Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images by Khaled Tarmissi, Jamal Alsamri, Mashael Maashi, Mashael M. Asiri, Abdulsamad Ebrahim Yahya, Abdulwhab Alkharashi, Monir Abdullah, Marwa Obayya

“…Recently, with the fast growth of deep learning (DL) and machine learning (ML) methods, object detection methods have been progressively used for cell detection. …”
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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…In this article, we present a new deep learning model to detect the occurrence of sepsis and the African vulture optimization algorithm (AVOA) to enhance the model performance. …”
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A Drilling Debris Tracking and Velocity Measurement Method Based on Fine Target Feature Fusion Optimization by Jinteng Yang, Yu Bao, Zumao Xie, Haojie Zhang, Zhongnian Li, Yonggang Li

“…Specifically, we enhance the multi-scale feature fusion capability of the YOLOv11 detection head by incorporating a lightweight feature extraction module, Ghost Conv, and a feature-aligned fusion module, FA-Concat, resulting in an improved model named YOLOv11-Dd (drilling debris). …”
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Enhanced Infrared Defect Detection for UAVs Using Wavelet-Based Image Processing and Channel Attention-Integrated SSD Model by Jining Zhao, RuiZhi Zhang, Shaogong Chen, Yanbo Duan, Zhiyuan Wang, Qingchen Li

“…In this paper, we develop a defect target detection algorithm based on image processing and feature matching to address background noise in the detection of defects in infrared images of Unmanned Aerial Vehicle (UAVs), as well as to improve real-time monitoring capabilities. …”
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. …”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. …”
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A Statistical Framework to Detect and Quantify Operator-Learning Curves in Medical Device Safety Evaluation by Ssemaganda HC, Davis SE, Govindarajulu US, Koola JD, Mao J, Westerman DM, Perkins AM, Speroff T, Ramsay CR, Sedrakyan A, Ohno-Machado L, Matheny ME, Resnic FS

“…Correctly attributing safety signals to learning or device effects allows for appropriate corrective actions and recommendations to improve patient safety.Objective: To develop and assess the statistical performance of an analytic framework to detect the presence of LE and quantify the learning curve (LC).Design and Setting: We generated synthetic datasets based on observed clinical distributions and complex feature correlations among patients hospitalized at US Department of Veterans Affairs facilities. …”
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Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection by Nwamaka Georgenia Ezeji, Kingsley Ifeanyi Chibueze, Nnenna Harmony Nwobodo-Nzeribe

“…To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection.   …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. …”
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Detection of Foreign Bodies in Transmission Line Channels Based on Fusion of Swin Transformer and YOLOv5 by XUE Ang, JIANG Enyu, ZHANG Wentao, LIN Shunfu, MI Yang

“…To address the challenges of complex detection background and poor detection performance for small targets, a transmission line channel security detection algorithm based on the fusion of window self-attention network and the YOLOv5 model is proposed. …”
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Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques by Amit Gupta, Rohan Raju Dhanakshirur, Kshitiz Jain, Sanil Garg, Neel Yadav, Amlesh Seth, Chandan J. Das

“… Objective The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and subtyping on computed tomography (CT): clear cell RCC (ccRCC) versus non-ccRCC using two-dimensional (2D) neural network architecture and feature consistency modules.…”
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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. …”
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YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 by Hao Hao, Xiang Yu

“…Second, we constructed a distributed focal detection head CLLAHead, to better capture the features at different scales. …”
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Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014-2019, by clinicians or machine learning alg... by David M Vu, Amy R Krystosik, