Enhancing Fake Review Detection Using Linguistic Exaggeration, BERT Embeddings, and Fuzzy Logic by Mohammed Ennaouri, Ahmed Zellou

“…Our method first preprocesses reviews and extracts key linguistic features based on extravagant words using a fuzzy logic-based membership function that highlights extreme linguistic patterns. Simultaneously, BERT embeddings capture the deep semantic meaning of the text. …”
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ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network by Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah

“…The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. Furthermore, ML-SOHOT achieves an average HO performance improvement of up to 96% compared to competitive algorithms from the literature. …”
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Application of artificial intelligence in insect pest identification - A review by Sourav Chakrabarty, Chandan Kumar Deb, Sudeep Marwaha, Md. Ashraful Haque, Deeba Kamil, Raju Bheemanahalli, Pathour Rajendra Shashank

“…AI-based detection methods use machine learning, deep learning algorithms, and computer vision techniques to automate and improve the identification of insects. …”
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Generative AI Models in Time-Varying Biomedical Data: Scoping Review by Rosemary He, Varuni Sarwal, Xinru Qiu, Yongwen Zhuang, Le Zhang, Yue Liu, Jeffrey Chiang

“…Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…This framework is interpretable and helps medical practitioners learn complex patterns in patients. <b>Method:</b> This study addresses these issues by employing boosting algorithms, for enhanced classification accuracy. …”
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Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus by Ji Yang, Yikun Zhou, Jiarui Zhang, Yongqin Zheng, Jundong He

“…Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. …”
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Advancements in the application of artificial intelligence in the field of colorectal cancer by Mengying Zhu, Mengying Zhu, Zhenzhu Zhai, Yue Wang, Fang Chen, Ruibin Liu, Ruibin Liu, Xiaoquan Yang, Guohua Zhao

“…In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…Existing GPM techniques often depend on parametric models, which may fail to capture the intricate patterns of groundwater distribution or adapt to varying data complexities. …”
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Integration of Bulk and Single-Cell Transcriptomics Reveals BCL2L14 as a Novel IGKC+ T Cell-Associated Therapeutic Target in Breast Cancer by He J, Akhtar A, Li J, Wei Q, Yuan Y, Ran J, Ma Y, Chen D

“…To pinpoint key regulatory genes, we applied machine learning algorithms. Based on the hub genes identified, we constructed a prognostic risk model and developed a nomogram to aid clinical decision-making. …”
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A Multivariate Phenotypical Approach of Sepsis and Septic Shock—A Comprehensive Narrative Literature Review by Alina Tita, Sebastian Isac, Teodora Isac, Cristina Martac, Geani-Danut Teodorescu, Lavinia Jipa, Cristian Cobilinschi, Bogdan Pavel, Maria Daniela Tanasescu, Liliana Elena Mirea, Gabriela Droc

“…Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. …”
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Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms by Zhi-Chuan He, Zheng-Zheng Song, Zhe Wu, Peng-Fei Lin, Xin-Xing Wang

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

“…By harnessing these technologies, we can analyze data to gain insights into symptoms and patterns, ultimately facilitating remote patient care. …”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…It is common for these systems to be constructed using conventional machine learning algorithms. So many common signs of attacks are ignored. …”
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A novel, rapid, and practical prognostic model for sepsis patients based on dysregulated immune cell lactylation by Chang Li, Mei He, PeiChi Shi, Lu Yao, XiangZhi Fang, XueFeng Li, QiLan Li, XiaoBo Yang, JiQian Xu, You Shang, You Shang

“…Patients were stratified into subgroups using k-means clustering based on lactylation levels. Machine learning algorithms, integrated with pseudotime trajectory reconstruction, were employed to map the temporal dynamics of lactylation. …”
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Modify a dual ridge horn antenna for drone jamming applications by Alaa Hussein Hilal, Ahmed Hameed Reja, Mohammed J. Mohammed

“…Handheld anti-drone jammer antennas require high-gain directivity beam patterns with narrow half-power beam widths (HPBW) while also covering multiple frequency bands that are used by drones. …”
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An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

“…Deep learning (DL) can successfully capture VIV patterns and generate accurate predictions by using a large amount of training data. …”
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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“…For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings. …”
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Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol by Mohammad Rifat Haider, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Xiaoming Li

“…Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). …”
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Editorial by Bulent Cavas

“…The eighth article, “Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms,” by Mustafaoğlu and Alkan (Türkiye), applies machine learning techniques to identify factors predicting students&#39; recycling behavior. …”
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Spatiotemporal variation of the association between climate dynamics and HFRS outbreaks in Eastern China during 2005-2016 and its geographic determinants. by Junyu He, George Christakos, Jiaping Wu, Bernard Cazelles, Quan Qian, Di Mu, Yong Wang, Wenwu Yin, Wenyi Zhang

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