Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

“…Traditional classification algorithms rely heavily on visual or sensor-based data (e.g., radar or image signals), often compromised by adverse weather, poor lighting, or occlusion. …”
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An Effective Method for Detecting Cyber Attacks on Computer Networks from the NSL-KDD Data Set by Shaik Aseena Babu, Reddy Rajeswara, Vullam Nagagopi Raju, Reddy Gondi Konda, Shaik Subhani

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Traffic Management System Using YOLO Algorithm by Pankaj Kunekar, Yogita Narule, Richa Mahajan, Shantanu Mandlapure, Eshan Mehendale, Yashashri Meshram

“…In order to maximize the number of vehicles that can cross safely with the least amount of waiting time, this paper presents an efficient method to use this algorithm, where traffic signal phases are based on the data obtained, primarily queue density and waiting time per vehicle. …”
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Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…A secondary CNN refines detections and reduces false positives. Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. …”
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A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms. by Andrew McDonald, Mark J F Gales, Anurag Agarwal

“…Moody PhysioNet challenge, where it won the first prize in both the challenge tasks. The algorithm's performance exceeded that of many end-to-end deep learning approaches that struggled to generalise to new test data. …”
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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

“…This study proposed a novel machine learning–driven methodology for detecting potential defects in computer numerical control (CNC) spindle manufacturing. …”
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Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm by G. Ayappan, S. Anila

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DETECTION OF NON-MELANOMA SKIN CANCER BY DEEP CONVOLUTIONAL NEURAL NETWORK AND STOCHASTIC GRADIENT DESCENT OPTIMIZATION ALGORITHM by Premananda Sahu, Srikanta Kumar Mohapatra, Prakash Kumar Sarang, Jayashree Mohanty, Pradeepta Kumar Sarangi

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Authorship identification methods in student plagiarism detection by A. I. Paramonov, I. A. Trukhanovich

“…Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data.Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments.…”
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Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs by Shiyun Li, Omar Dib

“…The resulting malicious URL detection system (MUDS) combines supervised machine learning techniques, tree-based algorithms, and advanced data preprocessing, achieving a high detection accuracy of 96.83% for known MURLs. …”
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SIP Flooding Attack Detection Technology of Multi-agent System Covert Network Based on BiGRU Algorithm by Tong Wu, Hengyu Liu, Tong Li, Wei Fan, Dawei Hu, Jianshi Bai

“…This technology is divided into two levels of detection. The primary detection collects and analyzes the hidden network data of multi-agent systems, and determines whether the traffic is abnormal by calculating the Renyi entropy value; abnormal traffic enters the second-level attack detection stage, extracting abnormal traffic from multi-agent covert networks and using the BiGRU model to learn features bidirectionally to determine whether it is an SIP flooding attack. …”
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Unraveling deep-learning detection of atmospheric Kármán vortex streets: dataset, baseline benchmarking, and optimization by Yihan Zhang, Chaoyue Wu, Qiao Su, Yuqi Zhang, Daoyi Chen

“…Different types of advanced algorithms were applied on the dataset, yielding baseline performance metrics and potential detection methods. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…<p>Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. …”
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AI-Based Ransomware Detection: A Comprehensive Review by Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam

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Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm by Puneet Kumar, Dalwinder Singh, Mamoona Humayun, Ali Alqazzaz, Arun Malik, Ibrahim Alrashdi, Isha Batra, Ghadah Naif Alwakid

“…After reviewing the state-of-the-art algorithms based on CNN learned that MRFasGCN is having the best performance compared to any other state-of-the-art algorithms for large data sets. …”
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Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

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Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning by Ping Zhao, Xiaojian Wang, Qing Zhao, Qingbing Xu, Yiru Sun, Xiaofeng Ning

“…The experimental results showed that the SG–SNV fusion hyperspectral data preprocessing algorithm and the KNN machine learning model were the most suitable for the detection of external defects in red-skin potatoes. …”
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SensorDBSCAN: Semi-Supervised Active Learning Powered Method for Anomaly Detection and Diagnosis by Petr Ivanov, Maria Shtark, Alexander Kozhevnikov, Maksim Golyadkin, Dmitry Botov, Ilya Makarov

“…In this paper, we propose SensorDBSCAN, a novel semi-supervised method for anomaly detection and diagnosis. The key innovation lies in achieving good performance with minimal labeled data - less than 1% of the dataset - by leveraging active and contrastive learning techniques. …”
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Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing by Marcio Luís Munhoz Amorim, Jorge Gomes Lima, Norah Nadia Sánchez Torres, Jose A. Afonso, Sérgio F. Lopes, João P. P. do Carmo, Lucas Vinicius Hartmann, Cicero Rocha Souto, Fabiano Salvadori, Oswaldo Hideo Ando Junior

“…This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. …”
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