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Based on the rapid development of intelligent technologies in recent years, the digital transformation of the whole industry and society has become increasingly important. Among them, digital twins and artificial intelligence have great potentials in improving industry processes and further enhancing productivity. This paper proposes an Intelligent Digital Twin System (IDTS) based on artificial intelligence and digital twins for the paper industry. The system includes the prediction models for the stirring speed of the dump chest, the water consumption of the deflaker, the supply air pressure of the dryer, and the exhaust air temperature of the dryer. The sensors, 5G network slices, and other equipment collect related data during the papermaking process for generating twin data, and we use the prediction models to analyze the data and monitor important indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) for the manufacturing processes, which are used to improve the energy utilization and production efficiency of the paper industry and thus facilitate cost saving. We apply this intelligent digital twin system and its associated prediction models to an actual paper manufacturing factory and show their effectiveness by improving the operational efficiency and saving labor and maintenance costs. [ABSTRACT FROM AUTHOR]

Post-hoc explaining approaches for deep learning (DL) models has attracted much attention in safety–critical applications such as rotating machinery intelligent fault diagnosis (IFD). However, with the help of the explanation techniques, the models are still fragile to domain shifts caused by varying speeds and loads without help for improving their cross-domain performance. Since humans in the decision-making loop are essential for a reliable diagnostic system to determine the reliability of diagnosis, this paper proposes a causal explaining guided domain generalization (CXDG) method to realize trust worthy IFD with human in the decision loop. Specifically, an explaining model is trained with the conditional mutual information, which is a causal strength metric, and utilized to tell the causal features in the input data as the attributions of the diagnostic model. A translation process of the attributions is proposed to make the explaining process understandable. Furthermore, the aim of this paper is not only explaining but also beyond that the diagnostic model is guided to focus on the causal features to improve its generalization ability in unseen domains. The effectiveness of the method is validated on two experiment datasets. The results show that the proposed method can both explain the attributions of the diagnosis model and be beneficial to the generalization ability. [ABSTRACT FROM AUTHOR]

Human–robot cooperative tasks have gained importance with the emergence of robotics and artificial intelligence technology. In interactive reinforcement learning techniques, robots learn target tasks by receiving feedback from an experienced human trainer. However, most interactive reinforcement learning studies require a separate process to integrate the trainer's feedback into the training dataset, making it challenging for robots to learn new tasks from humans in real-time. Furthermore, the types of feedback sentences that trainers can use are limited in previous research. To address these limitations, this paper proposes a robot teaching strategy that uses deep RL via human–robot interaction to learn table balancing tasks interactively. The proposed system employs Deep Q-Network with real-time sentiment feedback delivered through the trainer's speech to learn cooperative tasks. We designed a novel reward function that incorporates sentiment feedback from human speech in real-time during the learning process. The paper presents an improved reward shaping technique based on subdivided feedback levels and shrinking feedback. This function serves as a guide for the robot to engage in natural interactions with humans and enables it to learn the tasks effectively. Experimental results demonstrate that the proposed interactive deep reinforcement learning model achieved a high success rate of up to 99.06%, outperforming the model without sentiment feedback. • A robot teaching strategy for cooperative tasks via human–robot interaction is proposed. • Deep Q-Network is employed with real-time feedback delivered by the trainer's speech. • A novel reward function is designed to guide the robot in natural interactions. • The function incorporates the feedback with improved reward-shaping techniques. • Experiments on a robot proved the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• Adopting PRISMA methodology to investigate cybersecurity threats in FinTech. • Identifying the most effective defense strategies to negate the threats by examining 74 published papers. • Comparing the threats and defenses from different perspectives, including their impacts and technical details. • Proposing a novel and refined taxonomy of security threats and defense strategies in FinTech. • Recommending future research directions to address existing security gaps in current FinTech systems. The rapid evolution of the Smart-everything movement and Artificial Intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services. This paper introduces a novel and refined taxonomy of security threats in FinTech and conducts a comprehensive systematic review of defensive strategies. Through PRISMA methodology applied to 74 selected studies and topic modeling, we identified 11 central cyber threats, with 43 papers detailing them, and pinpointed 9 corresponding defense strategies, as covered in 31 papers. This in-depth analysis offers invaluable insights for stakeholders ranging from banks and enterprises to global governmental bodies, highlighting both the current challenges in FinTech and effective countermeasures, as well as directions for future research. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has become pervasive, enabling transformative advancements in various industries including smart city, smart healthcare, smart manufacturing, smart virtual world and the Metaverse. However, concerns related to risk, trust, and security are emerging with the increasing reliance on AI systems. One of the most beneficial and original solutions for ensuring the reliability and trustworthiness of AI systems is AI Trust, Risk and Security Management (AI TRiSM) framework. Despite being comparatively new to the market, the framework has demonstrated already its effectiveness in various products and AI models. It has successfully contributed to fostering innovation, building trust, and creating value for businesses and society. Due to the lack of systematic investigations in AI TRiSM, we carried out a comprehensive and detailed review to bridge the existing knowledge gaps and provide a better understanding of the framework from both theoretical and technical standpoints. This paper explores various applications of the AI TRiSM framework across different domains, including finance, healthcare, and the Metaverse. Futhermore, the paper discusses the obstacles related to implementing AI TRiSM framework, including adversarial attacks, the constantly changing landscape of threats, ensuring regulatory compliance, addressing skill gaps, and acquiring expertise in the field. Finally, it explores the future directions of AI TRiSM, emphasizing the importance of continual adaptation and collaboration among stakeholders to address emerging risks and promote ethical and enhanced overall security bearing for AI systems. [ABSTRACT FROM AUTHOR]

In this constantly evolving landscape of urbanization, the relationship between technology and automation, in regards to sustainability, holds immense significance. The intricate strands of human intelligence are seamlessly interwoven with the fabric of technological progress, giving rise to exquisite patterns of synergy and collaborative innovation. Automation is just another step in this process which started with the industrial revolution and now has paved way towards urbanization. Smart homes or home automation is a subset of Internet of Things (IoT) based automation that has added into the comfort, ease, and quality of our living standards and is now being integrated to form the concept of Smart Cities. In the past decade, various techniques and processes of smart home automation have been proposed and implemented. To extend and translate the existing methods into new one, the understanding of the former is imperative to the research procedure. This review stands as a comprehensive exploration, diving into the pivotal role of intelligent systems and expert knowledge in driving the transformation of smart homes into sustainable smart cities. By meticulously analyzing and aggregating an array of contemporary techniques used in smart homes, this paper offers profound contributions to the intersection of urban evolution and technological innovation. The review's holistic approach not only facilitates a deep understanding of smart homes' contributions but also charts a course for innovative strategies in city planning, infrastructure, and technological integration. In bridging the gap between technology and sustainable urban development, this exploration underscores the transformative power of leveraging smart home techniques to lay the foundation for harmonious and forward-thinking smart cities. The technologies cover a wide range of methodologies and intelligent systems used for communication, security and management in an urban infrastructure. The paper focuses on analysis of the technology to provide an outlook into achieving the goal of sustainable smart cities and deal with challenges like scalability and big data computation. Our comprehensive analysis yields a holistic set of technology comparisons and illuminates the promising future prospects within this domain. The information is highly insightful in creating a bigger picture for adopting state of the art technologies like Federated Learning (FL), Digital Twin and Embedded Edge computing in better planning and infrastructure management in smart cities. These findings offer reliable and potent methods to chart not only the course of research but also to enhance these technologies for the betterment of mankind's convenience and advancement. • Technical advancement needed for transformation of home automation to a next level. • Smart cities that build from the foundation of homes automations have been discussed. • Comparison with the existing papers to unlock new research directions in this area. [ABSTRACT FROM AUTHOR]

