Human mobility is well described by closed-form gravity-like models learned automatically from data by Oriol Cabanas-Tirapu, Lluís Danús, Esteban Moro, Marta Sales-Pardo, Roger Guimerà

“…Here, we show that simple machine-learned, closed-form models of mobility can predict mobility flows as accurately as complex machine learning models, and extrapolate better. …”
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Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model by Qi-Ang Wang, Jia Zhang, Jiandong Huang

“…However, there is no reliable and simple machine learning model for the prediction of the compressive strength. …”
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A Study on the Prediction of House Price Index in First-Tier Cities in China Based on Heterogeneous Integrated Learning Model by Yaqi Mao, Yonghui Duan, Yibin Guo, Xiang Wang, Shen Gao

“…To address the difficulty of low prediction accuracy, insufficient model stability, and certain lag associated with a single machine learning model in the prediction of house price, this paper proposes a multimodel fusion house price prediction model based on stacking integrated learning. …”
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Zero-Shot Classification of Art With Large Language Models by Tatsuya Tojima, Mitsuo Yoshida

“…Both traditional statistical methods and machine learning methods have been used to predict art prices. …”
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Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding by Ibrahim Almubark

“…This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. …”
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Towards Supercomputing Categorizing the Maliciousness upon Cybersecurity Blacklists with Concept Drift by M. V. Carriegos, N. DeCastro-García, D. Escudero

“…In this article, we have carried out a case study to optimize the classification of the maliciousness of cybersecurity events by IP addresses using machine learning techniques. The optimization is studied focusing on time complexity. …”
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A Comparative Analysis of Support Vector Machine and K-Nearest Neighbors Models for Network Attack Traffic Detection by Han Zhuoxi

“…This research centers on the use of advanced machine learning methods, particularly Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), to improve the detection of network attack traffic. …”
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CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data by Aulia Arif Wardana, Grzegorz Kołaczek, Parman Sukarno

“…CoAt-Set is compatible with standard machine learning frameworks, offering researchers and practitioners a comprehensive resource for developing, testing, and evaluating CIDS models. …”
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Linear SVM-Based Android Malware Detection for Reliable IoT Services by Hyo-Sik Ham, Hwan-Hee Kim, Myung-Sup Kim, Mi-Jung Choi

“…In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.…”
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Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach by Ernesto Lee, Furqan Rustam, Wajdi Aljedaani, Abid Ishaq, Vaibhav Rupapara, Imran Ashraf

“…This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. …”
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Expandable Orbit Decay Prediction Using Continual Learning by Junhua He, Hua Wang, Haitao Wang, Xuankun Fang, Chengyi Huo

“…Generalization performance of machine learning techniques (MLTs) is blocked by the universal challenge known as catastrophic forgetting, resulting in limited improvement on PODP. …”
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BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data by Mohammad Manzurul Islam, Md. Jubayer Ahmed, Mahmud Bin Shafi, Aritra Das, Md. Rakibul Hasan, Abdullah Al Rafi, Mohammad Rifat Ahmmad Rashid, Nishat Tasnim Niloy, Md. Sawkat Ali, Abdullahi Chowdhury, Ahmed Abdal Shafi Rasel

“…In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. …”
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Assisted Parkinsonism Diagnosis Using Multimodal MRI—The Role of Clinical Insights by Tobias Meindl, Alexander Hapfelmeier, Tobias Mantel, Angela Jochim, Jonas Deppe, Silke Zwirner, Jan S. Kirschke, Yong Li, Bernhard Haslinger

“…Results Clinical diagnosis was accurately confirmed using machine learning models with only small differences when using imaging and clinical signs versus imaging variables only (expected multiclass AUC of 0.95 vs. 0.92). …”
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SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network by Xiping Niu, Ling Sang, Xiaoling Duan, Shijie Gu, Peng Zhao, Tao Zhu, Kaixuan Xu, Yawei He, Zheyang Li, Jincheng Zhang, Rui Jin

“…Meanwhile, in the comparison of convolutional neural networks and machine learning, the CNN accuracy is much higher than the machine learning methods. …”
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AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study by Tai-Han Lin, Hsing-Yi Chung, Ming-Jr Jian, Chih-Kai Chang, Hung-Hsin Lin, Chiung-Tzu Yen, Sheng-Hui Tang, Pin-Ching Pan, Cherng-Lih Perng, Feng-Yee Chang, Chien-Wen Chen, Hung-Sheng Shang

“…Even promising existing machine learning approaches are restricted by reliance on complex clinical factors that could delay results, underscoring the need for faster, simpler, and more reliable diagnostic strategies. …”
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Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine by Amjad Rehman Khan, Tanzila Saba, Tariq Sadad, Seng-phil Hong

“…Cloud-based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. …”
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Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. by Muhammad Rizwan Khurshid, Sadaf Manzoor, Touseef Sadiq, Lal Hussain, Mohammed Shahbaz Khan, Ashit Kumar Dutta

“…While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches. …”
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Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network by Thuy-Anh Nguyen, Hai-Bang Ly, Hai-Van Thi Mai, Van Quan Tran

“…In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. …”
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GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force by Chandrasen Pandey, Diptendu Sinha Roy, Ramesh Chandra Poonia, Ayman Altameem, Soumya Ranjan Nayak, Amit Verma, Abdul Khader Jilani Saudagar

“…The article includes machine learning- and deep learning-based models to classify healthy and gait disorder patients using ground reaction force. …”
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Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification by Ahmad Ubai Dullah, Aditya Yoga Darmawan, Dwika Ananda Agustina Pertiwi, Jumanto Unjung

“…Along with the development of technology, various models of machine learning algorithms and data processing techniques have been developed to find models that can produce the best precision in classifying heart disease. machine learning algorithm model in classifying heart disease, so that it can improve the effectiveness of diagnosis and help in determining the right treatment for patients. …”
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