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Using instance segmentation and video inpainting provides a significant leap in real-time football video broadcast enhancements by removing potential visual distractions, such as an occasional person or another object accidentally occupying the frame. Despite its relevance and importance in the media industry, this area remains challenging and relatively understudied, thus offering potential for research. Specifically, the segmentation and inpainting of camera operator instances from video remains an underexplored research area. To address this challenge, this paper proposes a framework designed to accurately detect and remove camera operators while seamlessly hallucinating the background in real-time football broadcasts. The approach aims to enhance the quality of the broadcast by maintaining its consistency and level of engagement to retain and attract users during the game. To implement the inpainting task, firstly, the camera operators instance segmentation method should be developed. We used a YOLOv8 model for accurate real-time operator instance segmentation. The resulting model produces masked frames, which are used for further camera operator inpainting. Moreover, this paper presents an extensive “Cameramen Instances” dataset with more than 7500 samples, which serves as a solid foundation for future investigations in this area. The experimental results show that the YOLOv8 model performs better than other baseline algorithms in different scenarios. The precision of 95.5%, recall of 92.7%, mAP50-95 of 79.6, and a high FPS rate of 87 in low-volume environment prove the solution efficacy for real-time applications.

Abstract The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy and a hard-RIME puncture mechanism, along with an improved positive greedy selection mechanism, to resist getting trapped in local optima and enhance its overall search capabilities. The article also introduces Binary modified RIME (mRIME), a binary adaptation of the RIME algorithm to address the unique challenges posed by FS problems, which typically involve binary search spaces. Four different types of transfer functions (TFs) were selected for FS issues, and their efficacy was investigated for global optimization using CEC2011 and CEC2017 and FS tasks related to disease diagnosis. The results of the proposed mRIME were tested on ten reliable optimization algorithms. The advanced RIME architecture demonstrated superior performance in global optimization and FS tasks, providing an effective solution to complex optimization problems in various domains.

This paper looks at the incorporation of blockchain and Internet of Things (IoT) technologies into Forest 4.0, a sector that harnesses advanced tools such as artificial intelligence and big data for efficient and sustainable forest monitoring and management. The synergy of blockchain and IoT has gained significant attention, offering a secure and decentralized framework for data management, traceability, and supply chain oversight. The provided use cases demonstrate how these technologies improve forest practices, with insight into smart contract implementation and decentralized systems for sustainable forest management. The major findings imply that digital technologies such as blockchain, IoT, AI, WSNs, etc. can help improve forest management sustainability, efficiency and transparency, and integration of these technologies can provide significant information for decision-making and resource allocation, as well as improve supply chain transparency and sustainable forest practices.

The increasing severity of cyber-attacks against organizations emphasizes the necessity for efficient threat intelligence. This article presents a novel multi-layered architecture for threat intelligence that integrates diverse data streams, including corporate network logs, open-source intelligence, and dark web monitoring, to offer a comprehensive overview of the cybersecurity threat landscape. Our approach, distinct from previous studies, uniquely integrates these varied features into the machine-learning algorithms (XGBoost, Gradient Boosting, LightGBM, Extra Trees, Random Forest, Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, ridge Classifier, AdaBoost and Quadratic Discriminant Analysis) using various feature selection algorithms (information gain, correlation coefficient, chi-square, fisher score, forward wrapper, backward wrapper, Ridge classifier) to enhance real-time threat detection and mitigation. The practical LITNET-2020 dataset was utilized to evaluate the proposed architecture. Extensive testing against real-world cyber-attacks, including malware and phishing, demonstrated the robustness of the architecture, achieving exceptional results. Specifically, XGBoost demonstrated the highest performance with a detection accuracy of 99.98%, precision of 99.97%, and recall of 99.96%, Significantly surpassing traditional methods. Gradient Boosting and LightGBM also exhibited excellent performance, with accuracy, precision, and recall values of 99.97%. Our findings underscore the effectiveness of our architecture in significantly improving an organization’s capability to identify and counteract online threats in real-time. By developing a comprehensive threat intelligence framework, this study advances the field of cybersecurity, providing a robust tool for enhancing organizational resilience against cyber-attacks.