• A novel Iaah system is proposed in this paper. • A combined forecasting model (BWCE) is constructed in the Iaah system. • The Iaah system relaxes the assumptions of traditional Black-Litterman models. • The Iaah system expands the objective function of industry asset allocation. The B-L (Black-Litterman) model combines the subjective views of investors with the market's historical equilibrium return through the Bayesian theorem. This approach somewhat compensates for the limitations of the Mean-Variance model, making the asset allocation model attract significant attention. However, due to many investors lacking ample investment experience, they might struggle to provide appropriate subjective views. To address this issue, an Iaah (Industry Asset Allocation Hybrid) system based on the B-L model is proposed. First, a combined model is constructed, which simulates view return vectors and solves the problem of inaccurate view quantification. Then, the Iaah system introduces a dynamic condition correlation algorithm to optimize inherent parameters and explores asset allocation results in the time-varying effects. Next, it expands the types of objective functions and improves the deficiency of only considering the return and variance while ignoring the downside risk. Finally, based on a variety of performance metrics, the Iaah system is evaluated from multiple perspectives to test its practical value. The experiment results with the daily data of 10 SSE (Shanghai Stock Exchange) industry indexes show that the proposed system not only realizes the diversified allocation of industry assets and enhances the investment performance, but also exhibits strong robustness against changes in subjective parameters. It is conducive to managing personal wealth and achieving preservation and appreciation. [ABSTRACT FROM AUTHOR]

Seamless positioning ability has become an essential requirement in large-scale smart city scenes with the development of Artificial Intelligence of Things technology. The performance of seamless positioning is limited by the inaccurate crowdsourced navigation database, cumulative error of built-in sensors, and changeable measurement errors of different location sources. In order to solve these problems, this paper presents the CrowdLOC-S framework, which provides a concrete and accurate indoor/outdoor localization performance using the combination of crowdsourced Wi-Fi fingerprinting, Global Navigation Satellite System (GNSS), and low-cost sensors. A data and model dual-driven based trajectory estimator is developed for improving the long-term positioning performance of built-in sensors, and a hybrid one-dimensional convolutional neural network (1D-CNN), Bi-directional Long Short-Term Memory (Bi-LSTM), and Multilayer Perceptron (MLP) enhanced quality indicator is proposed for quality evaluation of crowdsourced trajectories and further Wi-Fi fingerprinting database construction. Besides, the transfer learning approach is applied in the quality indicator for autonomously predicting the location errors towards different indoor and outdoor location sources and realizing seamless scenes switching. Finally, a unified extended Kalman filter is developed to realize multi-source integration-based seamless localization using the positioning information provided by indoor and outdoor location sources and corresponding quality indicator results. Comprehensive experiments demonstrate that the presented CrowdLOC-S system is proven to realize precise and efficient indoor and outdoor positioning performance in complex and large-scale urban environments. [ABSTRACT FROM AUTHOR]

Managing stress is a crucial soft skill that affects cognitive performance and health. Stress detection through biometrics can be used to improve and evaluate stress management. However, measuring the effects of stress on biometrics and executive functions is difficult and dependent on the individual. Despite these challenges, this paper presents a case study that collects a comprehensive multimodal dataset with two stress metrics, four biometric signals, and twenty-two executive function metrics from Game-based Assessment (GBA) trace data specifically designed for this purpose. The experiments suggest that biometrics, especially the heart rate and skin temperature, are effective predictors of stress. Additionally, noteworthy correlations were observed between heart rate and certain executive function variables. The levels of GBA that measured shifting and processing speed showed a higher heart rate than the response inhibition levels. This case study, together with the developed stress detectors, enables the detection of persons who struggle to manage stress and measure their executive function performance under stressful situations. • We present a novel multimodal study on key topics for the 21st century society. • We introduce an innovative Game-based Assessment to measure executive functions. • For the first time, stress, biometrics, and executive functions are analyzed together. • The model allows the detection of users who cannot manage stress correctly. [ABSTRACT FROM AUTHOR]

Intelligent computing is an independent algorithm system that imitates the laws and principles of nature's biological chain to design and solve practical problems in modern society, it can help people better handle some big data problems through artificial intelligence technology and different cognitive characteristics. Especially, when dealing with group decision problems, it is often considered as human behavior based on cognitive computing. The q-rung orthopair fuzzy set (q-ROFS) is not only a generalization of Pythagorean fuzzy set (PFS), but also can deal with the decision problem of multi-source information in a wider area. In this paper, we first give the detailed explanations of the relationship between the q-ROFS and q-rung orthopair fuzzy number (q-ROFN)by an example, and itis pointed out that there are some defects in the existing two ranking methods for the Pythagorean fuzzy numbers (PFNs) and intuitionistic fuzzy numbers (IFNs) through counterexamples, and then all q-ROFNs are mapped into the unit triangle of the first quadrant through q-compression mapping, and the concept of distance factor for q-ROFNs is proposed by Euclidean distance between each q-ROFN and the maximum element (1,0).Secondly, a novel inverse score function for q-ROFNs are put forward by the distance factor and hesitancy degree, and the monotonicity of the inverse score function are discussed. Finally, a new ranking criterion of q-ROFNs is given by the new score formula, and then the rationality of the ranking criterion is proved, and through the method of solving the conditional extreme value of multivariate function (Lagrange multiplier method), there is indeed an extreme value point (corresponding to the maximum q-ROFN) on υ = μ. Besides, a q-rung orthopair fuzzy decision-making method for multi-source information is given by the new ranking criterion and aggregation operations, and the comparison of examples shows that the inverse score function and decision-making method do have many advantages in the q-ROFN environment. [ABSTRACT FROM AUTHOR]

Entities and relations extraction are the key tasks in the construction of biomedical knowledge graph, which play an important role in the biomedical artificial intelligence. However, extraction of entities and relations from biomedical texts is challenging because of the overlapping triples problem. The previous approaches typically divided the task into two separate sub-tasks. However, these methods failed to address the error propagation problem. Recent methods have been proposed to perform both sub-tasks simultaneously. Nonetheless, most current methods still encounter issues related to imbalanced interactions and independent features. In this paper, we propose a novel method based on feature partition encoding and relative positional embedding to joint extract biomedical entity and relation triples simultaneously. Compared to previous work, our method shows exceptional accurate in extracting entities and relations, while efficiently tackling the challenge of overlapping triples in biomedical texts. Our work has two contributions. Firstly, our method divides the features into task-specific and shared parts through entity, relation and sharing partitions at the encoding stage. And the encoded features will be aggregated according to the subsequent tasks. Secondly, we introduce a relative positional embedding method to capture the relative distance information between token pairs. In this way, our method can effectively deal with the sub-tasks interactions problem and improve entities and relations extraction. The experimental results show that our method improves the F1 scores of relations extraction by 3.2%, 2.1%, 3.4%, and 2.8% on four biomedical datasets, respectively. • Joint extraction is to recognize biomedical entities and relations simultaneously. • Feature partition encoding divides neurons into entity, relation, shared partition. • Relative positional embedding benefits both entities and relations extraction. • FPE is effective in addressing the issue of overlapping triples in biomedicine. [ABSTRACT FROM AUTHOR]

