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Computer scientists often publish their source code accompanying their publications, prominently using code repositories across various domains. Despite the concurrent existence of scholarly articles and their associated official code repositories, explicit references linking the two are often missing. Traditionally, identifying whether scholarly content and code repositories pertain to the same research project requires manual inspection, a time-consuming task. This paper proposes a deep learning-based algorithm for automatically matching scholarly articles with their corresponding official code repositories. Our findings indicate that the most common linking information includes the paper title and BibTeX entries, typically found in the repository’s readme document. In this study, we employed SPECTER for vector embedding of paper and repository metadata. Utilizing these embedding representations with the Light Gradient Boosting Machine (LGBM), our method achieved an F1 score of 0.94. Moreover, combining our best model with a rule-based approach improved performance by 5.31%. This study successfully delineates a connection between academic papers and associated official code repositories, minimizing reliance on explicit bibliographic information in repositories.

Software fault prediction (SFP) is a research area that helps development and testing process deliver software of good quality. Software metrics are of various types and are used in SFP for measurements. Inheritance is a prominent feature, which measures the depth, breadth, and complexity of object-oriented software. A few studies exclusively addressed the efficacy of inheritance in SFP. This provokes the need to identify the potential ingredients associated with inheritance, which can be helpful in SFP. In this paper, our aim is to collecting, organizing, categorizing, and investigating published fault prediction studies. Findings include identification of 54 inheritance metrics, 78 public datasets with various combinations of 10 inheritance metrics, 60% use of method level & use of private datasets, an increased number of studies using machine learning approaches. This study will facilitate scholars to studying previous literature on software fault prediction having software metrics, with their methods, public data sets, performance evaluation of machine learning algorithms, and findings of experimental results in a comfortable, and efficient way, emphasizing the inherited aspect specifically.

Artificial intelligence (AI) systems, based on neural networks are becoming ubiquitous. However, in many cases, their application is constrained by the lack of interpretability. One of the ways of overcoming this limitation is neuro-symbolic approaches, where the neural network is complemented by some symbolic structures, expressing existing knowledge and making it understandable to humans. Such approaches can make predictions more understandable and interpretable (due to the connection to human-understandable symbolic structures). Still, they can also have an additional advantage by allowing models to be trained with less data (due to leveraging prior knowledge). This paper focuses on a subset of neuro-symbolic approaches, where domain ontologies play the role of symbolic structures. The paper discusses the existing methods to build ontology-aware explainable neural networks and ways to leverage ontologies in forming the explanation. Based on the analysis, it proposes a computational framework for building ontology-aware self-explaining neural networks. The proposed framework allows several specializations, and it is shown that these specializations allow one to improve the prediction quality. Finally, the paper presents the results of a user study, showcasing that ontology-based explanations can improve the understandability of an AI model and the efficiency of human-AI interaction.

Transformer has been adopted to image recognition tasks and shown to outperform CNNs and RNNs while it suffers from high training cost and computational complexity. To address these issues, a hybrid approach has become a recent research trend, which replaces a part of ResNet with an MHSA (Multi-Head Self-Attention). In this paper, we propose a lightweight hybrid model which uses Neural ODE (Ordinary Differential Equation) as a backbone instead of ResNet so that we can increase the number of iterations of building blocks while reusing the same parameters, mitigating the increase in parameter size per iteration. The proposed model is deployed on a modest-sized FPGA device for edge computing. The model is further quantized by QAT (Quantization Aware Training) scheme to reduce FPGA resource utilization while suppressing the accuracy loss. The quantized model achieves 79.68% top-1 accuracy for STL10 dataset that contains $96\times 96$ pixel images. The weights of the feature extraction network are stored on-chip to minimize the memory transfer overhead, allowing faster inference. By eliminating the overhead of memory transfers, inference can be executed seamlessly, leading to accelerated inference. The proposed FPGA implementation accelerates the backbone and MHSA parts by $34.01\times $ , and achieves an overall $9.85\times $ speedup when taking into account the software pre- and post-processing. The FPGA acceleration leads to $7.10\times $ better energy efficiency compared to the ARM Cortex-A53 CPU. The proposed lightweight Transformer model is demonstrated on Xilinx ZCU104 board for the image recognition of $96\times 96$ pixel images in this paper and can be applied to different image sizes by modifying the pre-processing layer.

In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, two hundred and seven papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n=77 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features; the latter cluster concerns four fields of interest, being RADAR-based diagnosis (n=77), RADAR-based human behaviour monitoring (n=25), RADAR-based biometric authentication (n=19) and RADAR-based affective computing (n=9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n=41), infection detection (n=10), sleep stage classification (n=9), heart disease detection (n=9) and quality detection (n=8). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n=9), human detection (n=7), human localisation (n=4), human orientation (n=2), and activities classification (n=3).

Emotion recognition from speech signals plays a crucial role in Human-Machine Interaction (HMI), particularly in the development of applications such as affective computing and interactive systems. This review seeks to provide an in-depth examination of current methodologies in speech emotion recognition (SER), with a focus on databases, feature extraction techniques, and classification models. It has been done in the past using low-level descriptors (LLDs) like Mel-Frequency Cepstral Coefficients (MFCCs), linear predictive coding (LPC), and pitch-based features in methods like Support Vector Machines (SVM), Random Forests (RF), and Gaussian Mixture Models (GMM). But the development of deep learning techniques has completely changed the field. Models like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have shown that they are better at capturing the complex temporal and spectral features of speech. This paper reviews prominent speech emotion datasets, exploring their linguistic diversity, annotation processes, and emotional labels. It also analyzes the efficacy of different speech features and classifiers in handling challenges such as data imbalance, limited data availability, and cross-lingual variations. The review highlights the need for future work to address real-time processing, context-sensitive emotion detection, and the integration of multi-modal data to enhance the performance of SER systems. By consolidating recent advancements and identifying areas for further research, this paper aims to provide a clearer path for optimizing feature extraction and classification techniques in the field of emotion recognition.

