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In recent years, the research pertaining to speech recognition technology in the Kazakh language has gained significant importance. This is due to the increasing demand for natural language processing applications in the region where Kazakh is predominantly spoken. Thus, there exists an urgent requirement for precise and dependable speech recognition systems. The research study examines the application of sophisticated deep learning methodologies, such as Natural Language Processing (NLP) and Hidden Markov Model (HMM), in facilitating speech recognition for the Kazakh language. Additionally, the investigation delves into how various techniques, including data preprocessing, acoustic modeling, and language modeling, can aid in devising effective speech recognition systems. The article deliberates on the feasible uses of speech recognition technology in the geographic area where Kazakh language is spoken and outlines its future research prospects. The investigation underscores the significance of persistent inquiry in this realm to confront distinctive obstacles encountered in creating speech recognition systems for languages with restricted resources. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning enabled predictive nucleic acid amplification tests analysis. • On-site fluorescence signal processing powered by cloud computing. • Accurate prediction can be obtained using the early 22.5% data. • Approach can be universally extended to other areas of biomedical research. Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research. [ABSTRACT FROM AUTHOR]

With the latest development in information technology, smart campuses have become a topic that attracts attention in the world of education, especially in higher education. The smart campus has the potential to change the way of managing, accessing facil- ities, and teaching and learning process. The smart campus can collaborate various latest technologies such as edge computing, blockchain, deep learning, virtual reality, and other trend recent technologies. Therefore, this research is a guideline for developing an effective and efficient smart campus system with the latest information and communication technology. This paper explains smart campus technology, features, architecture that have been implemented in real life. Thirty-two papers will be analyzed using the Systematic Literature Review (SLR) method. Hopefully, this guideline can be a valuable reference for smart campus designers, researchers, and practitioners interested in developing innovative and efficient smart campus systems. [ABSTRACT FROM AUTHOR]

Municipal papers and polyethylene terephthalate (PET) wastes are primary constituents of municipal solid waste (MSW) which necessitates effective disposal. Co-pyrolysis is an efficient transformation technology for the production of valuable gas and liquid fuels. Therefore, co-pyrolysis characteristics, kinetics, gas and liquid products of cellulose and PET mixtures were investigated in a two-stage reactor by multiple characterizations coupling with deep learning approach. The results indicated that there exhibited positive interaction for cellulose and PET co-pyrolysis in terms of mass difference (δ m) and mass loss rate difference (δ r) between experimental and calculated values. The thermal decomposition process and synergistic effects (δ m and δ r) were accurately predicted by deep learning approaches (R 2 =0.9823–0.9993) using temperature, catalyst, mixing ratio and heating rate variables. Feature importance revealed that the temperature, catalyst, heating rate and mixing ratio were in the descending order to affect the remaining mass from TG curves. And the temperature played a predominant role in synergistic effects. The Flynn-Wall-Ozawa (FWO) method was more suitable for fitting the pyrolysis process to obtain activation energy (E a) with higher R 2 (0.94714–0.99640) compared with Kissinger-Akahira-Sunose (KAS) method. When the mixing ratio of cellulose and PET was 1:1, the E a was significantly reduced to 80.73 KJ/mol, and further reduced to 73.04 KJ/mol by introducing the catalyst, as compared to E a of individual cellulose (178.95 KJ/mol) and PET (198.34 KJ/mol). Co-pyrolysis of cellulose and PET led to a reduction of carbohydrates, furans, alcohols, ketones, and phenols, while the content of acids was remarkably increased which was consistent with the observation in FTIR that the absorption peak of C＝O became sharp and strong. The introduction of ZSM-11 resulted in an increase in aromatics and a decrease in acids content owing to aromatization and deoxygenation. This work is expected to offer beneficial guidance for the efficient disposal of MSW and the application of deep learning methods. [Display omitted] • The interactive pathway for catalytic co-pyrolysis of cellulose and PET was proposed. • The activation energy reached the lowest 73.04 KJ/mol when the mixing ratio was 1:1. • Optimized convolutional neural network predicted synergistic effects with R 2 > 0.99. • Feature importance for TG was temperature > catalyst > heating rate > mixing ratio. [ABSTRACT FROM AUTHOR]

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper's ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies. • The proposed framework can generate a comprehensive introduction based on users' brief inputs. • This paper demonstrates a three-testing-threshold approach to check the content quality. • The introduction generated through this method has less than 3% similarity to the original text by comparison. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (COD eff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SS eff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production. [Display omitted] • A new deep learning model was proposed for real-time estimation of water quality. • The new model was based on convolutional neural network, long-term short-term memory and attention mechanism. • The new model can guarantee wastewater reuse and reduce the wastewater treatment cost. [ABSTRACT FROM AUTHOR]