In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other. • Performing model-agnostic Explainable AI on classifiers. • Introducing the concept of dyscrasia of feature occurrences. • Assessing the relevance of a feature during classification. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, the Internet of Things and next generation mobile communication, the Internet of Medical Things (IoMT) as a typical application has attracted increasing attention as a typical application thereof owing to its convenience and practicality. To address certain privacy preservation issues concerning accessing patients records from past case databases as well as diagnosed patients' sensitive information on the Internet of Medical Things, an IoMT-secure privacy-preserving medical record search scheme (PMRSS) based on ELGamal blind signatures is proposed. In this paper, we first show that certain security flaws exists in the PMRSS. To address these flaws, we propose an improved privacy protection medical record searching scheme (iPMRSS) in IoMT for intelligent diagnoses based on the elliptical curve discrete logarithm problem (ECDLP). Security analysis demonstrates that our iPMRSS provides correctness, identity privacy is ensured based on the hard problem assumption of Elliptical curve discrete logarithm problem. Data security is satisfied based on the security of the Blind ECDSA Signature. Performance evaluations demonstrate that our theoretical results are consistent with simulations, thereby a comparative analysis with related state of art techniques show that our proposed iPMRSS scheme in about 1.5 ms has shorter time cost under some security level. • A privacy-preserving medical record searching scheme proposed by Sun et al. is not secure by a linear algebra attack. • A secure improved PMRSS scheme is proposed. • Security of the improved scheme achieves privacy and data security. • Performance of the improved scheme is reasonable for real applications. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease (COVID-19) caused a lot of mortality. The high mortality rate occurred because of the physicians' wrong or late identification of COVID-19 severity. So, developing Computer-Aided Design (CAD) systems using Artificial Intelligence (AI) techniques is critical to help physicians correctly identify the severity of COVID-19 in the early stages of the pandemic and then decrease the COVID-19 mortality percentage. In this paper, we develop a new green CAD system using a new hybrid handcrafted feature extraction algorithm and two-stage neural network architecture to grade the COVID-19 patient based on Computed Tomography (CT) scan images as having a moderate, severe, or critical infection. Because the proposed system uses handcrafted feature extraction algorithms, it consumes minimum resources and time than recent works. The proposed system consists of three phases: lesion segmentation, feature extraction, and diagnosis. Firstly, lesions from the CT scan image are manually segmented, and then three schemes are applied to extract salient features from the segmented lesions. These schemes are the Histogram of Oriented Gradients (HOG), Speeded Up Robust Features (SURF), and a new hybrid method that consists of cascading Discrete Wavelet Transform (DWT) and Gray-Level Co-Occurrence Matrix (GLCM). Then, the Cumulative Distribution Function (CDF) is computed for each scheme to extract the statistical markers. In the grading phase, a two-stage neural network approach is used. First, the extracted features are individually trained and tested for each scheme in the first neural network stage, and then the results of the first stage are combined to train and test each patient in the second neural network stage. The performance of the proposed system was assessed on a CT image dataset of 300 COVID-19-positive patients collected from the Cancer Imaging Archive (TCIA) website. The experimental results showed that our proposed system achieved 100% accuracy and kappa when the dataset was partitioned into 80% for training and 20% for testing. Also, it achieved 95.67% ± 0.47, 99.33% ± 0.77, and 100% ± 0 accuracies and 93.48% ± 0.74, 98.997% ± 1.16, and 100% ± 0 kappa values when the data was organized using 2, 4, and 10 folds, respectively. A green complexity algorithm analysis shows that this proposed system takes O(n) time complexity and 1 h and 20 min to train and test all cases. The performed green complexity analysis shows that the proposed system consumes 117.80 g Carbon Dioxide Equivalent (CO2e), 130.80 Wh, and 0.13 tree months for the carbon footprint, the energy needed, and the carbon sequestration, respectively. These results show that the proposed work consumes fewer resources and provides a green CAD system. • This paper proposes a CAD system for COVID-19 severity prediction. • The proposed system uses a two-stage neural network architecture. • The proposed system consists of two phases: feature extraction and severity identification. • A green complexity analysis is performed to prove the greenness of the proposed system. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies are increasingly applied to traffic flow prediction (TFP) to enhance prediction accuracy. This study utilizes bibliometric methods and network analysis measures to gain insights into the research status, development process, opportunities, and challenges of AI-based TFP research based on the literature data retrieved from the Web of Science core collection. The study first conducts basic statistical analysis of all papers. Subsequently, cooperation network analysis is conducted to identify the most productive countries/territories, institutions, and authors, the cooperative relationships, and the formed research communities. Co-citation network analysis is then employed to identify publications that have made outstanding contributions to the AI-based TFP field. Finally, the main path analysis of the paper citation network is used to analyze the knowledge diffusion process, while the keyword co-occurrence analysis is conducted to reveal the evolution characteristics of the research topics. Based on the bibliometric analysis results, we gain insights into the opportunities and challenges in this field from the perspectives of data, models, and applications, and provide pertinent suggestions for future research. Overall, this study can assist researchers in capturing the state-of-the-art and research directions in AI-based TFP. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) that utilizes neural networks (NNs) has a broad range of applications. However, NNs necessitate significant amounts of computation and data storage, which imposes considerable hardware demands and drives the need for AI chip research. To address this need, AI chip researchers must delve into extensive literature for entity information on hardware issues, architectures, optimizations, performances, and applications. Nevertheless, the influx of relevant papers has far surpassed their ability to read and absorb all the information. In this article, automatic information extraction in the AI chip domain using entity recognition techniques is conducted to alleviate the burden of paper reading for AI chip researchers. Our approach involves creating a manually annotated dataset called the ACER dataset to support automatic information extraction from literature in the field of AI chips. To address the challenge of recognizing entities with complex structures and lacking explicit features, the GIA-PME model is proposed, which utilizes a gated interaction attention mechanism and probability-matrix encoding. Our proposed approach enhances entity cognition by utilizing the keyword sequence of each entity type as prior knowledge. It also incorporates a dedicated embedding to learn the eigenvectors of entity structures. Finally, it combines the prior knowledge with eigenvectors using a designed gated interactive attention mechanism to assist with recognition. In addition, the proposed probability matrix encoding is used to detect nested entities to avoid information loss. Experimental results show that our GIA-PME model achieves the best performance compared to existing models, thus improving the strict/relaxed F1-score to 70.9/81.7 (+5.3 ∼ 9.1) on the ACER dataset. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has significant potential in Earth sciences, particularly in mineral identification for industrial exploration, geological mapping, and archaeological research. However, traditional methods are time-consuming, expensive, and complex. And existing mineral identification methods based on mineral photos face several critical challenges, including lack of consideration for natural image features captured in real environments, limitations of single-label classification which does not align with multi-mineral occurrences in nature, and growing computational complexity as the number of identifiable mineral labels increases. Therefore, this paper proposes an efficient mineral identification model based on multi-label image classification, focusing on natural environmental features. First, realistic feature datasets are created by simulating mineral photos in real environments. Then, the model uses the query-label (Query2Label) framework, with MaxViT-T (Multi-Axis Vision Transformer-Tiny) as the feature extraction network and the asymmetric loss function. Knowledge distillation is employed to improve identification accuracy while reducing computational complexity. The proposed model achieves an impressive average identification accuracy of 84.74% on a dataset of 495,756 mineral photos, surpassing existing models like ResNet-101, ML-GCN (Multi-Label Graph Convolutional Network), and SRN (Spatial Regularization Net). It maintains a lower parameter count and computational complexity. In the end, ablation experiments demonstrate the effectiveness of each optimization scheme. • Selecting image noise and color shift methods for realistic mineral data simulation. • Proposing an enhanced two-stage image multi-Label identification model framework. • Applying knowledge distillation for multi-label classification subtask decomposition. • Being with low computational complexity and high mineral identification accuracy. [ABSTRACT FROM AUTHOR]

Optimal path planning is a fundamental problem in artificial intelligence (AI) and has wide applications in areas such as robotics, transportation, and logistics. In this paper, we propose a novel approach for ordering algorithms in pathfinding problems using the concept of shell layers. Our approach aims to improve the computational efficiency and distance cost of ordering algorithms. To evaluate the effectiveness of our approach, we compare it with five state-of-the-art techniques commonly used in the field of ordering algorithms on eight different scenarios with varying configurations. Our results show that our proposed approach outperformed the state-of-the-art techniques in terms of time-computational complexity and distance cost in most of the scenarios, demonstrating its potential as a new state-of-the-art technique for ordering algorithms. Specifically, our approach outperformed the nearest neighbor, A*, branch and bound, Christofides, and genetics ordering algorithms. However, we also identified two specific situations where our approach was outperformed by two state-of-the-art algorithms due to the specific distribution of the goal nodes. These findings highlight the importance of evaluating and comparing ordering algorithms in various scenarios. Our approach has significant implications for AI research and development, as it has the potential to improve the performance of pathfinding algorithms in various applications. We also discuss the limitations of our approach and potential areas for further research, such as investigating the effectiveness of our approach in different types of graphs and exploring the potential use of machine learning techniques to optimize the shell layer construction. The code for this study is available at https://github.com/abdullah1aloush1/MuGONA. • Equally-spaced nodes formed over workspace with obstacles, optimizing search process. • Efficient neighboring graph built for equally-spaced nodes, reducing complexity. • Path optimization for navigation by ordering goal nodes, reducing time and distance. • Path smoothing for navigation via interpolation to yield a trajectory on path nodes. [ABSTRACT FROM AUTHOR]

