IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…The model demonstrated a 20.0% reduction in average traffic delay and a 25.0% enhancement in traffic flow efficiency. …”
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Indoor Multidimensional Reconstruction Based on Maximal Cliques by Yongtong Zhu, Lei Li, Na Liu, Qingdu Li, Ye Yuan

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Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals by Huaixia Shi, Huaqiang Si, Jiyun Qin

“…To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). …”
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Fast binary logistic regression by Nurdan Ayse Saran, Fatih Nar

“…This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. …”
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Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification by Christopher Liu, Juanjuan Fan, Barbara Bailey, Ralph-Axel Müller, Annika Linke

“…We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). …”
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Kernel-based response extraction approach for efficient configurable ring oscillator PUF by Enas Abulibdeh, Hani Saleh, Baker Mohammad, Mahmoud Al-Qutayri, Asif Hussain

“…The NIST tests are successfully passed, and the average prediction accuracy of machine learning models is found to be 65.3%.…”
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Automotive DNN-Based Object Detection in the Presence of Lens Obstruction and Video Compression by Gabriele Baris, Boda Li, Pak Hung Chan, Carlo Alberto Avizzano, Valentina Donzella

“…Recent advances in sensing, processing, machine learning, and communication technologies are accelerating assisted and automated functions development for commercial vehicles. …”
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Generating context-specific sports training plans by combining generative adversarial networks. by Juquan Tan, Jingwen Chen

“…Statistical significance is analyzed using ANOVA testing. The proposed GAN model outperforms traditional ML and rule-based methods, achieving a 22% reduction in MSE and a 45% improvement in generation time. …”
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Disentangling High-Paced Alternating I/O in Gaze-Based Interaction by Yulia G. Shevtsova, Artem S. Yashin, Sergei L. Shishkin, Anatoly N. Vasilyev

“…When playing the game, 15 volunteers selected screen objects using a 500 ms dwell time without additional actions for intention confirmation. By applying machine learning algorithms to gaze features and action context information, we achieved a threefold reduction in false positives, improved the quality of in-game decisions, and increased participant satisfaction with system ergonomics. …”
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Big Data Analytics for Uncovering Voxel Connectivity Patterns in Attention Deficit Hyperactivity Disorder by Caraka RE, Supardi K, Gio PU, Isnaniawardhani V, Chen RC, Djatmiko B, Pardamean B

“…The performance of different activation functions—ReLU, Sigmoid, and Tanh—was evaluated within deep neural networks.Results: Several key brain regions, including the Fusiform Gyrus, Thalamus, and Superior Temporal Gyrus, were identified as significant predictors for ADHD. The integration of machine learning models demonstrated improved classification accuracy, with ReLU-based neural networks outperforming others in most evaluation metrics.Discussion: The study demonstrates the potential of a robust, integrated machine learning framework to analyze high-dimensional neuroimaging data and identify biologically relevant markers of ADHD. …”
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ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime. by Chotirawee Chatpattanasiri, Federica Ninno, Catriona Stokes, Alan Dardik, David Strosberg, Edouard Aboian, Hendrik von Tengg-Kobligk, Vanessa Díaz-Zuccarini, Stavroula Balabani

“…Data-driven approaches such as Reduced Order Modeling (ROM) and Machine Learning (ML) are increasingly being explored alongside CFD to advance biomechanical research and application. …”
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Personalized prediction of esophageal cancer risk based on virtually generated alcohol data by Oswald Ndi Nfor, Pei-Ming Huang, Ming-Fang Wu, Ke-Cheng Chen, Ying-Hsiang Chou, Mong-Wei Lin, Ji-Han Zhong, Shuenn-Wen Kuo, Yu-Kwang Lee, Chih-Hung Hsu, Jang-Ming Lee, Yung-Po Liaw

“…We analyzed data from 86,845 individuals, including 763 diagnosed EC patients, sourced from the Taiwan Biobank. Eight machine learning models were employed: Bayesian Network, Decision Tree, Ensemble, Gradient Boosting, Logistic Regression, LASSO, Random Forest, and Support Vector Machines (SVM). …”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…However, the growing number and difficulty of such problems cause the developers to construct machine learning models with higher computational complexities, such as an increased number of hidden layers, epochs, learning rate, and rate decay. …”
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LightGBM-Based Human Action Recognition Using Sensors by Yinuo Liu, Ziwei Chen

“…Finally, using feature selection and dimensionality reduction, the efficiency of the model is further improved, achieving a 70.14% increase in time efficiency without reducing the accuracy rate.…”
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Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell by E. Acuña Espinoza, F. Kratzert, D. Klotz, D. Klotz, M. Gauch, M. Álvarez Chaves, R. Loritz, U. Ehret

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Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture by Khosro Rezaee, Safoura Farsi Khavari, Mojtaba Ansari, Fatemeh Zare, Mohammad Hossein Alizadeh Roknabadi

“…Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3–4% average accuracy improvement and a 10% variance reduction. …”
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Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing by Artur Samojluk, Piotr Artiemjew

“…An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. …”
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Exoplanet Classification Through Vision Transformers with Temporal Image Analysis by Anupma Choudhary, Sohith Bandari, B. S. Kushvah, C. Swastik

“…Traditional methods demand substantial effort, time, and cost, highlighting the need for advanced machine learning techniques to enhance classification efficiency. …”
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Food Waste Detection in Canteen Plates Using YOLOv11 by João Ferreira, Paulino Cerqueira, Jorge Ribeiro

“…This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner, and data augmentation techniques were applied to enhance the model’s robustness. …”
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A Novel Approach for Enhancing Plant Leaf Classification With the Binary Cuckoo Search Algorithm by Mohammad Subhi Al-Batah, Mohammad Ryiad Al-Eiadeh, Yazan Alnsour

“…In this study, we introduced a model for classifying a variety set of plant leaves using different techniques such as factorization machine (FM), dimensionality reduction (DR), and ensemble learning (EL). …”
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