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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]

Cloud Computing (CC) and virtualization concepts are two advanced technologies introduced to empower distance and blended learning. Besides, they play a crucial role in equipping learners with practical skills and fostering hands-on experience to defend against cyber-attacks. Many Higher Education Institutions (HEIs) in developed countries have already embraced the promise of CC to raise educational standards. However, the pace of its adoption in developing countries has stagnated. Moreover, existing solutions in the literature are not sustainable. They either rely on on-premise infrastructure or are bound by a single cloud service provider. Consequently, they are likely prone to failures and a sudden outage. To fill this gap, this paper is a first that comprehensively addresses the above issues and introduces a federated hybrid CC system based on an extension of Apache Virtual Computing Lab (VCL). The proposed system provides an independent open-source implementation, greater configuration flexibility, and methodological improvements as compared to existing studies in the literature. In addition, it promotes the sustainability of the CC services, extensible cloud architecture, and fault tolerance. VCL is primarily focused on provisioning Virtual Laboratories (VL) for remote cybersecurity and computer networks education, with potential expansion to other domains of engineering education. In addition, this paper introduces GPT-TerminalPro, a terminal-based tool driven by OpenAI's Generative Pretrained Transformer (GPT-3.5) that provides intelligent assistance to users while performing lab tasks. To experimentally evaluate the VCL's performance, the standard Linux tools as well as the Apache benchmark and httperf HTTP load generators are utilized. VCL has been tested with 30 users and 61 virtual user computing environments provisioning to validate the overall performance. The results are fascinating: the provisioning time including all VCL background tasks is always less than a minute and utilizes fewer computing resources while providing a better user experience. This paper will encourage the adoption of CC in low-income countries. • Breaking barriers for cloud adoption in developing countries education institutions. • Proposing a sustainable and open federated hybrid cloud computing system. • Effective provisioning of remote cybersecurity virtual laboratories. • Proposing GPT3-TerminalPro, an innovative chat-enabled AI tool based on GPT-3.5. • The VM ready time including all background tasks is less than one minute. [ABSTRACT FROM AUTHOR]

[Display omitted] Artificial intelligence (AI) in education (AIED) has evolved into a substantial body of literature with diverse perspectives. In this review paper, we seek insights into three critical questions: (1) What are the primary categories of AI applications explored in the education field? (2) What are the predominant research topics and their key findings? (3) What is the status of major research design elements, including guiding theories, methodologies, and research contexts? A bibliometric analysis of 2,223 research articles followed by a content analysis of selected 125 papers reveals a comprehensive conceptual structure of the existing literature. The extant AIED research spans a wide spectrum of applications, encompassing those for adaptive learning and personalized tutoring, intelligent assessment and management, profiling and prediction, and emerging products. Research topics delve into both the technical design of education systems and the examination of the adoption, impacts, and challenges associated with AIED. Furthermore, this review highlights the diverse range of theories applied in the AIED literature, the multidisciplinary nature of publication venues, and underexplored research areas. In sum, this research offers valuable insights for interested scholars to comprehend the current state of AIED research and identify future research opportunities in this dynamic field. [ABSTRACT FROM AUTHOR]

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritizing supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation. • This paper investigates a few DL approaches to extract features of SC data. • Both RNN and CNN are applied in the same model. • An improved CNN model has been proposed for feature extraction. • An online dataset is employed to validate the proposed DL architectures. [ABSTRACT FROM AUTHOR]

Intelligent applications are highly susceptible to uncertainty and inconsistency due to the intense and intricate interactions among their autonomous components (or agents), making their verification theoretically and practically challenging. This paper presents the design and implementation of a new open-source and scalable software tool for modeling and verifying intelligent applications with commitment and trust protocols under both uncertainty and inconsistency settings, using reduction-based multi-valued model checking techniques. The proposed tool is equipped with original and novel algorithms that transform our logics of multi-valued commitment (mv-CTLC) and multi-value trust (mv-TCTL) that we recently introduced to their classical two-valued commitment (CTLC) and trust (TCTL) logic versions as well as to Computational Tree Logic (CTL). Moreover, the tool transforms the mv-CTL to CTL, and it is applicable for the classical model checking by transforming the classical logics of trust and commitment to CTL. To demonstrate the practicality and applicability of the proposed tool in real settings, we present and report experimental results over two blockchain-based applications in the healthcare domain. Finally, we provide discussions and comparisons between the proposed approaches regarding scalability and efficiency. Moreover, we provide packages of more than 11 experiments, including the ones we conduct in this paper and enhanced experiments from previous works. Our findings ensure that the proposed approaches and the software tool that implements them are highly efficient and scalable, giving accurate results under varying conditions. • Design and implementation of an open-source scalable tool for systems model checking. • System modeling with multi-valued logic capturing uncertainty and inconsistency. • Practical demonstration using blockchain-based applications in healthcare domain. • Extensive experiments showing scalability, efficiency and reliability of the tool. [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]

