Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle by Gusti Ayu Putu Febriyanti, Anna Baita

“…The K-fold cross-validation method with K=10 and hyperparameter tuning were applied to obtain the optimal performance of both models. The results showed that SVM without undersampling achieved 92% accuracy, while with undersampling the accuracy decreased to 76%. …”
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Impact of monitor unit optimization in volumetric modulated arc therapy planning for nasopharyngeal carcinoma. by Huaqu Zeng, Zhen Li, Zongyou Chen, Shukui Tang, Qifu Lin, Minzhi Zhong, Zunbei Wen

“…Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. …”
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An accuracy-privacy optimization framework considering user’s privacy requirements for data stream mining by Waruni Hewage, R. Sinha, M. Asif Naeem

“…Experimental results show that the proposed framework archives an optimal privacy level above 97% while minimising the accuracy loss across various datasets. …”
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Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input by Jooyoung Kim, Wonkyung Lee, Jungwoon Park

“…This paper introduces the League of Legends Match Data Compactor (LoL-MDC), a tool designed to transform extensive match data into a concise and structured format optimized for LLM processing. By systematically summarizing structured match information—including match overviews, player and team statistics, timeline summaries, and algorithmically selected key events—the LoL-MDC significantly reduces the data size from approximately 80,000 tokens to under 2000 tokens while retaining analytical value. …”
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Optimized workflow for behavior-coupled fiber photometry experiment: improved data navigation and accessibility by Anna Athanassi, Amaury François, Emmanuel Bourinet, Marc Thevenet, Nathalie Mandairon

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Optimizing Airport Land Side Operations: Check-In, Passengers’ Migration, and Security Control Processes by Ludovica Adacher, Marta Flamini

“…The stochastic passengers’ behaviour and their preferences are simulated by a discrete event model, while the managing costs and the passengers’ discomfort are optimized by the Surrogate Method. …”
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A comprehensive review of AI and machine learning techniques in antenna design optimization and measurement by Pradnya A. Gajbhiye, Satya P. Singh, Madan Kumar Sharma

“…This review examines the latest advancements in applying AI/ML approaches to antenna design optimization. It explores the use of various AI/ML algorithms such as neural networks, decision trees, genetic algorithms, and particle swarm optimization in this context. …”
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URT-YOLOv11: A Large Receptive Field Algorithm for Detecting Tomato Ripening Under Different Field Conditions by Di Mu, Yuping Guou, Wei Wang, Ran Peng, Chunjie Guo, Francesco Marinello, Yingjie Xie, Qiang Huang

“…This study proposes an improved YOLOv11 model to address the limitations of traditional tomato recognition algorithms in complex agricultural environments, such as lighting changes, occlusion, scale variations, and complex backgrounds. …”
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Joint Optimization of UAV Placement and Resource Allocation in FDMA Wireless-Powered Sensor Networks by Omid Abachian Ghasemi, Mehdi Chehel Amirani

“…To solve it, the alternating minimization technique is used, where the problem is divided into subproblems with respect to each of the variables by fixing the others. Efficient algorithms based on the dual Lagrange method and Karush-Kuhn-Tucker (KKT) conditions are proposed for optimal transmission time scheduling and bandwidth allocation. …”
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Lightweight Anomaly-Based Detection Using Cuckoo Search Algorithm and Decision Tree to Mitigate Man-in-the-Middle Attacks in DNS by Ramahlapane Lerato Moila, Mthulisi Velempini

“…By integrating the Cuckoo Search Algorithm, the scheme minimizes false positives while improving the detection rate. …”
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Class-aware multi-source domain adaptation algorithm for medical image analysis using reweighted matrix matching strategy. by Huiying Zhang, Yongmeng Li, Lei He, Wenbo Zhang, Yuchen Shen, Lumin Xing

“…Additionally, first-order and second-order moment matching strategies are applied to effectively align the source and target domains, while an adaptive weighting mechanism is utilized to optimize the contributions of different source domains to the target domain. …”
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Inertial Subrange Optimization in Eddy Dissipation Rate Estimation and Aircraft-Dependent Bumpiness Estimation by Zhenxing Gao, Qilin Zhang, Kai Qi

“…In current vertical wind-based eddy dissipation rate (EDR) estimation algorithms based on flight data, the inertial subrange is determined empirically. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…This study reveals that while model selection is crucial, an appropriate optimization method and imputation technique significantly impact model performance. …”
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Multi-Objective Optimization of Machinability and Energy Consumption of Cast Iron Depending on Cooling Rate by Burak Öztürk, Fuat Kara

“…Energy efficiency in machining is crucial for sustainable production, and Specific Cutting Energy Consumption (SCEC) has become a key metric in evaluating machinability. Using genetic algorithms (GA) and Response Surface Methodology (RSM), this study optimized machining parameters for energy consumption and surface finish. …”
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Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review by Izabela Rojek, Dariusz Mikołajewski, Ewa Dostatni, Jan Cybulski, Mirosław Kozielski

“…The key findings highlight the growing emphasis on scalable model architectures, multimodal data integration, and the use of high-performance computing platforms. While existing research has focused on model decomposition, structural optimization, and algorithmic integration, there remains a need for fast DT platforms that support diverse user requirements. …”
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Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI by Myung-Joo Park, Hyo-Sik Yang

“…This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. …”
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Advancements in Optimization Techniques for Active Magnetic Bearing Systems: Current Trends and Future Directions by Tasnemul Hasan Nehal, Waleed M. Hamanah, Mohammad Ali Abido

“…This review delves into cutting-edge optimization techniques for AMBs, from time-tested methods like PID control to innovative approaches such as metaheuristic algorithms, multi-objective optimization, and AI-powered strategies including reinforcement learning and iterative learning control. …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…Publicly available multi-sensor satellite data, such as Landsat, Sentinel-1, and Sentinel-2, from 2018 to 2020 were employed, providing temporal observation frequencies of up to five days, which represents a 25% increase compared to traditional monitoring approaches. Sophisticated algorithms were developed and implemented to improve the accuracy of fire detection while minimizing false alarms. …”
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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

“…The proposed research aims to classify stress-induced emotions and predict stroke risk using advanced deep learning algorithms. The study utilizes EEG signals to categorize stress-related emotions, subsequently assessing stroke risk via an optimized deep learning model. …”
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Risk-Adjusted Deep Reinforcement Learning for Portfolio Optimization: A Multi-reward Approach by Himanshu Choudhary, Arishi Orra, Kartik Sahoo, Manoj Thakur

“…Recent advances in portfolio optimization have shown promising capabilities of deep reinforcement learning algorithms to dynamically allocate funds across various potential assets to meet the objectives of prospective investors. …”
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