Deep Learning in Visual Computing and Signal Processing by Danfeng Xie, Lei Zhang, Li Bai

“…In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. …”
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Fault detection in beam structure using adaptive immune based approach by Sasmita Sahu, Shakti P. Jena

“…Recently with the application of machine learning approaches and the soft computing, the damage can be detected easily. …”
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IoT enabled health monitoring system using rider optimization algorithm and joint process estimation by J. Prabin Jose, G. Jaffino, Mohammed Al Awadh, Koppula Srinivas Rao, Yan Yafang, Krishna Moorthy Sivalingam

“…Despite advances in health monitoring, existing methods often struggle with achieving high accuracy, sensitivity, and specificity in real-time detection. This work addresses the need for improved performance in health monitoring systems in real time sensor data. …”
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Automated fluid monitoring to optimize the follow-up of neovascular age-related macular degeneration patients in the Brazilian population by Virginia Mares, Gregor S. Reiter, Aniel Feitosa, Markus Gumpinger, Hrvoje Bogunovic, Ursula Schmidt-Erfurth, Marcio B. Nehemy

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Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals by Yinian Liang, Yan Wang, Fangjiong Chen, Hua Yu, Fei Ji, Yankun Chen

“…The proposed method automatically removes about 75% of clutter data from 1581.3MB of data in audio files and extracts 75.75 MB of the features detected by our algorithm. …”
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Detection of Hijaiyah Letters Handwritten in Early Childhood Using Yolov8 by Hidayatul Mustagfiroh, Aviv Yuniar Rahman, Rangga Pahlevi Putra

“…This study investigates the effectiveness of the YOLOv8 (You Only Look Once version 8) algorithm in detecting handwritten Hijaiyah letters among early childhood learners. …”
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Overview of smart sensors based on machine learning by Wang Shuaida, Lin Guanying, Wang Nuansheng, Li Yang, Yin Meihua

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EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

“…This research introduces EffiTriDimNet (ETDN), a multi-task convolutional neural network that combines one-dimensional pipeline defect leakage detection data into a unified feature map while simultaneously measuring the three-dimensional characteristics of the defects. …”
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Impact of AI and Machine Learning on Financial Services by Patil Smita, Mailcontractor Rahul

“…Advanced data analytics, automation, and predictive tools powered by AI (Artificial Intelligence) and Machine Learning (ML) are reshaping the global financial services sector, promoting innovation and enhancing efficiency across the sector. …”
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Detecting Anomalies in Sequences of Short Text Using Iterative Language Models by Cynthia Freeman, Ian Beaver, Abdullah Mueen

“…We introduce a technique for detecting anomalies in sequences of short textual data by adaptively and iteratively learning low perplexity language models. …”
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Entry point of machine learning in axial spondyloarthritis by Tiantian Sun, Qing Yu, Yuening Chen, Hongxiao Liu, Xinning Qu

“…By integrating large-scale data with diverse algorithms and using multidimensional data, such as patient medical records, laboratory examinations, radiological data, drug usage and molecular biology information, ML can be modelled based on real-world clinical issues. …”
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Development of Cyber Security Platform for Experiential Learning by Abhishek Vaish, Ravindra Kumar, Samo Bobek, Simona Sternad

“…The present research proposes a comprehensive web-based platform that can be used to communicate, collaborate and practice various use cases in the domain of network intrusion detection tools using machine learning algorithms and to evaluate user experience. …”
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Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction by Omar Abdullatif Jassim, Mohammed Jawad Abed, Zenah Hadi Saied Saied

“… Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. …”
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Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector by Germán Rodríguez-Galán, Eduardo Benavides-Astudillo, Daniel Nuñez-Agurto, Pablo Puente-Ponce, Sonia Cárdenas-Delgado, Mauricio Loachamín-Valencia

“…Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. …”
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A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models by Younis Bashar Younis, Fadwa Al Azzo

“…If untreated, this can cause blindness. To detect the classes of retinal detachment in various images, this paper introduces a novel method employing deep learning techniques involving the YOLOv8 algorithm. …”
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On-Chip Age Estimation Using Machine Learning by Turki Alnuayri, Saqib Khursheed, Daniele Rossi

“…This is carried out using Cadence simulations, implementing 13- and 51-stage ring oscillators (ROs) using a 22-nm CMOS technology and aging model provided by GlobalFoundries (GF). The machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, <inline-formula> <tex-math notation="LaTeX">$\tau _{dv}$ </tex-math></inline-formula>, f, aging time and inter-die and intra-die process variation (PV). …”
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Hybrid attention transformer integrated YOLOV8 for fruit ripeness detection by Jianyin Tang, Zhenglin Yu, ChangShun Shao

“…To solve these problems, this paper proposes an innovative fruit recognition model, HAT-YOLOV8, aiming to combine the advantages of Hybrid Attention Transformer (HAT) and YOLOV8 deep learning algorithm. This model improves the ability to capture complex dependencies by integrating the Shuffle Attention (SA) module while maintaining low computational complexity. …”
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Enhancing CNN-based network intrusion detection through hyperparameter optimization by Antanios Kaissar, Ali Bou Nassif, Bassel Soudan, MohammadNoor Injadat

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Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification by Loris Nanni, Matteo De Gobbi, Roger De Almeida Matos Junior, Daniel Fusaro

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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Six classification models—five machine learning algorithms (Multilayer Perceptron, Random Forest, Decision Tree, K-Nearest Neighbors, and Adaptive Boosting) and one deep learning model (Long Short-Term Memory, LSTM)—were evaluated for classifying four levels of misalignment severity. …”
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