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]

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

The approaches based on vision transformers (ViTs) are advancing the field of medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have developed artificial intelligence methods for cancer diagnosis based on ViTs. In this paper, 98 pertinent articles since 2020 were carefully chosen from digital databases, including Google scholar, Elsevier, and Springer Link, to review the research progress of artificial intelligence methods for cancer imaging based on ViT. Method : The basic structure of ViT is introduced, and corresponding modules such as patch embedding, positional embedding, transformer encoder, multi-head self-attention (MSA), layer normalization (LN), and residual connections, multilayer perceptron (MLP) are elaborated; a comprehensive review of improved ViT models in the medical field is presented. The application of ViT technology in cancer analysis based on medical images was reviewed. Results : ViT has achieved great success in cancer diagnosis based on medical images, showing its advantages in image classification, image reconstruction, image detection, image segmentation, image registration, image fusion, and other tasks. In these task studies, the most common task is cancer image classification and segmentation. There is still a lot of room for improvement in the aspects of multi-task learning, multi-modal learning, model generality, generalization ability, and explainability, and it also faces the mutual restriction of model scale and performance. Conclusion : The ViT training model for cancer diagnosis can potentially improve. The ViT model of self-supervised learning and semi-supervised learning mechanism is promising research. The lightweight attention module design, ViTs based on mobile networks, and the development of 3DViT will promote cancer diagnosis based on medical images to be more accurate and efficient. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is a crucial area in artificial intelligence that enables the visualization and analysis of high-dimensional data. The main use of dimensionality reduction is to lower the dimensional complexity of data, improving the performance of machine learning models. Non-linear dimensionality reduction approaches, which provide higher quality representations than linear ones, lack interpretability, prohibiting their application in tasks requiring interpretability. This paper presents LXDR (Local eXplanation of Dimensionality Reduction), a local, model-agnostic technique that can be applied to any DR technique. LXDR trains linear models around a neighborhood of a specific instance and provides local interpretations using a variety of neighborhood generation techniques. Variations of the proposed technique are also introduced. The effectiveness of LXDR's interpretations is evaluated by quantitative and qualitative experiments, as well as demonstrations of its practical implementation in diverse use cases. The experiments emphasize the importance of interpretability in dimensionality reduction and how LXDR reinforces it. • Non-linear dimensionality reduction is necessary in several use cases. • Interpretability is vital for most of those use cases. • LXDR agnostically interprets any dimensionality reduction technique. • LXDR is applicable to various domains and use cases. [ABSTRACT FROM AUTHOR]

Adopting various pricing policies has been highly regarded in recent years for setting prices and increasing firms' profits. One of the most common steps in pricing is to identify costs. Since a significant part of costs is related to the corresponding supply chain, many researchers in different fields have used decision-making for simultaneous pricing and network design. However, there is no such approach in the field of healthcare. This paper tries to fill this gap by formulating a mixed-integer nonlinear bi-level programming model examining the interaction of hospitals and their medicines suppliers. At the upper level, there is a competitive market where a new firm (entrant) intends to enter the market and faces the challenge of pricing medicines and making network design decisions. At the lower level, there is a private hospital competing with a public hospital, and it also struggles with healthcare services pricing and supplier selection. A comprehensive utility function that considers healthcare services prices, quality, waiting time, health insurance, readmission rate, and referral rate is extended at this level. Three novel meta -heuristic algorithms are recommended, including bi-level, improved fruit fly, jellyfish optimization, and forensic-based investigation optimization algorithms to solve the presented complex mathematical problem. [ABSTRACT FROM AUTHOR]

Bringing artificial intelligence on board Earth observation satellites unlocks unprecedented possibilities to extract actionable items from various image modalities at the global scale in real time. This is of paramount importance nowadays, as downlinking large amounts of imagery is not only prohibitively expensive but also time-consuming. However, building deep learning solutions that could be deployed on board an edge device is challenging due to the limited manually-annotated satellite datasets and hardware constraints of an edge device. This paper addresses these challenges through harnessing a blend of data-centric and model-centric approaches to build a well-generalizing yet efficient and resource-frugal deep learning model for multi-class satellite image classification in the few-shot learning settings. This integrated strategy is formulated to enhance classification performance while accommodating the unique demands of an image analysis chain on board OPS-SAT, a nanosatellite operated by the European Space Agency. The experiments performed over a real-world dataset of OPS-SAT images delves into the interactions between data- and model-centric techniques, underscores the significance of synthesizing artificial training data and emphasizes the value of ensemble learning. However, they also caution against negative transfer in domain adaptation. This study sheds light on effective model training strategies and highlights the multifaceted challenges inherent in deep learning for practical Earth observation, contributing insights to the field of satellite image classification within the constraints of nanosatellite operations. To ensure reproducibility of our study, the implementation is available at https://github.com/ShendoxParadox/Few-shot-satellite-image-classification-OPS-SAT. • We propose data- and model-centric approach to few-shot image classification. • We establish a framework for building deep learning models from limited samples. • We investigate our approaches over the benchmark images captured by OPS- SAT. • We ensure full reproducibility of our study. • The proposed techniques offer high classification accuracy and resource frugality. [ABSTRACT FROM AUTHOR]