3D printed ceramic parts often have defects due to their inherent brittleness. These defects are of various scale sizes. Detecting these defects, especially with multiple sizes, is a challenging task in the field of detection. Furthermore, there is a lack of surface defect real-time detection systems suitable for industrial applications in this field. Based on this, this paper makes two contributions: the design and development of a real-time defect detection system, and the proposal of a multiscale defect detection method for 3D printed ceramic surfaces within this system. Firstly, the paper establishes the overall structure of the real-time detection system for surface defects on 3D printed ceramic components, and describes the hardware and software components of the system. On this basis, a dataset of ceramic surface defect images is collected and constructed. Then, experimental analyses point out the shortcomings of the You Only Look Once version-5 (YOLOv5) model for multiscale defect detection. To address the shortcomings, the YOLOv5 model is optimized from three aspects, resulting in the Deep separable convolution + residual network-SKNetwork-Efficient Channel Attention Network-YOLOv5 (DepRes-SK-ECA-YOLOv5) model for multiscale defect detection. This model improves the ability to extract and fuse features of defects at different scales. The experimental results show that the DepRes-SK-ECA-YOLOv5 model can achieve 93.5 %, 91.6 %, 94.3 %, and 0.198 s for Precision, Recall, mAP, and Speed for the test set, respectively. Finally, the paper designs the workflow for the system software. The system hardware and system software are integrated to form a real-time detection system. The performance of the detection system is verified through experiments. [ABSTRACT FROM AUTHOR]

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening ailment characterized by the uncontrolled proliferation of cells. Breast cancer (BC) represents the most highly infiltrative neoplasms and constitutes the primary cause of mortality in the female population due to cancer-related complications. Consequently, the imperative for early detection and prognosis has emerged as a means to enhance long-term survival rates and mitigate mortality. Emerging artificial intelligence (AI) technologies are being utilized to aid radiologists in the analysis of medical images, resulting in enhanced outcomes for individuals diagnosed with cancer. The purpose of this survey is to examine peer-reviewed computer-aided diagnosis (CAD) systems that have been recently developed and utilize machine learning (ML) and deep learning (DL) techniques for the diagnosis of BC. The survey aims to compare these newly developed systems with previously established methods and provide technical details, as well as the advantages and disadvantages associated with each model. In addition, this paper addresses several unresolved matters, areas of research that require further exploration, and potential avenues for future investigation in the realm of advanced computer-aided design (CAD) models utilized in the interpretation of medical images. Furthermore, the integration of Internet of Things (IoT) in BC research and treatment holds immense significance by facilitating real-time monitoring and personalized healthcare solutions. IoT devices, such as wearable sensors and smart implants, enable continuous data collection, empowering healthcare professionals to track patients' vital signs, response to treatment, and overall health trends, fostering more proactive and tailored approaches to BC management. Moreover, the advent of 5G technology in BC applications promises to revolutionize communication speeds and data transfer, enabling rapid and seamless transmission of large medical datasets. This high-speed connectivity enhances the efficiency of remote diagnostics, telemedicine, and collaborative research efforts, ultimately accelerating the pace of innovation and improving patient outcomes in BC care. The present study aims to examine various classifiers utilized in ML and DL methodologies for the purpose of diagnosing BC. Research findings have demonstrated that DL has superior performance compared to standard ML methods in the context of BC diagnosis, particularly when the dataset is extensive. The existing body of research indicates that there are significant gaps in knowledge that need to be addressed in order to enhance healthcare outcomes in the future. These gaps highlight the pressing need for both practical and scientific research in the field. Finally, IoT and 5G will be how they can be used in order to enhance BC detection, treatment and patient care. • Cancer, characterized by uncontrolled cell proliferation, poses a significant threat to human life. • Breast carcinoma is highly invasive and a leading cause of female cancer-related mortality, emphasizing the need for early detection and prognosis. • Emerging AI technologies are assisting radiologists in analyzing medical images, improving cancer diagnosis outcomes. • This survey focuses on recent computer-aided diagnosis (CAD) systems using machine learning and deep learning for breast carcinoma diagnosis. • The paper identifies unresolved research areas and future investigation prospects in advanced CAD models for medical image interpretation. [ABSTRACT FROM AUTHOR]

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. [ABSTRACT FROM AUTHOR]

In document forensics, identifying ink mismatches is crucial for detecting forgeries and determining document authenticity. However, identifying and separating specific inks from paper can be challenging. Hyperspectral imaging can capture unique spectral patterns exhibited by inks composed of different materials, even when their colors appear identical. Hyperspectral document analysis (HSDI) can be employed to authenticate documents by analyzing the ink. Earlier studies had insufficiently accurate black ink detection, so this paper proposes a novel approach using deep learning and pre-processing methods to improve black ink detection accuracy. The proposed approach uses supervised deep learning to identify ink mismatches in hyperspectral document images. The study evaluated the performance of the proposed model using a hyperspectral image dataset consisting of UWA (University of Western Australia) writing ink in both blue and black colors and different types of artificially identical color inks mixed in various ratios to find ink mismatches. The results showed that the proposed system performed better than other systems reported in the literature, improving the average accuracy by up to 0.18% for blue and 0.36% for black. The proposed method accurately detects ink mismatches and identifies various inks based on their unique spectral response, rendering it highly beneficial for applications in document forensics. [ABSTRACT FROM AUTHOR]