The adoption of electronic health (e-health) systems has transformed healthcare delivery by harnessing digital technologies to enhance patient care, optimize operations, and improve health outcomes. This paper provides a comprehensive overview of the current state of e-health systems, tracing their evolution from traditional paper-based records to advanced Electronic Health Record Systems(EHRs) and examining the diverse components and applications that support healthcare providers and patients. A key focus is on the emerging trends in AI-driven cybersecurity for e-health, which are essential for protecting sensitive health data. AI’s capabilities in continuous monitoring, advanced pattern recognition, real-time threat response, predictive analytics, and scalability fundamentally change the security landscape of e-health systems. The paper discusses how AI strengthens data security through techniques like anomaly detection, automated countermeasures, and adaptive learning algorithms, enhancing the efficiency and accuracy of threat detection and response. Furthermore, the paper delves into future directions and research opportunities in AI-driven cybersecurity for e-health. These include the development of advanced threat detection systems that adapt through continuous learning, quantum-resistant encryption to safeguard against future threats, and privacy-preserving AI techniques that protect patient confidentiality while ensuring data remains useful for analysis. The importance of automating regulatory compliance, securing data interoperability via blockchain, and prioritizing ethical AI development are also highlighted as critical research areas. By emphasizing innovative security solutions, collaborative efforts, ongoing research, and ethical practices, the e-health sector can build resilient and secure healthcare infrastructures, ultimately enhancing patient care and health outcomes.

Quality of service provisioning in modern networks requires traffic to be classified as quickly as possible according to its requirements and service type. However, traffic classification (TC) becomes increasingly challenging as traffic encryption evolves. The Encrypted ClientHello (ECH) amendment to the most widespread encryption protocol, Transport Layer Security (TLS), conceals the most sensitive metadata of the TLS-encrypted flows including the Server Name Indication (SNI), which provides ground-truth early TC. Nevertheless, the backward compatibility and protocol limitations leave some non-random TLS metadata open. This paper designs a new early traffic classifier called hybrid Random Forest Traffic Classifier (hRFTC) that utilizes unencrypted TLS metadata together with the statistical features of the traffic flows extracted before the arrival of any application data from the server side. The paper collects an up-to-date diversified traffic dataset in various countries of North America, Europe, and Asia, which is available online and is one of the largest, most detailed, and diversified open-source TC datasets. The paper evaluates the performance of the state-of-the-art TC algorithms on the collected dataset. The results reveal that unencrypted in ECH scenario TLS settings are similar for many multimedia services. Consequently, the TC algorithms that rely solely on the TLS features achieve as low as 38.4% classification F-score. Meanwhile, the hybrid approach of the hRFTC dramatically enhances the TC efficacy. hRFTC achieves up to a 94.6% F-score on the collected dataset, which is superior to the best state-of-the-art algorithms.

The advancement in wearable technology with integrated biometric sensors allows real-time continuous monitoring of vital data such as heart rate and breathing rate. The utilization of this real-time data can yield significant insights into a person’s health status by facilitating the early identification of anomalies or patterns that may point to possible health problems. Moreover, it illustrates how the vitals alter or respond as the individual carries out a certain task. For a physically or mentally demanding task, understanding this response could ensure better organization of that task and minimal strain on human health by reducing stress and cognitive load. Unfortunately, it is not always feasible to continuously monitor biometric data. In this paper, we propose an alternative solution to the problem by using predictive models to forecast data points in the future. This paper presents a method with an ensemble learning approach capable of forecasting vitals from small chunks of data. The intelligent system proposed in the paper is connected to a Dielectric Elastomer to provide smart sensing to the user. The paper presents a new dataset containing vitals and contributes to classifying a particular task or action state based on recorded human vitals. While existing research focuses on the prediction of biometrics, our model is capable of forecasting the biometrics and outperforms standard machine learning predictive models by a margin across multiple evaluation metrics and the forecasted graph shows the effectiveness of our approach. This technique offers a viable alternative for data monitoring by data simulation, improving task management, and minimizing health strain.

This paper presents an adaptive pre-silicon integrated circuit verification algorithm that incorporates a machine learning algorithm. This approach can overcome the traditional corner-based process-voltage-temperature verification limitations considering variables coverage. The proposed solution employs an adaptive algorithm whose parameters have been analyzed in detail and optimized to achieve high performance. The optimal configurations for these parameters were demonstrated through the accuracy and efficiency of the algorithm. Additionally, this paper presents three methods that expand the use of the univariate algorithm to multivariate adaptive scenarios. These techniques aim to achieve the most accurate identification of worst-case circuit behavior through simultaneously modeling multiple electrical parameters (EP) outputs. The effectiveness of the proposed methods was validated through extensive testing on a large and diverse set of synthetic test functions that intend to replicate the behavior of real circuits. The algorithms consistency and accuracy are also validated on real Low Dropout Voltage Regulator (LDO) circuits.

Natural languages are written and spoken languages, and NLP (Natural Language Processing) is the ability of a computer program to recognize both written and spoken languages. Word Sense Disambiguation (WSD) is identified as a challenging area of research in Artificial Intelligence (AI), and Machine Translation (MT). WSD is the procedure for selecting the exact meaning of a word that has more than one meaning. This is an essential application for all-natural language processing applications. There are various knowledge-based, supervised, and unsupervised approaches to WSD process. The Naïve Bayes classifier as an example of approach supervised and unsupervised approaches is the most important method. In this paper, we emphasize on the use of the Naïve Bayes approach for text classification in WSD techniques. Bayes’ hypothesis is a probabilistic model and a reliable approach for text classification. Bayes’ hypothesis acknowledges that the occurrence of some other features is not dependent on the presence of a particular element in a class. This calculation can be used to solve multi-class prediction problems. This classifier performs better compared to the other methods of different approaches. This paper gives an itemized investigation of Naïve Bayes algorithms, which depicts its ideas, covering up Naïve Bayes,’ text characterization, traditional innocent Bayes,’ and machine learning. We have used the collocation method of feature extraction for the WSD of English sentences. Using this model we have disambiguate ambiguous English words by predicting part-of-speech inclusive of “noun,” “verb,” “adverb,” and “adjective.” This disambiguation module is an enhancement in machine translation. The system reported the performance measure of seventy-eight (78%) percent of the scale on F1-measure.

