An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

“…The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin, Tanzania, which lacks core data. Shapley additive explanations (SHAP) analysis identified thermal neutron porosity (TNPH), effective porosity (PHIE), and spectral gamma-ray (SGR) as the most critical parameters in permeability prediction. …”
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Characterizing climate change sentiments in Alaska on social media by Junjun Yin, Matthew Brooks, Donghui Wang, Guangqing Chi

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Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification by Xiaorou Zheng, Jianxin Jia, Shoubin Dong, Yawei Wang, Runuo Lu, Yuwei Chen, Yueming Wang

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Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data by Mehwish Naseer, Farhan Ullah, Samia Ijaz, Hamad Naeem, Amjad Alsirhani, Ghadah Naif Alwakid, Abdullah Alomari

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Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study by Lucio Caprioli, Amani Najlaoui, Francesca Campoli, Aatheethyaa Dhanasekaran, Saeid Edriss, Cristian Romagnoli, Andrea Zanela, Elvira Padua, Vincenzo Bonaiuto, Giuseppe Annino

“…Moreover, this system has proven effective in evaluating the technical skills of a group of players on the court and highlighting their areas for improvement, showing significant potential for practical applications in player training and performance analysis. <b>Conclusions:</b> Quantitatively assessing timing offers a new perspective for coaches and players to improve performance and technique, providing objective data to set training regimens and optimize game strategies.…”
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Aspect-Based Sentiment Analysis: A Comprehensive Review and Open Research Challenges by Waqas Ahmad, Hikmat Ullah Khan, Fawaz Khaled Alarfaj, Mohammed Alreshoodi

“…In this review study, we first focus on two diverse research tasks, aspect extraction, and aspect sentiment analysis and then we present a comprehensive review of existing studies in various classifications such as lexicon-based, graph data, topic models, machine learning, and deep learning. …”
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Identification of atrial fibrillation using heart rate variability: a meta-analysis by Ziwei Yin, Changxin Liu, Chenggong Xie, Zixing Nie, Jiaming Wei, Wen Zhang, Hao Liang

“…Recently, artificial intelligence (AI) algorithms have leveraged heart rate variability (HRV) patterns to enhance the accuracy of AF identification.MethodsWe conducted a systematic review of the literature by searching four major biomedical databases—PubMed, Web of Science, Embase, and Cochrane Library—spanning from their inception to December 13, 2024, following the PRISMA guidelines. We extracted data on true positives, false positives, true negatives, and false negatives from the included studies, which were then synthesized to evaluate sensitivity and specificity comprehensively.ResultsOur final analysis included 12 diagnostic studies. …”
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A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

Subjects: “…Web vulnerabilities, feature extraction, feature selection, data mining, supervised machine learning, phishing websites, cyber security, and phishing identification…”
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An Empirical Study on Data Augmentation for Pixelwise Satellite Image Time-Series Classification and Cross-Year Adaptation by Yuan Yuan, Lei Lin, Qi Xin, Zeng-Guang Zhou, Qingshan Liu

“…Data augmentation is a cost-effective strategy to prevent deep learning models from overfitting with limited labeled data, but its effectiveness for SITS has yet to be thoroughly explored. …”
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Social smart city research: interconnections between participatory governance, data privacy, artificial intelligence and ethical sustainable development by Samad Rasoulzadeh Aghdam, Samad Rasoulzadeh Aghdam, Behnaz Bababeimorad, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Mazdak Irani, Aapo Huovila

“…A deeper analysis of key terms used in recent research revealed the following hot topics: (1) governance and citizen participation, (2) artificial intelligence technologies such as machine learning, (3) blockchain, and (4) Internet of Things. …”
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Analysis of Students’ Motivation Formation to Study Physics in Modern Conditions by E. L. Kazakova, E. V. Moshkina, O. V. Sergeeva, E. V. Tikhomirova

“…The survey itself and survey data analysis were conducted using the functional features of the e-learning platform Blackboard Learn. …”
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Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks by Ayman Alzraiee, Richard Niswonger, Carol Luukkonen, Josh Larsen, Donald Martin, Deidre Herbert, Cheryl Buchwald, Cheryl Dieter, Lisa Miller, Jana Stewart, Natalie Houston, Scott Paulinski, Kristen Valseth

“…This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. …”
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Analysis of achievements for improvement in pedagogical competence of madrasah ibtidaiyah teachers by Ofi Rofiatul Janah, Mohammad Ali

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Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy by Yinzhen Tan, Bo Yu, Jianxin Pan, Wuliang Yin, Min Wang, Hua Wang, Kai Yang, Qingtai Xiao

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The future of precision orthopaedics: personalized data-driven practice by Deiary F. Kader, Andrew Coppola, Aditya Vijay, Andreas Fontalis, Fares S. Haddad

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OppIN: Optimal Path Intervention for Emergency Response Leveraging IoT and Big Data Technologies by Yassine Gacha, Takoua Abdellatif

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Exploring Purpose-Driven Methods and a Multifaceted Approach in Dam Health Monitoring Data Utilization by Zhanchao Li, Ebrahim Yahya Khailah, Xingyang Liu, Jiaming Liang

“…Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. …”
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FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain by Gabriel Chukwunonso Amaizu, Akshita Maradapu Vera Venkata Sai, Sanjay Bhardwaj, Dong-Seong Kim, Madhuri Siddula, Yingshu Li

“…However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. …”
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Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method by Danish Khan, Wasim Akram, Sajid Ullah

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From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary by Carlo Dindorf, Fabian Horst, Djordje Slijepčević, Bernhard Dumphart, Jonas Dully, Matthias Zeppelzauer, Brian Horsak, Michael Fröhlich

“…However, the emergence of wearable sensors and markerless motion capture has revolutionized this field, enabling data collection in real-world scenarios. This shift has also sparked interest in integrating machine learning (ML) into biomechanical workflows, promising to revolutionize data acquisition in field settings and refine data analysis (Halilaj et al., 2018). …”
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