Students' performance prediction employing Decision Tree by Abtahi Ahmed, Farzana Akter Nipa, Wasi Uddin Bhuyian, Khaled Md Mushfique, Kamrul Islam Shahin, Huu-Hoa Nguyen, Dewan Md. Farid

“…Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. …”
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Smart Charging and V2G: Enhancing a Hybrid Energy Storage System with Intelligent and Bidirectional EV Charging by Thomas Franzelin, Sarah Schwarz, Stephan Rinderknecht

“…This environment facilitates comprehensive investigations into EV behavior, charging strategies, control algorithms, and user interactions. It provides a platform for exploring the possibilities, limitations, and optimal use cases for smart charging and hybrid storage systems in practice.…”
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Battery Management System for Solar Power Plants in Uganda: An IoT-Driven Approach by Ssembalirwa, Denis, Cartland, Richard, Bature, U. I., Kitone, Isaac

“…Intelligent algorithms autonomously regulate charging and discharging cycles to prevent overcharging and deep discharge, optimizing battery performance. …”
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Urban Air Mobility Vertiport’s Capacity Simulation and Analysis by Antoni Kopyt, Sebastian Dylicki

“…The framework integrates dynamic grid-based spatial management, probabilistic mission duration algorithms, and EASA-compliant operational protocols to address the infrastructural and logistical demands of high-density UAS operations. …”
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Compact embedded RF sawtooth waveform generator for particle accelerator applications by Ashish Sharma, Subrat Kar, Bhuban Kumar Sahu

“…This paper elaborates on the construction of this instrument, including the design and implementation of multiple feedback controls and complex signal processing algorithms to generate a stable sawtooth waveform. The entire design is space and resource-optimized to be hosted on a single system-on-chip field programmable gate array board, which helped reduce the development effort. …”
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Data-driven decoding of quantum error correcting codes using graph neural networks by Moritz Lange, Pontus Havström, Basudha Srivastava, Isak Bengtsson, Valdemar Bergentall, Karl Hammar, Olivia Heuts, Evert van Nieuwenburg, Mats Granath

“…Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. …”
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Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…However, implementing accurate and automated object detection algorithms mitigates these challenges, allowing errors or defects to be detected before processing. …”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

“…When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments.…”
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The Analysis of the Possibility to Conduct Orbital Manoeuvres of Nanosatellites in the Context of the Maximisation of a Specific Operational Task by Magdalena Lewinska, Michal Kedzierski

“…The conclusions from the conducted analyses confirmed that an appropriate strategy of orbital manoeuvres may greatly improve the operational efficiency of the nanosatellite, while, at the same time, continuing to save fuel and energy. …”
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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

“…Understanding these decisions is essential for effective urban planning. While discrete choice models have been used to predict residential locations, their limitations in handling complex datasets have hindered their accuracy. …”
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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis by Esther Ugo Alum

“…This commentary aims to clarify how AI is improving the identification of novel biomarkers for optimal early diagnosis, focused treatment, and improved clinical outcomes, while also addressing certain obstacles and ethical issues related to the application of artificial intelligence in oncology. …”
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Challenges and Applications of Artificial Intelligence in Education: A Systematic Review by Saeid Arifnejad, Asadolah khadivi, Farhad Alipour

“…Additionally, it facilitates efficient teaching methodologies, automates grading and assessment, and optimizes resource allocation. The integration of AI can significantly improve the quality of education.Value: This research contributes to bridging the knowledge gap regarding AI applications in education while identifying key challenges for educators and policymakers.…”
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Understanding visitor flow and behaviour in developing tourism-service-oriented villages by space syntax methodologies: a case study of Tabian Rural Section of Qingshan Village, Ha... by Shuqin Wang, Yan Huang, Tianjie Li

“…The methodologies employed in this study are based on two space syntax algorithms: 3-D isovist analysis and agent-based models (ABMs). …”
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Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach by Seung-Yeon Kim, Haneul Ko

“…The results show that our scheme can achieve optimal transmission power and significantly improved network energy efficiency compared with conventional algorithms such as no transmit power control and random control. …”
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Faster? Softer? Or More Formal? A Study on the Methods of Enterprises’ Crisis Response on Social Media by Yongtian Yu, Weiming Ye, Kaihang Zhang

“…Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. …”
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Designing a sustainable and resilient agri-food closed-loop supply chain network for porcine products by Álvaro Ronaldo Elizalde Camacho, Fatemeh Gholian-Jouybari, Behzad Mosallanezhad, Neale R. Smith, Mostafa Hajiaghaei-Keshteli

“…The sustainability of agri-food supply chains is imperative for addressing global food security while mitigating environmental and socio-economic challenges. …”
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Energy-Efficient model for integrated berth allocation and quay crane management by Saeedeh Khalilpoor, Mehdi A. Kamran, Reza Babazadeh, Reza Kia

“…Several numerical experiments in different dimensions are defined and implemented with both algorithms. The results analysis gives an overall perspective, while both algorithms show their value and strong points and weaknesses in meeting demanding requirements of modern container terminal logistics. …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. An optimized loss function refines bounding box matching for small targets, while a pruning strategy removes redundant filters, boosting computational efficiency. …”
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IoT-edge anomaly detection for covariate shifted and point time series health data by Partha Pratim Ray, Dinesh Dash

“…We implement the ”IRKernel” to investigate the efficiency of exponentially weighted moving average (EWMA)-based light-weight statistical learning by using three variants e.g. probabilistic (P), shift-detect (SD), and two-stage shift-detect (TS-SD) over the EWMA algorithms. We perform evaluation of execution time of the algorithms in classical, incremental, and optimized modes while following the Kolmogorov–Smirnov hypothesis testing at the two-stage anomaly detection. …”
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