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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]

Abstract: This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques. [Copyright &y& Elsevier]

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]

Digital twins belong to ten of the strategic technology trends according to the Gartner list from 2019, and have encountered a big expansion, especially with the introduction of Industry 4.0. Sport, on the other hand, has become a constant companion of the modern human suffering a lack of a healthy way of life. The application of digital twins in sport has brought dramatic changes not only in the domain of sport training, but also in managing athletes during competitions, searching for strategical solutions before and tactical solutions during the games by coaches. In this paper, the domain of digital twins in sport is reviewed based on papers which have emerged in this area. At first, the concept of a digital twin is discussed in general. Then, taxonomies of digital twins are appointed. According to these taxonomies, the collection of relevant papers is analyzed, and some real examples of digital twins are exposed. The review finishes with a discussion about how the digital twins affect changes in the modern sport disciplines, and what challenges and opportunities await the digital twins in the future. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic exposed the importance of research on the spread of epidemic diseases. In this paper, we apply Artificial Intelligence and statistics techniques to build prediction models to estimate the SARS-CoV-2 seroprevalence in the United States, using multiple estimates of COVID-19 prevalence and other explanatory variables. We propose the use of stacking techniques based on multiple model building techniques (Linear and Beta Regression, Genetic Programming and Neural Networks) to obtain Predictive Ensemble Models. There has been extensive research on this field, but there has not been in-depth research on the application of stacking methods to estimate and forecast seroprevalence in the USA specifically. This paper provides a novel comparison of the behaviour and performance of different building techniques for stacking ensemble models and presents which methods are better for different scenarios. We find that Genetic Programming and Neural Networks are the best models with trained data within single states, and when multiple states are considered Genetic Programming is still better than the Regression models, but Neural Networks fail to estimate the seroprevalence accurately. Another novelty of our work is the use of cross-state validation to evaluate the models with new data, as well as temporal forecasting. Depending on how the data is processed, Linear Regression performs very well with cross-state validation and temporal forecasting, and Genetic Programming is very accurate with the former while Neural Networks work better with the latter. • COVID-19 seropositivity modelling in the USA. • Use of stacking and ensemble ML models. • Comparison of ML methods (linear regression, beta regression, genetic programming, neural network). • Trade-off between explainability and performance. [ABSTRACT FROM AUTHOR]

As diabetes management becomes more complicated, there is an increasing interest in understanding how to manage diabetes with physical activity. Our study aimed to investigate the role of wearable, non-invasive technologies in collecting data related to physical activity to model them via artificial intelligence methods for efficient diabetes management. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (also known as PRISMA) protocol and searched three databases, namely PubMed, Scopus, and Web of Science. Out of 960 titles, we included 32 in the full-text analysis. Results showed two main methods were used for the analysis, i.e., statistical and classification modeling. Results indicate among the employed regression methods, linear regression was used more than other methods, and the most common classification-based method for analyzing data was the Artificial Neural Network method. Assessing the quality of papers that used the classification method was done through Prediction model Risk Of Bias Assessment Tool (also known as PROBAST) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (also known as TRIPOD) tools. Based on PROBAST outcomes, although the risk of bias was low in most of the works, explaining the analyzing method specifically, the method of handling missing data needs more attention. Upon evaluating papers using the TRIPOD, it realized that there is a need to place emphasis on improving the quality of the presentation and explanation of the result. According to our review, the conjunction of non-invasive technologies and artificial intelligence is promising in managing diabetic risk factors for real-time monitoring of physical activities, enabling regular clinical intervention and optimized medical treatment. [ABSTRACT FROM AUTHOR]

Because of the prevalence of depression, its often-chronic course, relapse and associated disability, early detection and non-intrusive monitoring is a crucial tool for timely diagnosis and treatment, remission of depression and prevention of relapse. In this way, its impact on quality of life and well-being can be limited. Current attempts to use artificial intelligence for the early classification of depression are mostly data-driven and thus non-transparent and lack effective means to deal with uncertainties. Therefore, in this paper, we propose an end-to-end framework for extracting observable depression cues from diary recordings. Furthermore, we also explore its feasibility for automatic detection of depression symptoms using observable behavioural cues. The proposed end-to-end framework for extracting depression was used to evaluate 28 video recordings from the Symptom Media dataset and 27 recordings from the DAIC-WOZ dataset. We compared the presence of the extracted features between recordings of individuals with and without a depressive disorder. We identified several cues consistent with previous studies in terms of their differentiation between individuals with and without depressive disorder across both datasets among language (i.e., use of negatively valanced words, use of first-person singular pronouns, some features of language complexity, explicit mentions of treatment for depression), speech (i.e., monotonous speech, voiced speech and pauses, speaking rate, low articulation rate), and facial cues (i.e., rotational energy of head movements). The nature/context of the discourse, the impact of other disorders and physical/psychological stress, and the quality and resolution of the recordings all play an important role in matching the digital features to the relevant background. In this way, the work presented in this paper provides a novel approach to extracting a wide range of cues relevant to the classification of depression and opens up new opportunities for further research. [ABSTRACT FROM AUTHOR]

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-term memory and gated recurrent units. The nonlinear activation functions and backpropagation mechanisms used in these methods result in high computational complexity and correspondingly high energy consumption. This paper proposes a prediction model based on a green learning (GL) framework aimed at reducing energy consumption. The proposed GL model replaces the activation functions conventionally used for feature extraction with a hybrid scheme combining categorical and numerical features, such as seasonal climate features and multi-autoregression terms. The proposed GL model also eliminates the backpropagation methods used to optimize hyperparameters by employing seasonal centroids and the quantile auto-regression forest algorithm for classification/regression. In a case study, the proposed GL model significantly outperformed conventional DL models in terms of energy consumption without compromising accuracy. The energy savings of the proposed GL model are equivalent to the annual electricity consumption of 211 ∼ 565 households, increasing with model penetration. This paper demonstrates the importance of energy-saving models in mitigating the linkage effect of industrial carbon emissions. It also explores policy implications for the implementation of GL models. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis, detection, and prognostics (DDP) for complex equipment prognostics and health management (PHM) have achieved remarkable breakthroughs. Equipment in industrial scenarios often operates in normal conditions, resulting in missing anomalies, limited failures, and incomplete degradation paths. Thus the limited information on the state of the equipment collected from sensor readings severely hinders the cognitive capabilities of discriminative artificial intelligence (AI) for PHM. Data augmentation and generation (DA&G) techniques, represented by generative AI, have shown great promise in overcoming the limitations of PHM application scenarios. Research on DA&G has yielded significant achievements, but a comprehensive review in the mechanical field is still lacking. To this end, this paper provides a comprehensive review of DA&G techniques aimed at solving the DDP problems, which are divided into three categories insights of data, mechanism, and features. The data-based randomized approach applies controlled randomness for augmentation. The mechanism-based domain-specific techniques advocate for exploring relationships between the physical entity and monitoring data for generating by reasoned inference. The feature-based generative model aims to identify the latent space of data and subsequently resample it. Finally, the paper explores strategies for evaluating DA&G and provides a deep insight into the challenges and opportunities of DA&G techniques. [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]