Search

This paper investigates the amalgamation of sophisticated machine learning and data analytics algorithms to enhance real-time data analysis across diverse domains. Specifically, it concentrates on the utilization of machine learning methods for real-time data analysis, encompassing supervised, unsupervised, and reinforcement learning algorithms. The research underscores the significance of instantaneous processing, analysis, and decision-making in contemporary data-centric environments spanning industries like defense, exploration, public policy, and mathematical science. The paper explores data analytics strategies for real-time data analysis, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics techniques are explored for summarizing and visualizing extensive sensor data, while diagnostic analytics methodologies focus on detecting anomalies and irregular patterns in real-time data streams. Predictive analytics endeavors to predict forthcoming events based on historical data trends, thereby enabling proactive decision-making. Lastly, prescriptive analytics provides decision recommendations and optimization tactics grounded in predictive models and constraint logic. By offering a comprehensive examination of machine learning techniques and data analytics methodologies, the paper furnishes insights into augmenting real-time data analysis capabilities across various sectors. Additionally, it presents a case study on processing real-time data from an environmental monitoring system, illustrating the practical application of advanced machine learning and data analytics algorithms for proactive decision-making and environmental management. [ABSTRACT FROM AUTHOR]

Super-resolution (SR) is a technique aimed at improving the resolution of images. In blood cell imaging, it aids in the accurate identification and classification of cells. Improving the analysis process of microscopic images is necessary to achieve better disease diagnoses, especially the image quality, so that health professionals can reach a diagnosis closer to the ideal. For those aiming to implement SR algorithms to analyze microscopic blood cell images, it is crucial to determine which algorithms are in use, their intended purposes, future trends, and current gaps. No review of SR techniques focusing on blood cells was found in the literature. Therefore, this paper presents various techniques to improve the resolution of blood images. Data screening and inclusion followed the PRISMA method. Articles were grouped into four subtopics: generic (25.0%), vascular imaging (28.1%), cell imaging (9.4%), and blood cell imaging techniques (37.5%). Results revealed that more research efforts on cell imaging techniques would be required to achieve a more balanced distribution. This study contributes to knowledge by reviewing the most used techniques, their purposes, and applications, helping researchers find the best technique for their studies, especially for pathological researchers involved in image enhancement. [ABSTRACT FROM AUTHOR]

Federated learning (FL) presents a decentralized approach to model training, particularly beneficial in scenarios prioritizing data privacy, such as healthcare. This paper introduces AdaptiveMesh, an FL adaptive algorithm designed to optimize training efficiency in heterogeneous wireless environments. Through dynamic adjustment of training parameters based on client performance metrics, including central processing unit (CPU) utilization and accuracy trends, AdaptiveMesh aims to enhance model convergence and resource utilization. Experimental evaluations on heterogeneous client devices demonstrate the algorithm's effectiveness in improving model accuracy, stability, and training efficiency. Results indicate a significant impact on CPU adaptation in preventing client overloading and mitigating overheating risks. Furthermore, the results of the one-way analysis of variance (ANOVA) and regression analysis highlight significant differences in CPU usage, accuracy, and epochs between devices with varying levels of hardware capabilities. These findings underscore the algorithm's potential for practical deployment in real-world edge computing environments, addressing challenges posed by heterogeneous device capabilities and resource constraints. [ABSTRACT FROM AUTHOR]

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts. [ABSTRACT FROM AUTHOR]

The COVID-19 disease outbreak resulted in a worldwide pandemic. Currently, the reverse transcription-polymerase chain reaction (RT-PCR), which relies on nasopharyngeal swabs to examine the existence of the ribonucleic acid (RNA) of SARS-CoV-27, is still a popular approach to testing for the disease. Despite the high level of specificity of testing with RT-PCR, the sensitivity of the method could be relatively low, and there is significant variability in efficacy depending on different sampling methods and the time of occurrence of symptoms. It is therefore essential for us to develop a machine-learning algorithm that can analyze computerized tomography images to detect the presence of COVID-19. Besides COVID-19, lung computerized tomography (CT) scan images can detect many other diseases, such as lung cancer, pneumonia, etc. This paper deals with the implementation of an algorithm that takes lung CT scans and lung X-ray images as input and predicts a list of probable diseases and possible diagnoses that infect the lungs. Machine learning algorithms will be able to predict disease by scanning the tiniest of regions easily missed by the human eye. This paper presents a survey of various machine learning algorithms that aid in detecting multiple diseases in lung CT scan images. Apart from the study of standard algorithms best suited for COVID-19 detection, this paper also includes recent trends. One of the major recent trends that can be incorporated into COVID-19 detection is TinyML. Tiny ML is an emerging area in machine learning algorithms that can be used to detect multiple diseases in lung CT scan images with better accuracy and in less time. This tool can aid doctors in their diagnosis and treatment of patients and help increase the efficiency of the treatment process. While understanding the features and mapping them using a hidden layer, there is a probability of compressing the dataset, as well as the model to process and classify the low-bit images in real-time using TinyML. [ABSTRACT FROM AUTHOR]

