Geospatial brain-inspired navigation: a neurocognitive approach for autonomous systems in complex environments by Donghui Han, Tong Qin, Tianyu Yang, Hua Liao, Qiaosong Hei, Fangwen Yu, Bailu Si, Weihua Dong

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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

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Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems by Ka-Kyung Kim, Joon-Seok Kim, Ieck-Chae Euom

“…Anomaly detection systems, initially implemented for detecting device faults or failures, have increasingly become the focus of research aimed at identifying attack patterns as cyberattack techniques become more sophisticated and intelligent. …”
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Prediksi Stok dan Pengaturan Tata Letak Barang Menggunakan Kombinasi Algoritma Triple Exponential Smoothing dan FP-Growth by Kristoko Dwi Hartomo, Sri Yulianto Prasetyo, Rahmat Abadi Suharjo

“…Data processing in this research is called data mining using FP-Growth and Triple Exponential Smoothing algorithms. …”
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“DTH 1.0”: TOWARDS AN ARTIFICIAL INTELLIGENCE DECISION SUPPORT SYSTEM FOR GEOGRAPHICAL ANALYSIS OF HEALTH DATA by Dimitris KAVROUDAKIS, Phaedon C. KYRIAKIDIS

“…Decision trees methods belong to knowledge discovery methodologies and use computational algorithms for the extraction of patterns from data. …”
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A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks by Yuhong Wang

“…In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. …”
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Artificial Intelligence Implementation in Library Information Systems: Current Trends and Future Studies by Albertoes Pramoekti Narendra, Christine Dewi, Lanyta Setyani Gunawan, Ambrosius Sindu Ardi

“…This study examines recent advancements in AI integration within LIS over the past five years, focusing on the methodologies, tools, and algorithms used. Through a systematic literature review, the study identifies key trends, challenges, and examples of AI applications in LIS, providing insights and recommendations for future research and implementation.…”
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“DTH 1.0”: TOWARDS AN ARTIFICIAL INTELLIGENCE DECISION SUPPORT SYSTEM FOR GEOGRAPHICAL ANALYSIS OF HEALTH DATA by Dimitris KAVROUDAKIS, Phaedon C. KYRIAKIDIS

“…Decision trees methods belong to knowledge discovery methodologies and use computational algorithms for the extraction of patterns from data. …”
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Compression Sensitivity of the Burrows–Wheeler Transform and Its Bijective Variant by Hyodam Jeon, Dominik Köppl

“…The Burrows–Wheeler Transform (BWT) is a widely used reversible data compression method, forming the foundation of various compression algorithms and indexing structures. Prior research has analyzed the sensitivity of compression methods and repetitiveness measures to single-character edits, particularly in binary alphabets. …”
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Utilization of Data Mining Techniques in SIMPUS towards Smart Library at IAIN Kendari by Samrin, Badarwan, Raehang, Obaid Moh. Yahya, Syahrul

“…This study aims to analyze the application of data mining techniques in SIMPUS at IAIN Kendari, in order to improve the efficiency of library management and provide smarter services to users. By using algorithms such as association rules, clustering, and classification, historical data on borrowing and collection management can be processed to produce valuable insights, such as user behavior patterns, book need predictions, and collection recommendations. …”
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Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches by Yixin Yang, Kexuan Liu, Yunhui Gao, Chen Wang, Liangcai Cao

“…The ILT leverages optimization algorithms to generate mask patterns, outperforming traditional optical proximity correction methods. …”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

“…It also proposes the Solar FaultNet‐a novel deep learning‐based approach that significantly improves fault detection performance in solar PV systems and integrates the model with state‐of‐the‐art ML techniques like CNN and LSTM to capture inherent complex patterns and interdependencies of fault data. Besides, the proposed model outperforms conventional machine‐learning algorithms and state‐of‐the‐art deep‐ learning models for better performance by yielding higher accuracy, precision, recall, F1‐score, and low error rate on various fault types such as PV array faults, inverter faults, grid synchronization faults, and environmental faults. …”
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The effects of limb position and grasped load on hand gesture classification using electromyography, force myography, and their combination. by Peyton R Young, Kihun Hong, Eden J Winslow, Giancarlo K Sagastume, Marcus A Battraw, Richard S Whittle, Jonathon S Schofield

“…A prominent control technique employs surface electromyography (EMG) and pattern recognition algorithms to identify specific patterns in muscle electrical activity and translate these to device commands. …”
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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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Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques by Niloofar Alizadeh, Yasser Maghsoudi, Tayebe Managhebi, Saeed Azadnejad

“…The experimental results from three areas with a history of collapses demonstrate that the SBAS algorithm outperforms PSI in uncovering behavior patterns indicative of collapse and accurately pinpointing collapse points near real collapse sites. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…Integrating spatial and contextual factors with BER establishes a new standard for predictive accuracy in urban energy research.…”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

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Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features by Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan

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FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events by Yannek Nowatzky, Francesco Friedrich Russo, Jan Lisec, Alexander Kister, Knut Reinert, Thilo Muth, Philipp Benner

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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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