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Background/Objectives: This study investigates the classification of Major Depressive Disorder (MDD) using electroencephalography (EEG) Short-Time Fourier-Transform (STFT) spectrograms and audio Mel-spectrogram data of 52 subjects. The objective is to develop a multimodal classification model that integrates audio and EEG data to accurately identify depressive tendencies. Methods: We utilized the Multimodal open dataset for Mental Disorder Analysis (MODMA) and trained a pre-trained Densenet121 model using transfer learning. Features from both the EEG and audio modalities were extracted and concatenated before being passed through the final classification layer. Additionally, an ablation study was conducted on both datasets separately. Results: The proposed multimodal classification model demonstrated superior performance compared to existing methods, achieving an Accuracy of 97.53%, Precision of 98.20%, F1 Score of 97.76%, and Recall of 97.32%. A confusion matrix was also used to evaluate the model’s effectiveness. Conclusions: The paper presents a robust multimodal classification approach that outperforms state-of-the-art methods with potential application in clinical diagnostics for depression assessment.

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson’s disease. Sensor data collected from wearable gyroscopes located at the sole of the patient’s shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson’s as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen’s Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models.

Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles.

Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection. Magnetic resonance imaging (MRI) for earlier brain tumor diagnosis may improve the chance of survival for patients. The most common method of diagnosing brain tumors is MRI. The improved visibility of malignancies in MRI makes therapy easier. The diagnosis and treatment of brain cancers depend on their identification and treatment. Numerous deep learning models are proposed over the last decade including Alexnet, VGG, Inception, ResNet, DenseNet, etc. All these models are trained on a huge dataset, ImageNet. These general models have many parameters, which become irrelevant when implementing these models for a specific problem. This study uses a custom deep-learning model for the classification of brain MRIs. The proposed Disease and Spatial Attention Model (DaSAM) has two modules; (a) the Disease Attention Module (DAM), to distinguish between disease and non-disease regions of an image, and (b) the Spatial Attention Module (SAM), to extract important features. The experiments of the proposed model are conducted on two multi-class datasets that are publicly available, the Figshare and Kaggle datasets, where it achieves precision values of 99% and 96%, respectively. The proposed model is also tested using cross-dataset validation, where it achieved 85% accuracy when trained on the Figshare dataset and validated on the Kaggle dataset. The incorporation of DAM and SAM modules enabled the functionality of feature mapping, which proved to be useful for the highlighting of important features during the decision-making process of the model.

In the article by Jaime F [...]

Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations.

The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.

Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/‘X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate.

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

Abstract Images captured through a visual sensory system are degraded in a foggy scene, which negatively influences recognition, tracking, and detection of targets. Efficient tools are needed to detect, pre‐process, and enhance foggy scenes. Machine learning (ML) has a significant role in image defogging domain for tackling adverse issues. Unfortunately, regardless of contributions that were made by ML, little attention has been attributed to this topic. This paper summarizes the role of ML methods and relevant aspects in the image defogging research area. Also, the basic terms and concepts are highlighted in image defogging topic. Feature extraction approaches with a summary of advantages and disadvantages are described. ML algorithms are also summarized that have been used for applications related to image defogging, that is, image denoising, image quality assessment, image segmentation, and foggy image classification. Open datasets are also discussed. Finally, the existing problems of the image defogging domain in general and, specifically related to ML which need to be further studied are discussed. To the best knowledge, this the first review paper which sheds a light on the role of ML and relevant aspects in the image defogging domain.

Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality from breast cancer could be reduced by diagnosing and identifying it at an early stage. To detect breast cancer, various imaging modalities can be used, such as mammography. Computer-Aided Detection/Diagnosis (CAD) systems can assist an expert radiologist to diagnose breast cancer at an early stage. This paper introduces the findings of a systematic review that seeks to examine the state-of-the-art CAD systems for breast cancer detection. This review is based on 118 publications published in 2018–2021 and retrieved from major scientific publication databases while using a rigorous methodology of a systematic review. We provide a general description and analysis of existing CAD systems that use machine learning methods as well as their current state based on mammogram image modalities and classification methods. This systematic review presents all stages of CAD including pre-processing, segmentation, feature extraction, feature selection, and classification. We identify research gaps and outline recommendations for future research. This systematic review may be helpful for both clinicians, who use CAD systems for early diagnosis of breast cancer, as well as for researchers to find knowledge gaps and create more contributions for breast cancer diagnostics.

Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images.

The study presents a systematic review of 232 studies on various aspects of the use of artificial intelligence methods for identification of financial distress (such as bankruptcy or insolvency). We follow the guidelines of the PRISMA methodology for performing the systematic reviews. The study discusses bankruptcy-related financial datasets, data imbalance, feature dimensionality reduction in financial datasets, financial distress prediction, data pre-processing issues, non-financial indicators, frequently used machine-learning methods, performance evolution metrics, and other related issues of machine-learning-based workflows. The study findings revealed the necessity of data balancing, dimensionality reduction techniques in data preprocessing, and allow researchers to identify new research directions that have not been analyzed yet.

Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top–bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.

Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate any pathologies in the human body. One of the areas of interest is the human brain. Naturally, MR images are low-resolution and contain noise due to signal interference, the patient’s body’s radio-frequency emissions and smaller Tesla coil counts in the machinery. There is a need to solve this problem, as MR tomographs that have the capability of capturing high-resolution images are extremely expensive and the length of the procedure to capture such images increases by the order of magnitude. Vision transformers have lately shown state-of-the-art results in super-resolution tasks; therefore, we decided to evaluate whether we can employ them for structural MRI super-resolution tasks. A literature review showed that similar methods do not focus on perceptual image quality because upscaled images are often blurry and are subjectively of poor quality. Knowing this, we propose a methodology called HR-MRI-GAN, which is a hybrid transformer generative adversarial network capable of increasing resolution and removing noise from 2D T1w MRI slice images. Experiments show that our method quantitatively outperforms other SOTA methods in terms of perceptual image quality and is capable of subjectively generalizing to unseen data. During the experiments, we additionally identified that the visual saliency-induced index metric is not applicable to MRI perceptual quality assessment and that general-purpose denoising networks are effective when removing noise from MR images.

This paper analyzes the impact of the ongoing war in Ukraine on the productivity and collaboration networks of Ukrainian academics. As a case study, we analyze the publication patterns in open-access MDPI journals using bibliographic analysis methods and compare the research output published in 2022 with research papers published in the three preceding years (2019–2021) with at least one author having an Ukrainian affiliation. A total of 2365 publications were analyzed. The identified publication trends provide an interesting insight into the dynamics of the research network of Ukrainian researchers, which demonstrated a decline in diversity of international collaborations in 2022. The findings of this study emphasize the necessity of international research collaboration in a variety of fields in order to mitigate the detrimental effects of national crises and emergencies.

Maritime vessels provide a wealth of data concerning location, trajectories, and speed. However, while these data are meticulously monitored and logged to maintain course, they can also provide a wealth of meta information. This work explored the potential of data-driven techniques and applied artificial intelligence (AI) to tackle two challenges. First, vessel classification was explored through the use of extreme gradient boosting (XGboost). Second, vessel trajectory time series forecasting was tackled through the use of long-short-term memory (LSTM) networks. Finally, due to the strong dependence of AI model performance on proper hyperparameter selection, a boosted version of the well-known particle swarm optimization (PSO) algorithm was introduced specifically for tuning the hyperparameters of the models used in this study. The introduced methodology was applied to real-world automatic identification system (AIS) data for both marine vessel classification and trajectory forecasting. The performance of the introduced Boosted PSO (BPSO) was compared to contemporary optimizers and showed promising outcomes. The XGBoost model tuned using boosted PSO attained an overall accuracy of 99.72% for the vessel classification problem, while the LSTM model attained a mean square error (MSE) of 0.000098 for the marine trajectory prediction challenge. A rigid statistical analysis of the classification model was performed to validate outcomes, and explainable AI principles were applied to the determined best-performing models, to gain a better understanding of the feature impacts on model decisions.