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming an already trained Supervised Classification model, post-hoc explanations can be obtained via so-called counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the given Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single counterfactual explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, as in the single-instance single-counterfactual case, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactual explanations is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness. • A new model for Explainable Artificial Intelligence is proposed. • Counterfactual Explanations for a full group of individuals are sought. • Sparsity both at individual and group level is achieved. [ABSTRACT FROM AUTHOR]

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results. To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching. To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience. Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data. • Visual analytics platforms lack support for handling mobility data in ITS scenarios. • We present a visual-based toolkit to support mobility data analytics for ITS. • The solution allows practitioners to perform complex analyses without writing code. • The solution is open-source and integrated within the KNIME Analytics Platform. • The effectiveness of the solution is assessed in a real-world ITS analytics scenario. [ABSTRACT FROM AUTHOR]

• Developing an effective quantum self-supervised learning framework for time series data analysis. • Improving the learning capability of LSTM using quantum methods. • Identifying specific and effective time series data augmentation techniques for improved pretext learning. • Devising suitable quantum encoding technique specific for time series data in hand. • Improving the predictive learning ability for the downstream task in hand using transfer learning. Technologies such as Artificial Intelligence, Machine Learning, and Internet of Things has made unobtrusive mental health monitoring a reality. Since, obtaining a large-scale labelled dataset for mental health conditions is a big challenge; the self-supervised contrastive learning frameworks are more suitable for developing such systems. This paper presents a novel Quantum Long Short-Term Memory (LSTM) based Contrastive Learning framework for continuous mental health monitoring by leveraging LSTM's strengths in time series data analytics aiding it with the benefits of quantum computation, contrastive learning, and transfer learning. In the pretext task of the contrastive learning framework, a quantum guided LSTM base-encoder is developed for effective representational learning. The learnt model is then fine-tuned by training it with a small labelled dataset to further enhance its prediction capability. Experiments were carried out on seven benchmark datasets related to mental health conditions. With the enhanced representational and prediction abilities, the proposed model has shown superior performance over traditional ones. On heart rate variability dataset collected from (Schmidt et al., 2018), it achieves the greatest F1-score of 0.99. The paired t -test at 95% confidence level demonstrates that the proposed model outperforms the other related models. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is the latest designing and interacting technology used to support organizations in the competitive electronic commerce (E-commerce) context. The studies on AI in E-commerce remain fragmentary since few papers review the evolutionary process of knowledge infrastructure. To bridge this gap, we constructed a conceptual framework from the resource orchestration perspective, and retrieved 2252 documents from the Web of Science (WoS) database dating from 1998 to 2022 to synthesize the extant research on AI in E-commerce. Specifically, we integrated main path analysis with science mapping analysis covering the strategic diagram and evolution map. Through qualitative analysis of critical nodes in main paths, this article found that knowledge disseminated around recommender systems (RSs). This article also examined the performance traits and inter-relationships of significant themes over three consecutive time zones, as well as identified four main AI-enabled outcomes (thematic areas): Optimization and decision support , Trust and personalized recommendation , Sentiment analysis, and AI theories with allied technologies. Potential research prospects and implications are proposed based on these findings. Overall, this article provides state-of-the-art information on how AI can facilitate the implementation of E-commerce operations and could serve as a stepping stone to newcomers in the scientific community. [ABSTRACT FROM AUTHOR]

With the emergence of artificial intelligence, deep learning techniques have been widely deployed in forecasting stock markets. However, existing deep-learning-based models for news-based forecasts of stock trends are mostly black-box and difficult to explain. The procedure by which how final predictions are made within models keeps unknown, making it hard to interpret why one prediction should be better than the other. To provide explanations on predictions, this paper proposes to inject causal inference into model procedures and causally interpret predictions. We first generate a causal graph from financial news, and then integrate the information in the causal graph into a neural network model for stock trend prediction. Moreover, in order to better extract keywords from financial news we introduce a novel keyword extraction method named Distinguishable Word Filtering by Kolmogorov–Smirnov Test (DWF-KST). The experiment results on five financial datasets demonstrate that not only our proposed model explicitly provides an interpretation of prediction results, but also outperforms the state-of-art methods. the achieved results boost predictions of S&P 500 2-category from 89.7% to 97.4%, 3-category from 77.4% to 82.5%, and 5-category from 61.5% to 71.6%. For the other two indexes, the performances of Dow index improve from 86.2% to 90.2% and Nasdaq index improve from 76.4% to 78.9%. • We propose a method for stock trend prediction. • We use causal inference based on unstructured data. • Improve the interpretability of DL-based stock prediction models. • We propose DWF-KST for keyword selection in financial news. [ABSTRACT FROM AUTHOR]

Since there have been more and more incidents of women being harassed in the recent past, girls need to think twice before going out of their houses. Sometimes, they are not even safe in their house or workplace. These circumstance does not change for children of all genders who stay alone because of their working parents or for other reasons. Also, there is no such organized procedure to ensure safety and take women and children out of such violence and harassment. To address this problem, the authors of this paper developed an Android-based automated system to detect danger for women and children using audio from the surroundings. As the Android phone is available to everyone nowadays, they focused on using this device rather than developing a system on some external hardware. Different signal processing methods with deep learning techniques are used for this work. This work also addresses noise from the environment for any chaos and nullifies them using different noise reduction techniques such as Reduce Energy Noise, Reduce Mel Frequency Cepstrum Coefficient (MFCC) up Noise, Reduce Median Noise, Reduce Centroid Noise, Audio DeNoise, Noisereduce by Sainburf et al. & Butterworth high pass filter. The Noisereduce by Sainburg et al. along with the InceptionV3 model architecture turns out to be the best to classify audio with 95.51% accuracy. A new model called AudioViT is introduced. It uses a Visual Transformer and Residual Network to identify the audio signal. The Android application also takes necessary action when any unfavorable situation is detected. Android device users can use this application without any cost, which will pave the way to ensure the safety of women and children. • Taking help from technology to ensure our family member's security. • A self-sufficient application to detect danger from audio signals. • Uses Android and Python to collect, process, and analyze audio and take action. • Tried several deep learning and noise reduction models to reach the best combination. [ABSTRACT FROM AUTHOR]

AI-powered analysis of histopathology data has become an invaluable assistant for pathologists due to its efficiency and accuracy. However, existing deep learning methods still face some challenges in specifying cancer subtypes. For example, the ultra-high resolution of histopathological slides generally contains numerous redundant features, which are not useful for cancer subtype classification and thus lead to considerable computational costs. Moreover, the lack of manual annotations of disease-specific regions (i.e., patch-level annotations) from experts makes it more difficult to learn such histological features with only slide-level labels. In this paper, we propose an end-to-end weakly supervised learning framework called EWSLF to address these issues. First, we employ a cluster-based sampling strategy to refine the histological features for further training, which can improve classification accuracy and reduce computational cost. Second, we employ a multi-branch attention mechanism to produce patch-level pseudo-labels and aggregate the patch features into slide-level features, which can complement the missing patch-level labels from experts. Experimental results on both public and in-house datasets demonstrate the superiority and credible results of our model compared with the state-of-the-art methods for cancer subtype classification. Code: https://github.com/hongren21/ewslf. [ABSTRACT FROM AUTHOR]

