Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes by Xiaoping Li, Yujie Sun, Xinyue Liu, Shaoxuan Zhang

“…Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. …”
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Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…Abstract While multi-object tracking is critical for autonomous driving systems, traditional algorithms exhibit three fundamental limitations in complex scenarios: (1) blurred feature representation under occlusion and re-identification scenarios causing identity switches, (2) insufficient sensitivity to scale-variant targets due to fixed geometric constraints in conventional IoU-based loss functions, and (3) gradient degradation in deep convolutional layers hindering discriminative feature learning. …”
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Utilizing UAVs in Wireless Networks: Advantages, Challenges, Objectives, and Solution Methods by Mohammad Javad Sobouti, Amirhossein Mohajerzadeh, Haitham Y. Adarbah, Zahra Rahimi, Hamed Ahmadi

“…Additionally, we discuss the main methods and algorithms employed for UAV placement and trajectory optimization. …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…Enhanced strategies for effective fruit picking using the Eye-in-Hand system involve the development of more dexterous robotic hands and improved algorithms for precisely predicting the optimal picking point of each fruit. …”
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A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses by Shuailiang Chen, Aolong Liu, Fei Tang, Pei Hou, Yanli Lu, Pei Yuan

“…Greenhouse control involves regulating environmental parameters such as temperature, humidity, light, and CO₂ concentration to ensure an optimal growth environment for crops while conserving energy. …”
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Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output by Arlanova A. A., Hojamkuliyeva B. A., Babanazarov N. Sh., Arlanov M. S.

“…A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. …”
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Energy-Efficiency Maximization in Backscatter Communication-Based Non-Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches by Dingjia Lin, Tianqi Wang, Kaidi Wang, Zhiguo Ding

“…The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. …”
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WaveConstLib: A java library for signal analysis and wavelet construction by Çağla Sarvan Cibil, Nalan Özkurt

“…It provides tools to create wavelet functions tailored to specific signals while adhering to WT conditions. Unlike traditional methods, WaveConstLib employs multi-objective evolutionary algorithms (MOEAs) optimization to construct signal-specific wavelet functions, ensuring superior adaptability and performance. …”
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IDLEWiSE by Chiara Fusar Bassini, Leonard Hackel, Thorren Kirschbaum

“…This paper outlines an optimization tool using efficient machine-learning algorithms like decision trees to learn optimal shutdown policies online. …”
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Blockchain based solid waste classification with AI powered tracking and IoT integration by Aliaa M. Alabdali

“…IoT-connected bins transmit data to a central server, which uses blockchain to ensure secure, transparent data storage. AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. …”
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Resource allocation mechanism in the TWDM-PON and C-RAN joint architecture with hybrid energy supply by Ruyan WANG, Ningning XU

“…Aiming at the problems of low resource utilization rate,high energy consumption and poor user service quality in the existing virtualized Cloud Radio Access Network,an energy-aware virtualized resource allocation mechanism with hybrid energy supply was proposed.According to the energy sources and energy consumption of different network devices,energy arrival and energy consumption models were established.Furthermore,under the premise of guaranteeing the quality of user services,considering proportional fairness and energy consumption optimization,distributed algorithms based on asynchronous update were used to allocate resources and harvested energy for different types of virtual cloud radio access networks and user virtual base stations to effectively improve the energy efficiency of network.The simulation results show that the proposed resource allocation mechanism can reduce energy consumption while effectively reducing the latency and improving the throughput.…”
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Revolutionizing Diabetes Management Through Nanotechnology-Driven Smart Systems by Aayush Kaushal, Aanchal Musafir, Gourav Sharma, Shital Rani, Rajat Kumar Singh, Akhilesh Kumar, Sanjay Kumar Bhadada, Ravi Pratap Barnwal, Gurpal Singh

“…Diabetes is a global health challenge, and while current treatments offer relief, they often fall short in achieving optimal control and long-term outcomes. …”
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Decentralized Energy Swapping for Sustainable Wireless Sensor Networks Using Blockchain Technology by Umar Draz, Tariq Ali, Sana Yasin, Mohammad Hijji, Muhammad Ayaz, EL-Hadi M. Aggoune

“…The system is built using NS-3 to simulate node behavior, energy usage, and network dynamics, while Python manages the blockchain architecture, cryptographic security, and trading algorithms. …”
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A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions by Ioannis Syllaidopoulos, Klimis S. Ntalianis, Ioannis Salmon

“…This paper provides a comprehensive overview of AI methodologies, such as predictive analytics, Natural Language Processing (NLP), and machine learning architectures (e.g., Support Vector Machines – SVM and Long Short-Term Memory – LSTM), and optimization algorithms (e.g., Particle Swarm Optimization – PSO), assessing their effectiveness in security applications. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…Hyperparameter optimization (HPO) and comparisons were conducted using grid search and random search. …”
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Review of the Research on Underwater Explosion Ice-Breaking Technology by Xiao Huang, Zi-Xian Zhong, Xiao Luo, Yuan-Dong Wang

“…Key findings establish that shock waves initiate brittle fracture via stress superposition while bubble pulsation drives crack propagation through pressure oscillation; optimal ice fragmentation depends critically on charge weight, standoff distance, and ice thickness. …”
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

“…Accurate and reliable crop yield prediction is essential for optimizing agricultural management, resource allocation, and decision-making, while also supporting farmers and stakeholders in adapting to climate change and increasing global demand. …”
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AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis by Cong Feng, Ahmed Kateb Jumaah Al-Nussairi, Mustafa Habeeb Chyad, Narinderjit Singh Sawaran Singh, Jianyong Yu, Amirfarhad Farhadi

“…Abstract In the context of smart homes, efficiently managing temperature control while optimizing energy consumption and ensuring data security remains a significant challenge. …”
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Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis by Ali Nabavi, Mohammad Kashkooli, Sara Sadat Nabavizadeh, Farimah Safari

“…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…The predictive model used in this study provides reliable technical support for the optimization and engineering application of foam concrete, enhancing both prediction accuracy and practical efficiency.…”
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