The increasing complexity of software development demands efficient automated bug report priority classification, and recent advancements in deep learning hold promise. This paper presents a comparative study of contemporary learning paradigms, including BERT, vector databases, large language models (LLMs), and a simple novel learning paradigm, contrastive learning for BERT. Utilizing datasets from bug reports, movie reviews, and app reviews, we evaluate and compare the performance of each approach. We find that transformer encoder-only models outperform in classification tasks measured by the precision, recall, and F1 score transformer decoder-only models despite an order of magnitude gap between the number of parameters. The novel use of contrastive learning for BERT demonstrates promising results in capturing subtle nuances in text data. This work highlights the potential of advanced NLP techniques for automated bug report priority classification and underscores the importance of considering multiple factors when developing models for this task. The paper’s main contributions are a comprehensive evaluation of various learning paradigms, such as vector databases and LLMs, an introduction of contrastive learning for BERT, an exploration of applicability to other text classification tasks, and a contrastive learning procedure that exploits ordinal information between classes.

Health index is an essential tool to evaluate the condition of power transformers. Generally, there are two methodologies to evaluate the health index of power transformers: conventional methods and machine learning-based approaches. The main challenge in employing the health index to assess transformer’s condition is the lack of supporting data, which hinders its ability to accurately reflect the actual health condition of the transformers. While several studies on estimating the transformers health index using machine learning techniques can be found in the literatures, not much attention was given to the cumulative impact of multiple data unavailability on the calculation’s accuracy. This paper presents a comprehensive analysis of health index predictions using two machine learning methods: specifically, regression and classification against conventional methods of weighting and scoring. Furthermore, the paper presents some strategies to overcome the unavailability of multiple data. Six methods are presented and evaluated across 15 missing data scenarios to assess their effectiveness compared with scenarios featuring complete data. Results reveal that Gradient Boosting Regression has the most accurate accuracy in predicting the health index with a 95%, assuming complete data, and 97.87% considering data unavailability. The paper also presents an economic analysis to highlight the profitability associated with predicting missing data compared with the certainty level and evaluation using asset owner interpretation to assess the feasibility of the proposed method. The main contribution of the paper is the presentation of a comprehensive view for asset owners to make informed decisions in case of data unavailability.

The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

Smart farming, popularly called precision agriculture, refers to technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and drones that are rapidly transforming age-old farming traditions. This paper investigates how smart farming technologies are revolutionizing the practice of agriculture, specifically focusing on monitoring and control solutions of IoT devices and LoRaWAN networked. Therefore, the implementation of IoT devices would give the farmer access to current readings when needed for making decisions and optimizing their resources on the most important parameters for agricultural activity. And this is where IoT devices come in, making possible precision agriculture techniques with lowered costs by customizing farming practice for each crop or section of land. It means that even the most remote farms will benefit from monitoring and control solutions within LoRaWAN networks. Moreover, low power with long-range wireless connectivity technology devised by LoRaWAN ensures communication and data analysis to be a reliable one within the system. It introduces an intelligent LoRaWAN-based IoT device for the monitoring and controlling solutions with the parameters of performance evaluation, experimental setup, and the dataset used for its analysis. This paper elaborates on anomaly detection through Isolation Forest and proves it for the identification of anomalies in data related to temperature and humidity. Predominantly, the study also indicates precision in the temperature variation prediction model through the use of the predictive model based on the linear regression and random forest algorithms. This improves smart farming practices developed for precision agriculture in terms of efficiency, productivity, and sustainability.

Origin-Destination (OD) traffic flow estimations from traffic sensor data play an important role for transportation planning and management. This paper proposes a novel method to compare OD traffic estimated matrices (using data from traffic sensors). The proposed method uses the estimated OD traffic flow values together with COVID-19 incidence data in order to build a sequence of temporal graphs that are fed into a machine learning (ML) model. The ML model uses the input information to estimate/predict one-week ahead COVID-19 incidence data. A tailored Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) model is designed adapted to the input information. The paper evaluates the proposed method with 3 different OD estimation alternatives and compares the accuracy achieved by different configurations of the ML model with a traffic agnostic baseline model. Data from 44 provinces in Spain during 2021 providing daily COVID-19 incidence data and 635 geo-located traffic sensors providing monthly traffic counts are used to evaluate the results. The 3 traffic-aware OD estimation methods were able to outperform the baseline model, achieving model gains up to 136%. The major application of the results of this paper is a novel mechanism to validate prior OD traffic matrices.

This paper addresses the hypothesis that the preference of a worker toward a work spot depends on attribute information such as her or his age and position and the type of work task on which she or he is working on that day. The system proposed in this paper is designed to estimate work-spot preferences using machine learning (ML) for activity-based workplaces. The system collects a variety of items related to attribute information and work-spot preferences for each work task from individual workers in advance. It constructs ML models using the collected data and estimates the work-spot preference for a worker. We extend the system to improve the accuracy for the case where the estimation is performed with a limited number of samples for training data. To examine the system, this paper presents two types of experiments: with computer graphics (CG) and with a real environment. Through these experiments, we collect data from people and construct ML models for the estimation of work-spot preferences based on attribute information and types of work tasks. The results demonstrate that the proposed system works for estimating the work-spot preference for each individual worker in accordance with the attribute information and the type of work tasks.