This paper introduces a new visual dataset and framework to facilitate computer-vision-based traffic monitoring in high density, mixed and lane-free traffic (TRAMON). While there are advanced deep learning algorithms that can detect and track vehicles from traffic videos, none of the existing systems provides accurate traffic monitoring in mixed traffic. The mixed traffic flows in developing countries often includes the types of vehicles that are not widely known by the existing visual datasets. The computer vision algorithms also face difficulties in detecting and tracking a high density of vehicles that are not following lanes. This paper proposes a large-scale visual dataset of >282,000 labelled images of traffic vehicles, as well as a comprehensive framework and strategy to train common deep-learning-based computer vision algorithms to detect and track vehicles in high density, heterogeneous and lane-free traffic. A systematic evaluation of results shows that TRAMON, the proposed visual dataset and framework, performs well and better than the common visual dataset at all traffic densities. • We develop TRAMON, a large scale visual database and framework, for the detection and tracking of vehicles in high density, mixed and lane-free traffic • TRAMON outperforms popular 'generic' visual datasets such as Microsoft COCO at all traffic densities. • TRAMON can classify 15 types of urban vehicles in mixed traffic conditions. • TRAMON's highly accurate object detection enables us to not only estimate the traffic volume and density but also to track vehicle trajectories without interruption. [ABSTRACT FROM AUTHOR]

• Proposing physics-informed DCNN for HFI-SCB temperature field reconstruction. • Proposing Deep MC-QR method to quantify data uncertainty caused by noise. • Proposing HFI-SCB reliability analysis method based on interval multilevel BN. Temperature field reconstruction is essential for analyzing the reliability of a high-density functionally integrated satellite circuit board (HFI-SCB). As a representative deep learning model, the deep convolutional neural network (DCNN) is a powerful tool for reconstructing the HFI-SCB temperature field. However, DCNN needs a lot of labeled data to learn its parameters, which is contrary to the fact that actual satellite engineering can only acquire noisy unlabeled data. Thus, this paper proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing the HFI-SCB temperature field and quantifying the data uncertainty caused by sensor noise. The proposed method combines a DCNN with known physics knowledge to reconstruct an accurate HFI-SCB temperature field using only monitoring point temperatures. Besides, the proposed method can quantify the data uncertainty by the Monte Carlo quantile regression. Based on the reconstructed temperature field and the quantified data uncertainty, this paper builds an interval multilevel Bayesian Network to analyze the HFI-SCB reliability. Two case studies are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

Strain-based structural health monitoring technology has been widely used in the field of transportation. The existing strain damage identification methods have defects such as complex process, lag in state evaluation, and low intelligence. This paper adopts the deep learning method to establish a network model that uses the strain field information to map directly to the damage information, and takes a subway bolster as the engineering background to realise the end-to-end automatic damage identification. Firstly, the problem of damage identification in strain field is described, combined with the idea of fully convolutional network. The basic structure of damage identification network is modularised, and the overall design framework is proposed. Then, the damage simulation method is determined, and the feasibility of using this method to construct a strain field damage dataset is verified. The batch random damage model generation and the random noise signal addition program are coded, and the datasets of the bolster under static/dynamic force are obtained. Finally, the deep learning model is applied to the bolster damage dataset, and a residual module BolRes_Att that integrates spatial attention and channel attention mechanism is proposed. It has better damage identification performance without an increase in model parameters. The average number of faulty elements on the two bolster test sets of the improved damage identification network is 3.02 and 2.92, respectively, accounting for about 0.016% of all elements. The average processing time for a set of data is only 0.014 s. The results show that the deep learning model constructed in this paper can accurately and quickly identify the damage information of elements according to the strain field information. [ABSTRACT FROM AUTHOR]

The paper presents a novel solution to the issue of incomplete regions in 3D meshes obtained through digitization. Traditional methods for estimating the surface of missing geometry and topology often yield unrealistic outcomes for intricate surfaces. To overcome this limitation, the paper proposes a neural network-based approach that generates points in areas where geometric information is lacking. The method employs 2D inpainting techniques on color images obtained from the original mesh parameterization and curvature values. The network used in this approach can reconstruct the curvature image, which then serves as a reference for generating a polygonal surface that closely resembles the predicted one. The paper's experiments show that the proposed method effectively fills complex holes in 3D surfaces with a high degree of naturalness and detail. This paper improves the previous work in terms of a more in-depth explanation of the different stages of the approach as well as an extended results section with exhaustive experiments. [Display omitted] • We perform 3D surface reconstructions using generative inpainting techniques. • 2D representation of a 3D surface geometry based on its curvature. • Application of a general purpose neural network for inpainting. • Our approach does not require dataset nor training time. • Results outperform state-of-the-art quality and naturalness of the reconstructions. [ABSTRACT FROM AUTHOR]

Spatial locations of personnel, equipment, and materials are constantly changing as construction projects progress. The dynamic nature of the construction industry affects workers' performance of identifying hazards. Even though a great deal of effort has been made to improve construction safety, the construction industry still witnesses a high accident rate. In order to complement the existing body of knowledge relating to construction safety, this paper integrates Digital Twin (DT), Deep Learning (DL), and Mixed Reality (MR) technologies into a newly developed real-time visual warning system, which enables construction workers to proactively determine their safety status and avoid accidents. Next, system tests were conducted under three quasi-on-site scenarios, and the feasibility was proven in terms of synchronising construction activities over a large area and visually representing hazard information to its users. These evidenced merits of the development testing scenarios can improve workers' risk assessment accuracy, reinforce workers' safety behaviour, and provide a new perspective for construction safety managers to analyse construction safety status. • The proposed system generates simulated virtual construction sites based on the digital twin concept. • The virtual construction sites integrate BIM models and live video data for hazard identification. • Wearable mixed reality devices can present real-time hazard information to individual workers intuitively. • Three preliminary experiments have demonstrated the feasibility of the proposed system. [ABSTRACT FROM AUTHOR]