• One-step abductive multi-target learning with diverse noisy samples (OSAMTL-DiNS) is proposed. • OSAMTL-DiNS is applied to tumour segmentation for breast cancer (TSfBC). • OSAMTL-DiNS enables various approaches for noisy labels achieving more rational results in TSfBC. • A predictive model pre-trained with OSAMTL-DiNS for TSfBC is released. Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced technologies for different artificial intelligence applications. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced multi-target learning way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions. We released a model pre-trained with OSAMTL-DiNS for tumour segmentation in HE-stained pre-treatment biopsy images in breast cancer, which has been successfully applied as a pre-processing tool to extract tumour-associated stroma compartment for predicting the pathological complete response to neoadjuvant chemotherapy in breast cancer. [ABSTRACT FROM AUTHOR]

This research introduces Automated Cost-Duration Variance Prediction (CDVP) system to empower project managers to proactively predict project budget, duration, cost variance and duration variance. The research involved the development and comparison of a total of 48 machine learning (ML) algorithms, with 12 algorithms dedicated to each predictive model. To conduct the study, a dataset comprising 1277 historical cases of Field Canals Improvement Projects (FCIPs) was utilized. Factor analysis and stepwise regression analysis were employed for feature selection where five key drivers were identified from the initial 25 parameters. The paper optimized the performance of each algorithm based on Bayesian optimization. The proposed system produces four outputs: project cost, project cost variance, project duration, and duration variance. The developed ML models were able to predict all of the system's outputs with an adjusted determination coefficient R2 score greater than 0.93 and Mean Absolute Percentage Error (MAPE) less than 8.97. Environmentally, this research recommends to assess CO 2 emissions as a performance criterion to select the low environmental impact algorithms. As a result, Light Gradient Boosting Machine (LightGBM) and AdaBoost Regressor were green sustainable algorithms that emitted on average 58 and 69 CO 2e grams per computational hours. To enhance interpretability, the study employed the SHapley Additive exPlanations (SHAP) technique to provide explanations for the key drivers. [ABSTRACT FROM AUTHOR]

In artificial intelligence applications, advanced computational models, such as deep learning, are employed to achieve high accuracy, often requiring the execution of numerous operations. Conversely, lightweight computational models are typically more resource-efficient, making them suitable for various devices, including smartphones, tablets, and wearable technology. This paper presents an ultra-low-computation solution for interpreting sign languages to assist deaf and hard-of-hearing individuals without needing specialized hardware or significant computational resources. The proposed approach initially performs data abstraction on the input data. During this process, the image is systematically scanned from various perspectives, and the collected information is then encoded into a one-dimensional vector. Subsequently, the abstracted information undergoes processing through a Fully Connected Neural Network (FCN), resulting in highly accurate output. We also introduced two abstraction methods, namely Opaque and Glass, inspired by the interaction of light with different types of objects. The proposed abstractions facilitate the comprehension of the hand gesture's outer boundary as well as its row-wise and column-wise density of pixels. Our experiments on three datasets confirm the efficiency of the proposed method, achieving an accuracy of 99.4% in recognizing American Sign Language, 99.96% accuracy in recognizing Indian Sign Language, and 99.95% accuracy in recognizing Bangla Sign Language. Notably, the model size and the number of MAC operations are significantly smaller than state-of-the-art computational models trained on the same datasets. • We introduced an ultra-low-computation method for interpreting sign languages. • We developed a new encoding for the abstraction of hand gesture images. • Our approach significantly reduces both model size and MAC operations. • We achieved high accuracy in understanding American, Indian, and Bangla SLs. [ABSTRACT FROM AUTHOR]

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model. • Improved net output power through the integration of AI in a 2DOF control system. • Reduction of degradation using a data-driven 2DOF control system. • Development of data-driven reference generators and feedforward controllers. • Comparative performance evaluations of fuzzy models and neural networks. [ABSTRACT FROM AUTHOR]

