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

Passive non-invasive sensing signals from wearable devices and smartphones are typically collected continuously without user input. This passive and continuous data collection makes these signals suitable for moment-by-moment monitoring of health-related outcomes, disease diagnosis, and prediction modeling. A growing number of studies have utilized machine learning (ML) approaches to predict and analyze health indicators and diseases using passive non-invasive signals collected via wearable devices and smartphones. This systematic review identified peer-reviewed journal articles utilizing ML approaches for digital phenotyping and measuring digital biomarkers to analyze, screen, identify, and/or predict health-related outcomes using passive non-invasive signals collected from wearable devices or smartphones. PubMed, PubMed with Mesh, Web of Science, Scopus, and IEEE Xplore were searched for peer-reviewed journal articles published up to June 2024, identifying 66 papers. We reviewed the study populations used for data collection, data acquisition details, signal types, data preparation steps, ML approaches used, digital phenotypes and digital biomarkers, and health outcomes and diseases predicted using these ML techniques. Our findings highlight the promising potential for objective tracking of health outcomes and diseases using passive non-invasive signals collected from wearable devices and smartphones with ML approaches for characterization and prediction of a range of health outcomes and diseases, such as stress, seizure, fatigue, depression, and Parkinson’s disease. Future studies should focus on improving the quality of collected data, addressing missing data challenges, providing better documentation on study participants, and sharing the source code of the implemented methods and algorithms, along with their datasets and methods, for reproducibility purposes.

Contemporarily, two emerging techniques, blockchain, and machine learning are driving dramatic rapid growth in the field of network security. This paper describes a literature review of machine learning approaches for network security analytics and summarizes some applications of blockchain in the field of network security. We first illustrate three types of network security data, including network traffic, software binary, and security logs. Then we discuss the application of machine learning and deep learning approaches to analyze these data. We cover a broad array of attack types, including malware, spam, insider threats, network intrusions. We also summarize some applications and potential development direction of blockchain technology.

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) provides a powerful framework to probe and modulate human cortical and corticospinal excitability. In recent years, brain state-dependent EEG–TMS paradigms have gained increasing interest by synchronizing stimulation to ongoing neural activity. However, traditional approaches relying on single oscillatory features or fixed thresholds have yielded heterogeneous and often inconsistent results, motivating the adoption of machine learning (ML) and artificial intelligence (AI) methods to model brain state in a multivariate, data-driven manner. This review synthesizes current ML and deep learning (DL) approaches aimed at predicting cortical and corticospinal excitability from pre-stimulus EEG. We contextualize these methods within brain state-dependent EEG–TMS frameworks based on oscillatory phase, power, and network-level features, and within evolving definitions of brain state that move beyond local biomarkers toward distributed, large-scale, and dynamically evolving neural representations. The reviewed studies span feature-engineered models, data-driven decoding approaches, and emerging adaptive closed-loop frameworks. Finally, we discuss key methodological challenges, translational barriers, and future directions toward personalized, interpretable, and fully closed-loop neuromodulation systems.

The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.

Estimating water levels and discharges in tidal rivers and estuaries: Review of machine learning approaches

Artificial intelligence (AI) has become a transforming tool in changing how farming functions as cultivation encounters greater challenges from environmental degradation, depletion of resources, and rising food needs. This paper represents how predictive analytics of pest and early insects’ identification through artificial intelligence (AI) based solutions—especially machine learning (ML), deep learning (DL), and time series analysis that are transforming agricultural methods. These technologies enable growers to describe early indicators of crop conditions, prognosticate production issues, and maximise toxin and water operation by providing priceless perceptivity into crop growth, soil conditions, and environmental elements. By raising production overcome the hunger (SDG 2), encouraging innovation (SDG 9), and guaranteeing effective resource use (SDG 12), these advances greatly help achieve the goals of sustainable development. This paper gives a review of ML approaches applied in the agricultural fields, with particular attention on problems of bracket, discovery, and vaticination in crops. Establishing the foundation for a globally resilient and sustainable agriculture future, the study concludes with possibilities for making AI solutions more inclusive, interpretable, and ecologically responsible.