This paper presents the latest advances in machine learning techniques and highlights deep learning (DL) methods in recent studies. This technology has recently received great attention as it can solve complex problems. This paper focuses on covering one of the deep learning algorithms (deep neural network) and learning about its types such as convolutional neural network (CNN), Recurrent Neural Networks (RNN), etc. We have discussed recent changes, such as advanced DL technologies. Next, we continue analyzing and discussing the challenges and possible solutions of machine learning, such as big data and object detection, studying more papers in deep learning, and knowing the main experiments and future directions. In addition, this review also identifies some successful deep learning applications in recent years. Moreover, in this paper, one of the deep learning methods, convolutional neural networks, is applied to detect objects in images by using the You Only Look One model and comparing it with RetinaNet using pre-trained models. The results found that CNN (using YOLOv3) is a more accurate model than RetinaNet. [ABSTRACT FROM AUTHOR]

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus on different methods used for creating synthetic data. This paper explores several mitigating algorithms like SMOTE, AdaSyn, Fair-SMOTE, and BayesBoost. The optimized Bayesboost algorithm has been discussed. This approach showed more accuracy and addressed the error handling mechanism. [ABSTRACT FROM AUTHOR]

Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier. [ABSTRACT FROM AUTHOR]

The two main causes of blindness are diabetes and glaucoma. Routine diagnosis of blindness is based on the conventional robust mass-screening method. However, despite being cost-effective, this method has some problems as a human eye-disease detection method because there are many types of eye disease that are similar or that result in no visual changes in the eye image. These issues make it highly difficult to recognize blindness and control it. Moreover, the color of the macula of the spot can be very close to that of the affected macula in a variety of eye diseases, which suggests that the color of the macula spot can indicate various possibilities, rather than one. This paper discusses the shortcomings of current blindness-screening and monitoring systems and presents a feature-based blindness diagnosis approach using digital eye fundus images for the purpose of automated diagnosis of eye disorders, considering three conditions: healthy eye, diabetic retinopathy (DR), and glaucoma. As such, this paper develops a computer-aided diagnosis (CAD) method for automated detection of human blindness. The proposed approach integrates Gabor filter features, statistical features, colored features, morphological features, and local binary pattern features, then compares them with features drawn from a standard dataset of 1580 fundus images. Several classification techniques were applied to the extracted-features neural network (NN), support vector machine (SVM), naïve bias (NB). SVM classifiers show the most promising accuracy. They achieved 93.3% over the other classifiers. [ABSTRACT FROM AUTHOR]

Breast cancer is an illness that affects many women and can cause even death; this is a case of not being detected on time, which could be due to a human error during the analysis of radiographic images or not going on time in a health center. For this, using machine learning (ML) to analyze radiographic images is proposed as a support tool for radiologists aiming to reduce false diagnostic rates. While researching information, it was detected that this technology has many benefits in the health area; however, it also has limitations or disadvantages. The importance of this paper is to demonstrate that there are not enough clinical tests nor details about the methodologies that were used; there should be more to assert that ML is defined at the moment of making a diagnosis, which generates no conclusive results regarding effectiveness and therefore creates mistrust in doctors, and some people might rather use deep learning (DL) for its application in the detection of breast cancer because DL has more practical tests and fewer limitations than machine learning. [ABSTRACT FROM AUTHOR]

E-health systems rely on information and communication technology to support and improve various aspects of health services, delivery, and management. The success of artificial intelligence techniques has led to the emergence of a variety of systems designed to address a wide range of healthcare issues. In particular, gathering data on patient activity and behavior has enabled the development of reliable predictive systems for detecting chronic diseases and forecasting their progression. Human activity detection is a vast and emerging field, and various datasets have been collected for training different machine learning and deep learning (DL) models. The University of Milano Bicocca smartphone-based human activity recognition (UniMiB-SHAR) dataset is widely used for analyzing and recognizing human actions, including walking, running, and other daily activities. However, the autoencoder (AE) technique trained on this dataset yields poor performance. This paper aims to enhance the performance of AEs on the challenging UniMiB-SHAR dataset by introducing a convolutional AE model and employing novel preprocessing techniques, including normalization, magnitude, principal component analysis (PCA), and balancing methods such as SMOTEEN and ADASYNE. The experimental results demonstrate that the proposed AE model achieved successful performance, surpassing the state-of-the-art methods, with accuracies of 96.56% for activities of daily living (ADL), 98.86% for Fall, and 88.47% for the full dataset. [ABSTRACT FROM AUTHOR]

Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel 'FeatureBoostThyro' (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset. [ABSTRACT FROM AUTHOR]

The paper aims to evaluate precise location techniques with indoor devices using edge computing technologies, which are important for services such as smart homes and health. Despite their growing importance, indoor locations lack precise and standard methods, especially in complex environments. Solving this is being attempted through technologies such as reconfigurable surfaces and deep learning models, with attention to overcoming the challenges of indoor placement. The main objective of the study is to design a low-cost indoor location system using the ESP32 module and RSSI signals, integrated with embedded machine learning algorithms. The system to be developed will allow determining the location of objects or people with a location device through SSID signals from access points. The main objective is to evaluate the performance of three machine learning algorithms--random forest (RF), decision tree (DT) and support vector machine (SVM)--in the detection of four different locations (bathroom, kitchen, bedroom, and living room), involving the definition of system characteristics, data acquisition, the development of classifiers, and their integration in the ESP32 module to transmit location data wirelessly through the MQTT protocol. As a result of the evaluation, the DT model stands out for its efficiency under limited resource conditions during real-time implementation, but it may face challenges related to overfitting and resources at the implementation stage. [ABSTRACT FROM AUTHOR]

This paper analyzes the application of artificial intelligence (AI) techniques in lean construction (LC) and their potential to enhance project management (PM) for improved cost and schedule efficiency. The PRISMA methodology is used to select relevant articles in four steps. Furthermore, a bibliometric analysis of keywords and their occurrences is conducted. The study emphasizes the different methods of utilizing lean tools and AI techniques to attain optimal results in the construction industry. By combining a variety of tools and techniques, it is possible to create an environment that fosters improved project outcomes while minimizing risks and inefficiencies. According to the articles reviewed, the LC methodology and its tools are becoming increasingly relevant in general practice (GP). Machine learning (ML) techniques, particularly artificial neural networks (ANN), have been extensively researched as a tool to enhance construction projects by minimizing delays, fostering collaboration, cutting costs, saving time, and boosting productivity. Combining LC with ML can enhance profitability and align with lean principles, leading to successful outcomes for construction projects. [ABSTRACT FROM AUTHOR]

Web attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security's (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB). The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNN in terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916%. However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks. [ABSTRACT FROM AUTHOR]

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As the usage of digital money or plastic money grows in developing nations, so does the risk of fraud. To counter these scams, we need a sophisticated fraud detection method that not only identifies the fraud but also detects it before it occurs efficiently. We have introduced the notion of credit card fraud and its many variants in this research. Numerous ML fraud detection approaches are studied in this paper including Principal Component Analysis (PCA) data mining and the Fuzzy C-Means methodologies, as well as the Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The existing and proposed models for credit card fraud detection have been thoroughly reviewed, and these strategies have been compared using quantitative metrics including accuracy rate and characteristics curves. This paper discusses the shortcomings of existing models and proposes an efficient technique to analyze the fraud detection. [ABSTRACT FROM AUTHOR]

Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enable researchers to develop numerous fields in ways we had never imagined before. Intrusion Detection System (IDS) is one of these fields that benefits from the aforementioned techniques to identify and classify the security threats with more accuracy. In fact, IDS technology has become an essential component for any defense strategy, since it provides a healthy environment for businesses. Besides, it protects future network infrastructures from intruders and suspicious network activities. In this context, this paper presents a systematic literature review on ensemble learning-based IDS and the datasets used to evaluate the proposed methods. Furthermore, we illustrate the distribution of the reviewed papers by year of publication, datasets utilized and ensemble learning methods. Finally, we discuss the finding of this review and highlight some limitations of the aforementioned models. The overall purpose of this literature review is to provide a guideline on how to choose ensemble learning models to solve IDS issues based on the most efficient and recent trends. [ABSTRACT FROM AUTHOR]

Cloud computing has gained significant popularity due to its reliability and scalability, making it a compelling area of research. However, this technology is not without its challenges, including network connectivity dependencies, downtime, vendor lock-in, limited control, and most importantly, its vulnerability to attacks. Therefore, guarding the cloud is the objective of this paper, which focuses, in a novel approach, on two prevalent cloud attacks: Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks. To tackle the detection of these malicious activities, machine learning algorithms, namely Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN), are utilized. Experimental simulations of DDoS and MitC attacks are conducted within a cloud environment, and the resultant data is compiled into a dataset for training and evaluating the machine learning algorithms. The study reveals the effectiveness of these algorithms in accurately identifying and classifying malicious activities, effectively distinguishing them from legitimate network traffic. The finding highlights Decision Trees algorithm with most promising potential of guarding the cloud and mitigating the impact of various cyber threats. [ABSTRACT FROM AUTHOR]