• The paper explores AI decision-making to enhance transparency of CIN diagnosis using colposcopy images. • The study presents interpretable insights for clinical validation using different XAI methods. • Saliency method showed best result, using gradients to reveal likely malignancy traits. • Expert Clinic Validation of results conducted, along with statistical analyses. Although artificial intelligence techniques have performed well in analysing medical images, it is essential to consider the degree of understanding or interpretation in evaluations. This is especially important when diagnostic support is involved, as the trustworthiness of machine learning predictions needs to be clarified and evaluated. For deep learning algorithms to be translated into clinical practice, they must be made less opaque. To examine the classification of cervical dysplasia-diagnosed patients, we will explore explainable artificial intelligence (XAI) approaches, by implementing seven different interpretation techniques (Saliency, XRAI, Integrated Gradients, Smooth Gradients, Smooth Integrated Gradients, Grad-CAM, Smoothgrad-CAM). These XAI techniques shall be explored in order to assess the performance of four Convolutional Neural Network models (AlexNet, VGG16, MobileNet, and InceptionNet). Our results show that saliency qualities most closely match expert annotations, and there is a moderate to strong association between a model's sensitivity and the agreement between humans and computers. We also discovered a relevant relationship between expert insights and computational learning. This highlights the potential role of human expertise in influencing effective computer learning. [ABSTRACT FROM AUTHOR]

Advanced Process Control (APC) has become an increasingly pressing issue for the semiconductor industry, particularly in the new era of sub-5 nm process technology. To minimize human input in analyzing equipment state and output quality, Virtual Metrology (VM), one such incredibly reliable and resource-saving APC tool built on artificial intelligence and machine learning techniques, was introduced as an aide to real metrology and is aimed at predicting semiconductor product quality based on equipment status/process variables and sampled actual metrology. In this paper, VM efforts for semiconductor manufacturing applications are, for the first time, systematically reviewed. Based on comprehensive mining of literature, a generic architecture for semiconductor VM is presented, and the general requirements are discussed. Moreover, the state-of-the-art VM works for prominent processes involved in semiconductor manufacturing are surveyed, and the unique contributions of those VM predictive works are summarized and compared. Nevertheless, VM is still in its infancy stage, and there are significant hurdles that prevent wider VM implementation and acceptance in semiconductor industry. In this end, a critical analysis is provided to reveal the research gaps and discuss the challenges, such that further research can be simulated in the future. It is hoped that this work could illuminate researchers to understand the needs and future directions towards developing VM technology for semiconductor processes. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) and Artificial Intelligence (AI) are technologies pioneers in innovation and alteration in several domains. AR allows the creation of an entirely new and interactive experience for users. However, there are several drawbacks in developing AR applications, such as the marker identification process and the creation of content itself. These are very time-consuming procedures and require ad-hoc development. The advantages of using AI to solve AR limitations have recently been explored in literature. Motivated by these findings, in this paper it is proposed DeepReality, a software toolkit plug-in for Unity 3D. It is conceived for allowing developers to integrate any Deep Learning (DL) models into Unity, through AR Foundation and Barracuda inference engine. DeepReality is aimed at simplifying and streamlining the usage of DL models in conjunction with AR. As such, users skilled in Unity and DL can easily create mobile applications (iOS and Android) to: extract visual features of real-world objects (framed with the device camera) via DL; Show on-screen content on top of those real-world objects, via AR. DeepReality performs object semantic processing within the scene, and extended semantic effects for incongruent objects, overcoming the environmental tracking, which is feature-based. In order to test DeepReality usability, experiments have been performed on the execution time and memory usage data, demonstrating the feasibility and possibility of integrating and using DNNs models in mobile applications for AR. The complexity analysis confirms that DeepReality can be completely executed on mobile devices. DeepReality is also open-source and it is freely available in the Unity asset store. By fostering accessible AI-AR integration, DeepReality addresses key shortcomings in existing approaches, encapsulating contributions such as versatile DL integration, open-source accessibility, operational validation, and comprehensive metrics analysis. DeepReality empowers developers to transcend boundaries, enriching AR applications with AI's transformative potential. Our proposed framework fosters benchmarking, comparison, and a future harmonised by AR-AI synergy. [Display omitted] • DeepReality is an open-source toolkit, freely available in the Unity asset store. • DeepReality integrates Deep Learning models for streamlined mobile apps. • DeepReality leverages AR Foundation and Barracuda inference engine. • It demonstrates efficient execution and memory usage in mobile AR. • Complexity analysis confirms that it can be fully executed on mobile devices. [ABSTRACT FROM AUTHOR]