Customer lifetime value is an important measure for understanding long term profitability in e-commerce, where firms depend on effective customer retention and development in highly competitive digital markets. Traditional statistical approaches have supported early customer lifetime value estimation, but they often rely on assumptions that limit their ability to represent complex and changing customer behavior. This systematic review examined existing studies that applied machine learning methods to customer lifetime value prediction in e-commerce. Guided by the PRISMA framework, the review searched Scopus and IEEE Xplore for studies published between 2020 and 2026, and 12 studies met the inclusion criteria. The findings show that ensemble and deep learning approaches dominate recent research, with stacking, hybrid, and recurrent neural models frequently reporting strong predictive performance. However, the evidence also reveals important limitations, including restricted data accessibility, limited interpretability, high computational demands, and inconsistent evaluation methods, which reduce comparability across studies. The strong predictive performance of these models suggests their potential to support more effective customer segmentation and improved allocation of marketing resources. Future progress in customer lifetime value prediction will depend not only on improved model performance, but also on more transparent methods, publicly available benchmark datasets, and stronger attention to interpretability and practical applicability.

Thermoelectric (TE) materials have garnered significant interest due to their capacity to convert heat directly into electrical energy and vice versa, offering a sustainable route for energy harvesting and waste heat recovery. Nevertheless, many of the high-performance TE materials reported to date rely on elements that are scarce, costly, or environmentally hazardous, thereby limiting their large-scale deployment. To overcome these challenges, the development of efficient, earth-abundant, and environmentally benign alternatives is essential. Although first-principles methods provide valuable insights into the transport behavior of potential TE materials, their high computational cost restricts their utility in large-scale material screening. Recent progress in computational infrastructure, along with the advent of data-centric approaches such as machine learning (ML), has transformed the landscape of thermoelectric research. ML algorithms, trained on comprehensive datasets including experimental measurements, crystallographic data, and density functional theory (DFT) results can predict key TE metrics, such as the figure of merit (ZT), with remarkable speed and accuracy. This review explores the integration of ML into TE materials discovery, emphasizing its role in property prediction, descriptor engineering, and structural optimization. A systematic examination of ML-driven strategies promises to accelerate the discovery process and improve the efficiency of next-generation thermoelectric systems.

This review paper explores recent advances in machine learning approaches with emphasis on deep learning techniques for non-invasive cognitive impairment detection. We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric mobility. The paper provides an overview of relevant datasets, feature-extracting techniques, and deep-learning architectures applied to this domain. The paper comprises 60 peer-reviewed papers that mainly utilize deep learning models to detect cognitive impairment conditions. We have analyzed the performance of different methods across modalities and observed that speech and language-based methods generally achieved the highest detection performance. Studies combining acoustic and linguistic features tended to outperform those using a single modality. Facial analysis methods showed promise for visual modalities but were less extensively studied. Most papers focused on binary classification (impaired vs. non-impaired), with fewer addressing multi-class or regression tasks. Transfer learning and pre-trained language models emerged as popular and effective techniques, especially for linguistic analysis. Despite significant progress, several challenges remain, including data standardization and accessibility, model explainability, longitudinal analysis limitations, and clinical adaptation. Lastly, we propose future research directions, such as investigating language-agnostic speech analysis methods, developing multi-modal diagnostic systems, and addressing ethical considerations in AI-assisted healthcare. By synthesizing current trends and identifying key obstacles, this review aims to guide further development of deep learning-based cognitive impairment detection systems to improve early diagnosis and ultimately patient outcomes.

Abstract—Stock price prediction remains one of the most challenging tasks in financial forecasting due to the inherent volatility and nonlinearity of financial markets. This paper presents a comprehensive review of machine learning approaches for stock price prediction, comparing traditional statistical methods with modern deep learning architectures. We analyze various algorithms including Support Vector Machines (SVM), Random Forest, Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks. Our comparative analysis reveals that LSTM-based deep learning models achieve superior performance with prediction accuracies ranging from 85% to 89%, significantly outperforming traditional regression methods. The study examines key technical indicators, feature engineering techniques, and model optimization strategies that enhance prediction accuracy. We also discuss the challenges of market volatility, overfitting, and computational complexity. The findings demonstrate that sophisticated neural network architectures, particularly LSTM networks, offer more reliable predictions for investors and financial analysts in volatile market conditions. This research contributes to the growing body of knowledge on intelligent financial forecasting systems and provides practical insights for implementing machine learning-based trading strategies. Index Terms—Stock price prediction, machine learning, LSTM, deep learning, financial forecasting, technical analysis, time series prediction