In the era of lacking physical fitness, folks in society are facing vital health complications which can be due to a variety of reasons such as pollution, work pressure, food, and sleeping patterns, etc. One such critical health issue which can significantly affect the regular physical activity of patients is heart disease, emulating early predictions can protect lives by inculcating proposed work and improved methodology for early heart disease prediction. In this paper, an improved methodology for heart disease prediction is proposed using a logistic regression classifier, by tuning hyperparameter using a grid-based solver with tenfold cross-validation. The dataset used in the work is driven from the UCI Machine learning repository to evaluate the efficacy of the proposed model. Medical researchers or doctors can evaluate the model's accuracy and gets its performance. The enhanced performance rate of 90.16% has been shown in the experimental results which portrays it outclasses many prevailing models. Using this upgraded model can truly reduce the mortality rate of heart disease patients. In addition to the proposed model to make it user-friendly, a user interface has been designed where symptoms can be given as input and receives prediction as output. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become increasingly integrated into various aspects of everyday life, including the emergence of the AI tool ChatGPT. This study aims to explore the potential benefits of AI tools, such as ChatGPT for individuals with neurodevelopmental disorders. Through a comprehensive literature review, this paper examines the impact and efficacy of AI tools in enhancing personalized learning, assessment, and diagnosis. It highlights the potential of machine learning algorithms and AI models, which can gather input from multiple users, to contribute to these areas. However, it is important to note that there is currently no research evidence suggesting that AI tools can replace therapists and healthcare professionals in this context. [ABSTRACT FROM AUTHOR]

The exponential growth of the elderly population poses considerable obstacles to healthcare systems on a global scale, hence requiring the implementation of inventive strategies to identify and mitigate age-related illnesses at an early stage. The primary objective of this study is to explore the use of big data analytics to improve healthcare practices. Specifically, the emphasis is on identifying possible risk factors and developing proactive treatments for senior citizens. The research technique used in this study is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration of 2020. This approach is utilised to ensure a thorough and transparent review of the relevant literature. Moreover, the use of Rstudio software is prevalent in the field of data processing, statistical analysis, and visualisation. By conducting a comprehensive examination of academic databases and medical literature, this study undertakes an analysis of a collection of pertinent papers to explore the significance of big data analytics in the early diagnosis and prevention of diseases in senior populations. The studies that have been chosen include a wide range of healthcare fields, such as cardiology, neurology, cancer, and geriatrics. This selection aims to provide a thorough comprehension of existing practises and identify any possible areas that may need more attention. The results of this study emphasise the significant impact that big data analytics may have on healthcare for the elderly. Using extensive and varied datasets, sophisticated analytical methodologies such as machine learning algorithms and data mining allow the detection of nuanced patterns and correlations that might function as precursors for age-related ailments. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN. [ABSTRACT FROM AUTHOR]

Deep learning has gained significant popularity due to its exceptional performance in various machine learning and artificial intelligence applications. In this paper, we propose a comprehensive methodology for enhancing leaf disease detection using Convolutional Neural Networks (CNNs). Our approach leverages the power of CNNs and introduces innovative techniques to improve accuracy and provide insights into the inner workings of the models. The methodology encompasses multiple stages. We describe the methodology as follows: Firstly, we employ advanced preprocessing techniques to enhance the leaf image dataset, including data augmentation methods to augment the training data and improve model accuracy. Secondly, we design and implement a robust Convolutional Neural Network architecture with multiple layers and ReLU activation, enabling the network to effectively learn complex patterns and features from the input images. To facilitate monitoring and control of the CNN processes, we introduce a novel network visualization module. This module offers a filter-level 2D embedding view, providing real-time insights into the inner workings of the network and aiding in the interpretation of the learned features. Additionally, we develop an interactive module that enables real-time model control, allowing researchers and practitioners to fine-tune the model parameters and optimize its performance. To evaluate the effectiveness of our proposed methodology, we conduct extensive experiments using the PlantVillage dataset, which contains a diverse range of plant diseases captured through a large number of leaf images. Through rigorous analysis and evaluation, we demonstrate the superior performance of our approach, achieving classification accuracy exceeding 99%. [ABSTRACT FROM AUTHOR]