Intelligent Decision Support Systems (DSS) have gained considerable interest within the field of construction engineering through the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies. These systems possess the capacity to enhance decision-making processes and optimize the results of projects. Nevertheless, it is imperative to conduct a thorough and inclusive examination that incorporates and evaluates the various methodologies employed within this particular discipline, thereby bridging the existing void in research. The current state of research in the field of AI and ML in construction engineering is characterized by a dearth of comprehensive reviews that encompass the wide array of methodologies utilized. Although certain studies have delved into specific facets of this subject, there remains a novelty and research gap in terms of a holistic examination of the topic. The objective of this review is to conduct a comprehensive analysis of AI and ML methodologies in the field of construction engineering, with a specific emphasis on their practical applications, advantages, and constraints. The study also ascertains potential areas for future research and improvement in the domain of Intelligent DSS. This study employed a rigorous approach that involved an extensive review of scholarly literature and empirical investigations from 2001 to June 2023. The findings of the analysis indicate a noticeable increase in the number of research papers focusing on Intelligent DSS within the field of construction engineering during the last twenty years. The field has witnessed the emergence of key research areas, namely the utilization of Internet of Things (IoT), explainable AI, supply chain optimization, sustainable construction practices, and human–machine collaboration. The results of this study hold significant implications for the construction industry. The incorporation of AI and ML techniques within Intelligent DSS can effectively tackle various issues such as cost overruns, project delays, and safety concerns. [ABSTRACT FROM AUTHOR]

Proactive identification and the management of disruption risks play a crucial role in the achievement of a global supply chain's aims. Given the velocity and volume by which such disruption events occur, it is impractical to expect supply chain managers to determine the occurrence of such events manually. Given the pressures facing global supply chains due to the COVID-19 crisis, it is important for supply chain managers to proactively identify disruption risks to their supply chains and manage them to either achieve the outcomes or develop plans by which resilience against them can be built. In this paper, we demonstrate how the integration of natural language processing and reinforcement learning, which are fundamental artificial intelligence methods, can be used to assist supply chain risk managers in the timely identification of such disruption events. We explain in detail our proposed approach, namely RL-SCRI and show its superiority over the current models in achieving its aim. [ABSTRACT FROM AUTHOR]

The significant proliferation of AI-empowered systems and machine learning (ML) across various examined domains underscores the vital necessity for comprehensive and customised explainability frameworks to lead to usable and trustworthy systems. Especially in the medical domain, where validation of methodologies and outcomes is as important as the adoption rate of such systems, the requirements of the depth and the level of abstraction of the explainability are particularly important and necessitate a systemic approach to ensure a proper definition. Explainability and interpretability are important usability and trustworthiness properties of AI-empowered systems and, as such, constitute important factors for technology acceptance. In this paper, we propose a novel framework for explainability requirements in AI-empowered systems using the Technology Acceptance Model (TAM). This framework employs targeted ML (hierarchical clustering, k-means or other) to acquire a user model for personalised, multi-layered explainability. Our novel framework integrates a rule-based system, which guides the degree of trustworthiness to be achieved based on user perception and AI literacy level. We test this methodology in the case of AI-empowered medical systems to (1) assess and quantify the doctors' abilities and familiarisation with technology and AI, (2) generate layers of personalised explainability based on user ability and user needs in terms of trustworthiness and (3) provide the necessary environment for transparency and validation. To assess and quantify the doctors' abilities we have considered Rapid Estimate of Adult Literacy in Medicine (REALM) a tool commonly used in the medical domain to bridge the communication gap between patients and doctors. • Framework for personalised explainability in Artificial intelligence, based on Levels. • Levels refer to AI literacy level and level of abstraction. • Behavioural models were used that measure perception towards an innovation. • Framework is evaluated and implemented in an AI-empowered medical application. [ABSTRACT FROM AUTHOR]

The design of real-world engineering systems deals with high complexity at the system and subsystem levels, as well as the interaction between the subsystems involved. Multidisciplinary Design Optimization (MDO), which aims to integrate the optimization algorithms in both system and subsystem levels along with exploring mechanisms of synergistic action among subsystems, is a renowned methodology to design complex engineering systems. In Multi-Agent Systems (MASs), as a complex system, to achieve the best system performance, agents have to work their individual tasks in a coordinated manner and in cooperation with other agents. Given some architectural resemblance between MDO and MAS, especially in subsystems interaction (agents, in MAS case), this paper addresses proposing a multi-agent multidisciplinary system design optimization strategy. This strategy by taking advantage of the capabilities of MDO proposes a consolidated framework for the optimal design of real world engineering systems. This strategy is extendable to multi-agent systems in a diverse range of missions and tasks. In this regard to illustrating its application a distributed cooperative search and coverage (CSC) mission in uncertain environments is considered as a case study for designing a MAS. The proposed framework contributes to designing a MAS by applying the history data of previously completed missions and the normalized cross-correlating (NCC) algorithm as well. The effectiveness of the proposed framework is demonstrated via simulations and comparison. [ABSTRACT FROM AUTHOR]

