Review Of Machine Learning Approaches Research Articles

A Comprehensive Review of Machine Learning Approaches in Livestock Health Monitoring

Livestock health monitoring has emerged as a crucial area in agricultural technology, where Machine Learning (ML) approaches offer promising solutions for disease detection, heat stress management, and behavioral anomaly identification. This review explores the latest advancements in applying machine learning models to livestock health monitoring, focusing on methods such as deep learning, IoT integration, and multimodal frameworks. Techniques like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and hybrid models have significantly improved early detection of communicable and non-communicable diseases. Additionally, AI-based systems monitoring heat stress and behavioral changes have enhanced overall livestock management, reducing economic losses and improving animal welfare. This review synthesizes current trends and challenges in deploying ML models in real-world farming environments, particularly integrating IoT devices to collect and analyze health data. Moreover, it highlights future directions in improving the accuracy and scalability of ML models, including optimizing data collection and leveraging real-time analytics. This review aims to guide future developments in AI-driven livestock health monitoring systems by providing a comprehensive overview of current research.

Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data

In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using opensource datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing data-related and model-related issues and evaluating the limitations of these competitions. The potential solutions to address these challenges are also summarized. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for next-generation ML-PHM development in PHM domain.

A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation

Global warming and carbon emissions have drawn attention to the need to decarbonize transport. Promoting electric vehicles (EVs) has become an important strategy towards this goal. Although EVs have significant advantages in emission reduction and energy saving, their wider adoption is limited by factors such as range and charging infrastructure. Accurate prediction and modeling of EV energy consumption is the key to solving these challenges. This review first summarizes traditional energy consumption estimation models and, thereafter explores the application of interpretable machine learning, emphasizing its importance in improving model transparency and practicality. The potential of neural networks to enhance prediction accuracy through feature extraction and pattern recognition is also discussed. This paper aims to provide a systematic review of the latest advances in EV energy consumption forecasting. It serves as a reference for researchers and practitioners to optimize and upgrade urban transport systems for sustainable development.

Advancements and Challenges in Solar Radiation Prediction: A Review of Machine Learning Approaches

Solar irradiance prediction holds paramount importance in optimizing the efficacy and dependability of solar power systems. This evaluative manuscript delves into an array of machine learning methodologies applied in solar irradiance prediction, encompassing conventional methodologies. It further delves into the ramifications of meteorological data, geographical and temporal parameters, data preprocessing, feature curation methodologies, amalgamated learning methodologies, and composite models. Moreover, the manuscript investigates the latent advantages of amalgamating orbital visuals and terrestrial sensors to augment prognostic capacities. Ultimately, the review underscores the exigency for broader datasets, refined feature curation methodologies, and composite models to attain heightened precision and resilience.

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

A Review of Machine Learning Approaches for Semantics-Based String Matching

A Review of Machine Learning Approaches for Semantics-Based String Matching

A Review of Machine Learning Approaches for Rumour Detection: Techniques, Challenges, and Future Directions (Machine Learning for Rumour Detection)

Rumours and misinformation propagate rapidly across online social networks, posing significant challenges to maintaining the integrity of information dissemination. In recent years, machine learning (ML) techniques have emerged as promising tools for automating the detection and mitigation of rumours. This review paper provides a comprehensive examination of the advancements in rumour detection using ML approaches. The paper begins by outlining the landscape of rumour dissemination in online social networks, highlighting the characteristics and challenges associated with rumour detection. Subsequently, it systematically categorizes and analyzes various ML methods employed for rumour detection, including supervised, unsupervised, and semi-supervised learning approaches. Furthermore, the review delves into the diverse features and representations utilized in ML models for rumour detection, such as textual content, user engagement patterns, network structures, and temporal dynamics. It discusses the strengths and limitations of different feature sets and their impact on the effectiveness of rumour detection systems. Moreover, the paper explores the intricacies of dataset construction and evaluation methodologies for training and testing rumour detection models. It examines commonly used benchmark datasets and evaluation metrics, emphasizing the importance of robust evaluation frameworks for assessing the performance of ML-based rumour detection systems accurately. Additionally, the review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models. It also discusses potential directions for future research aimed at advancing the state-of-the-art in rumour detection and mitigation.