Sentiment analysis becomes an essential part of every social network, as it enables decision-makers to know more about users' opinions. Arabic sentiment analysis has a small number of datasets relative to English. This paper introduces active learning to the Arabic NLP community and demonstrates its power. Active learning is the process of selecting the data before labelling it to get the best benefits from the labelled data and improve performance. As it takes much time and effort to label large datasets, a better way is to label selected data and get higher performance. This paper applies active learning to the ArSarcasm-v2 dataset, which is labelled for sentiment, sarcasm, and dialect, by using the SAIDS "Sentiment Analysis Informed of Dialect and Sarcasm" model that predicts sarcasm and dialect and then uses them to predict the sentiment of the text. The paper runs multiple active learning experiments with different setups. The paper achieves state-of-the-art performance on ArSarcasm-v2 for sentiment analysis by achieving a 76.71 F1-score using 95% of the training dataset which means that adding some data points makes the performance worse. The paper also demonstrates that by using active learning, we achieve 97%, 99%, and 100% of the highest F1-score reached by all training data using only 10%, 20%, and 27% of the training data respectively. This means that using about one-quarter of the training data attains the same F1-score as using the full training data. As per our knowledge, this is the first paper to introduce active learning to the Arabic NLP community. [ABSTRACT FROM AUTHOR]

Wind turbines and solar photovoltaic (PV) systems are intermittent and uncertain energy sources that disturb grid planning operations. In this paper, we establish a multi-objective distributionally robust optimization model for optimal locations of wind turbines and solar photovoltaics (PV) that minimize the variance of renewable energy sources and maximize power production. Moreover, this paper evaluates the accuracy of the Autoregressive Moving Average (ARMA), Deep Learning Gated Recurrent unit (GRU), and Deep Learning Long Short-Term Memory (LSTM) as forecasting models for wind speed and solar irradiation and compares their root mean square errors (RMSE). Using the forecasting error information, we characterize the uncertain variables in the ambiguity set, incorporating the bounds, means, and covariance values. Furthermore, we propose a modified multi-objective non-dominated sorting genetic algorithm (NSGA-II) approach to achieve a tractable Pareto front solution. To verify the effectiveness of the model, we use the actual candidate sites for wind turbines and solar photovoltaic (PV) systems in Saudi Arabia. The results demonstrate that our proposed model is an attractive and less conservative solution than a multi-objective robust optimization model when considering forecasting uncertainties. [ABSTRACT FROM AUTHOR]

The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a selectable Transformer and Gist CNN network (STGC-Net). It designs a subspace similar recombination module (SSR-Module) based on non-negative matrix factorization (NMF) and the self-attention mechanism for feature decomposition. This can alleviate the redundant information of multi-modal data and extract their own singular and common features. Considering that the MS and the PAN images exhibit different advantageous properties, a selectable self-attention spectral feature extraction module (S 3 FE-Module) and a multi-stream Gist spatial feature extraction module (MGSFE-Module) are proposed for the different singular features. The former can refine the Transformer's input and simultaneously characterize the sequence information between channels for the MS image. The latter introduces the positional relationship between local features while extracting spatial features for the PAN image, thereby improving the accuracy of scene classification. Experimental results indicate that the proposed method performs better than the other methods. The relevant code of this paper is provided at: https://github.com/ru-willow/ST-GC-Net. • A subspace similar recombination module is proposed. • The selectable self-attention spectral feature extraction module is designed. • The multi-stream Gist spatial feature extraction module is introduced. [ABSTRACT FROM AUTHOR]

The rise of Android malware has become a growing concern for individuals, institutions, and governments around the world. As the popularity of Android devices grows continuously, cybercriminals are finding new ways to exploit vulnerabilities in the Android operating system to distribute malware, steal data, and launch attacks. This research paper explores the present state of Android malware, including the types of malware that are most prevalent, the techniques used by cybercriminals to distribute malware, and the impact of Android malware on individuals and organizations. The paper also examines the strategies that can be used to protect Android devices from malware and the legal and ethical implications of using malware to monitor or control Android devices. The findings of this paper have important implications for individuals, organizations, and policymakers, and can help inform the development of more effective strategies for protecting against Android malware. In this work, the Long Short-Term Memory (LSTM) approach is proposed to detect Android malware. To validate our proposed approach, we have computed performance metrics including accuracy, precision, recall, and F1-Score. Additionally, we conducted a comparison with existing work. [ABSTRACT FROM AUTHOR]

Technological advancement is something we can no longer ignore, deep learning being one of those advancements. With deep learning, emotions can be extracted and analyzed in textual data to be utilized in various sectors, involving human behavior analysis. The main objective of this paper is to investigate and discover basic emotional categories that are typically exhibited by humans, such as sadness, joy, anger, fear, and disgust. Sectors focused on business can utilize emotion detection for various applications, such as to create personalized services, and in medical scenarios to help develop specialized mental treatments. This paper enables other researchers to enrich their knowledge and develop a more accurate and efficient deep learning model. To create an emotion detection model, analysis regarding the best performing model has to be done. This paper discusses the difference in performance across three different RNN models, namely LSTM, BiLSTM, and GRU. The RNN based models, in theory, share the same architecture and will be evaluated using the ISEAR dataset to create an emotion detection model. The result of this paper concluded that the simplest RNN model, GRU, achieved the highest score across four scoring metrics: accuracy, recall, precision, and F1 score. The GRU model achieved an accuracy of 60.26%, BiLSTM with 59.3%, and LSTM with 57.65%. Hence, based on the results, the GRU-based models are a strong choice for emotion detection when tested on the ISEAR dataset. [ABSTRACT FROM AUTHOR]