Highlights • Ensemble classifier system is applied to biometric verification. • For each base classifier, the classifier's competence is separately calculated. • A lip geometrical features measurements are proposed. • Experiments confirmed our method outperforms other state-of-the-art methods. Abstract Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of classifiers, also known as an ensemble, can outperform the ensemble's individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data. We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biometric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection. [ABSTRACT FROM AUTHOR]

Highlights • An integrated scheme is proposed for prognostics of Lithium-ion batteries. • The prognostic scheme contains pre-processing and prediction modules. • Novel imputation techniques handle battery data with missing observations. • The proposed multiple imputation technique reveals the uncertainty of estimations. • Extreme learning machines predict the remaining useful life over a long horizon. Abstract This paper focuses on the development of a prognostic scheme for estimating the remaining useful life (RUL) of Lithium-ion batteries with missing observations. The scheme has two main modules based on extreme learning machines: pre-processing and prediction. The pre-processing module uses novel single and multiple imputation techniques to estimate the missing observations. The prediction module aims to obtain precise predictions even in the presence of missing observations and with the related uncertainty. The pre-processing module sends imputed subsets of samples to the prediction module, which makes use of extreme learning machines for one-step and multi-steps predictions. The prediction module contains various multi-steps prediction strategies including iterative, direct and DirRec, which use the constant-current experimental capacity data for the long-term prediction of the remaining useful life. Accurate prediction of RUL requires continuity in the time-series dataset. The proposed scheme is designed to build an intelligent prediction system with the ability to handle time-series data containing missing values and is robust enough to generate a complete time-series dataset and, then, make short or long term predictions. The experimental results confirm that the proposed framework can be beneficial for intelligent diagnostic and prognostic systems related to battery as well as other wide range of applications. The main focus of the paper is the development of an integrated imputation-prediction scheme and not the evaluation of individual performances of the imputation or prediction techniques. [ABSTRACT FROM AUTHOR]

Highlights • Process model for transformation of formalized sentences into relational database. • Algorithms of functions and procedures for each step of transformation. • Meta model for the structure and content of formalized sentences in the relational database. Abstract Verbalized text contains knowledge necessary and sufficient for transfer of numerous human cognitions. The question is how this knowledge was saved into text. Authors believe that they found an important idea how to assemble knowledge into nodes of knowledge. Similar methods in the field of knowledge networks exist, but none of them does it in the same way as the Node of Knowledge (NOK) method. Basic terms and concepts in a language are represented in words whose meaning is final and cannot be divided in subterms. More complex meaning can be achieved by combining words in sentences. According to authors' opinion, each sentence includes connective medium for words in the sentence (related to semantic reasons), which is inwrought in the Node of Knowledge method. Using the Node of Knowledge method each sentence can be presented as a network of connected words. This network is enriched with links between words so a computer can interpret meaning and knowledge of the sentence in the same way an intelligent person does. A formalized and semantically enriched record of sentences (called Formalized Node of Knowledge – FNOK) is developed. Authors find that in this way, even without statistical text analysis, an algorithm can give correct answer to a question set based on written text. This paper presents the system for transformation of textual knowledge expressed in natural language sentences into a relational database. The system is a part of a larger knowledge-based system based on the Node of Knowledge (NOK) conceptual framework for knowledge-based system development. This paper starts with sentences written in the formalized and enriched form for which a logical transformation into the structure of a relational database is proposed. The system and algorithms for the transformation of formalized sentences into n-tuples of the relational database are distributed and represented in several steps. The research has shown that it is possible to store semantically enriched sentences in relational databases. The solution presented in this paper is important for further development of the system for receiving questions from users and providing answers (i.e. question-answering system), with the ability to use well-developed relational SQL languages. Relational database of texts enables numerous applications in the field of expert and intelligent systems. [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]