Soaring energy demand in power sector demands an intelligent electricity grid which is able to meet the energy requirements of consumers with higher efficiency and reliability. This paper discusses about how Smart Grid (SG) is the solution for such requirements in the energy sector. SG constitutes of three main components: Information Technology (IT), Operational Technology (OT), and Advanced Metering Infrastructure (AMI). It is important to synchronize these three components in order to seamlessly deliver electricity to the consumers connected to the electric network. Cyberattack is increasing sharply in the SG infrastructure and triggers disrupting or malicious manipulation of entire electricity network. It is crucial to maintain the data traffic, power transmission and distribution with high reliability. This paper aims at arriving at the taxonomy of attacks that are subjected to IT, OT and AMI components of SG. This exhaustive taxonomy further opens avenues to evolve mitigation strategies for these attacks which will eventually aid in SG protection. Elaborate discussion about various mitigation strategies for various types of attacks is performed. The systematic approach of methods and techniques along with the type of solution it provides to SG applications is discussed. It is evident from literature that Machine Learning (ML), Deep Learning (DL) and signal processing techniques are very efficient and robust in attack detection and retaliating in SG. ML model pro. Hence the importance of framework and tools in SG environment is analyzed. A case study is also presented that emphasizes on SG Attacks on the specific system and its mitigation strategies.

Alzheimer’s disease is a progressive disease for which researchers have yet to discover the main cause, but believe it probably involves a combination of age-related changes in the brain, genetic, environmental and lifestyle factors. Alzheimer’s is an irreversible disease that still has no cure. Therefore, its early diagnosis is very important to prevent its progression. Developing Machine Learning algorithms in healthcare, especially in brain disorders such as Alzheimer’s disease, provides new opportunities for early diagnosis and recognition of important biomarkers. This paper presents an overview of advanced studies based on Machine Learning techniques for diagnosing Alzheimer’s disease and different stages of mild cognitive impairment based on magnetic resonance imaging (MRI) images in the last 10 years. Also, this paper comprehensively describes the commonly efficient Machine Learning algorithms in each stage of magnetic resonance imaging processing used in the papers, which can facilitate the comparison of algorithms with each other and provide insight into the impact of each technique on classification performance. This review can be a valuable resource to gain a new perspective on the various research methods used in recent studies on Alzheimer’s disease.

The overall network volume in backbone optical networks is constantly growing, composed of many smaller network services with increasing trends and various seasonality. Due to the recent advances in machine learning algorithms, short- and long-term traffic fluctuations can be forecasted. Consequently, the backbone optical network can be adapted to traffic changes aiming to improve its performance. However, an important challenge lies in developing effective optimization methods capable of adapting to traffic changes to leverage the knowledge about the traffic. To this end, this paper addresses the time-varying traffic in spectrally-spatially flexible optical networks (SS-FONs), which are a promising technology to mitigate backbone network requirements of vast traffic volume transmission. The main contribution of this paper is twofold. Firstly, we introduce a new traffic prediction method using multioutput regression and temporal features to forecast traffic between all node pairs and integrate this prediction method into an optimization framework developed for dynamic resource allocation in translucent SS-FONs with time-varying traffic. Secondly, we evaluate potential network performance improvements from periodic lightpath reallocation through extensive numerical experiments. According to the results of experiments run on two representative optical network topologies, the proposed approach with periodic resource allocation allows achieving up to 7.8 percentage points reduction of bandwidth blocking compared to the reference scenario without reallocation. Consequently, the network requires up to 23.4% fewer transceivers to deliver the same traffic in considered scenarios and thus the power consumption savings are provided.

Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several Machine Learning (ML) algorithms in predicting solar power generation through a detailed performance comparison. This paper analyzes six algorithms: CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF). Using a dataset of 4213 sets of solar power generation data, each model was trained and tested, with performance evaluated based on R-squared (R2) scores for the whole dataset, training set, and test set. Also, this study examined the mean and standard deviation of test set predictions to gauge how consistent each model was. The results showed that RF had the highest overall R2 score of 0.940 and a training set score of 0.971. XGBoost demonstrated exceptional performance on the test set, attaining a high R2 score of 0.822. CatBoost and GBMs exhibited strong performance, albeit with slightly lower R2 values of 0.786 and 0.829, respectively. Although the MLP regressor and SVMs exhibited high training scores, they encountered difficulties in generalizing to unfamiliar data. This paper highlights the effectiveness of combining XGBoost and RF techniques in improving the accuracy of solar power forecasts. The investigation focuses on enhancing the precision and reliability of renewable energy projections through a comprehensive comparison of various contemporary ML techniques.

The security of Industrial Supply Chain (ISC) has emerged through the integration of Industrial Internet of Things (IIoT) and Blockchain (BC) technology. This new era involves effectively protecting IIoT systems from various threats and ensuring their smooth operation and resilience against potential cyber-attacks. Within the ISC ecosystem, combining machine learning (ML)-based security models for cyber-attack detection can play a crucial role in enhancing the ISC security and proactively identifying potential threats. This paper presents a BC-enabled ISC that embed ML security model integrated within a multi-layered approach. We conducted a comparative study and performance analysis of several ML classification techniques, with a focus on supervised methods to identify the lightweight model for cyber-attack detection suitable for deployment in resource-constrained IIoT environment. We investigate the performance of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and three ensemble techniques, namely Bagging, Stacking, and Boosting. The study employs the WUSTL-IIOT-2021 imbalanced dataset, which contains samples representing four types of attacks, including denial of service (DoS), SQL injection, reconnaissance, and backdoor. The paper addresses the imbalance in class representation by customizing the dataset for training and testing the ML models. Both Mutual Information (MI) and Extra-trees (ET) are applied as a one-stage ensemble feature selection. The performance of the ML models are investigated using classification accuracy (Acc), precision, recall, F1 score, Matthews correlation coefficient (MCC), model size (Mem), training time (TT) and prediction time (PT).

With increasing realised traffic on transport networks, greenhouse gas emissions show a similar trend. Reducing them is a modern aspiration, creating a better place to live and moving towards sustainability. Expanding the infrastructure is often not an appropriate solution, as the system would only be fully utilised at peak times, while at less frequent times it would not even approach capacity and would require huge investment costs. An alternative to further construction work is the implementation of intelligent traffic systems, where smoother flows can achieve higher capacity by reducing the variability in the system. In a motorway environment, a common approach is Variable Speed Limit Control, where the road is divided into zones and individual speed limits are used to increase or decrease the load on the cells. This paper proposes a solution in which individual cells make decisions cooperatively, in contrast to classical state machine-based methods. Thanks to the jointly formulated goal of the agents, a predictive control method is created that leads to a reduction in emissions due to avoided shock waves and reduced waiting times. This paper presents a solution that provides a universal solution across multiple application lengths, illustrating the power of deep learning. https://github.com/istvan-knab/Variable-speed-limit-control.

