Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale by Harsh Vazirani, Xiaofeng Wu, Anurag Srivastava, Debajyoti Dhar, Divyansh Pathak

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Efficient tree mapping through deep distance transform (DDT) learning by Jan Schindler, Ziyi Sun, Bing Xue, Mengjie Zhang

“…The increase in available remote sensing data and advances in automated object detection makes it feasible to map trees over large areas in unprecedented detail. …”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…Our scRNA-seq data confirmed the signature genes identified by machine learning algorithms: Vimentin (VIM) and chloride intracellular channel 1 (CLIC1) were upregulated in monocytes, while integrin subunit beta 2 (ITGB2) was elevated in NK cells of KD. qRT-PCR results also validated the bioinformatic analysis. …”
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Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data by Netra R. Regmi, Nina D.S. Webb, Jacob I. Walter, Joonghyeok Heo, Nicholas W. Hayman

“…Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. …”
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Explainable AI supported hybrid deep learnig method for layer 2 intrusion detection by Ilhan Firat Kilincer

“…The study, CL2-IDS dataset and hybrid DL model, combinations of CNN and Bi-LSTM algorithms, facilitates the intrusion detection and exemplifies how DL models and XAI techniques can be used to support IDS systems.…”
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Comparative study of multiple algorithms classification for land use and land cover change detection and its impact on local climate of Mardan District, Pakistan by Farnaz, Narissara Nuthammachot, Muhammad Zeeshan Ali

“…This study aims to evaluate the performance of various machine learning classifiers, including Support Vector Machine (SVM), Random Forest Algorithm (RFA), K-Nearest Neighbor (KNN), and Maximum Likelihood (MLH), in detecting Land Use and Land Cover [LULC] changes in Mardan District, Pakistan, from 2015 to 2023. …”
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Comparative study of IoT- and AI-based computing disease detection approaches by Wasiur Rhmann, Jalaluddin Khan, Ghufran Ahmad Khan, Zubair Ashraf, Babita Pandey, Mohammad Ahmar Khan, Ashraf Ali, Amaan Ishrat, Abdulrahman Abdullah Alghamdi, Bilal Ahamad, Mohammad Khaja Shaik

“…However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data by F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell

“…In this context, we present an unsupervised machine learning approach to identify candidates for spectroscopic follow-up using data from the Southern Photometric Local Universe Survey (S-PLUS). …”
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Automatic Detection of Urban Trees from LiDAR Data Using DBSCAN and Mean Shift Clustering Methods in Fatih, Istanbul by Z. Cetin, N. Yastikli

“…The remaining high vegetation points were subsequently segmented using the machine learning-based Mean Shift clustering algorithm to obtain individual tree crowns. …”
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Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder by Nvgui LIN, Lanxiu HONG, Daoshan HUANG, Yang YI, Zhixuan LIU, Qifeng XU

“…Firstly, the data of normal electricity users are employed as training samples, and the effective features of the data are automatically extracted by AE; and then the data is reconstructed to calculate the detection threshold. …”
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Machine learning for Internet of Things (IoT) device identification: a comparative study by Hamid Tahaei, Anqi Liu, Hamid Forooghikian, Mehdi Gheisari, Faiz Zaki, Nor Badrul Anuar, Zhaoxi Fang, Longjun Huang

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Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework by Arnd Pettirsch, Alvaro Garcia-Hernandez

“…Thermal imaging overcomes these issues, enabling reliable detection across all conditions without collecting personal data. …”
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Fire and Smoke Detection Based on Improved YOLOV11 by Zhipeng Xue, Lingyun Kong, Haiyang Wu, Jiale Chen

“…However, complex backgrounds, large environmental changes, and data requirements pose great challenges to high-precision outdoor smoke detection. …”
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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

“…Future directions include integrating multi-omics data, developing explainable AI frameworks, and addressing ethical concerns, such as data privacy and algorithmic bias. …”
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Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

“…This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. …”
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A novel machine learning-based approach to thermal integrity profiling of concrete pile foundations by Javier Sánchez Fernández, Agustín Ruiz López, David M.G. Taborda

“…This work demonstrates the applicability and robustness of machine learning algorithms in enhancing nondestructive TIP testing of concrete foundations, thereby improving the safety and efficiency of civil engineering projects.…”
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UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning by Yaxi Liu, Xulong Li, Boxin He, Meng Gu, Wei Huangfu

“…Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. …”
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An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

“…Abstract With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

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