IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization by Lihua Song, Ying Han, Yufei Guo, Chenying Cai

“…The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9% in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks. …”
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies by Mohamed Ahwiadi, Wilson Wang

“…This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. …”
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Majority clustering for imbalanced image classification by Keshav Sharma, Jyoti Arora, Pooja Kherwa, Zainab Alansari

“…The results highlight the significance of proposed model as a practical approach to address the challenges posed by imbalanced data distributions in machine learning tasks.…”
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Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification by Sanghyun Seo, Hiskias Dingeto, Juntae Kim

“…This approach ensures that the most relevant and informative data are utilized during the learning process, optimizing learning efficiency and model performance. …”
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An interpretable multi-transformer ensemble for text-based movie genre classification by Faheem Shaukat, Naveed Ejaz, Zeeshan Ashraf, Mrim M. Alnfiai, Nouf Nawar Alotaibi, Salma Mohsen M. Alnefaie

“…After pre-processing the text plots, three transformer-based models, Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, and Robustly Optimized BERT Pre-training Approach (ROBERTa), are used to generate genre predictions, combined through a weighted soft-voting method. …”
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Leveraging deep learning for risk prediction and resilience in supply chains: insights from critical industries by Waleed Abdu Zogaan, Nouran Ajabnoor, Abdullah Ali Salamai

“…When compared to traditional machine learning models, our DL models demonstrated superior accuracy and performance. …”
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Resting-state EEG microstate features for Alzheimer's disease classification. by Xiaoli Yang, Zhipeng Fan, Zhenwei Li, Jiayi Zhou

“…The results show that in the Alpha (8-13 Hz) sub-band, the microstate feature set as model input to SVM is optimal for the recognition of AD, with a classification accuracy of 99.22%. …”
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Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings by Özge Öztürk Hacar, Müslüm Hacar, Fatih Gülgen, Luca Pappalardo

“…The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. …”
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Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking by Gang Yu, Shuang Zhang, Min Hu, Y. Ken Wang

“…Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. …”
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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people by Mahir Mohammed Sharif Adam, Hussah Nasser AlEisa, Samah Al Zanin, Radwa Marzouk

“…Furthermore, the proposed AODSA-MPAVCP model utilizes the YOLOv10 model for object detection. …”
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A Deep Learning Approach to Unveil Types of Mental Illness by Analyzing Social Media Posts by Rajashree Dash, Spandan Udgata, Rupesh K. Mohapatra, Vishanka Dash, Ashrita Das

“…The optimal resulting model is selected by training and testing all of the models on the publicly available Reddit Mental Health Dataset. …”
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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…Objective: To develop and validate a robust machine learning model for classifying breast ultrasound images into benign, malignant, and normal categories, aiming to enhance diagnostic accuracy using advanced feature extraction and ensemble learning techniques. …”
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Research on Suppression of Vibration Caused by Combustion Pressure Pulsation in Aircraft Engines by Liang Hong, Chao-Ping Zang, He-Jiong Ji, Qiu-Xia Yu, Yun-Fan Jiang, Lian Shen

“…By adjusting the air/fuel equivalence ratio at the head of the combustion chamber flame tube, the working conditions of the combustion chamber were optimized. Then, according to the results through the main combustion chamber component tests and overall machine tests, the narrow frequency high-energy components in the airflow pulsation and overall machine vibration disappeared, which meant that the vibration of the aircraft engine was effectively suppressed. …”
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Enhanced Pilot Attention Monitoring: A Time-Frequency EEG Analysis Using CNN–LSTM Networks for Aviation Safety by Quynh Anh Nguyen, Nam Anh Dao, Long Nguyen

“…This study addresses a fundamental question in sensor-based condition monitoring: how can temporal- and frequency-domain EEG sensor data be optimally integrated to detect precursors of system failure in human–machine interfaces? …”
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Cloud Computing Resource Scheduling Algorithm Based on Unsampled Collaborative Knowledge Graph Network by Haichuan Sun, Liang Gu, Chenni Dong, Xin Ma, Zeyu Liu, Zhenxi Li

“…Based on graph convolutional neural networks, analyze the target load of cloud platforms, construct multi hop data transmission paths one by one, and perform deep level information load balancing; Establish a multiplexing information transmission model, correct the initial weights of graph convolutional neural networks, combine reverse transmission calculation methods, integrate and balance cloud computing resources, and confirm the optimal resource scheduling plan; Integrating class convolution and human-machine interaction attention mechanism, the value of the previous time series neural unit is transferred to the current neural unit, and the classification output sequence of knowledge graph relational data feature fragments is analyzed. …”
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Slope deformation prediction based on GA–BP neural networks by Wenhui TAN, Kai LI, Huimin LIU, Meifeng CAI, Qifeng GUO

“…This algorithm optimizes the initial weights and thresholds of the BP neural network, leading to the establishment of the time-series deformation prediction model of slopes based on the GA–BP neural network. …”
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Theoretical Analysis and Experiment of the Five DOF Hybrid Robot P(RPR/RP)RR by Xuejian Ma, Xiaoyu He, Yundou Xu, Jiantao Yao, Yongsheng Zhao

“…Based on three-dimensional model of the hybrid robot P(RPR/RP)RR established by these dimensions, the kinematics and stiffness of the hybrid robot are simulated and verified. …”
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PM2.5 concentration 7-day prediction in the Beijing–Tianjin–Hebei region using a novel stacking framework by Xintong Gao, Xiaohong Wang, Fuping Li, Wenhao Jiang, Meng Zhe, Jiaxing Sun, Ao Zhang, Linlin Jiao

“…This approach enhanced the accuracy of prediction to a degree of approximately 17% in comparison with a solitary machine learning model. The findings of this study demonstrated that the integration of the LSTM-RF model with the fusion-based Stacking algorithm led to a substantial enhancement in the accuracy of PM2.5 predictions. …”
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Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems by Pennipat Nabheerong, Warissara Kiththiworaphongkich, Watcharaporn Cholamjiak

“…Moreover, we present the advantage of our algorithm by comparing it with existing machine learning methods. The highest performance value of 85.03% accuracy, 82.56% precision, 87.65% recall, and 85.03% F1-score show that our algorithm performs better than the other machine learning models.…”
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MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT by Zhenning Chen, Xinyu Zhang, Siyang Wang, Youren Wang

“…Different from conventional federated learning (FL), which relies on a central server for model aggregation, decentralized FL (DFL) exchanges models among edge servers, thus improving the robustness and scalability. …”
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