The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as “a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections”. In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector.

This paper proposes a novel method to predict the fresh weight of lettuce at the shipping stage in a plant factory using the early-stage growth images. It is well-established that the size and shape of plants correlate with their fresh weight. The proposed method captures chlorophyll fluorescence-based growth images daily and extracts geometric features such as projection area, edge length, and skeleton length. We design a regression model to predict the fresh weight using the dimensionality-reduced historical features. However, without considering growth statuses, the dimensionality reduction approach leads to decreased predictive performance for mature and slower-growing plants. In this paper, we generate a plant growth model that simulates the growth process by integrating multiple growth records based on the comparison of growth statuses. The proposed method then reduces the dimensionality by fitting historical features to the plant growth model to obtain future features. Experimental results demonstrate that the proposed method accurately predicts the fresh weight and achieves the coefficient of determination of 0.885, root mean square error of 8.790 g, and mean absolute error of 6.684 g when predicting the fresh weight ten days ahead using growth images from the past ten days.

The usage of artificial intelligence and machine learning in wireless communications is the stepping stone towards a technological breakthrough in the current limitations of wireless communication systems. The trend of future coding schemes towards 6G appears to be based on rateless schemes and machine learning. Channel coding is important when transmitting data or information reliably as it provides error-correcting purposes. However, there is still a demand for more research regarding machine learning for channel coding. There is also a lack of a specific term or classification for existing machine learning applications for channel coding. This paper explores and compiles current trending machine learning techniques for channel coding. We are also introducing and proposing a new type of machine learning classification for channel coding purposes, as well as surveying some of the papers that fall under the respective class. This paper also discusses current challenges and future machine learning trends for channel coding, which are expected to impact future wireless communications development, especially in channel coding advancements.

Situation Awareness (SA) is a process of sensing, understanding and predicting the environment and is an important component in complex systems. The reception of information from the environment tends to be continuous and of a multimodal nature. AI technologies provide a more efficient and robust support by subdividing the different stages of SA objectives into tasks such as data fusion, representation, classification, and prediction. This paper provides an overview of AI and multimodal methods used to build, enhance and evaluate SA in a variety of environments and applications. Emphasis is placed on enhancing perceptual integrity and persistence. Research indicates that the integration of artificial intelligence and multimodal approaches has significantly enhanced perception and comprehension in complex systems. However, there remains a research gap in projecting future situations and effectively fusing multimodal information. This paper summarizes some of the use cases and lessons learned where AI and multimodal techniques have been used to deliver SA. Future perspectives and challenges are proposed, including more comprehensive predictions, greater interpretability, and more advanced visual information.

The rapid evolution of cryptocurrency markets and the increasing complexity of trading strategies necessitate a comprehensive understanding of price-prediction models and their direct impact on trading efficacy. While extensive research has been conducted separately on price prediction methods and trading strategies, there remains a significant gap in studies explicitly correlating precise price forecasts with successful trading outcomes. This review paper addresses this gap by critically examining the role of accurate cryptocurrency price predictions in enhancing trading strategies. We conducted a systematic review of sufficient scholarly articles and web resources, focusing on the methodologies and effectiveness of various predictive models and their integration into cryptocurrency trading strategies. Our selection criteria ensured the inclusion of papers that demonstrate methodological rigor, relevance, and recent contributions to the field, spanning from economic theories and statistical models to advanced machine learning techniques. The findings reveal that precise price predictions significantly contribute to the development of adaptive and risk-managed trading strategies, which are crucial in the highly volatile cryptocurrency market. The review also identifies current challenges and proposes directions for future research, emphasizing the need for interdisciplinary approaches and ethical considerations in predictive modeling. This synthesis aims to bridge the existing research gap and guide future studies, thereby fostering more sophisticated and profitable trading strategies in the cryptocurrency domain.

This paper proposes an accurate harmonic identification strategy for microgrids and distributed power systems. The harmonic identification strategy is one of the complex tasks in microgrids due to the need of high computational burden in terms of memory and computational time. The complexity of the considered problem is resulted from solving the transcendental nonlinear equations that characterize harmonics especially in real time is considered as a highly challenged problem. The proposed identification strategy aims at detecting individual and total harmonic distortion levels that is generated from several harmonic sources. In the current paper, the Machine Learning Regression Analysis (MLRA) including location-specific data and the Artificial Neural Networks (ANNs) are proposed to identify the harmonic distortion. To enhance the identification and prediction performance, the standard IEEE 34-bus test feeder with verified harmonic sources power system is emulated for various scenarios using Electric Transient Analysis Program (ETAP). An extracting procedure for individual and total harmonic components are employed. The higher reduction level of error with approximately maximum median of 3.7127e $-^{15}$ % assures the accurate prediction of harmonic components. In addition, this work investigates the impact of several practical cases when the voltages of the renewable clean energy source arrays vary (e.g., solar cell, wind turbine, and EV), and a double-stage topology is needed to have the same amount of voltage at the input of the inverter before inversion.