The applications of nanofluids are widely beneficial in heat transmission and cooling systems. Nanofluid viscosity and thermal conductivity have a substantial effect on heat transfer applications and on devices such as solar and geothermal systems. Machine learning models enable faster, less expensive modeling of nanofluid thermophysical properties. These models are secure for future studies and in the development of nanotechnology. In this review, shape, size, temperature, and volume concentration are considered as inputs to develop several machine learning methods, such as artificial neural networks, support vector regression, decision trees, and random forests. These models were analyzed by comparing their R2 values, and the results indicated that machine learning-based models generally exhibited more reliable performance than the other approaches. The observation in this review was that thermal conductivity increases with temperature and volume fractions, whereas viscosity decreases with size, temperature, and volume fractions. To determine the optimal nanoparticle type, size, and concentration for specific applications such as data center cooling and high-heat-flux electronics, future research may employ ML-based optimization techniques.

Food safety is being transformed by artificial intelligence (AI), which is boosting contamination detection, real time monitoring and transparency of food supply chain. AI based techniques like machine learning, deep learning and computer vision help to detect chemical, microbial and physical contaminants in food more accurately and efficiently. These advancements have led processes to be automated, minimize the impact of human error and facilitate better decision taking. Other innovations include rapid, automated detection and traceability using AI driven spectroscopy, sensor based monitoring and block chain integration. Challenges in adopting AI, however, include fragmented and proprietary data, lack of model interpretability, the sheer implementation costs, and regulatory hurdles. Implementing AI has cost and technical challenges for small and medium sized businesses. Also, the AI models must be explainable and FMV compliant to provide the necessary transparency and reliability. Future research will consist of building upon the AI models developed in this thesis, incorporation of AI with IoT and edge computing for real time monitoring as well as setting up of ethical and regulatory frameworks. Trust in AI driven food safety will be developed with standardized AI regulations, unbiased predictions, and data privacy protections. Although AI presents some hurdles, it has the power to contribute in building a much safer, more efficient and transparent global food supply chain.

Healthcare is one of the important real-life applications of machine learning. Machine learning has delivered very powerful automated systems to the humanity such as automated vehicles and voice-enabled personal assistant system. Now in the health domain, digitization of patient data produces a lot of information. This information is used to analyze the health records and predict the future health conditions of the patients. In this chapter, we elaborate various algorithms of machine learning. We analyze the basic understanding of the concept of these algorithms in healthcare domain and working fashion. We discuss the current trends of machine learning in identifying and diagnosing diseases using medical imaging techniques, drug discovery, robotic surgery, and smart fitness equipment. We have elaborated many ML algorithms which are already working in this domain like SVM, logistic regression, random forest, linear regression, etc. In this study, we also talk about the various challenges for the medical care industry and threats. And as the conclusion, we find that algorithms are being optimized for better performance and the selection of an optimal algorithm is a challenging task in AI. And engineers of AI devices should be aware of the possible unintended consequences of their methods and guarantee that the calculations are done in view of the worldwide network.KeywordsAIMLSVMLogistic regressionLinear regressionRoboticsDrug discoveryHealth

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

Incorporating embedded energy resources (EERs) into demand-side management (DSM) has the potential to optimize the operational efficiency of distributed energy systems. It can be achieved by leveraging the reserve capacity of these resources. The effective use of this reserved capacity results in enhanced planning, scheduling, and operation optimization. Forecasting the power consumption of EERs is an essential part of reserved capacity calculation. This research presents a review of machine learning (ML)-based techniques utilized for short-term power consumption forecasting for a category of EERs specifically identified as thermostatically controlled loads (TCLs). Given the complicated characteristics and the difficulty in forecasting these resources, the existing research in this area is quite limited and there is still space for further studies. Moreover, this review underscores the importance of dedicating resources to further research, including developing replicable case studies and exploring novel technologies in this field.