Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals by Faisal Saleem, Zahoor Ahmad, Jong-Myon Kim

“…This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. …”
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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

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Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…It is shown that the proposed method has higher fault detection and classification accuracy compared to three traditional classification approaches, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and three state-of-the-art methods: 1) Stockwell transform +SVM, 2) Fast Fourier Transform + SVM, and 3) Hilbert-Huang transform of vibration data and power spectral density + Artificial Neural Network. …”
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Image enhancement for detection of underwater moulted crabs in greenhouse soft-shell crab farming using deep learning by Mohammad Affaiq Bin Aini, Siow Hoo Leong, Yueh Tiam Yong, Beng Yong Lee, Xiaomin Zhao, Sharifah Raina Manaf, Firdaus Abdullah, Heng Yen Khong

“…Image enhancement methods are proposed to address these challenges and improve the performance of crab detection using the YOLOv7 deep-learning algorithm. …”
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A systematic review of AI-enhanced techniques in credit card fraud detection by Ibrahim Y. Hafez, Ahmed Y. Hafez, Ahmed Saleh, Amr A. Abd El-Mageed, Amr A. Abohany

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Construction of a Real-Time Detection for Floating Plastics in a Stream Using Video Cameras and Deep Learning by Hankyu Lee, Seohyun Byeon, Jin Hwi Kim, Jae-Ki Shin, Yongeun Park

“…A YOLOv8 algorithm was trained using field video data to automatically detect and count four types of floating plastic debris such as common plastics, plastic bottles, plastic film and vinyl, and fragmented plastics. …”
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A machine learning based authentication and intrusion detection scheme for IoT users anonymity preservation in fog environment. by Khondokar Oliullah, Md Whaiduzzaman, Md Julkar Nayeen Mahi, Tony Jan, Alistair Barros

“…Furthermore, we introduce a staked ensemble learning approach for intrusion detection that achieves 99.86% accuracy, 99.89% precision, 99.96% recall, and a 99.91% F1-score in detecting anomalous instances, with a support count of 50,376. …”
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Using Cuckoo Search Algorithm to Predict Corporate Financial Risks and Alleviate Economic Uncertainty by Muqiao Cai

“…This framework combines a Backpropagation Neural Network (BPNN) with the Cuckoo Search Algorithm (CSA) to build an adaptive learning system. …”
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Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images by M. Eliazer, Sibi Amaran, K. Sreekumar, A. Vikram, Gyanendra Prasad Joshi, Woong Cho

“…Nowadays, researchers have begun to implement an image-processing model for analyzing jaundice. Paediatricians can detect and classify neonatal jaundice with machine learning (ML) and deep learning (DL) techniques. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary. ObjectiveThis study aims to develop an ensemble learning framework that adaptively integrates multimodal physiological data collected from wearable wristbands and digital cognitive metrics recorded on tablets, thereby improving the accuracy and practicality of MCI detection. …”
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Analyzing social psychological impact on emotional expression through peer communication using crayfish optimization algorithm with deep learning model by Umkalthoom Alzubaidi

“…This paper uses the Crayfish Optimization Algorithm and Deep Learning (SPIEEPC-COADL) method to analyze the Social Psychological Impact on Emotional Expression through Peer Communication. …”
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Advanced driver assistance system (ADAS) and machine learning (ML): The dynamic duo revolutionizing the automotive industry by Harsh SHAH, Karan SHAH, Kushagra DARJI, Adit SHAH, Manan SHAH

“…Subsequently, the critical algorithms used in ADAS technology, which include face detection, head-pose estimation, gaze estimation, and link detection are discussed. …”
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Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV by Guohua Wei, Ding Zhou, Xiaojun Yuan

“…The developed system uses deep learning algorithms for real-time data collection to enhance monitoring efficiency and accuracy, as well as advanced object detection algorithms to compare real-time construction images with BIMs. …”
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SecFedMDM-1: A Federated Learning-Based Malware Detection Model for Interconnected Cloud Infrastructures by Daniella Mughole Kalimumbalo, Joke A. Badejo, Kennedy O. Okokpujie, Emmanuel Adetiba, Molo Mbasa Joaquim, Nzanzu Vingi Patrick, Gabriel Oyeyemi, Claude Takenga

“…This method protects users’ privacy as numerous devices can collaborate to build machine learning models without sharing data. Three distinct deep-learning algorithms were chosen for the models’ training, validation, and testing phases. …”
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Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera by Ashish Sharma, Taraprasad Das, Bryan Ong, Florian Mickael Savoy, Divya Parthasarathy Rao, Jun Kai Toh, Anand Sivaraman

“…The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera.Design, setting A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used.Participants The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. …”
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Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms by Xin Liu, Xingming Shu, Yejiang Zhou, Yifan Jiang

“…Finally, the study employed LIME for more precise prediction and interpretation of individual predictions.ConclusionThe machine learning algorithms effectively predicted postoperative lower limb deep vein thrombosis in colorectal cancer patients. …”
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DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection by Babak Masoudi

“…In recent years, deep learning methods have provided promising results for solving anomaly detection problems in any data types, especially in images. …”
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Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks? by Jowaria Khan, Rana Elfakharany, Hiba Saleem, Mahira Pathan, Emaan Shahzad, Salam Dhou, Fadi Aloul

“…Smart cities rely on these systems seamlessly functioning with one another, and any successful cyberattack could have devastating effects, including large-scale data theft. In such a context, the proposed machine learning model offers a robust solution for detecting and mitigating multi-step cyberattacks in these critical environments. …”
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Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of <i>Helicobacter pylori</i> Infection by Sultan Noman Qasem

“…The development of HeliEns involved rigorous data preprocessing steps, including data cleaning, encoding of categorical variables, and feature scaling, to ensure the dataset’s suitability for quantum machine learning algorithms. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. …”
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