Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm for Selecting Features in Cervical Cancer Data by Afidatul Masbakhah, Umu Sa'adah, Mohamad Muslikh

“…Along with the development of technology and in an effort to detect cervical cancer early, machine learning algorithms have been widely used to analyze the risk of cervical cancer, one of which is Random Forest (RF). …”
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Leveraging SARs and Advanced Deep Learning Techniques for Oil Spill Detection in UAE by M. Alkhalifa, A. Alowais, A. Manousakis, A. Manousakis

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Cyberattack detection on SWaT plant industrial control systems using machine learning by Shadi Jaradat, Md Mostafizur Komol, Mohammed Elhenawy, Naipeng Dong

“…The dataset, sourced from the Singapore University of Technology and Design, includes data from 51 sensors and actuators. The research employs a Long Short-Term Memory (LSTM) network alongside traditional machine learning algorithms like Random Forest (R.F.), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) to classify cyberattacks. …”
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Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination by Yajun Pang

“…Sports health is gradually attracting attention, and computer vision technology is integrated into sports health to improve the quality of sports and increase the motivation of athletes. A deep learning sports health video propagation detection and recognition system is built through the mode of video propagation to provide real-time training information for sports and scientific body index parameters and exercise data for sports health programs. …”
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Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning by Emerson Ferreira Vilela, Cileimar Aparecida da Silva, Jéssica Mayara Coffler Botti, Elem Fialho Martins, Charles Cardoso Santana, Diego Bedin Marin, Agnaldo Roberto de Jesus Freitas, Carolina Jaramillo-Giraldo, Iza Paula de Carvalho Lopes, Lucas de Paula Corrêdo, Daniel Marçal de Queiroz, Giuseppe Rossi, Gianluca Bambi, Leonardo Conti, Madelaine Venzon

“…The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

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Cybersecurity of smart grids: Comparison of machine learning approaches training for anomaly detection by S. V. Kochergin, S. V. Artemova, A. A. Bakaev, E. S. Mityakov, Zh. G. Vegera, E. A. Maksimova

“…K-means and One-Class SVMs are less effective in detecting abrupt anomalies but are useful for general clustering of data and detecting both abrupt and smooth changes, respectively.Conclusions. …”
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Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models by Kingsley Ifeanyi Chibueze, Nwamaka Georgenia Ezeji, Nnenna Harmony Nwobodo-Nzeribe

“…Future recommendations include leveraging advanced ML techniques like deep learning and reinforcement learning and exploring ensemble learning methods to enhance congestion detection models further. …”
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Detection System Design and Implementation for Foreign Objects in Automatic Platform Door Gap by YU Qingguang, WANG Shi, GAO Bonan, CHEN Yuxuan, XIAO Chengbo, LIU Youqi, WANG Yujin, ZHAO Ming, LI Le, CAI Guanzhi

“…Method Based on the video and LiDAR algorithm fusion technology, a dual-criterion AI detection strategy that combines video image recognition with LiDAR point cloud data is proposed. …”
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Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review by Shaohua Wang, Dachuan Xu, Haojian Liang, Yongqing Bai, Xiao Li, Junyuan Zhou, Cheng Su, Wenyu Wei

“…This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. …”
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A lightweight deep-learning model for parasite egg detection in microscopy images by Wenbin Xu, Qiang Zhai, Jizhong Liu, Xingyu Xu, Jing Hua

“…Abstract Background Intestinal parasitic infections are still a serious public health problem in developing countries, and the diagnosis of parasitic infections requires the first step of parasite/egg detection of samples. Automated detection can eliminate the dependence on professionals, but the current detection algorithms require large computational resources, which increases the lower limit of automated detection. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

“…The study objective is to build a model based on machine learning that can predict the detection of SARS-CoV-2 from medical data. …”
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A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion by Yadong Yao, Yurui Zhang, Zai Liu, Heming Yuan

“…In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. …”
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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. …”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Unlike conventional methods, FLA incorporates machine learning methods to improve fault detection, whereas FIA provides an optimized isolation strategy, decreasing operational delays and reducing power disruption. …”
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Enhancing Autonomous Truck Navigation in Underground Mines: A Review of 3D Object Detection Systems, Challenges, and Future Trends by Ellen Essien, Samuel Frimpong

“…It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. …”
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Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm by Suhendro Y. Irianto, Ryan Yunandar, M. S. Hasibuan, Deshinta Arrova Dewi, Nualyai Pitsachart

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A comprehensive review of predictive analytics models for mental illness using machine learning algorithms by Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, Ferdaus Anam Jibon, Md. Sahidullah

“…This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. …”
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Evaluating Label Encoding and Preprocessing Techniques for Breast Cancer Prediction Using Machine Learning Algorithms by Mukesh Kumar, Vivek Bhardwaj

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Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights by Priya Metri, Swetta Kukreja

“…Suicidal ideation prevalence among students is a growing concern that requires urgent attention.This review systematically analyzes 28 studies on the application of machine learning techniques for the early detection of suicidal ideation. …”
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