Although artificial intelligence enables the detection of abnormalities in medical images and is widely used as a computer vision technology, many researchers have focused on the detection of only one disease related to diabetes, which is diabetic retinopathy. In fact, patients face a significant risk of two other illnesses: cataract and glaucoma. In this article, we examined the diagnosis of these three eye diseases caused by diabetes and compared four approaches to classify these conditions. The proposed approaches are based on the transfer learning technique. We started by filtering, preparing, and augmenting the dataset, then applied transfer learning for feature extraction using two different architectures: VGG16 and RESNET50. We also investigated the impact of using contrast limited adaptive histogram equalization on the accuracy and precision of the models. This filter was used in a pre-training step for diabetic retinopathy diagnosis and in this paper proved its efficiency for glaucoma and cataract too. The final layers were replaced by Random Forest for classification. Models performed acceptable accuracies of 89.17% and 85.64% without operating contrast-limited adaptive histogram equalization and achieved better results when applying contrast-limited adaptive histogram equalization, with an accuracy of 97.48% and 96.66% for VGG16 and RESNET 50, respectively. [ABSTRACT FROM AUTHOR]

The use of advanced computer technology in the healthcare industry has the potential to improve patient care and therapeutic results. The goal of this project is to improve data security, privacy, and decentralisation in healthcare by integrating blockchain and Internet of Things (IoT) technologies. The adoption of IoT devices makes it possible to gather and analyse patient sensory data in real-time; however centralised processing and storage present problems such as data manipulation and privacy issues. The study investigates the creation of a decentralised IoT-based e-healthcare system that takes these issues into account by utilising blockchain technology. In addition, the paper also emphasises how blockchain use has advanced smart contract technologies. Smart contracts provide safe user authentication for IoT device access, assuring responsibility, traceability, and data integrity. The study investigates the potentially game-changing applications of blockchain technology in healthcare, such as enhanced data interoperability, patient-cantered care, reduced administrative procedures, and increased transaction transparency. The report also highlights the significance of blockchain in managing pharmaceutical supply chains, considering the essential influence on patient welfare and safety. Effective management is essential in the healthcare business because supply chain interruptions or breaches can have serious implications. The present level of research in blockchain-enabled IoT applications for healthcare is examined comprehensively using the PRISMA framework and records from the Scopus database. The three most important research topics are cloud computing, fog computing, and medical services. The results highlight the important role that blockchain-enabled IoT applications have played in enhancing data security and privacy in the healthcare industry. Real-time data gathering, precise diagnoses, individualised treatments, and simplified administrative procedures are all made possible by the integration of blockchain and IoT. Additionally, scalable solutions and insightful data for healthcare decision-making are provided via fog computing, cloud computing, machine learning, and smart contracts. [ABSTRACT FROM AUTHOR]

Thanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset. [ABSTRACT FROM AUTHOR]

Coronavirus 2 virus is responsible for the spread of the infectious disease COVID-19 (also known as Coronavirus disease). People around the globe who got infected with the virus experienced a respiratory illness that could become as serious as leading someone to lose their life. However, the upside of the pandemic is that it has led to numerous types of research and explorations, majorly in the medical science field. Since a systematic survey of previous research activities and bibliometric analysis gives a brief idea about such contributions and acts as a reference to future research, this study aims to cover the research related to COVID-19 in the computer technology domain. It is limited to the works accepted and accessible with the keywords – Covid-19, prediction, and pandemic, in the Scopus search engine to justify the scope of this survey. Further, the paper highlights a few prior works used for predictive analysis and presents a quantitative angle on their algorithms. Earlier works showcase Time Series Analysis using ARIMA/SARIMA models for predicting the vaccination rates, and Extreme Gradient Boosting (XGBoost), Xtremely Boosted Network (XBNet) Regression, and Recurrent Neural Network (RNN) for Confirmed, Cured, and Death cases. Amongst the algorithms used in the latter use case, XBNet regression performed better than XGBoost regressor. [ABSTRACT FROM AUTHOR]

Cancer is a serious disease that causes death by genomic disorder combination and diversity of unreasoning changes. This paper study the major deep learning techniques that are addressing medical image analysis and summarizes over 200 contributed articles to the subject, in particular those studies that are published in the last 6 years (since 2016). The main purpose of this paper study is to survey the deep learning algorithms for cancer detection and diagnosis. the results show that the convolutional neural network (CNN) is the most broadly used when it comes to deep learning and medical image analysis. [ABSTRACT FROM AUTHOR]

The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language. [ABSTRACT FROM AUTHOR]

The outburst of the pandemic COVID-19 in 2019 from the land of China to the entire world has brought a major change in people's living style. The severity of the disease is so much that more than 20 million people are affected by it worldwide. The recommended preventive measure to avoid the spread of this fatal disease is to maintain social distance leading to a significant economic disruption that could submerge the economies globally. However in order to keep the economies moving, the work from culture is adopted globally wherein the employees are connected via the internet and the work continues to happen. This paper aims to discover how people adopt such an attempt in the working culture. Also, the purpose of the paper is to find out its bearing on the employees' working capacity and whether they would like to continue with this arrangement in the future. The research employs primary data collected from 400 employees across various industries who are currently working from home. The research proposes advantages and challenges in working from home with a majority of employees preferring the method. [ABSTRACT FROM AUTHOR]

