Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Ramiro Saltos, Ignacio Carvajal, Fernando Crespo, Richard Weber

“…However, unlike static clustering, where a plethora of validation indices exist to assess the solution’s quality, evaluating the effectiveness of dynamic clustering algorithms remains a challenge. …”
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Multimodal Classification Technique for Fall Detection of Alzheimer’s Patients by Integration of a Novel Piezoelectric Crystal Accelerometer and Aluminum Gyroscope with Vision Data... by V. Mohan Gowda, Megha P. Arakeri, Vasireddy Raghu Ram Prasad

“…Smart expert systems line up with various applications to enhance the quality of lifestyle of human beings, such as major applications for smart health monitoring systems. …”
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Dataset of apples for grading by sweetness, ripeness and varietyMendeley Data by Shilpa Gaikwad, Sonali Kothari, Ignisha Rajathi G

“…The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems.The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…Data augmentation can help address these limitations, but its use in biomechanical time-series data remains insufficiently evaluated.<h4>Objective</h4>This scoping review on data augmentation for biomechanical time-series data focuses on examining current techniques, evaluating their effectiveness, and offering recommendations for their application.…”
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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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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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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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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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Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review by Yinglin Guo, Ning Li, Chonghui Song, Juan Yang, Yinglan Quan, Hongjiang Zhang

“…Radiomics, an emerging field, focuses on extracting high-dimensional quantitative features from medical imaging data and utilizing them to construct predictive models for disease diagnosis, prognosis, and treatment evaluation. In recent years, the integration of artificial intelligence (AI) with radiomics has significantly enhanced the process of analyzing and extracting meaningful features from large and complex radiomic datasets through the application of machine learning (ML) and deep learning (DL) algorithms. …”
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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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An Efficient Biobjective Heuristic for Scheduling Workflows on Heterogeneous DVS-Enabled Processors by Pengji Zhou, Wei Zheng

“…Energy consumption has recently become a major concern to multiprocessor computing systems, of which the primary performance goal has traditionally been reducing execution time of applications. In the context of scheduling, there have been increasing research interests on algorithms using dynamic voltage scaling (DVS), which allows processors to operate at lower voltage supply levels at the expense of sacrificing processing speed, to acquire a satisfactory trade-off between quality of schedule and energy consumption. …”
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A Review of Developments and Metrology in Machine Learning and Deep Learning for Wearable IoT Devices by Minh Long Hoang

“…It also examines how algorithm selection influences model performance metrics and computational efficiency, which are critical for real-time and resource-constrained applications. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The findings demonstrate that deep learning algorithms offer more accurate and earlier detection of diseases, highlighting their potential for application in plant growing. …”
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Machine Learning Approaches to Code Similarity Measurement: A Systematic Review by Zixian Zhang, Takfarinas Saber

“…Following a rigorous systematic review protocol, we identified and analyzed 84 primary studies on a broad spectrum of dimensions covering application type, devised Machine Learning algorithms, used code representations, datasets, and performance metrics, as well as performance evaluations. …”
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Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review by Beyza Demirtaş Yılmaz

“…<b>Results:</b> Machine learning applications were classified as preoperative evaluation, speech perception, and speech understanding in noise and other studies. …”
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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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Ranking nodes in bipartite systems with a non-linear iterative map by Andrea Mazzolini, Michele Caselle, Matteo Osella

“…Abstract Ranking nodes in networks according to a defined measure of importance is an extensively studied task, with applications in ecology, economic trade networks, and social networks. …”
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Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization by Serdar Ekinci, Davut Izci, Veysel Gider, Laith Abualigah, Mohit Bajaj, Ievgen Zaitsev

“…To ensure optimal performance, the parameters of the RPIDD2 controller are optimized using metaheuristic algorithms, including the flood optimization algorithm (FLA), reptile search algorithm (RSA), particle swarm optimization (PSO) and differential evolution (DE). …”
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QoS-Aware Fault Detection in Wireless Sensor Networks by Alessandra De Paola, Giuseppe Lo Re, Fabrizio Milazzo, Marco Ortolani

“…The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.…”
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Reliable Graph Routing in Industrial Wireless Sensor Networks by Jing Zhao, Yajuan Qin, Dong Yang, Junqi Duan

“…Our design is evaluated using simulation where we show that our algorithm could achieve a balance between routing reliability and overhead.…”
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