Image Enhancement Techniques for Images at Blur Motion and Different Noises by Sahlah Ali

“…The images get degraded due to environmental conditions and atmospheric difference, it is therefore important to retrieve original images using different algorithms of image processing. There are widespread applications to restore images in our world today. …”
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A Quantum-Inspired Optimization Strategy for Optimal Dispatch to Increase Heat and Power Efficiency by K. Vanitha, B. Jyothi, R. Seshu Kumar, V. S. Chandrika, Arvind R. Singh, R. M. Naidoo

“…The proposed algorithm also incorporates a classical optimizer to refine the numerical evaluations acquired from the quantum operations. …”
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Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…The purpose of this study is to evaluate the effectiveness of various machine learning algorithms in predicting the ultimate tensile strength (UTS) of friction stir welded joints. …”
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Before hands disappear: Effect of early warning visual feedback method for hand tracking failures in virtual reality. by Mucahit Gemici, Vrushank Phadnis, Anil Ufuk Batmaz

“…However, current hand tracking algorithms are prone to errors, which can disrupt the user experience and hinder task performance. …”
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A survey on key technologies of privacy protection for machine learning by Zishan LIU, Qiang CHENG, Bo LV

“…With the development of information and communication technology,large-scale data collection has vastly promoted the application of machine learning in various fields.However,the data involved in machine learning often contains a lot of personal private information,which makes privacy protection face new risks and challenges,and has attracted more and more attention.The current progress of the related laws,regulations and standards to the personal privacy protection and data safety in machine learning were summarized.The existing work on privacy protection for machine learning was presented in detail.Privacy protection algorithms usually have influence on the data quality,model performance and communication cost.Thus,the performance of the privacy protection algorithms should be comprehensively evaluated in multiple dimensions.The performance evaluation metrics for the privacy protection algorithms for machine learning were presented,given with the conclusion that the privacy preservation on machine learning needs to balance the data quality,model convergence rate and communication cost.…”
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Analysis of disease severity and mortality prediction using machine learning during COVID-19 by Hodjat (Hojatollah) Hamidi, Mostafa Moradi

“…This paper focuses on how machine learning (ML) algorithms and applications have been used to analyze disease severity and mortality prediction in COVID-19 research. …”
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Transformed L1 Regularization for Sparse Photoacoustic Tomography by Xueyan Liu, Lu Wang, Xin Wang, Ziqi Hao, Xiaolong Hu

“…In this study, a transform L1 (TL1) regularization model was proposed to improve the quality of PAT images, which was evaluated by numerical simulation and tissue phantom experiments. …”
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DEEP LEARNING-BASED SUPER-RESOLUTION TECHNIQUES: A COMPARATIVE ANALYSIS WITH RECENT INSIGHTS by Renuka Sambhaji Sindge, Maitreyee Dutta, Jagriti Saini

“…Current researchers have employed machine-learning techniques, neural networks, and Deep Learning based methods to enhance the quality of LR images obtained from different field applications. …”
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Machine Learning Approaches for Software Defect Prediction by Hijab Zehra Zaidi, Ubaid Ullah, Muddassira Arshad, Hanan Aljuaid, Muhammad Arslan Rauf, Nadeem Sarwar, Rimsha Sajid

“…This paper analyses existing research about machine learning approaches in software defect prediction as a key element for improving software reliability and quality. The paper reviews the use of machine learning algorithms in software defect prediction framework’s bug prediction while assessing their performance across multiple environments. …”
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Framelet transform based edge detection for straight line detection from remote sensing images by Vidhya Rangasamy, Sulochana Subramaniam

“…Rosenfeld evaluation metric is used to measure the quality of the edge detection methods, which shows the framelet based edge detection produce sound results than other methods. …”
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Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction by Xuru Li, Kun Wang, Yan Chang, Yaqin Wu, Jing Liu

“…The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. …”
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A Swarm-Based Multi-Objective Framework for Lightweight and Real-Time IoT Intrusion Detection by Hessah A. Alsalamah, Walaa N. Ismail

“…Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. …”
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Universal Modeling for Non-Destructive Testing of Soluble Solids Content in Multi-Variety Blueberries Based on Hyperspectral Imaging Technology by Lingqi Meng, Guoliang Chen, Dayang Liu, Ning Tian

“…The soluble solids content (SSC) of blueberry is a key index for evaluating its quality. In view of the demand for rapid non-destructive testing of blueberry SSC and the shortcomings of the existing single-variety testing models in cross-variety applications, a universal prediction model construction method based on hyperspectral imaging (HSI) technology is proposed in this study. …”
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Advanced control parameter optimization in DC motors and liquid level systems by Serdar Ekinci, Davut Izci, Mohammad H. Almomani, Kashif Saleem, Raed Abu Zitar, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu, Laith Abualigah

“…Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Hybrid approaches combining geostatistics with ML algorithms (e.g., RF, Boost, SVM, ANN) demonstrate promise in addressing spatial uncertainty, while RS data enhances covariate enrichment and near-real-time applications. …”
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Neuropathic pain: proposal of a mechanism-based treatment by Laura Demartini, Cesare Bonezzi

“…Some authors have suggested that the poor results in the treatment of neuropathic pain may be related to the different mechanisms present in each patient and have tried to correlate them with clinical characteristics in order to evaluate possible targeted treatments. This approach has been used in some studies evaluating the response to specific pharmacotherapies in clusters of patients, with encouraging results but still limited applicability to clinical practice. …”
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A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences by Christopher Schnur, Payman Goodarzi, Yannick Robin, Julian Schauer, Andreas Schütze

“…In practical machine learning (ML) applications, covariate shifts and dependencies can significantly impact model robustness and prediction quality, leading to performance degradation under distribution shifts. …”
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Adaptation of mathematical educational content in e-learning resources by Yuliya V. Vainshtein, Victoria A. Shershneva, Roman V. Esin, Tatyana V. Zykova

“…An upcoming trend in improvement the quality of studying mathematical disciplines is the development and application of adaptive electronic educational resources. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. …”
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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

“…Abstract The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a hot topic of interest in both industry and academia. …”
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