A systematic literature review of machine learning approaches for class-wise recognition of Alzheimer’s disease using neuroimaging-based brain disorder analysis

A systematic literature review of machine learning approaches for class-wise recognition of Alzheimer’s disease using neuroimaging-based brain disorder analysis

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids.

This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area.

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review.

Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment. The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images. We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB). In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB. Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.

Construction accident prevention: A systematic review of machine learning approaches.

The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety. This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention. This study examined 73 scientific articles through bibliometric research and descriptive analysis. The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine - SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data). The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management.

A Review of Machine Learning Approaches to Soil Temperature Estimation

Soil temperature is an essential factor for agricultural, meteorological, and hydrological applications. Direct measurement, despite its high accuracy, is impractical on a large spatial scale due to the expensive and time-consuming process. On the other hand, the complex interaction between variables affecting soil temperature, such as topography and soil properties, leads to challenging estimation processes by empirical methods and physical models. Machine learning (ML) approaches gained considerable attention due to their ability to address the limitations of empirical and physical methods. These approaches are capable of estimating the variables of interest using complex nonlinear relationships with no assumptions about data distribution. However, their sensitivity to input data as well as the need for a large amount of training ground truth data limits the application of machine learning approaches. The current paper aimed to provide a review of ML techniques implemented for soil temperature modeling, their challenges, and milestones achieved in this domain.

Photoacoustic imaging with limited sampling: a review of machine learning approaches.

Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.

A critical review of machine learning approaches for predicting diabetes mellitus

Diabetes is a complex, chronic metabolic condition that progresses gradually and causes the patient to have increased blood glucose levels. The three most prevalent kinds of diabetes are type 1, type 2, and gestational. It results in serious health problems such renal problems, heart attacks, foot ulcers, and visual impairment. One of the primary symptoms is hyperglycemia. Early illness progression detection aids in the reduction of additional consequences. The classification of quantitative data most typically uses machine learning techniques. It makes it easier to pinpoint the important elements that are most responsible for the development of diseases. During the pre-processing stage, the least important properties are removed. The classification or forecasting of the condition Diabetes Mellitus using conventional. The discussion and analysis of supervised machine learning techniques focused on their predictive accuracy. The most popular and effective machine learning model among the several extant algorithms is examined using a variety of tools, datasets, and measures. One of the most effective machine learning models is the Deep learning algorithm of artificial neural networks, which is favored by many scientists and researchers in the field of health.

A Review of Machine Learning Approaches in Synchrophasor Technology

As power systems (PS) move toward smart grids and microgrids in modern times, complexity eventually rises as a result of integration with various distributed energy resources, demand-side management, and cybersecurity issues. Additionally, frequent PS network blackouts will have an impact on a country’s social, economic, and financial position. To ensure the reliability as well as security of the network’s smart-driven power system, more advanced monitoring and measurement technology, which is dominated by SCADA systems, is required. This is where synchrophasor-based phasor measurement units (PMUs) come into play, which process and analyze massive amounts of data in real-time more precisely to identify systemic anomalies. Due to their quick response times, high computing speeds, accuracy, and scalability, machine learning (ML)-based techniques are becoming more popular for handling real-time big data. The many ML algorithms used recently in synchrophasor technology, which enhances cybersecurity, fault detection and classification, transient stability assessment, voltage stability assessment, and forced oscillation localization are thoroughly reviewed in this paper. With the help of more than 190 pertinent papers, this work effectively discusses the ML applications in the synchrophasor technology where PMUs and μPMUs are deployed. The article also shows that several concerns have not yet been resolved and are still up for consideration by researchers in the future. One of them is the detection and observation of oscillation and line-tripping occurrences in the distribution area where μPMUs are installed. To address these problems, sophisticated DL approaches are recommended. Future possibilities to decrease bandwidth usage and improve processing delay using edge computing technology are also mentioned in this paper. The research potential for ML and DL approaches also extends to power line communication, wide area monitoring systems, and 5G and 6G network communications.

A comprehensive review of machine learning approaches for dyslexia diagnosis

A comprehensive review of machine learning approaches for dyslexia diagnosis

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.

Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. To predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist "Quality assessment of machine learning studies" was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence. In total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.

A review of machine learning approaches for drug synergy prediction in cancer.

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.

Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.