Clinical text classification of electronic medical records is a challenging task. Existing electronic records suffer from irrelevant text, misspellings, semantic ambiguity, and abbreviations. The approach reported in this paper elaborates on machine learning techniques to develop an intelligent framework for classification of the medical transcription dataset. The proposed approach is based on four main phases: the text preprocessing phase, word representation phase, features reduction phase and classification phase. We have used four machine learning algorithms, support vector machines, naïve bayes, logistic regression and k-nearest neighbors in combination with different word representation models. We have applied the four algorithms to the bag of words, to TF-IDF, to word2vec. Experimental results were evaluated based on precision, recall, accuracy and F1 score. The best results were obtained with the combination of the k-NN classifier, and the word represented by Word2vec achieving an accuracy of 92% to correctly classify the medical specialties based on the transcription text. [ABSTRACT FROM AUTHOR]

Aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN. [ABSTRACT FROM AUTHOR]

In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system's layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system's limitations. [ABSTRACT FROM AUTHOR]

An infection caused by bacteria can lead to severe complications affecting bloodstream disease. At present, blood cultures are used to identify bacteria. However, blood culture is a time-consuming and labor-intensive method of diagnosing disease. The effect of delayed early diagnosis is that it influences the mortality risk. Thus, it is urgent to develop an initial prediction model to identify patients with bloodstream infections. This paper focused on classifying the bacteria using a deep-learning approach. Besides, deep learning techniques can enhance the bacterial classification process more effectively. The transfer learning-based convolutional neural network technique is used to develop our model. In addition, we compared the proposed model with another model used to find the best results. Compared to other models, the proposed model achieved an evaluation score with high accuracy of 98.62%. Medical decision-making may benefit from the proposed approach. [ABSTRACT FROM AUTHOR]

In a newborn baby, the particular complication is jaundice. Jaundice is due to many reasons. An increase in the bilirubin level denotes the presence of jaundice. Neonatal hyperbilirubinemia is most common in the first postnatal week. Early prediction and classification of jaundice are essential to reduce the mortality rate. Diagnosis of the disease using computer vision techniques helps to achieve accuracy in prediction and classification. Here, I studied and extracted the information from medical data mining and achieved the goal. It involves examining, analyzing, extracting, selecting, and classifying the features. Finally, it provides treatment suggestions. It enables the physician to perform early prediction and provide appropriate jaundice treatment. Computer vision and machine learning techniques make the process easy and simple. The optimized method of classification increases accuracy. The review provides a detailed description of the diagnosis of neonatal jaundice using the skin region. The entire survey is performed based on their processing steps. Examining a huge number of medical images can reduce the risk. However, the medical images incorporate a huge amount of information about the bilirubin level on the baby's skin, and the increase in the number of images makes their accurate assessment a challenging task. This paper reviews various promising methods developed in computer vision to detect and classify neonatal jaundice. It presents a comprehensive analysis of various approaches. The analysis focuses on the advantages and disadvantages of various methods. [ABSTRACT FROM AUTHOR]

Retinopathy is an eye disease caused by diabetes, and early detection and treatment can potentially reduce the risk of blindness in diabetic retinopathy sufferers. Using retinal Fundus images, diabetic retinopathy can be diagnosed, recognized, and treated. In the current state of the art, sensitivity and specificity are lacking. However, there are still a number of problems to be solved in state-of-the-art techniques like performance, accuracy, and being able to identify DR disease effectively with greater accuracy. In this paper, we have developed a new approach based on a combination of image processing and artificial intelligence that will meet the performance criteria for the detection of disease-causing diabetes retinopathy in Fundus images. Automatic detection of diabetic retinopathy has been proposed and has been carried out in several stages. The analysis was carried out in MATLAB using software-based simulation, and the results were then compared with those of expert ophthalmologists to verify their accuracy. Different types of diabetic retinopathy are represented in the experimental evaluation, including exudates, micro-aneurysms, and retinal hemorrhages. The detection accuracies shown by the experiments are greater than 98.80 percent. [ABSTRACT FROM AUTHOR]