In the rapidly evolving domain of artificial intelligence, chatbots have emerged as a potent tool for various applications ranging from e-commerce to healthcare. This research delves into the intricacies of chatbot technology, from its foundational concepts to advanced generative models like ChatGPT. We present a comprehensive taxonomy of existing chatbot approaches, distinguishing between rule-based, retrieval-based, generative, and hybrid models. A specific emphasis is placed on ChatGPT, elucidating its merits for frequently asked questions (FAQs)-based chatbots, coupled with an exploration of associated Natural Language Processing (NLP) techniques such as named entity recognition, intent classification, and sentiment analysis. The paper further delves into the customization and fine-tuning of ChatGPT, its integration with knowledge bases, and the consequent challenges and ethical considerations that arise. Through real-world applications in domains such as online shopping, healthcare, and education, we underscore the transformative potential of chatbots. However, we also spotlight open challenges and suggest future research directions, emphasizing the need for optimizing conversational flow, advancing dialogue mechanics, improving domain adaptability, and enhancing ethical considerations. The research culminates in a call for further exploration in ensuring transparent, ethical, and user-centric chatbot systems. • Explore chatbot technologies, including ChatGPT and applications in domains. • Investigate the pros and cons of rule-based, retrieval, generative, and hybrid chatbots. • Emphasize ChatGPT in FAQs chatbots and NLP integration. • Discuss ChatGPT customization, knowledge base, and ethics. • Highlight real-world use of generative chatbots and emphasize research on flow and ethics. [ABSTRACT FROM AUTHOR]

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed, and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better. [ABSTRACT FROM AUTHOR]

• We propose a novel hybrid model namely MEEMD-Mix-KELM to forecast crude oil price. • The emerging MEEMD, with mode-splitting prevention, is used to decompose oil price. • We create a new mix-kernel for ELM by absorbing merits of local and global kernels. • Empirical results show that the proposed hybrid model achieves significant effects. It is of vital importance for governments, enterprises, and investors to forecast crude oil prices accurately, while this task is beset with difficulties and challenges due to the complex patterns in oil prices. This paper aims to propose a novel hybrid method to model and forecast the crude oil price by integrating median ensemble empirical mode decomposition (MEEMD) and mix-kernel extreme learning machine (Mix-KELM). Firstly, the emerging MEEMD is employed to decompose the crude oil price into several simple subseries. Secondly, a novel mix-kernel is developed for extreme learning machine (ELM) by combining the advantage of the local kernel (i.e., Radial Basis Function in learning ability) and global kernel (i.e., Sigmoid in generalization ability), with weights of the kernels optimized through genetic algorithm. Thirdly, the proposed Mix-KELM is applied to forecast the subseries of crude oil price, and the sub-forecasting results are integrated to generate the final results. The empirical results show that our proposed MEEMD-Mix-KELM model with different forecasting horizons significantly outperforms the benchmarks in terms of forecasting accuracy and robustness test. Taking one-step-ahead forecasting as an example, the proposed model exhibits the lowest prediction errors in terms of mean absolute error, symmetric mean absolute percentage error, and root mean squared error with values of 1.1767, 0.0135, and 1.5717, respectively. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed. [ABSTRACT FROM AUTHOR]

• To design an automatic multi-disease detection in CXR images by ensemble model. • To adopt ODeepLabv3 to perform segmentation process for multi-disease detection. • To adopt a new ensemble model named Optimized Ensemble Transfer Learning (OETL). • To design a Mutation Rate-based Lion Algorithm (MR-LA) to obtain high accuracy. • To optimize the epochs in ensemble classifier with the help of MR-LA algorithm. Chest radiography is a comparatively low-cost, most probably applied in a medical process that brings crucial information to discover diagnostic conclusions. Chest X-rays (CXR) are used in the prognosis of chest diseases that includes asthma, lung cancer, pneumonia, and COVID-19. Artificial intelligence detects the multi-disease automatically to enhance its proficiency and performance by solving image detection complications with various machine learning and deep learning approaches. Convolutional Neural Network (CNN) has been designed for the advancement of computerized recognition systems. For the classification of medical images, texture, shape, size, and tissue constitution are essential features for detecting diseases. Hence, huge input features are merged with deep CNN validated to increase the effectiveness of CXR analysis. In addition, multi-scale features are visualized in CNN for the detection of variable sizes of thoracic diseases. This paper has implemented a novel multi-disease diagnosis model using chest X-ray images through deep learning approaches, and thus, it helps to minimize the computational cost. The standard CXR images are gathered from the standard datasets. After, the pre-processing is performed for cleaning and contrast the images. Further, the image segmentation is carried out using Optimized DeepLabv3 (ODeepLabv3), where the optimization of parameters in DeepLabv3 is done by a new Mutation Rate-based Lion Algorithm (MR-LA). Then, multi-disease classification is carried out through Optimized Ensemble Transfer Learning (OETL), where the OETL is carried out through VGG16, ResNet, ImageNet, MobileNet, GoogleNet, Inception, and Xception. Here, the parameter optimization of all models is done by the same MR-LA. The proposed model improved the effectiveness and performance in expressions of sensitivity, precision, and specificity factors and get higher classification and detection accuracy. The outcome of the recommended method over other models is identified by the relative analysis conducted. [ABSTRACT FROM AUTHOR]