The Computational Fluid Dynamics (CFD) simulations at different fidelity levels are a common tool for predicting the aerodynamic performance in many engineering applications such as; automotive and aerospace to cite a few. However, the associated computational costs with these simulations are not trivial, and using the CFD simulations might easily become unpractical specially at the early design stages. The number of the required CFD simulations could be reduced immensely by using Artificial Intelligence (AI) methods which can predict, for example, the aerodynamic coefficients based on previous and limited CFD simulations. Having discussed the AI potential to minimize the CFD usage, the objective of this work is to investigate and compare the ability of different AI methods to predict the aerodynamic coefficients using the same CFD database from the literature. In this study, three different AI methods, namely; machine learning (e.g. Extreme Gradient Boosting (XGB), CatBoost, Bagging, Light Gradient Boosting Machine (Light-GBM), Random Forest (RFR), Gradient Boosting), deep learning (e.g. One Dimensional Convolutional Neural Network (Conv1D)) and surrogate models (e.g. Gaussian Process Regression (GPR)) have been utilized to estimate a sample CFD database (e.g. NACA0012, RAE2822) from the existing literature, and the prediction performance of these methods have been compared. The results show that, the aerodynamic coefficients predicted using CatBoost, XGB and Bagging are in good agreement with the reference CFD results. Furthermore, the proposed Conv1D method with LeakyReLU activation function showed a promising results in the development of aerodynamic models which can be used in the early stages of the air vehicles design. The obtained results from this work indicate that the CatBoost and the XGB models required much smaller training time compared to the Conv1D method, moreover, the proposed regression trees; CatBoost, Bagging and XGB can reduce the required number of CFD simulations significantly. • Various artificial intelligence methods were studied to predict aerodynamic data. • Regression tree models such as XGB, Catboost and LightGBM were used. • Surrogate models and deep learning models (i.e. Conv1D) also were used. • Detailed comparison were presented between the aforementioned models. • The paper suggests the suitability of XGB and Catboost for aerodynamics cases. [ABSTRACT FROM AUTHOR]

As an emerging digital product, artificial intelligence models face the risk of being modified. Malicious tampering will severely damage model functions, which is different from normal modifications. In addition, tampering localization for targeted repair can effectively reduce the cost. Therefore, it is crucial to achieve model content authentication and locate the tampering location. We proposed a novel semi-fragile neural network watermarking method in this paper to address these issues. Specifically, with the precondition of maintaining model performance, we proposed a method that generates a set of semi-fragile samples for a model to achieve the content authentication and tampering localization. The experiment results show that the content authentication of the model can be achieved by analyzing the output results of the model for the semi-fragile samples. When the model is processed normally, the output results are consistent with the expected label, while when the model is maliciously tampered with, the model produces unstable output. Furthermore, the tamper localization of the model can be further achieved through the information hidden in the semi-fragile samples, resulting in an average accuracy of more than 99.42%. In addition, our method is also effective for other deep neural networks. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic's profound impact on global health and the economy underscores the need for new computational strategies to rapidly identify potential antiviral drugs against emerging and re-emerging infectious diseases. AI-based drug repurposing methods have the potential to significantly shorten the drug discovery process, rendering them a crucial and efficacious approach for screening anti-SARS-CoV-2 compounds. In this paper, we proposed GINCM-DTA, an AI-based graph isomorphism network with protein contact map representation for drug–target affinity (DTA) prediction, and presented the COVID-DTA dataset for SARS-CoV-2-specific drug repurposing. Pre-training GINCM-DTA on large-scale Davis and KIBA datasets achieved state-of-the-art performance, while fine-tuning on the COVID-DTA dataset using transfer learning resulted in a mean square error of 0.026 and concordance index of 0.967. Utilizing the fine-tuned GINCM-DTA model, we predicted DTAs for drugs against SARS-CoV-2 main targets, focusing on key conserved proteins and unmutated spike proteins for virtual screening. We integrated GINCM-DTA predictions with molecular docking simulations to identify five candidates. In vitro assays revealed Hydralazine (5.737 nM) as a potential inhibitor of ACE2 and TMPRSS2, effectively blocking SARS-CoV-2 infection. Moreover, the results highlight the potential of Hydralazine as a broad-spectrum drug candidate, demonstrating its efficacy in obstructing the binding between ACE2 and spike proteins of multiple Omicron variants, including the currently dominant strains BQ.1, BQ.1.1, XBB.1.5, and XBB.1.16. Our method offers a novel computational strategy for COVID-19 antiviral screening and prepares for potential future epidemics incited by emerging viruses. • Proposed GINCM-DTA: a novel GIN model for DTA prediction with SOTA performance. • The fine-tuned GINCM-DTA achieved excellent performance: MSE=0.026, CI=0.967. • Identified Hydralazine as a potential inhibitor to block SARS-CoV-2 infection. • Potential effective against Omicron subvariants: BQ.1, BQ.1.1, XBB.1.5, XBB.1.16. • New computational strategy for COVID-19 antiviral screening & future virus epidemics. [ABSTRACT FROM AUTHOR]

High-dose single exposures of long-wavelength ultraviolet-A (UV-A) radiation may trigger severe biological and skin tissue damage in humans and animals, as well as photosynthetic damage in plants. In humans, the highly abundant UV-A is also linked to an increased risk of skin cancer. This paper develops a new forecasting framework tailored for very short-term UV-A predictions using a Bayesian optimized ensemble Neural Basis Expansion Analysis for Interpretable Time Series (B-E-NBEATS) method, integrated with cloud cover predictors and the solar zenith angle. The design phase of the proposed model entails feature selection using an efficient neighborhood component analysis algorithm and hyperparameter tuning with a Bayesian optimizer. To further enhance the predictive performance and reliability, the B-E-NBEATS model is calibrated via uncertainty quantification by adopting an ensemble approach with single-point models trained on three key loss functions. The prescribed model generates interpretable forecasts of 20-minute ahead UV-A by empowering the ensemble members to learn seasonal and trend components in UV-A datasets. With respect to the four benchmark models, the statistical metrics and visual infographics of predicted and observed UV-A reveal a superior forecasting capability of the proposed B-E-NBEATS model. The model yields a comparatively high correlation coefficient of 0.910, 0.869, 0.933, and 0.900 for the spring, summer, autumn, and winter testing phases, respectively. The superior performance by the newly designed hybrid ensemble model ascertains its potential utility in UV-A monitoring and mitigating subsequent harmful exposure risk for the general public, and animal and plant life. [Display omitted] • An interpretable AI method is designed to predict solar ultraviolet-A radiation. • Deep learning ensemble approach is adopted to enhance forecasting performance. • The model is optimized with neighborhood component analysis and Bayesian optimizer. • Model calibration by quantifying uncertainty offers reliable ultraviolet-A forecasts. • The proposed model predicts cloud-affected solar ultraviolet-A with high accuracies. [ABSTRACT FROM AUTHOR]

Finding a more efficient trading strategy has always been one of the main concerns in financial market trading. In order to create trading strategies that lead to higher profits, historical data must be used. Due to a large amount of financial data and various factors affecting them, algorithmic trading and, more recently, artificial intelligence are employed to overcome the decision-making complexity. This paper aims to introduce a new approach using Twin-Delayed DDPG (TD3) and the daily close price to create a trading strategy. As a continuous action space deep reinforcement learning algorithm, in contrast to the discrete ones, the TD3 provides us with both the number of trading shares and the trading positions. In order to evaluate the performance of the proposed algorithm, the comparison results of our approach and other commonly-used algorithms such as technical analysis, reinforcement learning, supervised learning, stochastic strategies, and deterministic strategies are reported. By employing both position and the number of trading shares, we show that the performance of a trading strategy can be improved in terms of Return and Sharpe ratio. • Proposing an autonomous trading algorithm. • Providing smart trading capability in stock markets. • Employing a continuous action space DRL model. • Facilitating Money management. [ABSTRACT FROM AUTHOR]

Blockchain has been viewed as a breakthrough and an innovative technology due to its privacy, security, immutability, and data integrity characteristics. The consensus layer of the blockchain is the backbone and the most important layer of the blockchain architecture because it acts as the performance and security manager of the blockchain. The detection of Long-Range Attacks (LRA) on the Proof-of-Stake (PoS) blockchain is a complex task. Earlier studies have shown various challenges in detecting long-range attacks and monitoring the activities of validator nodes on the blockchain network. Thus, this paper proposes a novel dataset for node classification on a proof-of-stake permissionless blockchain and proposes a Deep Learning method that can be used to classify nodes into malicious or non-malicious nodes to mitigate long-range attacks with high accuracy. The performance metrics for the model are compared and measured which suggest the developed performance of the proposed model. The proposed solution can serve as a guide on how future researchers and blockchain developers can simulate and curate proof-of-stake datasets and goes further to demonstrate that artificial intelligence models can be used as a mitigating checkpoint for long-range attacks. The dataset in the paper is publicly available and can be used by other researchers to detect other activities and behaviors on a permissionless blockchain. These techniques can further enhance security, performance and create fairness on the proof-of-stake consensus. [ABSTRACT FROM AUTHOR]

