BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers. by Tanzir Hossain, Ar-Rafi Islam, Md Humaion Kabir Mehedi, Annajiat Alim Rasel, M Abdullah-Al-Wadud, Jia Uddin

“…The use of traditional machine learning algorithms, deep learning architectures, and hybrid models, including novel stacking classifiers, was a part of our experiment. …”
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Analysis on Acoustic Disturbance Signals Expected During Partial Discharge Measurements in Power Transformers by Michał KUNICKI

“…As a result, an energy patterns analysis based on the wavelet decomposition is found as the most reliable tool for identification of PD signals. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…This transformation improves feature extraction by capturing more discriminative patterns, leading to superior performance compared to conventional methods. …”
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Efficient Real-Time Pathfinding for Visually Impaired Individuals by Tadeh Ghahremanians, Hossein Mahvash Mohammadi

“…Traditional methods usually focus on the detection of specific patterns or objects, requiring custom algorithms for each object of interest. …”
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Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Due to the influence of multiple factors, the cooling load in manufacturing plants exhibits complex characteristics, including multi-peak patterns, periodic fluctuations, and short-term disturbances during meal periods. …”
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Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

“…Against this backdrop, research on Intrusion Detection Systems (IDSs) leveraging machine learning in IoT environments has been actively conducted. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. …”
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THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DEVELOPMENT OF PREDICTIVE COMPETENCE IN MODERN SPECIALISTS by Viacheslav Osadchyi, Maksym Pavlenko, Liliia Pavlenko, Oleksii Sysoiev, Vladyslav Kruglyk

“…At the same time, diverse adaptation patterns to AI use necessitate a rethinking of the role of human judgement and raise concerns about technological dependency, algorithmic bias, and unequal access to innovation. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

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Application of artificial intelligence technologies in HR management by S. V. Okladnikova, A. S. Pankrashov

“…Within the framework of the conducted research, the historical patterns that led the field of personnel management to transformation and which made the use of AI technologies relevant in this area were considered. …”
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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis by Esther Ugo Alum

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South-Oriented PV Trackers: A Solution for Tree Plantations? by Jean-Baptiste Pasquier, Jules Chéron, Farida Amichi

“…We propose innovative tracking algorithms to mitigate shading effects during different seasons, preserving both electrical output and agricultural productivity. …”
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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

“…The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. …”
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Faster? Softer? Or More Formal? A Study on the Methods of Enterprises’ Crisis Response on Social Media by Yongtian Yu, Weiming Ye, Kaihang Zhang

“…Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. …”
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Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine by Karishma Sahoo, Prakash Lingasamy, Masuma Khatun, Sajitha Lulu Sudhakaran, Andres Salumets, Vino Sundararajan, Vijayachitra Modhukur

“…Aberrant methylation patterns enable early cancer detection and therapeutic stratification; however, their complex patterns necessitates advanced analytical tools. …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

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Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation by Lalitha Krishnasamy, Siva C, Rajesh Kumar Dhanaraj, Mahmoud Ahmad Al-Khasawneh, Taher Al-Shehari, Nasser A. Alsadhan, Shitharth Selvarajan

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QSAR, docking and pharmacokinetic studies of 2,4-diphenyl indenol [1,2-B] pyridinol derivatives targeting breast cancer receptors by Auwal Isa, Adamu Uzairu, umar Umar, Muhammad Tukur Ibrahim, Abdullahi Umar

“…A robust quantitative structure-activity relationship (QSAR) model, developed using genetic algorithms and multilinear regression analysis, predicts chemical activity (pGI50) against breast cancer receptors. …”
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Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…Machine learning is a technique that uses algorithms to find useful patterns in data. Based on previous NST data, it can be designed as a school readiness prediction model that will facilitate teachers and parents in knowing the readiness of children to enter elementary school. …”
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Syllable-, Bigram-, and Morphology-Driven Pseudoword Generation in Greek by Kosmas Kosmidis, Vassiliki Apostolouda, Anthi Revithiadou

“…Pseudowords are essential in (psycho)linguistic research, offering a way to study language without meaning interference. …”
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