Seamless positioning and navigation requires an integration of outdoor and indoor positioning systems. Until recently, these systems mostly function in-silos. Though GNSS has become a standalone system for outdoors, no unified positioning modality could be found for indoor environments. Wi-Fi and Bluetooth signals are popular choices though. Increased adoption of different machine learning techniques for indoor–outdoor context detection and localization could be witnessed in the recent literature. The difficulty in precise data annotation, need for sensor fusion, the effect of different hardware configurations pose critical challenges that affect the success of indoor–outdoor (IO) positioning systems. Wireless sensor-based techniques are explicitly programmed, hence estimating locations dynamically becomes challenging. Machine learning and deep learning techniques can be used to overcome such situations and react appropriately by self-learning through experiences and actions without human intervention or reprogramming. Hence, the focus of the work is to present the readers a comprehensive survey of the applicability of machine learning and deep learning to achieve seamless navigation. The paper systematically discusses the application perspectives, research challenges, and the framework of ML (mostly) and DL (a few) based positioning approaches. The comparisons against various parameters like the technology used, the procedure applied, output metric and challenges are presented along with experimental results on benchmark datasets. The paper contributes to bridging the IO localization approaches with IO detection techniques so as to pave the way into the research domain for seamless positioning. Recent advances and hence, possible future research directions in the context of IO localization have also been articulated. • IO localization methods are presented from pragmatic view of seamless positioning. • ML techniques are categorized based on their applications in seamless positioning. • Performance results of ML techniques on benchmark datasets are reported. • The open issues along with future directions are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper proposes a novel approach to train generators without any training data or shaped rewards by learning at multiple sizes. • This paper presents an application of the multi-size training approach to train generative flow networks for controllable 2D tile-based game level generation at various sizes. • This paper modifies diversity sampling for compatibility with generative flow networks, and to expand the generators' expressive ranges. A level generator is a tool that generates game levels from noise. Training a generator without a dataset suffers from feedback sparsity, since it is unlikely to generate a playable level via random exploration. A common solution is shaped rewards, which guides the generator to achieve subgoals towards level playability, but they consume effort to design and require game-specific domain knowledge. This paper proposes a novel approach to train generators without datasets or shaped rewards by learning at multiple level sizes starting from small sizes and up to the desired sizes. The denser feedback at small sizes negates the need for shaped rewards. Additionally, the generators learn to build levels at various sizes, including sizes they were not trained for. We apply our approach to train recurrent autoregressive generative flow networks (GFlowNets) for controllable level generation. We also adapt diversity sampling to be compatible with GFlowNets. The results show that our generators create diverse playable levels at various sizes for Sokoban, Zelda, and Danger Dave. When compared with controllable reinforcement learning level generators for Sokoban, the results show that our generators achieve better controllability and competitive diversity, while being 9 × faster at training and level generation. [ABSTRACT FROM AUTHOR]

The large number of near-Earth asteroids (NEAs) has greatly impacted human space activities and Earth security. However, detecting NEAs in astronomical images with complex, varying backgrounds is still extremely challenging. In this paper, we propose a deep segmentation assisted asteroid tracking algorithm, termed DSAT, to construct a possible pipeline for faint NEA tracking in astronomical images. First, the single-frame object detection problem is converted to a segmentation problem, enabling robust extraction of faint potential moving objects. Then, a multiframe motion prior-based moving object tracking algorithm is proposed to find real NEAs. We further propose a distance tolerance criterion to help DSAT achieve effective tracking in practical situations when detection has partially failed. Finally, the pipeline is tested with both simulated and real astronomical images at different SNRs and in crowded fields. The results showed that our pipeline has the potential to detect and track faint NEAs in complex backgrounds. Our code is publicly available at https://github.com/zhenhongdu/DeepSegAsteroidTracker. [ABSTRACT FROM AUTHOR]

The paper explores the tensile behaviour of plain weave ceramic matrix composites (CMCs) by employing a nonlinear constitutive model for fibre tow composites, driven by in-situ X-ray computed tomography data. Through the application of a deep-learning-based image segmentation method, the study successfully identified material damage modes, including matrix cracks, fibre pull-out, and interface debonding. Three-dimensional characterisation and quantitative analyses of damage have been conducted. At the mesoscopic level, a theoretically derived nonlinear constitutive model for fibre tow composites, accounting for continuous damage to both fibre and matrix, is established by using damage data. At the macroscopic level, a unit cell model is constructed for the numerical simulation of plain weave CMCs. Material's initial defects are prone to induce crack initiation. External matrix cracks can propagate to tows, and crack deflection may occur during this process. Furthermore, adjacent microcracks have the potential to connect, forming a larger one under high stress. Theoretical results predicted by the nonlinear constitutive model for fibre tow composites under tension exhibit good agreement with test data. The built unit cell model accurately predicts the tensile mechanical behaviour of plaine weave CMCs. [ABSTRACT FROM AUTHOR]