This paper lays the foundations for Physical Layer Security (PLS) as a native 6G communication operation mode. 6G mobile network will enable intelligent applications, automated services, and low-latency connectivity to data networks. Security performance improvements become vital as 6G ubiquitously integrates with critical infrastructure. This paper presents 6G PLS frameworks that combine a Deep Neural Network (DNN) with a shared key-based PLS scheme at the decoding stage to achieve higher security performance. The paper analyzes and models an Artificial Intelligence (AI)/Machine Learning (ML) PLS Layer-1 security detection scheme that achieves an impressive approximately $10-15$ dB of improvement in Signal to Noise Ratio (SNR) performance over the linear PLS detection model in multiple fading environments modelled in COST 259 channel model and accompanying AWGN noise prior to error correction coding (ECC). An analysis of raw BER versus SNR is presented for multiple MIMO antenna sizes and codebook modulation orders. An analysis of the scheme shows that eavesdroppers cannot perfectly decode the secret information even under significantly improved SNR case scenarios. In contrast, legitimate receivers achieve nearly perfect secrecy at lower SNR cases by adopting the ML-based PLS model. In addition, the paper develops the theory and analysis, for the first time to our knowledge, of Alamouti Space-Time Coding (STC) over adjacent periods within a shared key-based PLS scheme with 2M transmit and 2M receive antennas. New modes of STC-based PLS show vast gains of approximately 15 dB over existing linear shared key-based PLS for multi-codebook modulation order in COST 259 environments and noise. Finally, the paper also proposes an operational overview of AI/ML integrated PLS with shared key-agreement protocol in an Open Radio Access Network (O-RAN) architecture model. The paper illustrates a secure, low latency, Layer-1 security O-RAN architecture scheme for 6G communication based upon “security Apps” for latency mitigation.

Unmanned aerial systems/vehicles (UAS/UAVs) are increasingly utilized for inspecting high-voltage (HV) Tx lines. Operating on batteries, these UAVs are equipped with various electrical sensors, microprocessors, and motors. All of these sensors are susceptible to electric (E) and magnetic (H) fields, which may affect sensing, communication, and control operations of UAS. This paper is first-of-its-kind large scale study that gathered field data (E/H) across 5 different Tx lines ( $69 kV_{AC}$ , $230 kV_{AC}$ , $345 kV_{AC}$ , $500 kV_{AC}$ , and $250 kV_{DC}$ ). Additional flight data on a microwave tower was also gathered. Multiple types of DJI UAVs (M2EA, M30, and M300) are used to carry E/H field sensors, and radio frequency (RF) sensors as payload for this study. Specific measurements include the E field in V/m, H field in mG, Battery voltage in V, Battery current in A, Battery percentage, Battery Temperature in °F, latitude, and longitude. The overall goal of the paper is to quantify E/H field distribution in HVTx lines which aided in realization of FAA rule making on how close UAVs could fly safely. The preliminary findings indicate that significantly higher E/H field level were reported near AC than DC Tx lines. The paper discusses conditions influencing E/H field strength during UAV operations. Additionally, hybrid machine learning (HML) models (RFR-SVM, RFR-KNN) were used to forecast battery drain. The results indicate that the hybrid RFR-KNN model yields lower MAPE values compared to stand-alone and other hybrid models.

The Open Radio Access Network (RAN) emerges as a revolutionary architecture promising unprecedented levels of openness, flexibility, and intelligence within radio access networks. Central to this innovation is the integration of Machine Learning (ML) and Artificial Intelligence (AI) within the RAN Intelligent Controller (RIC), aimed at optimizing network operations and enhancing control mechanisms. This paper undertakes a thorough examination of Open RAN, particularly focusing on its energy consumption aspects, which are pivotal for ensuring the sustainability of future wireless networks. In this paper, we review and compare Open RAN architecture with previous network architectures. In particular we focus on O-RAN Alliance specifications. Additionally, we explore the deployment of ML across various facets of Open RAN and highlights how to estimate the energy consumption of ML models. Through constructing explicit energy consumption models for key O-RAN components, we provide a granular analysis of their energy profiles. Finally we compare the energy dynamics of O-RAN against traditional RAN architectures, delineating the impact of virtualization and disaggregation on energy efficiency.

The integration of intelligent reflecting surfaces (IRS) into cooperative non-orthogonal multiple access (NOMA) systems revolutionizes wireless networks by enhancing signal strength, mitigating interference, and optimizing spectral efficiency. The cooperative NOMA (CNOMA) framework, empowered by IRS technology, further promises enhanced performance, robustness, and scalability for next-generation wireless networks as compared to NOMA only systems. This paper explores the synergy between IRS and NOMA to leverage cooperative techniques for superior wireless system design. Fundamental principles, technological advancements, and potential applications of IRS-assisted CNOMA systems are discussed, highlighting existing works. Both underlay and overlay NOMA principles are examined in conjunction with IRS in the paper. Spatial modulation-aided CNOMA is explored for multiple-input multiple-output (MIMO) systems, along with its advantages and practical challenges. Additionally, the paper discusses fundamental principles and technological advancements of IRS-assisted CNOMA systems, emphasizing solutions to potential challenges and the role of machine learning (ML)/deep learning (DL) in resource optimization like transmit power and IRS phase settings. Simulation results are presented to highlight the benefits of IRS-aided CNOMA system design. Finally, the paper outlines future directions and potential research topics in IRS-aided CNOMA.

According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

In the rapidly evolving landscape of digital technology, the security of web and mobile applications stands paramount. As these platforms become increasingly integrated into our daily lives, the need for robust safety measures becomes imperative. This research paper delves into the intricate realm of web and mobile request safety, unraveling a multi-faceted exploration that combines traditional feature engineering with state-of-the-art machine learning models. Beginning with foundational models like TextCNN and TextRNN, we scrutinize their effectiveness in discerning the safety of requests. Advancing our investigation, we delve into the capabilities of sophisticated architectures, including Bidirectional LSTMs, DistilBERT, and RoBERTa. Beyond individual assessments, we introduce hybrid models that synergize the strengths of various approaches, establishing a comprehensive defense against emerging security threats. Throughout this research, we navigate the intricacies of model training, evaluation, and performance metrics. From accuracy and precision to recall and confusion matrices, each metric paints a nuanced picture of the efficacy of these models in ensuring the safety of web and mobile interactions. In a world where cyber threats loom large, the significance of this research lies not only in its technical contributions but also in its practical implications. By providing insights into innovative strategies for enhancing the security and resilience of digital applications, this paper contributes to the ongoing discourse on fortifying the digital infrastructure.

One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to e-commerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI.

