An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

“…Primarily, the EALAI-CFDNBD model utilized the linear scaling normalization (LSN) model to ensure that the input features were appropriately scaled for subsequent analysis. Furthermore, the binary olympiad optimization algorithm (BOOA)-based feature selection is utilized to extract the most informative features, reducing the data dimensionality. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

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Enhancing Heart Disease Diagnosis Using ECG Signal Reconstruction and Deep Transfer Learning Classification with Optional SVM Integration by Mostafa Ahmad, Ali Ahmed, Hasan Hashim, Mohammed Farsi, Nader Mahmoud

“…The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integrating adaptive preprocessing, histogram-based lead separation, and robust point-tracking techniques into a unified framework. …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…In the regression analysis conducted within the scope of Scenario-1, the model we proposed to detect “cheating” behavior, which is one of the unethical learner behaviors, was found to be a 5-layer DNN model with a test performance success of 80.9%. In the binary classification analysis for Scenario-2, students who “copied” from unethical behaviors were obtained with an accuracy rate of 96.9% by the model established by the 10-layer DNN algorithm we proposed. …”
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Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine by Toru Aonishi, Tatsuya Nagasawa, Toshiyuki Koizumi, Mastiyage Don Sudeera Hasaranga Gunathilaka, Kazushi Mimura, Masato Okada, Satoshi Kako, Yoshihisa Yamamoto

“…By modifying the sequence control code for the calculation control module, other algorithms such as Simulated Bifurcation (SB) can also be implemented. …”
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The association between dietary diversity and patterns in Chinese university students with abnormal uterine bleeding: a cross-sectional study by Sha Yu, Lei Lai, Xiang Li, Lifeng Lei, Xuan Zhang, Leyi Liu, Peize Song, Xinyue Zhang, Yuxin Ma, Qianqian Lan, Yufang Liu, Yuanqun Chen, Yuping Tang, Zhiying Yu

“…Abstract Background Abnormal uterine bleeding (AUB) is a common gynecological issue during reproductive years. Researches on how dietary diversity and patterns influence AUB in Chinese university students are limited, with some studies not clearly defining irregular menstruation. …”
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Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers by Mengwei Ma, Weimin Xu, Jun Yang, Bowen Zheng, Chanjuan Wen, Sina Wang, Zeyuan Xu, Genggeng Qin, Weiguo Chen

“…Conclusions The radiological characteristics of breast tumors extracted from CEM were found to be associated with breast cancer subtypes in our study. Future research is needed to validate these findings.…”
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Artificial intelligence driven mental health diagnosis based on physiological signals by P.R. Naregalkar, A.A. Shinde, M.V. Patil

“…The machine learning algorithms, like Decision tree and kernel support vector machine, are employed for dope classification tasks. …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…By constructing training and test datasets on all SNPs, intersected SNPs, and randomly generated SNPs, we developed a Machine learning (ML) framework using twelve different algorithms. Then, we compared the performances of the various ML models and used the SHapley Additive exPlanations (SHAP) framework to decipher why and how decisions are made within the optimal algorithm. …”
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An evaluation of Kd-Trees vs Bounding Volume Hierarchy (BVH) acceleration structures in modern CPU architectures by Ernesto Rivera-Alvarado, Julio Zamora-Madrigal

“…In this research, we made an up-to-date evaluation of both acceleration structures, using state-of-the-art BVH and Kd-Trees algorithms implemented in C, and found out that the Kd-Trees acceleration structure provided better performance in all defined scenarios on a modern x86 CPU architecture. …”
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Spatiotemporal evolution of interseismic coupling and stress accumulation near an asperity on a vertical strike-slip fault: Insights from three-dimensional viscoelastic numerical s... by LI Yebo, HUANG Luyuan, YAO Rui, TIAN Yiwei, YANG Shuxin

“…Our innovative approach, referred to as the “binary fault-locking approach”, simplifies fault behavior into a binary system, categorizing states as either “locked” or “unlocked”. …”
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An invitation to the sample complexity of quantum hypothesis testing by Hao-Chung Cheng, Nilanjana Datta, Nana Liu, Theshani Nuradha, Robert Salzmann, Mark M. Wilde

“…The final part of our paper outlines and reviews how sample complexity of quantum hypothesis testing is relevant to a broad swathe of research areas and can enhance understanding of many fundamental concepts, including quantum algorithms for simulation and search, quantum learning and classification, and foundations of quantum mechanics. …”
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Approximate Techniques in Solving Optimal Camera Placement Problems by Jian Zhao, Ruriko Yoshida, Sen-ching Samson Cheung, David Haws

“…While the theoretical foundation of the optimal camera placement problem has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor networks. …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Regression and classification models were developed using gradient boosting, random forest, and CatBoost algorithms. Regression models predicted vault height as a continuous variable, while classification models categorized vault heights into binary and multi-class tasks. …”
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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…Six machine learning algorithms—Decision Trees, k-nearest neighbors, Gaussian Support Vector Machine, Neural Network, Support Vector Machines kernel, and Logistic Regression Kernel—were evaluated for both binary and multi-class classification using feature sets of 4, 12, 23, 50, and 79 features. …”
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Biomedical Data Analysis Systems for the Diagnosis of Skin Neoplasms by O. O. Myakinin

“…Algorithms for a dermatoscopic module are based on the Haar transform, Local Binary Patterns and color features. …”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…Early diagnosis and efficient categorization of plant diseases are of utmost importance to support agricultural production and profitability. Besides, this research aims to investigate the feasibility that employs data mining framework together with optimization algorithms as approaches towards plant diseases identification and categorization systems. …”
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DIGITAL TWINS AND THEIR APPLIANCE IN TRANSPORT ECONOMICS by O. N. Rimskaya, I. V. Anokhov

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Shape Penalized Decision Forests for Imbalanced Data Classification by Rahul Goswami, Aindrila Garai, Payel Sadhukhan, Palash Ghosh, Tanujit Chakraborty

“…Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. …”
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