This paper proposes an end-to-end optical camera communications (OCC) system using an autoencoder neural network trained to recover the transmitted symbols. Although OCC techniques have been extensively studied in the literature, using an autoencoder that learns the transmitter and receiver functions jointly is a novel concept with significant prospects. Furthermore, we investigate the performance impact caused by the overlooked optical-to-electrical (O2E) conversion process of real-world OCC receivers. The autoencoder learning model captures these typically undesired changes in image sensors for the design of constellation symbols and reception schemes. For the simulation, we constructed an end-to-end autoencoder for a color space-based OCC system and measured the O2E performance effect. The proposed autoencoder communication system is analyzed and compared using the symbol error rate (SER) across various OCC detection systems. Despite the subtle spectral responsivity variations in image sensors, our numerical results indicate that the autoencoder model can learn to recover the transmitted data while minimizing SER and meeting the lighting requirements. These findings may interest a broad range of applications, particularly in IoT sensor networks. Among all the image sensors we studied, the OCC system with Bayer CFA-based signal detection showed superior performance. [ABSTRACT FROM AUTHOR]

The tremendous growth the pharmaceutical field witnessed especially concerning drug-development, caused drug-related experiments such as those concerned with estimating their interactions with targets harder to conduct. This issue led researchers to transition from vivo and vitro to selico experimenting methods, which deploy artificial intelligence (AI) techniques such as recommender systems (RS) to solve problems at hand. However, most RSs depend on global features such as indices, which render them incapable of fully understanding the behaviors of individuals. In this paper, we introduce our Convolutional Recurrent Deep Learning model-based RS approach, or 'RSCRDL', which tackles the interaction prediction problem from a natural language processing approach's (NLP) point of view. RSCDRL processes the textual representations of both protein sequences and drugs in order to learn the implicit features of the tokens forming them, rendering it able to process unseen and non-reactive individuals indirectly. We also present a greedier variant of RSCRDL that can exploit additional drugs explicit features for better predictive abilities. Experiments performed on two real-world datasets show significant outperformance achieved by our models over two general-purpose state-of-the-art RSs, while other experiments conducted on orphan protein sequences helped deduce drug candidates that are assumed to be capable of inhibiting their activities. [ABSTRACT FROM AUTHOR]

Predictive maintenance advocates the use of artificial intelligence to analyze big data and provides support for monitoring health conditions and planning maintenance activities in smart manufacturing systems. However, deep networks with massive parameters incur high computational costs, hindering its practical applications. Knowledge distillation (KD) transfers knowledge from a deep network to a lightweight network at the cost of an evident decline in diagnostic accuracy. Instead of an accuracy-size trade-off, a framework for creating a lightweight residual network with higher diagnostic accuracy is proposed in this paper. First, the generalization ability of the teacher network is enhanced based on a domain-adversarial neural network (DANN) with normalization attention mechanism (NAM) and ResNet18. Then, a small student network, ResNet6, efficiently learns the knowledge from its well-learned teacher network through the improved probability KD (IPKD) and uniform quantization. The IPKD is designed to obtain the utmost knowledge, represented as probability distributions of labels and feature space, and narrow the gap between teacher and student networks. Meanwhile, the uniform quantization is incorporated into the distillation process for quantization and distillation co-optimization, further compressing the student network. Experiments on two open-source and one wheelset bearing sets are conducted for performance verification and comparison. The results demonstrate that the obtained lightweight student network has a similar accuracy to the deep network for cross-domain fault diagnosis of bearings with various damages and speeds, even bearing sources, and its quantized network has a much smaller size while retaining a comparatively similar accuracy, which makes it more practical for actual industrial equipment. [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]

This paper deals with the topic of learning from unlabeled or noisy-labeled data in the context of a classification problem. In the classification problem the outcome yields one of a discrete set of values thus, assumptions on them could be established to obtain the most likely prediction model at the training stage . In this paper, a novel case-based model selection method is proposed, which combines hypothesis testing from a discrete set of expected outcomes and feature extraction within a cross-validated classification stage. This wrapper-type procedure acts on fully-observable variables under hypothesis-testing and improves the classification accuracy on the test set, or keeps its performance at least at the level of the statistical classifier. The model selection strategy in the cross validation loop allows building an ensemble classifier that could improve the performance of any expert and intelligence system, particularly on small sample-size datasets. Experiments were carried out on several databases yielding a clear improvement on the baseline, i.e., SPECT dataset A c c = 86.35 ± 1.51 , with S e n = 91.10 ± 2.77 , and S p e = 81.11 ± 1.61 . In addition, the CV error estimate for the classifier under our approach was found to be an almost unbiased estimate (as the baseline approach) of the true error that the classifier would incur on independent data. [ABSTRACT FROM AUTHOR]

Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI. [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]