Research on load labeling method for big data in novel power system by ZHANG Ximing, XU Huan, YANG Qiuyong

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Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm by Fatemeh Hataminia, Anahita Azinfar

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A hybrid model of ARIMA and MLP with a Grasshopper optimization algorithm for time series forecasting of water quality by Jie Su, Ziyu Lin, Fengwei Xu, Gholamreza Fathi, Khalid A. Alnowibet

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Clustering Countries of the World Based on the Trend of the COVID-19 Incidence: An application of shape-based k-means algorithm by Arefeh Dehghani Tafti, Yunes Jahani, Sara Jambarsang, Abbas Bahrampour

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Identification of Major Bleeding Events in Postoperative Patients With Malignant Tumors in Chinese Electronic Medical Records: Algorithm Development and Validation by Hui Li, Haiyang Yao, Yuxiang Gao, Hang Luo, Changbin Cai, Zhou Zhou, Muhan Yuan, Wei Jiang

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Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm by Javad Shahlaei Bagheri

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Generation and Classification of Novel Segmented Control Charts (SCC) Based on Hu’s Invariant Moments and the K-Means Algorithm by Roberto Baeza-Serrato

“…Finally, a grouping is made to classify the samples in clear and simple language as excellent, good, or regular using the k-means algorithm. The results visually display the total pattern behavior of the samples and their interpretation when they are classified intelligently. …”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The results indicate a promising predictive capacity of both models, showcasing their effectiveness in capturing intricate patterns within the dataset. In the case of ANN, the Root Mean Squared Error (RMSE) is reported at 3.806, demonstrating the model's ability to approximate the true energy consumption values. …”
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Optimizing the Design of TES Tanks for Thermal Energy Storage Applications Through an Integrated Biomimetic-Genetic Algorithm Approach by Nadiya Mehraj, Carles Mateu, Gabriel Zsembinszki, Luisa F. Cabeza

“…Inspired by natural thermal distribution patterns found in vascular networks, the AI-driven methodology explored 13 geometric parameters, focusing on branching structures and spatial distribution, and resulted in computationally generated designs with a 29% increase in heat transfer surface area while maintaining manufacturability constraints within a fixed tank diameter of 150 mm and height of 155 mm. …”
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An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior by Hodjat (Hojatollah) Hamidi, Bahare Haghi

“…The results demonstrate that the proposed approach, particularly when combined with spectral clustering and optimally weighted features, significantly surpassing the performance of traditional static segmentation techniques. This research not only provides a more accurate framework for uncovering hidden patterns in customer behavior but also delivers actionable insights for targeted marketing and personalized customer strategies.…”
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Diagrammatic prevalence index: a new algorithm to evaluate pine wilt disease prevalence at the sub-compartment scale by Yanjun Zhang, Yanjun Zhang, Siyuan Zheng, Jinjuan Bai, Jiafu Hu, Yongjun Wang, Yongjun Wang

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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…This research presents the development of a novel object detection algorithm designed to identify humans in natural outdoor environments using minimal computational resources. …”
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The Fast-Greedy algorithm reveals hourly fluctuations and associated risks of shark communities in a South Pacific city by Ibtissam Chafia, Jihad Zahir, Christophe Lett, Tarik Agouti, Hajar Mousannif, Laurent Vigliola

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Subject based feature selection for hybrid brain computer interface using genetic algorithm and support vector machine by Nida Mateen, Mehreen Naeem, Muhammad Jawad Khan, Talha Yousaf, Ahsan Ali, Wael A. Altabey, Mohammad Noori, Sallam A Kouritem

“…Feature selection is of great importance in hybrid BCI systems to reduce dimensionality, improve interpretability, and optimize the classification performance. In this research, we present a subject-specific feature selection technique based on the modified genetic algorithm (GA) and support vector machine (SVM) classifier. …”
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Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis by Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

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Evaluation of K-Means Algorithm for Faulted Landforms Extraction and Offset Measurement With an Example From the Eastern Kunlun Fault by Shengchao Zhou, Zhou Lin

“…Accurate offset measurement is crucial for recovering the size of past earthquakes and understanding the recurrence patterns of strike-slip faults. Traditional methods, which rely on manual delineation of displaced geomorphic markers from satellite images, often introduce significant uncertainties. …”
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Modeling uncertainties associated with decision-making algorithms based on similarity measures of possibility belief interval-valued fuzzy hypersoft setting by Mamika Ujianita Romdhini, Faisal Al-Sharqi, R.H. Al-Obaidi, Zahari Md. Rodzi

“…Furthermore, we introduce a similarity measure to facilitate pattern recognition in PIVFHS-sets, aiding users in recruitment processes. …”
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Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine by Hojin Moon, Lauren Tran, Andrew Lee, Taeksoo Kwon, Minho Lee

“…Methods: To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. …”
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Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasksMendeley Data by Ali Nirabi, Faridah Abd Rahman, Mohamed Hadi Habaebi, Khairul Azami Sidek, Siti Yusoff

“…The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress.The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. …”
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Enhancing Creativity and Validation in Explanatory Deep Learning-Based Symbolic Music Generation: A Hybrid Approach With LSTM and Genetic Algorithms by Ahmad Zainul Fanani, Arry Maulana Syarif, Ika Novita Dewi, Abdul Karim

“…These findings support the effectiveness of the proposed model in fostering creative exploration of new tonal patterns aligned with the target genre.…”
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