Rolling bearings constitute one of the most vital components in mechanical equipment, monitoring and diagnosing the condition of rolling bearings is essential to ensure safe operation. In actual production, the collected fault signals typically contain noise and cannot be accurately identified. In the paper, stochastic resonance (SR) is introduced into a spiking neural network (SNN) as a feature enhancement method for fault signals with varying noise intensities, combining deep learning with SR to enhance classification accuracy. The output signal-to-noise ratio(SNR) can be enhanced with the SR effect when the noise-affected fault signal input into neurons. Validation of the method is carried out through experiments on the CWRU dataset, achieving classification accuracy of 99.9%. In high-noise environments, with SNR equal to −8 dB, SRDNs achieve over 92% accuracy, exhibiting better robustness and adaptability. • Stochastic resonance is combined with spiking neural networks to improve accuracy. • Diverse neurons are employed in Stochastic Resonance Deep Residual Networks (SRDN). • SRDN exhibits superb robustness and adaptability under different noise conditions. [ABSTRACT FROM AUTHOR]

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T- X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies. [ABSTRACT FROM AUTHOR]

The process of manually detecting and removing screws during disassembly can be time-consuming and demanding. Automated systems utilizing imaging and deep learning can rapidly and accurately identify screws based on features like head shape and size. Different deep learning methods have varying screw detection capabilities. Choosing the right method significantly impacts system effectiveness, robustness and efficiency. Several generic deep learning architectures have been proposed to address this problem. However, there is little guidance on their suitability for specific scenarios. This paper aims to provide qualified guidance for selecting suitable deep learning methods for screw head detection. The proposed approach involves researching existing object detection methods and choosing two representatives – one employing a one-stage approach, the other using two-stage. These selected methods will be implemented identically and their performance evaluated via rigorous analysis and comparison. The dataset comprises synthetic and real images of 6 screw types across 3 products. YOLOv5 was selected for one-stage detectors, Faster R-CNN for two-stage. Results show the one-stage YOLOv5 generally outperforms Faster R-CNN in precision, recall, speed and training times, while Faster R-CNN delivered superior recall with smaller training datasets. [ABSTRACT FROM AUTHOR]

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis. Thus, denoising needs to be utilized as an essential step of vibration signal processing. Traditional denoising methods need expert knowledge to select hyperparameters. And data-driven methods based on deep learning lack interpretability and a clear justification for the design of architecture in a "black-box" deep neural network. An approach to systematically design neural networks is by unrolling algorithms, such as learned iterative soft-thresholding (LISTA). In this paper, the multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived by embedding a designed multi-layer sparse coder to the convolutional extension of LISTA. Then the multi-layer convolutional dictionary learning (ML-CDL) network for mechanical vibration signal denoising is proposed by unrolling ML-CLISTA. By combining ML-CDL network with a classifier, the proposed denoising method is applied to the explainable rolling bearing fault diagnosis. The experiments on two bearing datasets show the superiority of the ML-CDL network over other typical denoising methods. • The multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived. • The multi-layer convolutional dictionary learning network is proposed. • The proposed method is applied to the explainable rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

• A solar image captioning method that applies a transformer-based deep learning natural language processing is proposed. • A DeepSDO description dataset for training solar image captioning models including nine solar events is provided. • The experimental results show that the proposed method outperforms other benchmark methods in terms of four evaluation metrics. Solar images are essential for identifying and predicting solar phenomena, and have been used as key information for analyzing space weather. In this paper, we propose a solar image captioning method that applies a transformer-based deep learning (DL) natural language processing method. In addition, we provide a new DeepSDO description dataset for training solar image captioning models. First, we develop the DeepSDO description dataset using solar image data from Korean Data Center for solar dynamics observatory (SDO) and scripts from the National Aeronautics and Space Administration (NASA) SDO gallery website. The DeepSDO description dataset includes nine solar events: sunspots, flares, prominences, prominent eruptions, coronal holes, coronal loops, filaments, active regions, and eclipses. Second, we train the DL-based image captioning model, the meshed-memory transformer, using the DeepSDO description dataset. The experimental results show that the proposed method outperforms other benchmark methods in terms of four evaluation metrics. This study demonstrates that DL-based image captioning can successfully generate solar image captions for multiple solar features, and could potentially be used in other themes of solar physics and space weather. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a novel hybrid deep learning approach, SSA-CNN-LSTM, for forecasting solar power generation. The approach combines Singular Spectrum Analysis (SSA), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to leverage temporal and spatial dependencies in real-time greenhouse solar power generation data. Through a comprehensive comparative analysis, SSA-CNN-LSTM is compared against three established models, CNN-LSTM, SSA-CNN, and SSA-LSTM, employing real solar power generation data over a two-year period. The findings prominently demonstrate SSA-CNN-LSTM's exceptional performance, particularly in the 1-hour ahead prediction horizon. With an hour-ahead Mean Absolute Error (MAE) of 0.1202, SSA-CNN-LSTM surpasses the forecast precision of CNN-LSTM (0.6269), SSA-CNN (0.2354), and SSA-LSTM (0.2049). This excellence extends to the 2-hour-ahead forecast, where SSA-CNN-LSTM maintains its superiority with an MAE of 0.1400. In the day-ahead forecast, SSA-CNN-LSTM upholds its competitiveness, demonstrating an MAE of 0.1774. These outcomes underscore the immense potential of SSA-CNN-LSTM as a formidable tool for precise solar power forecasting. The model's effectiveness empowers greenhouse operators and energy management systems to optimize resource allocation, ultimately fostering elevated energy efficiency and overall greenhouse productivity. [ABSTRACT FROM AUTHOR]

