UAV imaging for spectral characterization of Coffee Leaf Miner (<i>Leucoptera coffeella</i>) infestation in the Cerrado Mineiro region by V. S. W. Orlando, M. D. L. B. T. Galo, G. D. Martins, A. M. Lingua, V. Andaló

“…Conversely, the group of least-infested plants obtained the best result with the Random Forest algorithm, achieving an RMSE of 32.47%. These promising results indicated that CLM can be detected using aerial multispectral imaging data.…”
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Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment by Mohammad Saleem, Abigail E. Watson, Aisha Anwaar, Ahmad Omar Jasser, Nabiha Yusuf

“…Deep learning algorithms demonstrated high accuracy in tumor microenvironment analysis, including tumor region identification and lymphocyte detection. …”
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Harnessing artificial intelligence role in oral cancer diagnosis and prediction: A comprehensive exploration by Archana Behera, N. Aravindha Babu, Remya Rajan Renuka, Mukesh Kumar Dharmalingam Jothinathan

“…Deep learning techniques, particularly neural networks are an important aspect of oral cancer early detection, and they drastically minimize human error. …”
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Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization by Stefano Fornasaro, Aleksander Astel, Pierluigi Barbieri, Sabina Licen

“…To address this challenge, multivariate data analysis is widely used with an increasing interest in neural networks and deep learning networks along with well-established chemometrics methods and receptor models. …”
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Using Digital Intelligence in Promoting Mechanism for Medical Care Insurance for Rare Diseases: Concepts and Applications by YANG Xinyu, ZHANG Yuzheng, WANG Shengfeng, GUO Wudong

“…By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. …”
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Color correction methods for underwater image enhancement: A systematic literature review. by Yong Lin Lai, Tan Fong Ang, Uzair Aslam Bhatti, Chin Soon Ku, Qi Han, Lip Yee Por

“…However, challenges persist across all categories, including algorithmic limitations, data dependency, computational complexity, and performance variability across diverse underwater environments. …”
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Semi-automated annotation for video-based beef cattle behavior recognition by Zhiyong Cao, Chen Li, Xiujuan Yang, Shuai Zhang, Ling Luo, Hao Wang, Hongbo Zhao

“…Data annotation was automated using the YOLOv8 target detection model and the ByteTrack multi-target tracking algorithm to annotate each beef cow’s coordinates and identity codes. …”
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Modelling trial-by-trial changes in the mismatch negativity. by Falk Lieder, Jean Daunizeau, Marta I Garrido, Karl J Friston, Klaas E Stephan

“…Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors.…”
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Counting Cattle in Argentina with AI and Satellite Images by S. Torrusio, J. Gonzalez, J. Gonzalez, M. Errázquin, A. Cereceda, M. J. Cassano, N. Morales, R. Saucedo

“…The objective was the development of a Deep Learning algorithm for the detection of livestock, cows in particular, in very high-resolution satellite images (provided by the Argentine Space Agency (CONAE)) and its subsequent counting. …”
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A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring by Mangali Sravanthi, Sravan Kumar Gunturi, Mangali Chinna Chinnaiah, Siew-Kei Lam, G. Divya Vani, Mudasar Basha, Narambhatla Janardhan, Dodde Hari Krishna, Sanjay Dubey

“…The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. …”
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IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception by Joseph Bao, Murat Kantaciourglu, Yevgeniy Vorobeychik, Charles Kamhoua

“…A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator. We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices. …”
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Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke by Danis Rifa Nurqotimah, Ahsanun Naseh Khudori, Risqy Siwi Pradini

“…The researchers implemented a method to classify stroke with the Support Vector Machine (SVM) algorithm. The SVM is a learning method used in medical diagnosis for classification, the researchers processed data sets using the Orange tool. …”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

“…When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. …”
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The role of artificial intelligence in managing hospitalized patients with mental illness: a scoping review by Md Samiun, Moustaq Karim Khan Rony, Sumaiya Yeasmin, Mia Md Tofayel Gonee Manik, Anupom Debnath, Mustakim Bin Aziz, Afia Fairooz Tasnim, Sadia Islam Nilima

“…Despite these advancements, challenges were noted, such as algorithmic biases due to non-diverse training data, privacy and security risks for sensitive patient information, and clinician resistance stemming from concerns about autonomy and ethical implications of AI-supported clinical decisions. …”
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Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling by Pimolkan Piankitrungreang, Kantawatchr Chaiprabha, Worathris Chungsangsatiporn, Chanat Ratanasumawong, Peemdej Chancharoen, Ratchatin Chancharoen

“…By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. …”
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Comparative Analysis of Random Forest and Logistic Regression Methods in Predicting Leukemia Blood Cancer Using Microscopic Blood Cell Images by Jepri Banjarnahor, Galuh Wira Relungwangi

“…While both algorithms have potential as clinical decision support, future research can test deep learning techniques and larger datasets to improve the accuracy and reliability of the model.…”
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Current application of artificial intelligence in laparoscopic cholecystectomy by S.M. Chooklin, S.S. Chuklin

“…The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. …”
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Aircraft Sensor Fault Diagnosis Based on GraphSage and Attention Mechanism by Zhongzhi Li, Jinyi Ma, Rong Fan, Yunmei Zhao, Jianliang Ai, Yiqun Dong

“…These signals are then transformed into graph data with a specific topology reflecting the overall sensor status of the aircraft using K-nearest neighbor and Radius classification algorithms. …”
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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

“…Firstly, based on the generalized S-transform, the fault characteristic information in the frequency domain is obtained to construct the input data for GRNN. Secondly, the chaos quantum particle swarm optimization (CQPSO) algorithm is used to optimize the parameters of the generalized regression neural network, form an ideal network model based on the principle of the lowest fitness function, and better learn the fault characteristics of the ultra-high voltage DC transmission line. …”
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Bayesian Regularization Backpropagation Neural Network for Glass Beams in Lateral–Torsional Buckling by Saddam Hussain, Chiara Bedon, Gaurav Kumar, Zaheer Ahmed

“…The potential and accuracy of artificial neural networks (ANN), based on ML methods, are addressed based on validation toward literature data. In particular, to detect the best-performing data-driven ML model, the load-bearing capacity of LG beams (i.e., Fcr and dLT) is set as output response, while geometric properties (length, width, thickness) and material features (for glass and interlayers) are used as input variables. …”
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