One of the most crucial elements in end-of-life judgment is life expectancy. For example, good forecasting aids in determining the course of therapy and planning for the acquisition of wellness services and infrastructure. Physicians, on the other hand, tend to overestimate life expectancy, missing the window of opportunity to begin a plan of care. This study examines the feasibility of estimating life expectancy from a WHO dataset collected from Kaggle using machine learning techniques. Even though much research has been conducted in the past on factors influencing life expectancy, including demographic factors, economic distribution, and death rates. It was observed that the impact of immunizations on the standard of living was not previously considered. In this paper, we analyzed life expectancy based on various features, including immunization features (Polio, Hepatitis B, Diphtheria, etc..), HDI factors (schooling, GDP, etc.) of various countries for 15 years period. We also proposed machine learning algorithms for the prediction of life expectancy. We applied regression algorithms logistic regression, SVM, Decision Tree, and random forest regression and achieved a good r-squared value with the random forest algorithm. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delay in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions. [ABSTRACT FROM AUTHOR]

As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a continuum disorder. The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results. [ABSTRACT FROM AUTHOR]

Nowadays, numerous attacks can be considered high risks in terms of the security of Wireless Sensor Network (WSN). As a result, different applications are introduced to manage the data and information exchange and related security sides to be saved in the transmission of data. Recently, most of the security attacks are classified as cyber ones. These attacks interest in the system halting and destroying the data rather than stealing the data. In this paper, a cyber-attacks detection system is proposed based on an intelligent hybrid model that uses deep and machine learning technologies. The proposed model improves the cyber-attack detection speed. In addition, a feature reduction model is proposed using machine learning methods (PCA and SVD) to select the most related features to the adopted classes of attacks. This can affect positively the deeplearning model complexity. The obtained results demonstrate the superiority of the proposed hybrid model-based cyber detection system in comparison to the traditional ones in reaching an accuracy of 99.98%, 100%, 100%, 100% for precision, recall, and F1-measure respectively, and reducing the time to 23s for the datasets of Message Queuing Telemetry Transport-Dataset (MQTT-DS) and Wireless Sensor Networks Dataset (WSN-DS). [ABSTRACT FROM AUTHOR]

One of the most crucial stages in the building of a Machine Learning (ML) model is the evaluation and analysis of classifier model performance. The agricultural sector is the economic backbone of India and needs extensions to provide solutions to the problems faced by the farmers. This paper presents agriculture soil health analysis using Machine Learning approaches for best model and tool selection and also bibliometric analysis to identify different sources and author's keywords for finding the area of focus for proposed work. Models are built on SK-Learn, KNIME, WEKA and Rapid Miner tools using different ML algorithms. Nave Bayes, Random Forest (RF), Decision Tree (DT), Ensemble learning (EL), and k-Nearest Neighbor (KNN) are used to analyze soil data on these tools. Results show that Decision Tree model outperforms other algorithms, followed by RF algorithm which is a set of multiple Decision tree algorithms and SK-Learn tool gives better accuracy followed by WEKA tool then KNIME tool. Maximum accuracy obtained by Decision Tree algorithm is 98.40% using SK-Learn followed by KNIME tool with 73.07%, Maximum accuracy obtained by Naïve Bayes algorithm is 69.50% using SKLearn followed by KNIME tool with 68.14%, maximum accuracy obtained by Random Forest algorithm is 85.00% using SK-Learn followed by 73.06% using WEKA tool, maximum accuracy obtained by Ensemble algorithm is 89.00% using SK-Learn followed by 73.06% using WEKA tool and for KNN it is 95.50% using SK-Learn followed by 71.85% using WEKA tool. [ABSTRACT FROM AUTHOR]

Wind turbine power curve (WTPC) plays an important role for energy assessment, power forecasting and condition monitoring. The WTPC captures the nonlinear relationship between wind speed and output power. Many modeling approaches have been proposed by researches to improve the WTPC model performance. In this paper, we present a hybrid approach of wind turbines power curve modeling based on Case Based Reasoning approach, multi agent system, the K-Means unsupervised machine learning method, and then the supervised machine learning algorithm, which is the K-Nearest Neighbors KNN method. The both of the Machine Learning algorithms, K-means and KNN, are used in the retrieve step of the Dynamic Case Based Reasoning (DCBR) cycle to facilitate the search of wind turbines with similar characteristics to our target case. These wind turbines are first grouped into homogeneous classes and then sorted on the basis of a feature similarity measure using the K-Nearest Neighbors supervised machine learning method. Finally, a set of WTPC with similar characteristics of the target case are proposed. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is a degenerative disease of the brain that results in memory loss due to the death of brain cells. Alzheimer's disease is more common as people get older. Memory loss happens over time, and as a result, the person loses the ability to react appropriately to their surroundings. Microarray technology has emerged as a new trend in genetic research, with many researchers utilizing it to look at the changes in gene expression in particular organisms. Microarray experiments can be used in various ways in the medical field, including the prediction and detection of disease. Large amounts of unprocessed raw gene expression profiles sometimes contribute to computational and analytic difficulties, including selecting dataset features and classifying them into an appropriate group or class. The large dimensions, lesser sample size, and noise in gene expression data make it difficult to attain good Alzheimer classification accuracy using the entire collection of genes. The categorization process necessitates careful feature reduction. As a result, a comprehensive review of microarray Alzheimer's disease studies is presented in this paper, focusing on feature selection techniques. [ABSTRACT FROM AUTHOR]