Smart and connected health (SCH) accelerates the development and integration of information science and engineering approaches to support the digital transformation of health and medicine in populated societies. Sensor data based human activity recognition (HAR) as an effective means of SCH is promising for healthcare monitoring and ambient assisted living. This paper focuses on multi-position sensor data fusion enabled HAR, and proposes a multi-channel deep learning framework. The main contributions include: (1) A multi-channel hybrid deep learning model (1DCNN-Att-BiLSTM) that merges a one-dimensional convolutional neural network, a bidirectional long short-term memory model, and an attention mechanism is proposed to significantly improve the recognition performance of HAR models by extracting and selecting local and global behavioral features in the spatial and temporal domains; (2) Publicly accessible datasets Shoaib AR, Shoaib SA, and HAPT are collected and processed to build multi-position sensor data pool for model's evaluation; (3) Thoroughly evaluating the classification performance of seven sensor data fusion patterns from different body positions within the parallel network structure of the multi-channel framework; (4) Comparing the recognition performance metrics of traditional machine learning models, deep learning models, and hybrid models with our proposed model. Extensive experiments demonstrate that our approach achieves competitive performance. [ABSTRACT FROM AUTHOR]

High-performance sparse matrix multipliers are essential for deep learning applications, and as big data analytics continues to evolve, specialized accelerators are also needed to efficiently handle sparse matrix operations. This paper proposes a modified, configurable, outer product based architecture for sparse matrix multiplication, and explores design space of the proposed architecture. The performance of various architecture configurations has been examined for input samples with similar characteristics. The proposed architecture has been implemented on Xilinx Kintex-7 FPGA using 32-bit single precision floating-point arithmetic and also in 8-bit, 16-bit and 32-bit fixed-point arithmetic formats. The effect of quantization in the proposed architecture has been analyzed extensively and the results have been reported. The performance of the proposed architecture has been compared with state-of-the-art implementations, and an improvement of 9.21% has been observed in the performance. [ABSTRACT FROM AUTHOR]

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer. A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis. 31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73–0.86], 0.82 [95% CI, 0.78–0.86], and 0.88 [95%CI, 0.85–0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72–0.87], 0.73 [95% CI, 0.62–0.82], and 0.84 [95%CI, 0.80–0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results. This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately. • AUCs in both training and validation cohorts were higher than 0.80. • Multiple sequences MRI and deep learning methods improve diagnostic performance. • DWI performed better than contrast-enhanced sequences. • The general quality of the included articles was poor. • Further studies should use independent validation cohorts. [ABSTRACT FROM AUTHOR]

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper. • This pre-Editorial provides an overview of some hot topics in AI for life. • The benefit for the reader is to get a short, crisp, concise intro into AI. • Authors interested in this Special Issue will get a scope of topics. • The take away message is that in the future no biotechnology can do without AI. [ABSTRACT FROM AUTHOR]

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing nonmonotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems. [ABSTRACT FROM AUTHOR]

This paper reviews the recent development of augmented reality (AR) head-tracking techniques. AR is an emerging technology that provides users with digital augmentation layered on the real-life environment. AR systems use tracking to maintain the point of reference and enable the device to follow the user's movements and position within the physical world. Head-tracking is a crucial component of AR that inputs and interprets the movement of the device and the user's physical location in real-time. The main challenge in head-tracking is the appearance change that results from head rotation, which necessitates high accuracy and long-range tracking in a noisy environment. Latency and errors in head-tracking can reduce the system's overall effectiveness and cause jarring or unsettling effects for the user. This paper's contribution is to shed light on the best methods for tracking and registering in augmented reality, which will guide future advancements in the field. The review covers recent work proposed to improve head-tracking performance and reduce latency, highlighting the challenges and future directions of head-tracking techniques in AR. [ABSTRACT FROM AUTHOR]

Moving beyond classical single-label classification tasks proves to be crucial nowadays since the majority of real-life problems are inherently multi-label. Various tasks require involving, for one single instance as an input, several classes simultaneously or none of them. Particularly, detecting abusive texts in Arabic social networks intuitively carries several manifestations with regard to the targets' referred characteristics like gender, race, sexual orientation, etc. Existing multi-label algorithms either invest considerable complexity to handle labels' correlation or explicitly ignore this information to deal with each label as an independent binary problem. To remedy the abovementioned problems, this paper proposes a new multi-label classification solution based on deep neural networks. The main idea is to exploit the correlation between all the dataset's labels to break it into ordered subsets. Then, follow that order to implement a chain of BERT classifiers. Each classifier is therefore trained on a corresponding subset and passes its output via feature space to the following one. For a new instance, we gather all the classifiers' results and pick the best one for each class. Experiments showed that our approach significantly outperformed the single classifier's method using BERT as well as other neural network architectures on our Arabic abusive language dataset. 91%,76%, and 82% were achieved respectively for precision, recall, and F1-score. Moreover, we further investigated the performance of our approach on Kaggle multi-label research paper dataset and achieved very promising results. [ABSTRACT FROM AUTHOR]