Federated Learning (FL) allows task initiators (servers) to utilize data from task participants (clients) to train machine learning models while protecting data privacy. However, in the FL system, when the client data are non-independently identically distributed (Non-IID), appropriate metrics are chosen to accurately evaluate the quality of the client data, accordingly to select a reasonable subset of clients, and thus ensure the accuracy of the FL aggregation model. In this paper, based on the experimental results, a data distribution evaluation model is proposed, which is based on two metrics: the volume of client data and its increment and the balance of global client data. This data distribution evaluation model enables more accurate evaluation of clients with Non-IID characteristics. Based on this evaluation model, this paper further proposes an FL client subset selection algorithm. This algorithm accurately evaluates the data value of each client, enabling the server to select the most valuable subset of clients before FL training, thus improving the accuracy of the federated learning aggregation model in scenarios with Non-IID client data. When training the FL aggregation model using the proposed method on datasets composed of CIFAR-10, Fashion-MNIST, and DEAP distributions, compared to the optimal baseline, the average precision scores increased by 5.99%, 4.79%, and 4.29% respectively. The improvement in accuracy is more pronounced in scenarios with Non-IID data, such as in the DEAP dataset distribution with the highest Non-IID degree, where the accuracy increased by 5.30% compared to the optimal baseline.

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

Traffic collisions pose a significant global concern, necessitating innovative solutions to enhance safety. Post-collision, it becomes crucial for the police to determine the responsibilities of the involved parties, distinguishing between criminal acts and non-criminal incidents. Insurance companies also rely on such investigations to compensate victims. Assessing responsibility after a crash is a complex task requiring advanced knowledge of road rules. For straightforward scenarios like crashes with traffic lights, decisions are fast and easy. However, in situations such as crashes without any traffic signs, expert knowledge is essential. Automating such tasks demands innovative approaches, representing a necessity for the future of the automobile and insurance industries. Despite these critical needs, there has been limited research in the domain. This paper introduces a system that is capable of detecting vehicle collision and implements an original responsibility assessment process to assess drivers responsibilities. This paper is an extended version of our previous work. In the previous work, the system supported only three types of head-on/angle crash scenarios without traffic lights within three weather conditions, in addition to those with traffic lights. This study presents the improved system which now accommodates six different types of head-on/angle crash scenarios without traffic lights within six harsh weather conditions, such as foggy and snowy days. Additionally, extensive experiments are conducted with results showing that the system performs better than its previous version, mainly during nighttime without traffic lights (up to 93% accuracy against up to 82.5% obtained previously). Moreover, through case studies and comparisons with existing research, the effectiveness and superiority of the system are demonstrated.

The breath rate can now be monitored remotely due to the advancements in digital stethoscope sensor technology, signal processing, and machine learning. Automatic breathing rate classification, on the other hand, provides additional benefits in medical diagnostics. In this paper, a lightweight convolutional neural network is proposed for automatic breathing rate classification utilizing the piezoresistive sensor data. In the proposed work, the raw signals from the piezoresistive sensor are pre-processed using a continuous wavelet transform to generate the corresponding images. These images are then fed into a lightweight convolutional neural network, which efficiently classifies the breathing rate into six classes based on the number of breaths per minute. Through extensive results, we show that the proposed model results in a classification accuracy of 96.40% which is higher than all the benchmark models considered in this paper. We also evaluate the performance of the proposed model using edge computing devices such as Raspberry Pi, Nvidia AGX Xavier, and Nvidia Jetson Nano.

Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and ma-chine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper’s findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

The field of cybersecurity has witnessed a significant shift towards Programmable Data Planes with the emergence of the P4 programming P4. The existing literature lacks a comprehensive taxonomy that provides collaborative classifications and characterization of P4-based cybersecurity solutions. In this paper, we propose a novel taxonomy to better characterize published works in the field of cybersecurity solutions developed leveraging P4 and Programmable Data Planes. Our taxonomy introduces three main categories: detection techniques, mitigation actions, and deployment platforms. Unlike existing classification approaches present in literature, our taxonomy allows categorizing a given work according to different criteria, thus enabling collaborative classifications and the identification of shared features by highlighting intra-category and inter-category relationships that can be established among different works. Through our comprehensive characterization of the works identified in the literature, we present key findings that contribute to extend the current understanding of the field. By identifying the diverse range of techniques employed and the platforms utilized, we aim at addressing the needs to understand the area, which is fundamental for future advancements. Notably, we emphasize the significance of security provisioning through the adoption of cybersecurity functional abstractions. These abstractions enable the emulation of the behavior of conventional security devices, leveraging the capabilities of P4. Furthermore, we discuss several research challenges and possible future directions that have emerged from our analysis. This paper focuses on providing valuable insights and knowledge for researchers and practitioners in the field of cybersecurity solutions based on Programmable Data Planes leveraging P4, with the final goal of opening avenues for new research.

With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed in many industries have been improved. Particles and impurities of any size, shape and in any condition can be detected using advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird’s nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI methods in particle and impurity detection, applications of AI in particle and impurity detection in related industries including in EBN and AI applications in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field.

In urban traffic, a Dynamic Traffic Light System (DTLS) is an important aspect of automatic driving. DTLS estimates the time of the light signal from images of dynamically changing road traffic. In conventional traffic light systems, light signals are enabled at predefined or fixed time intervals without having information on the current traffic density on the road. This static behavior of the traffic light system increases unnecessary waiting time on the road, eventually creating traffic jams, environmental pollution, and other health emergencies. The smart traffic light system addresses these issues with self-learning algorithms and dynamically allows traffic to pass by learning current traffic density. In this paper, a vision-based DTLS is proposed using the YOLO (You Only Look Once) object detection algorithm that detects and counts the total number of vehicles on the roads of a traffic signal junction. The traffic signals are tuned based on the computed traffic to minimize the overall delay at that junction. Moreover, the traffic junctions are facilitated to communicate with the adjacent junctions to transmit the cumulative traffic delay observed. This delay is used to prioritize traffic passing through salient blocks like schools, offices, hospitals, etc. The paper aims to minimize the overhead incurred in both computations of traffic (using approximate computing) and in communication networks (using low-power technologies of IEEE 802.15.4 standard, specifically DSME MAC and/or LoRaWAN). The proposed system accomplishes its objective of smart city infrastructure by optimizing the traffic flow. Further, the paper provides a mechanism for green traffic corridors for emergency vehicles.

