A secure framework for the Internet of Things anomalies using machine learning by Vijay Prakash, Olukayode Odedina, Ajay Kumar, Lalit Garg, Seema Bawa

“…The integration of machine learning algorithms with IoT infrastructure allows for real-time monitoring and proactive anomaly detection in diverse IoT applications. …”
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Identification of high-risk COVID-19 patients using machine learning. by Mario A Quiroz-Juárez, Armando Torres-Gómez, Irma Hoyo-Ulloa, Roberto de J León-Montiel, Alfred B U'Ren

“…We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. …”
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Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification by Lochana Mendis, Debjyoti Karmakar, Marimuthu Palaniswami, Fiona Brownfoot, Emerson Keenan

“…Efforts to address this issue have focused on data-driven deep-learning methods to detect fetal compromise automatically. …”
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Computational intelligence to detect bearing faults using optimal features from motor current signals by G. Geetha, P. Geethanjali

“…The data referring to faults needs to be studied under distinct operating conditions, with effective features. …”
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CLDN9 and hsa-miR-4496 as non-invasive biomarkers for gastric cancer detection by Qiongxia Hu, Lu Han, Jinglin Wang, Fei Li, Hongfei Pu, Yue Shi

“…The identification of microRNAs regulating CLDN9 utilized machine learning techniques, such as LASSO regression and random forest algorithms. …”
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Applications of machine learning in gravitational-wave research with current interferometric detectors by Elena Cuoco, Marco Cavaglià, Ik Siong Heng, David Keitel, Christopher Messenger

“…In data analysis, machine learning has already been applied as an alternative to traditional methods for signal detection, source localization, noise reduction, and parameter estimation. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…However, persistent challenges include data scarcity in developing regions (only 12% of Sub-Saharan African monitoring stations provide real-time data), algorithmic bias (65% sensitivity loss in cross-regional models), and regulatory skepticism toward "black-box" systems. …”
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Detection of Alzheimer and mild cognitive impairment patients by Poincare and Entropy methods based on electroencephalography signals by Umut Aslan, Mehmet Feyzi Akşahin

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Research on new energy power plant network traffic anomaly detection method based on EMD by Danni Liu, Shengda Wang, YutongLi, Ji Du, Jia Li

“…Methodology This research propose Network Quality Assessment (NQA) traffic management algorithms to prevent illegal access and data breaches, this involves strong security measures such as encryption, firewalls, and encrypted communication methods. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

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Transfer Learning for Induction Motor Health Monitoring: A Brief Review by Prashant Kumar

“…These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. …”
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Towards a multi-modal Deep Learning Architecture for User Modeling by Ange Tato, Roger Nkambou

“…The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. The resulting model was evaluated on a public multi-modal dataset, achieving better results than state-of-the-art algorithms for a similar task: opinion polarity detection. …”
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Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning by Wan-Peng Sun, Ji-Guo Zhang, Yichao Li, Wan-Ting Hou, Fu-Wen Zhang, Jing-Fei Zhang, Xin Zhang

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Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…Our approach, with its unique ability to address multidimensional healthcare data, reassures the method’s robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. …”
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Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning by Ciyun Lin, Hongli Zhang, Bowen Gong, Dayong Wu

“…We compare results obtained by four machine-learning-enabled forecasting approaches on a real-life data set. …”
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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning. by Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun

“…Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. …”
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TransC-GD-CD: Transformer-Based Conditional Generative Diffusion Change Detection Model by Yihan Wen, Zhuo Zhang, Qi Cao, Guanchong Niu

“…Change detection (CD) methodologies have garnered substantial attention owing to their capability to monitor alterations in geographic spaces across temporal intervals, especially with the acquisition of high-resolution remote sensing images. …”
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Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction by Ashika T, Hannah Grace, Nivetha Martin, Florentin Smarandache

“…In order to lower death rates and enhance treatment results, early detection is critical. Neutrosophic Set Theory (NST) and machine learning (ML) approaches are integrated in this study to provide a novel hybrid methodology (NS-ML) that improves breast cancer diagnosis. …”
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Advanced Plant Phenotyping Technologies for Enhanced Detection and Mode of Action Analysis of Herbicide Damage Management by Zhongzhong Niu, Xuan Li, Tianzhang Zhao, Zhiyuan Chen, Jian Jin

“…The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. …”
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