Alzheimer’s disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete picture of the changes in the brain that occur over time in AD. Medical image fusion is crucial in that it combines data from various image modalities into a single, better-understood output. The present study explores the feasibility of employing Pareto optimized deep learning methodologies to integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images through the utilization of pre-existing models, namely the Visual Geometry Group (VGG) 11, VGG16, and VGG19 architectures. Morphological operations are carried out on MRI and PET images using Analyze 14.0 software and after which PET images are manipulated for the desired angle of alignment with MRI image using GNU Image Manipulation Program (GIMP). To enhance the network’s performance, transposed convolution layer is incorporated into the previously extracted feature maps before image fusion. This process generates feature maps and fusion weights that facilitate the fusion process. This investigation concerns the assessment of the efficacy of three VGG models in capturing significant features from the MRI and PET data. The hyperparameters of the models are tuned using Pareto optimization. The models’ performance is evaluated on the ADNI dataset utilizing the Structure Similarity Index Method (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), and Entropy (E). Experimental results show that VGG19 outperforms VGG16 and VGG11 with an average of 0.668, 0.802, and 0.664 SSIM for CN, AD, and MCI stages from ADNI (MRI modality) respectively. Likewise, an average of 0.669, 0.815, and 0.660 SSIM for CN, AD, and MCI stages from ADNI (PET modality) respectively.

This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).

Abstract Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework.

The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications.

The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results. In this paper, we explore the use of the Unity ML-Agents toolkit to train intelligent agents to navigate a racing track in a simulated environment using RL algorithms. The paper compares the performance of several different RL algorithms and configurations on the task of training kart agents to successfully traverse a racing track and identifies the most effective approach for training kart agents to navigate a racing track and avoid obstacles in that track. The best results, value loss of 0.0013 and a cumulative reward of 0.761, were yielded using the Proximal Policy Optimization algorithm. After successfully choosing a model and algorithm that can traverse the track with ease, different objects were added to the track and another model (which used behavioral cloning as a pre-training option) was trained to avoid such obstacles. The aforementioned model resulted in a value loss of 0.001 and a cumulative reward of 0.068, proving that behavioral cloning can help achieve satisfactory results where the in game agents are able to avoid obstacles more efficiently and complete the track with human-like performance, allowing for a deployment of intelligent agents in racing simulators.

One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.

Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely used machine-learning-based research differs in terms of the datasets, training methods, performance evaluation, and comparison methods used. In this paper, we surveyed 224 papers published between 2003 and 2022 that employed machine learning for text classification. The Preferred Reporting Items for Systematic Reviews (PRISMA) statement is used as the guidelines for the systematic review process. The comprehensive differences in the literature are analyzed in terms of six aspects: datasets, machine learning models, best accuracy, performance evaluation metrics, training and testing splitting methods, and comparisons among machine learning models. Furthermore, we highlight the limitations and research gaps in the literature. Although the research works included in the survey perform well in terms of text classification, improvement is required in many areas. We believe that this survey paper will be useful for researchers in the field of text classification.

Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.

Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of tumor volume instead of size may improve the accuracy of survival prediction. In response to this need, we propose a novel model, the enhanced brain tumor identification and survival time prediction (ETISTP), which computes tumor volume, classifies it into low- or high-grade glioma, and predicts survival time with greater accuracy. The ETISTP model integrates four parameters: patient age, survival days, gross total resection (GTR) status, and tumor volume. Notably, ETISTP is the first model to employ tumor volume for prediction. Furthermore, our model minimizes the computation time by allowing for parallel execution of tumor volume computation and classification. The simulation results demonstrate that ETISTP outperforms prominent survival prediction models.

Abstract Human gait analysis is a novel topic in the field of computer vision with many famous applications like prediction of osteoarthritis and patient surveillance. In this application, the abnormal behavior like problems in walking style is detected of suspected patients. The suspected behavior means assessments in terms of knee joints and any other symptoms that directly affected patients’ walking style. Human gait analysis carries substantial importance in the medical domain, but the variability in patients’ clothes, viewing angle, and carrying conditions, may severely affect the performance of a system. Several deep learning techniques, specifically focusing on efficient feature selection, have been recently proposed for this purpose, unfortunately, their accuracy is rather constrained. To address this disparity, we propose an aggregation of robust deep learning features in Kernel Extreme Learning Machine. The proposed framework consists of a series of steps. First, two pre-trained Convolutional Neural Network models are retrained on public gait datasets using transfer learning, and features are extracted from the fully connected layers. Second, the most discriminant features are selected using a novel probabilistic approach named Euclidean Norm and Geometric Mean Maximization along with Conditional Entropy. Third, the aggregation of the robust features is performed using Canonical Correlation Analysis, and the aggregated features are subjected to various classifiers for final recognition. The evaluation of the proposed scheme is performed on a publicly available gait image dataset CASIA B. We demonstrate that the proposed feature aggregation methodology, once used with the Kernel Extreme Learning Machine, achieves accuracy beyond 96%, and outperforms the existing works and several other widely adopted classifiers.

Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and requires deep expert knowledge. In this work, a hybrid and fully-automated classification system is developed for detecting different types of defects in EL images. The system fuses the deep feature representations extracted from two different deep learning models (Inception-V3 and ResNet50) to form more discriminative feature vectors. These feature vectors are then fed into the classifier layer to assign them into one of different types of defects. A large-scale, challenging solar cells dataset composed of 2,624 EL images was used to assess the performance of the proposed system in both the binary classification (functional vs defective) task and multi-class classification (functional, mild, moderate, and severe) task. The proposed system has managed to detect the correct defect type with less than 1 s per image with an accuracy rate of 98.15% and 95.35% in the binary classification and multi-classification task, respectively.

Because of its benefits in providing an engaging and mobile environment, virtual reality (VR) has recently been rapidly adopted and integrated in education and professional training. Augmented reality (AR) is the integration of VR with the real world, where the real world provides context and the virtual world provides or reconstructs missing information. Mixed reality (MR) is the blending of virtual and physical reality environments allowing users to interact with both digital and physical objects at the same time. In recent years, technology for creating reality-based 3D models has advanced and spread across a diverse range of applications and research fields. The purpose of this paper is to design, develop, and test VR for kinaesthetic distance learning in a museum setting. A VR training program has been developed in which learners can select and perform pre-made scenarios in a virtual environment. The interaction in the program is based on kinaesthetic learning characteristics. Scenarios with VR controls simulate physical interaction with objects in a virtual environment for learners. Learners can grasp and lift objects to complete scenario tasks. There are also simulated devices in the virtual environment that learners can use to perform various actions. The study’s goal was to compare the effectiveness of the developed VR educational program to that of other types of educational material. Our innovation is the development of a system for combining their 3D visuals with rendering capable of providing a mobile VR experience for effective heritage enhancement.

The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications.

A serious game is a type of game that is designed for a primary purpose other than entertainment. Instead, serious games are intended to achieve specific goals, such as education, training, or health promotion. The goal of serious games is to engage players in a way that is both enjoyable and effective in achieving the intended learning or behavior change outcomes. Recently, several systematic reviews on the development and application of serious games and on the application of gamification techniques have been published, which indicate high activity and ongoing progress in this area of research. Such an extensive body of review papers raises the need to analyze and extract the current state and the prevailing trends of the serious games and gamification (SGG) domain by analyzing and summarizing the systematic review articles. This study presents a systematic meta-review, i.e., a review of the 53 survey papers on the domain of serious games and gamification. The systematic review follows the PRISMA guidelines, while constructive and cross-sectional methods are used to analyze and present the results. Finally, this study identifies the future trends and challenges for the domain. As a result, the meta-review helps the reader to quickly assess the present status of SGG and serves as a reference for finding further information on each technology utilized in SGG. Using the criterion of the citations, the meta-review analysis provides insight into the quantity and academic relevance of the published SGG articles. Moreover, 53 articles published in journals were selected as important surveys in the research field. The study found that serious games and gamification techniques are increasingly being used for a wide range of health conditions and the focus is shifting towards the use of mobile and digital platforms, virtual reality, and machine learning to personalize and adapt interventions. The existing research gaps include the lack of standardization in development and evaluation, insufficient understanding of underlying mechanisms of action, limited understanding of integration into existing healthcare systems, limited understanding of specific game mechanics and design elements for promoting health behaviors, and limited research on scalability, adoption, and long-term effects. These research gaps highlight the need for further research to fully understand the potential and limitations of serious games and gamification for health and how to effectively apply them.

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.

The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a ploninomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB-based two-dimensional (2D) skeleton recognition algorithms, with the goal of producing a full 3D model as a final result. The suggested method extracts the entire humanoid character motion curve, which is then connected to a three-dimensional (3D) mesh for real-time preview. Our method maintains high joint mapping accuracy with smooth motion frames while ensuring anthropometric regularity, producing a mean average precision (mAP) of 0.950 for the task of predicting the joint position of a single subject. Furthermore, the suggested system, trained on the MoVi dataset, enables a seamless evaluation of posture in a 3D environment, allowing participants to be examined from numerous perspectives using a single recorded camera feed. The results of evaluation on our own self-collected dataset of human posture videos and cross-validation on the benchmark MPII and KIMORE datasets are presented.

Much news is available online, and not all is categorized. A few researchers have carried out work on news classification in the past, and most of the work focused on fake news identification. Most of the work performed on news categorization is carried out on a benchmark dataset. The problem with the benchmark dataset is that model trained with it is not applicable in the real world as the data are pre-organized. This study used machine learning (ML) techniques to categorize online news articles as these techniques are cheaper in terms of computational needs and are less complex. This study proposed the hyperparameter-optimized support vector machines (SVM) to categorize news articles according to their respective category. Additionally, five other ML techniques, Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), were optimized for comparison for the news categorization task. The results showed that the optimized SVM model performed better than other models, while without optimization, its performance was worse than other ML models.

Breast cancer is one of the leading causes of death in women worldwide—the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan–Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation.

Speech impairment analysis and processing technologies have evolved substantially in recent years, and the use of voice as a biomarker has gained popularity. We have developed an approach for clinical speech signal processing to demonstrate the promise of deep learning-driven voice analysis as a screening tool for Parkinson’s Disease (PD), the world’s second most prevalent neurodegenerative disease. Detecting Parkinson’s disease symptoms typically involves an evaluation by a movement disorder expert, which can be difficult to get and yield varied findings. A vocal digital biomarker might supplement the time-consuming traditional manual examination by recognizing and evaluating symptoms that characterize voice quality and level of deterioration. We present a deep learning based, custom U-lossian model for PD assessment and recognition. The study’s goal was to discover anomalies in the PD-affected voice and develop an automated screening method that can discriminate between the voices of PD patients and healthy volunteers while also providing a voice quality score. The classification accuracy was evaluated on two speech corpora (Italian PVS and own Lithuanian PD voice dataset) and we have found the result to be medically appropriate, with values of 0.8964 and 0.7949, confirming the proposed model’s high generalizability.