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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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization by Sumet Mehta, Fei Han, Muhammad Sohail, Bhekisipho Twala, Asad Ullah, Fasee Ullah, Arfat Ahmad Khan, Qinghua Ling

“…The analysis of high-dimensional microarray gene expression data presents critical challenges, including excessive dimensionality, increased computational burden, and sensitivity to random initialization. Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. …”
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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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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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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

“…We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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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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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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ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network by Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah

“…The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. …”
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Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid by Josue N. Otshwe, Bin Li, Songsong Chen, Feixiang Gong, Bing Qi, Ngouokoua J. Chabrol

“…System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. …”
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Delivering data: A real-world dataset for last-mile delivery optimizationZenodo by Anna Vrani, Savvas D. Apostolidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos

“…The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. …”
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Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach by Hassan S. Ahmed, Ahmed J. Abid, Adel A. Obed, Ameer L. Saleh, Reheel J. Hassoon

“…This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. …”
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