• Review of papers on the diagnosis of brain diseases using the fusion of neuroimaging modalities is presented. • For each brain disease, the most appropriate neuroimaging modalities and best combinations are introduced. • Advantages and disadvantages of fusion levels and different modalities are introduced. • Details of various types of deep neural networks used for the task at hand are also examined. • Finally, future directions and challenges in the field are discussed. Brain diseases, including tumors and mental and neurological disorders, seriously threaten the health and well-being of millions of people worldwide. Structural and functional neuroimaging modalities are commonly used by physicians to aid the diagnosis of brain diseases. In clinical settings, specialist doctors typically fuse the magnetic resonance imaging (MRI) data with other neuroimaging modalities for brain disease detection. As these two approaches offer complementary information, fusing these neuroimaging modalities helps physicians accurately diagnose brain diseases. Typically, fusion is performed between a functional and a structural neuroimaging modality. Because the functional modality can complement the structural modality information, thus improving the performance for the diagnosis of brain diseases by specialists. However, analyzing the fusion of neuroimaging modalities is difficult for specialist doctors. Deep Learning (DL) is a branch of artificial intelligence that has shown superior performances compared to more conventional methods in tasks such as brain disease detection from neuroimaging modalities. This work presents a comprehensive review paper in the field of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and Autoencoders (AEs). First, neuroimaging modalities and the need for fusion are discussed. Then, review papers published in the field of neuroimaging multimodalities using AI techniques are explored. Moreover, fusion levels based on DL methods, including input, layer, and decision, with related studies conducted on diagnosing brain diseases, are discussed. Other sections present the most important challenges for diagnosing brain diseases from the fusion of neuroimaging modalities. In the discussion section, the details of previous research on the fusion of neuroimaging modalities based on MRI and DL models are reported. In the following, the most important future directions include Datasets, DA, imbalanced data, DL models, explainable AI, and hardware resources are presented. Finally, the main findings of this study are presented in the conclusion section. [ABSTRACT FROM AUTHOR]

Spam is some unwanted material that may be put in the form of images. While many machine learning approaches are effective at detecting textual spam, this is not true for image spam. In this paper, a new framework for identifying image spams is proposed. The images are divided into two categories: undesirable material contained in the form of images which is referred to as a spam image, whereas anything else is referred to as a ham image. Our proposed methodology is based on applying different pre-trained deep learning models, including InceptionV3, Densely Connected Convolutional Networks 121(DenseNet121), Residual Networks (ResNet50), Visual Geometry Group (VGG16) and MobileNetV2, to filter out the unwanted spam images. Different standard test datasets such as Dredze Dataset, Image Spam Hunter (ISH) Dataset and Improved Dataset are utilized in this paper for performance testing. Furthermore, transfer learning and data augmentation are employed to address the issue of a shortage of labeled data. In our implementation, the fully connected (FC) layer in the aforementioned pre-trained models is replaced with a Support Vector Machine (SVM) classifier, resulting in an improved accuracy. The obtained results reveal that ResNet50 model yields the best performance achieving 99.87% accuracy, 99.88% area under the curve (AUC), 99.98% sensitivity, 99.79% precision, 98.99% F1 score and a computational testing time of in the order of one to two seconds for the ISH dataset. [ABSTRACT FROM AUTHOR]

Current disrupting trends in manufacturing like mobile robot-based intralogistics and digital twins require precise indoor positioning. 5G-based positioning has the potential to be more cost-efficient than other positioning systems due to shared hardware for positioning and communication. But first it has to achieve high positioning accuracy and stability. No positioning technique has yet been established as standard in the industry in combination with 5G because traditional positioning approaches can deliver unstable results when multi-path fading, reflections, or missing line-of-sight effects occur. Identifying promising techniques is still an open research topic. It is established in the literature that deep learning-based approaches can mitigate these effects better than traditional ones by learning unique fingerprints that correspond to precise positions. However, fully-connected neural networks tend to overtrain on these fingerprints which again leads to unstable positioning due to a lack of generalization. This can be explained by the inability of fully connected neural networks to learn structural interrelationships in high dimensional data. This is because their input data is flattened, which means the structure is removed. Convolutional neural networks are being widely used with image data and can learn structural interrelationships. This paper presents a technique that uses convolutional neural networks in combination with a channel-smoothing approach to position mobile robots in the research factory ARENA 2036. The results presented in this paper indicate an indoor positioning accuracy similar to other industrial positioning systems, with improved stability compared to a fully-connected approach. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the long short-term memory network (LSTM). The ResNet boosts the accuracy in deep neural networks, and LSTM improves the classifier's performance by passing the time-series previous state information to the current state. This paper demonstrates that the proposed model achieves 92% peak recognition accuracy at 18 dB SNR. It is higher than the ResNet by 11.4%, the CNN network by 4.7%, and the CLDNN network by 2%. Moreover, it delivers more than 90% classification accuracy at SNR above 0 dB. Additionally, it improves the classification accuracy at low SNR by achieving 85.5% accuracy at −2 dB SNR. Furthermore, it advances the recognition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB. [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the o n e − v s − r e s t strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms to prove their effectiveness. • The first combination of twin support vector machines (TSVM) and deep neural networks. • Nesting of more advanced auto encoder networks. • Incorporating multi-view learning capabilities. • Mutual negotiation and simultaneous learning between deep neural networks and TSVM. • The experiments prove the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]