Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

“…Recent studies have delved into the application of Machine Learning (ML) in the software testing domain, where the high cost and time consumption associated with data annotation have prompted the use of unsupervised algorithms. …”
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SCHEDULING PROBLEMS OF STATIONARY OBJECTS WITH THE PROCESSOR IN ONE-DIMENSIONAL ZONE by N. A. Dunichkina, D. I. Kogan, Yu. S. Fedosenko

“…Correspondingly, the bicriteria problem with the mentioned evaluation criteria is fundamentally intractable, computational complexity of the schedule structure algorithm is exponential. …”
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Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning by anfal A. Fadhil, Asmaa’ H. AL_Bayati, Ibrahim Ahmed Saleh

“…This goal is accomplished by calculating the parameters of software reliability growth models (SRGMs). The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). …”
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Active Inference-Driven Multi-Armed Bandits: Superior Performance through Dynamic Correlation Adjustments by Lin Xiaoqi

“…In recent years, Multi-Armed Bandit (MAB) algorithms have gained substantial attention due to their effectiveness in real-world applications, such as recommendation systems, autonomous systems, and dynamic resource allocation. …”
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Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era by Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li

“…Image preprocessing, which involves standardizing images for training or deployment of various AI algorithms, encompasses essential steps such as image quality evaluation, alignment, stacking, background extraction, gray-scale transformation, cropping, source detection, astrometry, and photometry. …”
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Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“…The research not only deepens the understanding of emotion classification algorithms but also provides valuable perspectives for their application in crucial areas such as mental health and personalized medical assistance. …”
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A BERT-Based Classification Model: The Case of Russian Fairy Tales by Валерий Дмитриевич Соловьев, Марина Ивановна Солнышкина, Andrey Ten, Николай Аркадиевич Прокопьев

“…Method: We pre-train BERT using a collection of three classes of documents and fine-tune it for implementation of a specific application task. Focused on the mechanism of tokenization and embeddings design as the key components in BERT’s text processing, the research also evaluates the standard benchmarks used to train classification models and analyze complex cases, possible errors and improvement algorithms thus raising the classification models accuracy. …”
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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

“…Future research can further explore the application of quantum-inspired optimization algorithms in other energy systems and optimize the algorithm’s parameters to improve its performance.…”
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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

“…The inclusion of the DBI in the k-means++ clustering algorithm facilitated an objective evaluation of cluster quality, ensuring that the selected number of clusters accurately represents the underlying data patterns. …”
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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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Lateral shear interferometry for shape accuracy measurements of 3D-printed micro-optics by Zhao Yanqiu, Wang Lunwei, Siegle Leander, Giessen Harald

“…For micro-lens production and quality control, performance assessment is invariably necessary to assure diffraction limited imaging in the different optical applications. …”
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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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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis by Tengfei Zhou, Tengfei Zhou, Xinming Tian, Wei Wang, Zhe Chu

“…All models showed a high risk of bias according to the PROBAST evaluation, mainly due to the lack of external validation and methodological shortcomings.ConclusionCurrent SAE prediction models demonstrate moderate discriminatory ability, but their methodological quality remains poor, and they are not yet suitable for routine clinical application. …”
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Study of methods for extracting contours of objects on raster images of amber samples by Yu.O. Podchashynskyi, A.V. Ryzhuk

“…The use of these operators in TVS provides the most complete and reliable information for building a three-dimensional model, classifying and evaluating the quality of amber samples. The open source computer vision library was taken as the algorithmic and software basis of the conducted research. …”
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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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A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China by Xiaofei Shang, Huawei Zhao, Shengxiang Long, Taizhong Duan

“…Moreover, the workflow involves multiple model integration processes, which is the key to model quality. The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. …”
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Detection of soluble solid content in table grapes during storage based on visible-near-infrared spectroscopy by Yuan Su, Ke He, Wenzheng Liu, Jin Li, Keying Hou, Shengyun Lv, Xiaowei He

“…The soluble solid content (SSC) in grapes significantly influences their flavour and plays an integral role in evaluation of the quality and consumer acceptance. This study employed visible near-infrared (Vis-NIR) spectroscopy to rapidly quantify SSC in table grapes during storage. …”
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Literature review for the topic of automation of scheduling classes and exams in higher education institutions by Wadhah M Al-Gabri

“…Schedule is considered as part of the educational process support system, and, in its turn, has features of the system combining such objects as students, teachers, disciplines, and classrooms. The application of the system analysis methods allows allocating essential features of the implemented systems of timetabling, classifying and evaluating them. …”
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Nonreference Medical Image Edge Map Measure by Karen Panetta, Chen Gao, Sos Agaian, Shahan Nercessian

“…It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. …”
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