Emotionally pleasant experiences trigger repetition in humans whilst their emotional opposite lead to avoidance/refusal of activities. Human interest, a key factor in everyday human life, can be quite useful in the evaluation of participant engagement on activities and their optimization, so as to maximize interest and motivation. A glaring issue however comes from subtle demonstrators associated with human interest, which converge onto its complex assessment/quantization. Yet, there is an inherent correlation between human interest and its provoked emotional response which can be explored in tandem with emotion recognition for the development of an engagement metrics tool. Such a mechanism would be highly beneficial for the improvement of several activities involved with learning and security, by enabling precise control over participant enthusiasm. In this paper we present an interest mapping technique which provides the user with spatio-temporal information extracted from a participant crowd. The technique aims to extract emotional cues from participant facial data, assessing its spatial and temporal distributions over the course of scenarios such as therapy sessions and lectures. The goal is to demonstrate where and when activities must be improved in order to retain attention, maximize efficacy and assure emotional pleasantness in participants. For validation, this study makes use of data collected over a college lecture so as to provide readers with a real demonstration of the technique's advantages. [ABSTRACT FROM AUTHOR]

In recent years, the amount of data has been increased dramatically, driven by many real-world fields such as marketing, learning, social media, multimedia, medicine...etc. Because of that, data mining algorithms have extensively used on these data to serve as one of the newest data modeling and analytical tools, by which, a knowledge-rich environment can be generated and decision-making can be improved. Data mining tools can be employed for reducing these tests and predicting future trends by valuable information-driven decisions. There are two categories of data mining algorithms: descriptive and predictive. The rules of clustering, association, summarization, and sequence discovery will be associated with descriptive type. On the other hand, predictive type will compromise classification, regression and time series analysis rules. In this paper, a study have been presented for helping specialists and physicians in Iraq to investigate heart problems via (Weka 3.8.3) software focusing on four data mining classification techniques (1BK, J48, Naïve Bayes and REPTREE). The predictive precision tests, the ROC curve, and the AUC value are calculated based on a compiled dataset that have been received from the hospital of Baghdad medical city and the hospital of Ibn al-Bitar. The performance of the J48 technique (94.5%) indicates optimum performance based on SMO no performance factor of Baghdad medical city. [ABSTRACT FROM AUTHOR]

Human action recognition using skeletons has been employed in various applications, including healthcare robots, human-computer interaction, and surveillance systems. Recently, deep learning systems have been used in various applications, such as object classification. In contrast to conventional techniques, one of the most prominent convolutional neural network deep learning algorithms extracts image features from its operations. Machine learning in computer vision applications faces many challenges, including human action recognition in real time. Despite significant improvements, videos are typically shot with at least 24 frames per second, meaning that the fastest classification technologies take time. Object detection algorithms must correctly identify and locate essential items, but they must also be speedy at prediction time to meet the real-time requirements of video processing. The fundamental goal of this research paper is to recognize the real-time state of human fighting to provide security in organizations by discovering and identifying problems through video surveillance. First, the images in the videos are investigated to locate human fight scenes using the YOLOv3 algorithm, which has been updated in this work. Our improvements to the YOLOv3 algorithm allowed us to accelerate the exploration of a group of humans in the images. The center locator feature in this algorithm was adopted as an essential indicator for measuring the safety distance between two persons. If it is less than a specific value specified in the code, they are tracked. Then, a deep sorting algorithm is used to track people. This framework is filtered to process and classify whether these two people continue to exceed the programmatically defined minimum safety distance. Finally, the content of the filter frame is categorized as combat scenes using the OpenPose technology and a trained VGG-16 algorithm, which classifies the situation as walking, hugging, or fighting. A dataset was created to train these algorithms in the three categories of walking, hugging, and fighting. The proposed methodology proved successful, exhibiting a classification accuracy for walking, hugging, and fighting of 95.0%, 87.4%, and 90.1%, respectively. [ABSTRACT FROM AUTHOR]

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to the automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm AdaBoost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms. [ABSTRACT FROM AUTHOR]

This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. [ABSTRACT FROM AUTHOR]

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world & time-series datasets. [ABSTRACT FROM AUTHOR]