GEM-CRAP: a fusion architecture for focal seizure detection by Jianwei Shi, Yuanyuan Zhang, Ziang Song, Hang Xu, Yanfeng Yang, Lei Jin, Hengxin Dong, Zhaoying Li, Penghu Wei, Yongzhi Shan, Guoguang Zhao

“…Abstract Background Identification of seizures is essential for the treatment of epilepsy. Current machine-learning and deep-learning models often perform well on public datasets when classifying generalized seizures with prominent features. …”
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Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation by Shan Zhu

“…Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners’ pronunciation errors but also assessing their overall pronunciation level. …”
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Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks by Seok-Woo Jang, Gye-Young Kim, Siwoo Byun

“…Subsequently, it groups the ports using an improved clustering algorithm, allowing an artificial neural network to learn the extracted features and to automatically detect and classify normal traffic data, DDoS attacks, DoS attacks, or Internet Worms. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

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Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model by Gee-Sern Jison Hsu, Jie Syuan Wu, Yin-Kai Dean Huang, Chun-Chieh Chiu, Jiunn-Horng Kang

“…Participants lifted boxes of varying sizes and weights while their movements were recorded from multiple angles and heights to ensure comprehensive data capture. We used the OpenPose algorithm to detect and extract key body points to calculate relevant biomechanical features. …”
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Fast Processing of Massive Hyperspectral Image Anomaly Detection Based on Cloud-Edge Collaboration by Yunchang Wang, Jin Sun, Yang Xu, Zhihui Wei, Shangdong Zheng, Zebin Wu

“…With the improvement of hyperspectral image resolution, existing anomaly detection algorithms find it challenging to quickly process large volumes of hyperspectral data while fully exploiting spectral information. …”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

“…Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.…”
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Low-Light Image and Video Enhancement for More Robust Computer Vision Tasks: A Review by Mpilo M. Tatana, Mohohlo S. Tsoeu, Rito C. Maswanganyi

“…The review concludes by highlighting major findings such as that although supervised learners obtain the best results, due to a lack of real-world data and robustness to new data, a shift to zero-shot learners is required.…”
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TATPat based explainable EEG model for neonatal seizure detection by Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva

“…Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. …”
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Using A One-Class SVM To Optimize Transit Detection by Jakob Roche

“…As machine learning algorithms become increasingly accessible, a growing number of organizations and researchers are using these technologies to automate the process of exoplanet detection. …”
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Ensemble Transformer–Based Detection of Fake and AI–Generated News by Md. Ishraquzzaman, Mohammed Ashraful Islam Chowdhury, Shahreen Rahman, Riasat Khan

“…This work leverages advanced natural language processing, machine learning, and deep learning algorithms to effectively detect fake and AI–generated content. …”
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Crack Detection in Civil Infrastructure: A Method-Scenario Review by Chang Haochen, Gu Weifan, Guo Baohua, Bassir David

“…Ensuring the structural safety of civil infrastructure is vital for public welfare and cost-effective maintenance. Crack detection, as a key indicator of structural health, has transitioned from traditional image processing to advanced deep learning methods. …”
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Simultaneous multi-class detection of interplanetary space weather events by Nguyen Gautier, Bernoux Guillerme, Ferlin Antoine

“…Such studies benefit directly from the rapid and reproducible expansion of these catalogs, made possible by automatic event detection from in-situ time series data. Previous studies revealed the efficiency of deep-learning based methods for this task over traditional threshold-based techniques. …”
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Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees by Cheng Shen, Yuewei Liu

“…Some machine learning algorithms and artificial intelligence models for defect detection, such as Convolutional Neural Networks (CNNs), present outstanding performance, but they are often data-dependent and cannot provide guarantees for new test samples. …”
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Semi-supervised permutation invariant particle-level anomaly detection by Gabriel Matos, Elena Busch, Ki Ryeong Park, Julia Gonski

“…However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. …”
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Impact of Lexical Features on Answer Detection Model in Discussion Forums by Atif Khan, Muhammad Adnan Gul, Abdullah Alharbi, M. Irfan Uddin, Shaukat Ali, Bader Alouffi

“…Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.…”
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Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning by Yusuf Alaca

“…The images of the pancreatic CT were transformed into graph structures using the Harris Corner Detection algorithm, which enables the capture of complex spatial relationships. …”
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Optimization of IMU-based bending strain solving algorithm and full-scale experimental validation by Tong SHI, Xiaoben LIU, Lin ZHANG, Jun WANG, Rui LI, Ting Xie, Qingshan FENG, Qiyu HUANG

“…Moreover, a solving algorithm optimized through an ANNExtraTree deep learning model was introduced. …”
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Elevator Operation Health Diagnosis using Vibration Region Segmentation Algorithm via Internet by Lin Hsiung-Cheng, Peng Yan-Hao

“…To develop the system, the elevator vibration data is collected by a 3D accelerometer and then processed in the PC using the proposed algorithm. …”
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Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing by Arief Setyanto, Theopilus Bayu Sasongko, Muhammad Ainul Fikri, Dhani Ariatmanto, I. Made Artha Agastya, Rakandhiya Daanii Rachmanto, Affan Ardana, In Kee Kim

“…Although state-of-the-art deep learning-based OD methods achieve high detection rates, their large model size and high computational demands often hinder deployment on resource-constrained edge devices. …”
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