Instructional Process of Design-Based Learning Integration on Computational Thinking: A Framework for Effective Teaching in Course of Physics Experiment Design by Suritno Fayanto, I Nyoman Sudana Degeng, Syaad Patmanthara, Saida Ulfa

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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. …”
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CFTformer: End-to-End Cross-Frame Multi-Object Tracking With Transformer by Abdollah Amirkhani, Seyed Alireza Khoshnevis

“…To access model’s performance in AV applications, the BDD100K dataset was utilized for training and evaluation where the proposed approach achieved a 1.9% improvement in the IDF1 compared to other transformer-based models.…”
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SFMattingNet: A Trimap-Free Deep Image Matting Approach for Smoke and Fire Scenes by Shihui Ma, Zhaoyang Xu, Hongping Yan

“…It provides precise foreground opacity values and attribute annotations. (2) Evaluation of existing image matting baseline methods. …”
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Neighborhood socioeconomic inequality and sarcopenia in community-dwelling older adults: a cross-sectional study by Xiangyu Zhai, Doris S. F. Yu

“…This study examined the association between neighborhood socioeconomic inequality and sarcopenia.Materials and Methods Data from three impact evaluation studies aimed at promoting healthy aging by using the WHO Integrated Care for Older People Model, tailored exercise training, and social prescribing strategies. …”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…Through comprehensive evaluation using both synthetic and real-world traces from deep learning training workloads, our method demonstrates substantial improvements over state-of-the-art algorithms: achieving up to 45.1% reduction in average I/O latency, 32.5% improvement in throughput for critical applications, and 28.8% reduction in storage costs. …”
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QSAR, Molecular Docking, and Pharmacokinetic Studies of 1,8-Naphthyridine Derivatives as Potential Anticancer Agents Targeting DNA Topoisomerase II by Garba Suleiman, Auwal Salisu Isa, Adamu Uzairu, Nabil El Brahmi, Saïd El Kazzouli

“…Molecular descriptors were calculated via PaDEL software and further processed via Data Pretreatment Software V.WPS 1.2. The Kennard–Stone algorithm in the dataset division graphical user interface 1.2 split the dataset into training and test sets. …”
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YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

“…We de-veloped a comprehensive YOLO-RM dataset comprising 57,500 images to facilitate model training and evaluation. Several innovations were introduced to the YOLOv8n framework: (1) An ALKBlock-based C2f module incorporating DACov structure to enhance spatial perception; (2) An EffQA-FPN feature pyramid network inspired by QARepVGG to mitigate precision loss during quantization and reparameterization; (3) A DynamicHead attention mechanism with multi dimensional perception capabilities; and (4) EIoU and Adaptive Wing Loss functions to optimize bounding box and key point regression. …”
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Performance of Sentiment Classification on Tweets of Clothing Brands by Muhammad Shafiq Jalani, Hu Ng, Timothy Tzen Vun Yap, Vik Tor Goh

“…The evaluation of performance was measured with accuracy, precision, recall and F1-Score. …”
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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The basic algorithm for the delivery forecasting model was the Decision Tree algorithm. …”
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The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review by Yuchen Ma, Mohan Li, Huiqun Wu

“…The pooled area under the receiver operating characteristic (AUROC) curve for ML models predicting MACEs was 0.7879 in the training set and 0.7981 in the testing set. Logistic regression (LR), the most commonly used algorithm, achieved an AUROC of 0.8229 in the testing group and 0.7983 in the training group. …”
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An Approach to Finding a Robust Deep Learning Model by Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev

“…The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of a large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. …”
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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…A detailed comparative analysis was conducted, examining key parameters such as learning rate, optimization algorithms, and the number of training epochs to determine the most effective approach. …”
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Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…Various ophthalmic parameters were input into the above algorithms for model training, and the performance of the algorithms was analyzed from the difference between the prediction value and the ground truth. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…The data was divided into 70% for training and 30% for screening. The experimental results showed that the logistic regression model performed better than the nearest neighbor algorithm with a precision of 96%, recall of 98%, and F1- score of 97% in the thalassemia intermedia category, while it had a precision of 97%, recall of 95%, and F1- score of 96% in the thalassemia major category, indicating that logistic regression performed well in distinguishing between these two categories. it has been shown that logistic regression is more effective than the K-nearest neighbor algorithm in classifying thalassemia patients, especially those with thalassemia major. …”
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Pix2Pix-Based Modelling of Urban Morphogenesis and Its Linkage to Local Climate Zones and Urban Heat Islands in Chinese Megacities by Mo Wang, Ziheng Xiong, Jiayu Zhao, Shiqi Zhou, Qingchan Wang

“…The research framework comprises four key components: (1) acquisition of LCZ maps and urban form samples from selected Chinese megacities for training, utilizing datasets such as the World Cover database, RiverMap’s building outlines, and integrated satellite data from Landsat 8, Sentinel-1, and Sentinel-2; (2) evaluation of the Pix2Pix algorithm’s performance in simulating urban environments; (3) generation of 3D urban models to demonstrate the model’s capability for automated urban morphology construction, with specific potential for examining urban heat island effects; (4) examination of the model’s adaptability in urban planning contexts in projecting urban morphological transformations. …”
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RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings by Fengwei Yuan, Guoning Ma, Qinghao Zeng, Jinghong Liu, Zhang Xiao, Zhenhong Zou, Xiangjiang Wang

“…Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production.…”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Univariate and multivariable logistic regression identified six independent risk factors including body mass index (BMI), fracture classification, concomitant ipsilateral foot and ankle fractures, smoking, quality of fracture reduction, and fracture type. Performance evaluation demonstrated that Extreme Gradient Boosting (XGBoost model) achieved high AUC values with superior specificity and sensitivity in both the training and testing sets. …”
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Building an Otoscopic screening prototype tool using deep learning by Devon Livingstone, Aron S. Talai, Justin Chau, Nils D. Forkert

“…Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network. …”
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A versatile framework for attitude tuning of beamlines at light source facilities by Peng-Cheng Li, Xiao-Xue Bi, Zhen Zhang, Xiao-Bao Deng, Chun Li, Li-Wen Wang, Gong-Fa Liu, Yi Zhang, Ai-Yu Zhou, Yu Liu

“…It supports flexible input/output ports, easy integration of diverse evaluation functions and free selection of optimization algorithms. …”
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