The inability to measure energy production accurately in photovoltaic power stations is an important issue affected by photovoltaic plants. In certain contexts, the imputation of missing values can be complicated given the lack of similar measurements from other environments and/or the difficulty of generating predictive models when there are simply too many unknowns, when other measurements are not available, or when there are no external correlated signals (e.g., solar irradiation, ambient temperature, modules temperature or wind speed). For such limited environments, this paper proposes a solution based on an artificial neural network with an architecture built in the form of an encoder–decoder. The proposed model can be trained with time series of energy production noting missing data of any kind. This paper demonstrates that the proposed model can perform imputations of missing data comparable to those made by auto-regressive and clustering techniques, even when the time series to be predicted is the same as the one used for training. The model is well suited for environments where the severity of missing values range between 30% and 70%, being especially indicated for environments where the severity of missing values reaches 50%, which surpasses in all the evaluated metrics other methods. • Imputation of absences in PV energy production under contexts of large absences. • A proposal of neural network that supports missing values in the training set. • The model reconstructs missing values even when trained with single partial series. • The model can reconstruct the missing values in the same series used for training. • The model outperforms other methods in highly restrictive missing value scenarios. [ABSTRACT FROM AUTHOR]

Ensuring a certain clustering of distributed sensor nodes is significant for energy efficiency of wireless sensor networks (WSNs). Recently, several energy efficient artificial intelligence based-methods that provide cluster scheduling schemes have been proposed. However, there are few methods providing balanced energy exhausting of the number of nodes in clusters. In this paper, we present a novel cluster scheduling scheme for WSNs using local gravitational method to adaptively determine the number of time-varying clusters based on a sequence of vectors. The main idea in this method is to be inspired by the physical features of gravity force among the mass units. These features are considered to be units of mass of nodes in vector space. It is based on the fact that with clustering, the highest gravitational force on moving the units of mass is applied by the area with feature vectors that are high-density. Cooperative and distributed clustering is formed and the number of clusters is found by sharing the gravitational force between neighboring nodes through a distribution-optimization scheme. The results of this study have been compared with the existing methods in various scenarios and with many performance criteria through the Matlab programming. Especially in dense networks, the proposed algorithm has increased the number of clusters and ensured the clustering balance as much as possible. The proposed method has scaled down the energy consumption, and enhanced the network lifetime. [ABSTRACT FROM AUTHOR]

Early fault detection, both in equipment and the products in process, is of paramount importance in industrial processes to ensure the quality of the final product, avoid abnormal operating conditions, expensive repairs, and even production process shutdown. The growing complexity of industrial systems and the increase in the amount of available data have encouraged the development of intelligent systems for automatic fault prediction/detection, mainly based on Industry 4.0 technologies and, particularly, those based on deep learning methodologies. However, the vast majority of proposals and research carried out to date define specific solutions for specific cases, which still requires a high level of expert knowledge for scaling the solutions to industrial environments. Actually, one of the major issues towards the industrialization of deep learning solutions is the determination of the optimal, or near-optimal, hyper-parameters. In this paper, we propose a low-level set-up effort intelligent failure detection system that integrates deep neural networks with a Bayesian Optimization algorithm for self-tuning of the system hyper-parameters. In addition, to facilitate the industrialization of the proposal and its incorporation into current industrial systems, we embedded the proposal in our previously formulated and tested Simulai architecture which allows for containing and interaction with multiple and heterogeneous technological components of manufacturing processes. Finally, our proposal is tested in two real cases of a different nature. The obtained results show a successful performance and demonstrate the easy online integration and interaction in a real production system. • Industrialization of artificial intelligence through a self-adjusting system. • Improvement of the decision-making process in the detection of industrial failures. • Optimization of deep learning for the improvement of industrial health and quality. • Demonstration of the proposal's adaptability to real cases of different natures. [ABSTRACT FROM AUTHOR]

Food security is currently a major concern due to the growing global population, the exponential increase in food demand, the deterioration of soil quality, the occurrence of numerous diseases, and the effects of climate change on crop yield. Sustainable agriculture is necessary to solve this food security challenge. Disruptive technologies, such as of artificial intelligence, especially, deep learning techniques can contribute to agricultural sustainability. For example, applying deep learning techniques for early disease classification allows us to take timely action, thereby helping to increase the yield without inflicting unnecessary environmental damage, such as excessive use of fertilisers or pesticides. Several studies have been conducted on agricultural sustainability using deep learning techniques and also semantic web technologies such as ontologies and knowledge graphs. However, the three major challenges remain: (i) the lack of explainability of deep learning-based systems (e.g. disease information), especially to non-experts like farmers; (ii) a lack of contextual information (e.g. soil or plant information) and domain-expert knowledge in deep learning-based systems; and (iii) the lack of pattern learning ability of systems based on the semantic web, despite their ability to incorporate domain knowledge. Therefore, this paper presents the work on disease classification, addressing the challenges as mentioned earlier by combining deep learning and semantic web technologies, namely ontologies and knowledge graphs. The findings are: (i) 0.905 (90.5%) prediction accuracy on large noisy dataset; (ii) ability to generate user-level explanations about disease and incorporate contextual and domain knowledge; (iii) the average prediction latency of 3.8514 s on 5268 samples; (iv) 95% of users finding the explanation of the proposed method useful; and (v) 85% of users being able to understand generated explanations easily—show that the proposed method is superior to the state-of-the-art in terms of performance and explainability and is also suitable for real-world scenarios. • Combining deep learning and semantic technology improves accuracy on noisy dataset. • Semantic technology enables generating user friendly natural language explanation. • 95% of users find explanations based on the proposed approach useful. • 85% of users can comprehend the explanations provided by the proposed approach. [ABSTRACT FROM AUTHOR]

Current research on artificial intelligence (AI) algorithms in safety–critical areas remains extremely challenging due to their inability to be fully verified at design time. In this paper, we propose an RTA-IR architecture, which bypasses the formal verification of the AI algorithm by incorporating runtime assurance (RTA) and provides safety assurances for the AI controllers of complex autonomous vehicles (such as those obtained using neural networks) without excessive performance sacrifice. RTA-IR consists of a high-performance and unproven advanced controller and two verifiable safety controllers and a decision module designed based on the Responsibility Sensitive Safety Model (RSS). The advanced controller is designed based on attention generating adversarial imitation learning(GAIL), which can imitate the efficient policies of experts from a set of expert demonstrations. RSS provides verifiable safety criteria and switching logic for the decision module, and RTA-IR provides safety for autonomous vehicles when the advanced controller produces unsafe control, as well as restoring control of the vehicle by the advanced controller under conditions that confirm safety. We tested and evaluated RTA-IR separately for two levels of traffic density in one driving task. Experiments have shown that RTA-IR exhibits superior performance in terms of both safety and efficiency compared to the baseline method. [ABSTRACT FROM AUTHOR]