Contemporary mobile networks, offering advanced services such as highly dependable and fast communication (URLLC) and extensive device-to-device connectivity (mMTC), are paving the way for the upcoming 6G era. These networks are expanding their capabilities beyond traditional voice and short messaging services, enabling diverse devices to connect to the cellular network. However, with this increased connectivity comes a heightened vulnerability at the radio interface, which is the primary access medium for mobile network communication. This research work focuses on safeguarding the availability of the radio interface in the face of emerging threats. Threats to radio interface availability can originate either directly from exploiting the 3GPP radio protocol stack within base stations or indirectly through the IP protocol stack carried over the user plane. In particular, this research paper delves into user plane DDoS attacks leveraging the IP protocol stack to generate excessive traffic. It introduces a novel detection method situated within the Radio Access Network (RAN). This method analyzes the patterns of radio protocols and their functionalities to identify user plane DDoS attacks initiated from User Equipment (UEs). Importantly, the method does not rely on directly inspecting user plane packets like IP packets but rather leverages the characteristics of 3GPP radio protocols (e.g., MAC, RLC, PDCP) to detect IP DDoS attacks closer to their origin. This early detection capability helps prevent DDoS traffic from propagating to the transport network. The implications of this research extend beyond the current generation of networks, as it lays a foundation for enhancing security in the forthcoming 6G networks. As 6G aims to deliver even more advanced services and connectivity across a diverse array of devices, the robust security measures proposed in this work will be instrumental in ensuring the reliability and availability of these cutting-edge networks. The analysis employed in this paper showcase the performance with accuracy of 98.9% for DDoS attack detection.

With the development of artificial intelligence, there have been many attempts to incorporate artificial intelligence into algorithmic trading. In particular, reinforcement learning, which aims to solve dynamic decision-making problems, is attracting attention because of its high utilization in algorithmic trading. In this paper, we will implement a simple Deep Reinforcement Learning (DRL) trading robot to check the performance of DRL. In addition, we tried to find out how much performance improvement can be achieved by comparing a robot that learned a single stock data with a robot that learned stock data, market index, and interest rate data. This paper aims to develop a stock investment robot using a Proximal Policy Optimization (PPO) reinforcement learning algorithm and analyze the performance of the robot. The first robot used only the stock data of APPL INC, a single stock, as input, and the second robot used stock data of APPL INC and the S&P 500 index together with US interest rate data as input data. Afterward, the stock investment performance of the two robots for APPL INC was comparatively analyzed using the test data.

The application of Machine Learning (ML) in healthcare has opened unprecedented avenues for predictive analytics, diagnostics, and personalized medicine. However, the sensitivity of healthcare data and the ethical dilemmas associated with automated decision-making necessitate a rigorous ethical framework. This review paper aims to provide a comprehensive overview of the existing ethical frameworks that guide ML in healthcare and evaluates their adequacy in ad-dressing ethical challenges. Specifically, this article offers an in-depth examination of prevailing ethical constructs that oversee healthcare ML, spotlighting pivotal concerns: data protection, in-formed assent, equity, and patient autonomy. Various analytical approaches including quantitative metrics, statistical methods for bias detection, and qualitative thematic analyses are applied to address these challenges. Insights are further enriched through case studies of Clinical Decision Support Systems, Remote Patient Monitoring, and Telemedicine Applications. Each case is evaluated against existing ethical frameworks to identify limitations and gaps. Based on our com-prehensive review and evaluation, we propose actionable recommendations for evolving ethical guidelines. The paper concludes by summarizing key findings and underscoring the urgent need for robust ethical frameworks to guide ML applications in sensitive healthcare environments. Future work should focus on the development and empirical validation of new ethical frameworks that can adapt to emerging technologies and ethical dilemmas in healthcare ML.

With the rapid development of information communication and mobile device technologies, smart devices have become increasingly popular, providing convenience to households and enhancing the level of intelligence in daily life. This trend is also driving innovation and progress in various fields, including healthcare, transportation, and industry. However, as technology continues to proliferate, network security concerns have become increasingly prominent, making the protection of digital life and data security an urgent priority. Intrusion detection has always played an important role in the field of network security. Traditional intrusion detection systems predominantly rely on anomaly detection technology to identify potential intrusions by detecting abnormal patterns in network traffic. With technological advancements, machine learning-based methods have emerged as the cornerstone of modern intrusion detection, enabling more precise identification of abnormal behaviors and potential intrusions by learning the patterns of normal network traffic. In response to these challenges, this paper introduces an innovative intrusion detection model that amalgamates the Attention-CNN-BiLSTM (ACBL) and Temporal Convolutional Network (TCN) architectures. The ACBL and TCN models excel in processing spatial and temporal features within network traffic data, respectively. This integration harnesses diverse neural network structures to elevate overall model performance and accuracy. Furthermore, a unique approach inspired by dung beetles’ natural behavior, incorporating Tent mapping-enhanced Dung Beetle Optimization Algorithm (TDBO), is leveraged for both optimizing feature selection parameters and searching for optimal model hyperparameters. The feature selection parameters obtained from TDBO are then combined with the importance ranking from the Random Forest algorithm, ensuring optimal features can be better selected to enhance model performance. This paper introduces a novel intrusion detection model, the TDBO-ACBLT model, and validates its performance using the UNSW-NW15 dataset. TDBO excels in feature selection compared to common algorithms and achieves superior parameter optimization accuracy over Harris’s Hawk Optimization (HHO), Particle Swarm Optimization (PSO), and Dung Beetle Optimization (DBO). The proposed model achieves higher accuracy than prevalent machine learning models.