Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazeln... by Derick Malavi, Katleen Raes, Sam Van Haute

“…This study applies near-infrared hyperspectral imaging (NIR-HSI) and machine learning (ML) to detect EVOO adulteration with hazelnut, refined olive, and olive pomace oils at various concentrations (1%, 5%, 10%, 20%, 40%, and 100% m/m). …”
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Plasma cfDNA multi-omic biomarkers profiling for detection and stratification of gastric carcinoma by Shiyi Song, Xiuli Zhang, Pin Cui, Weihuang He, Jiyuan Zhou, Shubing Wang, Yong Xiong, Shu Xu, Xiaohui Lin, Guozeng Huang, Xiaohua Tan, Qinglong Xu, Yongling Liu, Qingqun Li, Kehua Yuan, Mingji Feng, Hanming Lai, Hui Yang, Shaorong Zhang

“…The multi-omic biomarkers in this study, including fragmentation profile, end motif and genome-wide Copy Number Variations (CNV) of plasma cfDNA, are recently developed means for cancer detection and monitoring. And these biomarkers were extracted from WGS data to build machine learning algorithm based classifiers, prediction models, to discriminate GC patients from healthy individuals, achieving extremely high precision of sensitivity at 94.87% and specificity at 99.35%. …”
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Invisible Manipulation: Deep Reinforcement Learning-Enhanced Stealthy Attacks on Battery Energy Management Systems by Qi Xiao, Lidong Song, Jong Ha Woo, Rongxing Hu, Bei Xu, Kai Ye, Ning Lu

“…Our method employs deep reinforcement learning (DRL) to generate synthetic measurements (e.g., battery voltage, current) that mimic real data, bypassing residual-based bad data detection (BDD) and misleading Extended Kalman-filter (EKF) based State-of-Charge (SoC) estimations. …”
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Classification of Chicken Carcass Breast Blood-Related Defects Using Hyperspectral Imaging Combined with Convolutional Neural Networks by Liukui Duan, Juanfang Bao, Hao Yang, Liuqian Gao, Xu Zhang, Shengjie Li, Huihui Wang

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Optimizing ML models for cybercrime detection: balancing performance, energy consumption, and carbon footprint through multi-objective optimization by Romil Rawat

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Application of the LDA model to identify topics in telemedicine conversations on the X social network by Mario Sierra Martín, Fang-Wei Chen, Pilar Alarcón Urbistondo

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Artificial intelligence (AI) in restorative dentistry: current trends and future prospects by Mariya Najeeb, Shahid Islam

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A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

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Research on Spam Filters Based on NB Algorithm by Su Shengyue

“…The SpamAssassin dataset is used in this study to explore the use of the Naive Bayes (NB) algorithm for spam detection. The algorithm demonstrated high accuracy and efficiency in classifying large-scale text data, achieving an accuracy of 97.74%, a recall rate of 96.60%, and a precision rate of 96.8%, with an F1 score of 0.97. …”
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Real-Time Anomaly Detection in IoMT Networks Using Stacking Model and a Healthcare- Specific Dataset by Hadjer Goumidi, Samuel Pierre

“…This study addresses these challenges by proposing a real-time anomaly detection model based on machine learning (ML) techniques, designed to detect and mitigate diverse cyber threats effectively. …”
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C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles by Thanh Binh Ngo, Long Ngo, Anh Vu Phi, Trung Thị Hoa Trang Nguyen, Andy Nguyen, Jason Brown, Asanka Perera

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Tools for forecasting regional economic growth using big data and business intelligence technologies by Afanasev Kirill, Kalinin Aleksandr

“…The research methodology includes modified machine learning algorithms specifically adapted for regional data analysis, using both structured and unstructured information sources. …”
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Improved Multi-Size, Multi-Target and 3D Position Detection Network for Flowering Chinese Cabbage Based on YOLOv8 by Yuanqing Shui, Kai Yuan, Mengcheng Wu, Zuoxi Zhao

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A Novel Deep Learner for Human Behavior Prediction Over Public Video Surveillance by Bayan Alabdullah, Bisma Batool Fatima, Haifa F. Alhasson, Mohammed Alshehri, Yahya AlQahtani, Nouf Albhassabi, Hui Liu

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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…This study examines the impact of various partitioning techniques on crime forecasting performance, comparing the traditional static division of the city into police districts with machine learning approaches, specifically density clustering algorithms, for detecting crime hotspots. …”
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Simple energy detector for two-stage classification for antidrone systems by Zurovac Snežana, Petrović Nikola, Joksimović Vasilija, Pokrajac Ivan, Mikanović Darko, Sazdić-Jotić Boban

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Hybrid Naïve Bayes Models for Scam Detection: Comparative Insights From Email and Financial Fraud by Lebede Ngartera, Mahamat Ali Issaka, Saralees Nadarajah

“…This study revisits the Naïve Bayes algorithm—often underestimated in modern cybersecurity—as a core building block for effective scam detection. …”
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A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system by Yufan He, Wenjuan Du, Qiang Fu, H.F. Wang

“…To solve the problem, this study proposes a novel model-free method to detect SSO risk and locate the source. The proposed method applies the deep learning support vector data description and label spreading approaches. …”
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Detection of Agricultural Terraces Platforms Using Machine Learning from Orthophotos and LiDAR-Based Digital Terrain Model: A Case Study in Roya Valley of Southeast France by Michael Vincent Tubog, Karine Emsellem, Stephane Bouissou

“…This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. …”
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Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications by Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Siddheshwari Bankar, Sartaj Bhuvaji, Sungroul Kim, Choon-Sik Park

“…However, prediction models are sensitive to the size and distribution of the data they are trained on. ML algorithms rely heavily on vast quantities of training data to make accurate predictions. …”
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