In a world increasingly pervaded by mobile and IoT devices, position-related information is gaining more and more importance. Highly accurate and standardized positioning techniques are not yet available for indoor scenarios, unlike for the outdoor case. The most commonly used method for indoor positioning is WiFi fingerprinting, which, despite its well-recognized advantages, still suffers from some notable limitations. Recently, approaches relying on deep learning showed promising results even though their lack of interpretability is still a significant drawback. In this paper, for the first time, we propose a domain-specific concept of interpretability, based on identifying the access points that are most relevant to a position estimate. The goal is to enhance the positioning process by gaining novel scientific knowledge and operational insights, without worsening the performance of the task. We show how it is possible to practically achieve both a local and a global notion of interpretability by means of a deep learning model equipped with an attention module, applied to a ranking based fingerprint representation. Since off-the-shelf application of attention does not guarantee to achieve a faithful nor plausible interpretation, we verified through a series of thoroughly designed quantitative and qualitative clustering based experiments the existence of a strong relationship between the obtained interpretations and the positioning domain. Finally, as by-product, we showed an example of how the new knowledge can be used in principle to improve positioning performance. • Interpretability in indoor positioning is defined and achieved through attention. • The model uses ranked-based fingerprinting, deep learning (RNN) and attention. • Attention has a strong spatial characterization in the indoor positioning context. • Different kinds of attention mechanisms exhibit rather different behaviours. • Attention contributes to multiple positioning-related tasks, e.g., improving. accuracy. [ABSTRACT FROM AUTHOR]

An evaluation is administered to measure students' learning outcomes, which nowadays become challenging for instructors as student growth increases exponentially. Several models are proposed in the literature based on selected artificial intelligence algorithms that are once trained and then deployed. The problem with these kinds of systems is that the trained models are locked and cannot adjust to dynamically changing circumstances, leading to a drop in performance. Moreover, these systems only considered basic parameters for computing the semantic similarity, resulting in less accuracy. This paper develops an intelligent student evaluation model based on a predictive optimization approach, which considers question type, structure, necessary keywords, language, and conceptual aspects to evaluate the student's answer. In order to enhance the performance of the proposed evaluation system, we have proposed a predictive optimization approach where a deep neural network is used as a learning module to learn from training data, and particle swarm optimization and gradient descent are used as an optimization scheme to optimize weighting parameters for the deep neural network. The proposed work uses and analyzes the real dataset of NTNU students' exams to validate the proposed platform's practicability. Initially, we will employ the natural language processing technique of deep learning in which semantic similarity score and other features will be used to compute the degree of relevance between actual answers and students' provided answers. The proposed semantic similarity score algorithm is based on the WordNet library and Growbag dataset to check the solution's semantics, conceptual aspects, and creativity. The resulting score will be used as a supervised machine-learning classification system feature. Performance of the classification model will be ensured using standard evaluation measures, including Precision, recall, and f-measure. The end goal of this platform is to acquire the grade against the student's answer given as input in the developed platform. • Develop an ISE model for open-ended question based on optimal learning. • Develop a semantic similarity module using wordnet and Growbag dataset. • Minimize feature space based on correlation index to reduce computational costs. • Proof of concept of the proposed ISE is presented based on comprehensive analysis. • Compare the similarity rate of the ISE with baseline models. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) models are widely used in intelligent devices. However, challenging vibration environments can seriously affect the environmental adaptability of AI-based devices. In this paper, an environmental adaptability assessment method of AI-based devices is proposed. The formation mechanism of optical blurred image is elaborated, and measure function is used to transform the stress magnitudes of environment into distances of distribution discrepancy. To ensure that the obtained optical blurred images are fully controllable, we generate vibration-blurred images using an explicitly quantifiable extended dataset. Additionally, the generated vibration-blurred image is evaluated for consistency with the actual image. Finally, the environmental adaptability of multiple state-of-the-art models is evaluated based on extended datasets. The assessment results show that the environmental adaptability of the visual perception system is mainly limited by the vibration amplitude under high frequency. Under low frequency vibration, the environmental adaptability of AI models is also frequency-dependent. [ABSTRACT FROM AUTHOR]

In recent days, the research on student's intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analysing the intelligence level of the learners. Each learner's knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child's learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student's intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student's reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system's performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilization are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customized teaching easier; hence, this article involves determining a student's cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorizes students' cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students. • Cognitive Knowledge Representation Model (CKRM) to assess students' reasoning level. • Knowledge level assessment of students based on Cognitive Performance Test. • Computation of Cognitive Performance Metrics and Reasoning Coefficient. • Performance analysis using expert judgements and cognitive evaluation metrics. • Average accuracy score using expert judgemental analysis is 0.914. [ABSTRACT FROM AUTHOR]

Clinicians typically use semantic features to judge the malignant status of nodules, while artificial intelligence systems (AI) tend to extract unknown features to diagnose nodules. The former relies on clinical knowledge, while the latter explores AI knowledge. Although many studies indicate that fusing clinical and AI knowledge can help computer-aided diagnosis (CAD) systems improve diagnostic accuracy and gain clinician approval, how to effectively fuse them is still an open question. This paper proposes a simple and effective pipeline (abbreviated as CKAK), which fuses clinical and AI knowledge at both feature and decision levels for accurate lung nodule malignancy classification and semantic attributes characterization. The feature-level fusion can retain rich information in high-dimensional features and improve the model's accuracy; the decision-level fusion can provide some interpretability for the model's decision-making process, which is expected in clinical applications. Specifically, the proposed CKAK consists of two sequential stages: (i) the initial prediction stage (IPS); and (i i) the prediction refine stage (PRS). The IPS predicts eight radiologist-interpreted semantic attributes and an initial malignancy diagnosis in parallel. Then, these results are fed to the subsequent PRS to refine the diagnosis further by fully fusing them at feature and decision levels. Besides, to enhance the ability of feature learning, we propose a novel scale-aware feature extraction block (SAFE). It integrates multi-scale contextual features with a lightweight Transformer rather than adding or concatenating them roughly. Extensive experiments at the LIDC-IDRI data set show that the proposed CKAK can achieve superior benign-malignant classification accuracy with minor radiologist-interpreted semantic scores error, meeting the need for a reliable CAD system. • We fully fuse clinical and AI knowledge at the feature and decision-making levels. • We integrate multi-scale information to improve model's feature learning ability. • Experiments show our method has reliable performances in lung nodule diagnosis. [ABSTRACT FROM AUTHOR]

Making the best voltage/current regulation against disturbances and uncertainties and, most importantly, to minimize chattering effect in power electronics circuits, the hysteresis modulation based (HM) sliding mode control (SMC) is widely used. There are various research on this subject in the literature. However, the variable switching frequency that HM-SMC brought about has regrettably overlooked in the majority of these research. This problem is addressed in depth in this paper by demonstrating how the switching frequency is affected by various factors. Next, the fuzzy logic control (FLC) is proposed for the first time as a solution for switching frequency regulation in a power electronics circuit. The proposed FLC for switching frequency regulation fortifies the system described in this study and sets it apart from many other HM-SMC studies in the literature with its advantages against the difficulties caused by variable switching frequency such as excessive switching and driver power losses. On the other hand, the fast terminal SMC (FTSMC) which has significantly superior convergence performance than its counterparts, has been recommended for voltage regulation. The proposed FTSMC technique employs only one voltage sensor different from the past literature. This simplifies the proposed control mechanism in practical implementation and also makes the system economical. The two proposed controllers' compatibility with one another has been tested through experimental tests, including the steady-state condition, variations in switching frequency reference value, step change of load resistance, output voltage reference value variation, and finally step change of input voltage. The outcomes of the experiments demonstrate that the switching frequency and output voltage are successfully regulated with good dynamic and steady state performance. Finally, the proposed techniques have been compared with six recent approaches from the existing literature to highlight their performance. • FLC is employed for the first time in power electronics to regulate switching frequency. • Digital design has been made for the measurement of the switching frequency. • FTSMC is employed to deliver significantly superior convergence performance. • Proposed FTSMC provides better dynamic reaction due to its strong dynamic surface. [ABSTRACT FROM AUTHOR]

The stock market is a financial market where shares of publicly listed corporations are purchased and sold. It is an indicator of a country's economic health, reflecting the performance of companies and the overall business environment. The prices of stocks are determined by supply and demand. Investing in the stock market can be risky, but it can offer the potential for significant returns over the long term. Artificial intelligence, including the stock market, has become increasingly prevalent in the financial sector. Long Short-Term Memory (LSTM) is a type of artificial neural network that is often used in time series analysis. It can effectively predict stock market prices by handling data with multiple input and output timesteps. Metaheuristic algorithms, such as Artificial Rabbits Optimization algorithm (ARO), can be used to optimize the hyperparameters of an LSTM model and improve the accuracy of stock market predictions. In this paper, an optimized deep LSTM network with the ARO model (LSTM-ARO) is created to predict stock prices. DJIA index stocks are used as the dataset. LSTM-ARO is compared with one artificial neural network (ANN) model, three different LSTM models, and LSTM optimized by Genetic Algorithm (GA) model. All the models are tested on MSE, MAE, MAPE, and R2 evaluation criteria. The results show that LSTM-ARO overcomes the other models. [ABSTRACT FROM AUTHOR]

AI virtual assistants are steadily expanding their way into multiple domains such as healthcare, hospitality, and telecommunication. However, such virtual assistants still suffer from a limited level of distinctive characteristics of domain-specific knowledge. In this paper, we improve the Named Entity Recognition (NER) ability by facilitating (i) the assistants' understanding of both general and domain-specific terms by building new datasets in the automotive domain and using adversarial training and (ii) NER's robustness against spacing errors using multi-task learning. We verify the proposed method in both Korean and English NER datasets and the experimental results demonstrate that the proposed NER model outperforms the baseline model in both general and automotive domains. The proposed method improves automotive NER performance by 1.85 points and 1.13 points in English and Korean, respectively. Furthermore, we validate proposed NER model on the in-house question answering system where queries are taken from real users. • We construct the automotive NER dataset in both Korean and English. • We propose ADMit that utilizes domain adversarial and multi-task learning for NER. • We show that ADMit achieves state-of-the-art in the automotive NER datasets. • ADMit improves the performance of automotive FAQ system for real user's queries. [ABSTRACT FROM AUTHOR]

Sentiment analysis using artificial intelligence is a great tool for analyzing human linguistic capabilities. The deep learning techniques combined with Natural Language Processing (NLP) has said to offer higher performance in analyzing the sentiments in implicit aspects present in high-quality training datasets. However, the different complexities associated with deep learning techniques such as high computational and space complexity, sensitivity to learning rate, high training time, etc make them hard to implement in real-time applications. However, very few researchers have used the randomization-based neural network for sentimental analysis which offers different benefits such as increased generalization capacity, improved accuracy, low training time, and universal approximator for non-linear functions. Motivated by this problem, this paper presents an Artificial Rabbits optimized Robust Random Vector Functional Link Network (RRVFLN) for improving sentiment analysis accuracy. Initially, the data is preprocessed and the word vectors are efficiently analyzed by the Fast text method by considering each token as an n-gram character. Artificial Rabbits Optimization (ARO) is utilized to RRVFLN model's input weights and to optimize the hidden layer biases. The ARO-optimized RRVFLN model classifies the outcomes obtained in the three datasets namely the News Headline Sentiment dataset, Sentiment140 dataset, and 4000 Short Story dataset into positive and negative. The simulation results obtained by comparing the proposed model with the various existing techniques display the effectiveness in terms of F1 score, precision, classification accuracy, error rate prediction, and kappa statistics. [ABSTRACT FROM AUTHOR]

Data-driven intelligent systems provide a possible solution to condition-based maintenance of wind turbines without experts' knowledge or mechanism models. However, the accuracy of fault diagnosis results is easily impaired by the data imbalance in real-world applications. In this paper, a novel method is proposed to address the mentioned problem. Specifically, spatial and temporal information of supervisory control and data acquisition (SCADA) data is extracted, and a contrastive learning strategy is developed to obtain the data representations correlated with the health conditions. Additionally, the bollards of the data distributions are determined in the representation space, and they are matched with the data centers of each health state. Under this circumstance, the unexpected effects of data imbalance on data representations are relieved, leading to effective decision boundaries regardless of the sample numbers. Then, a classifier is trained on the learned representations to recognize the faults in wind turbines. Compared with several baselines and state-of-the-art approaches, the impressive performance of the proposed method is demonstrated with simulated data and actual measurements from a utility-scale wind turbine. This research provides useful insights into the efficient development of wind energy with data-driven intelligent systems. [ABSTRACT FROM AUTHOR]

Dempster–Shafer Theory (DST) of belief function is a basic theory of artificial intelligence, which can represent the underlying knowledge more reasonably than Probability Theory (ProbT). Because of the computation complexity exploding exponentially with the increasing number of elements, the practical application scenarios of DST are limited. In this paper, we encode Basic Belief Assignments (BBA) into quantum superposition states and propose the implementation and operation methods of BBA on quantum circuits. We decrease the computation complexity of the Matrix Evolution on BBA (MEoB) on quantum circuits. Based on the MEoB, we realize the quantum belief functions' implementation, the similarity measurements of BBAs, evidence Combination Rules (CR), and probability transformation on quantum circuits. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) have obtained wide attention in recent years, which is relied on their attractive socio-economic benefits and potential to improve safety. One of the main characteristics of MASS is that it can use perception information to replace Officers on Watch (OOW) to make different navigation decisions through expert and intelligent systems, so as to make navigation more intelligent, reliable and safer, which is mainly based on the Maritime Autonomous Navigation Decision-making System (MANDS). However, the state-of-the-art study lacks a systematic description and research on MANDS, and most of them mainly focus on solving a single navigation decision-making task, which is not aligned with the meaning of autonomous navigation. Therefore, this study develops a novel MANDS and installs it on a 300TEU intelligent container ship to successfully carry out real ship trials of autonomous navigation. In this paper, the definition of autonomous navigation is first proposed, which mainly contains four different navigation tasks, namely "Keep route", "Route Optimization", "Collision Avoidance", "Return Back". A well-designed architecture for MANDS is presented which includes corresponding decision-making models and communication interfaces with external systems. Furthermore, in order to realize the switching and deployment between different navigation tasks in the process of autonomous navigation, a novel hierarchical decision-making mechanism is proposed and analyzed, and finally the MANDS is integrated with perception system and ship control system respectively. Before the real ship trial, this system is tested and verified in a simulation-based environment which is composed of the electronic chart platform and ship motion simulator, we also design a scenario library for the testing work. At last, real ship trials of autonomous navigation for MANDS are carried out, which can convincingly prove the performance of MANDS. This study proposes a relative complete solution for autonomous navigation, including system modelling, integration and real ship trial. These findings offer new insight into the research of ship autonomous navigation and promote the improvement of ship intelligence and informatization level. [ABSTRACT FROM AUTHOR]

This paper shows the development of a tool with which to solve the most critical aspect of the porthole die design problem using a predictive model based on machine learning (ML). The model relies on a large amount of geometrical data regarding successful porthole die designs, information on which was obtained thanks to a collaboration with a leading extrusion company. In all cases, the dies were made of H-13 hot work steel and the billet material was 6063 aluminium alloy. The predictive model was chosen from a series of probes with different algorithms belonging to various ML families, which were applied to the analysis of geometrical data corresponding to 596 ports from 88 first trial dies. Algorithms based on the generation of multiple decision trees together with the boosting technique obtained the most promising results, the best by far being the CatBoost algorithm. The explainability of this model is based on a post-hoc approach using the SHAP (SHapley Additive exPlanations) tool. The results obtained with this ML-based model are notably better than those of a previous model based on linear regression as regards both the R2 metric and the results obtained with the application examples. An additional practical advantage is its explainability, which is a great help when deciding the best way in which to adjust an initial design to the predictive model. This ML-based model is, therefore, an optimal means to integrate the experience and know-how accumulated through many designs over time in order to apply it to new designs. It will also provide an aid in generating the starting point for the design of high-difficulty dies, in order to minimise the number of FEM (finite element method) simulation/correction iterations required until an optimal solution is achieved. It is not aimed to eliminate FEM simulation from the design tasks, but rather to help improve and accelerate the whole process of designing porthole dies. The work presented herein addresses a validation model for a very common porthole die typology: four cavity and four port per cavity dies for 6xxx series aluminium alloys. However, a wide range of research regarding the generalisation of this model or its extension to other porthole die typologies must still be carried out. [ABSTRACT FROM AUTHOR]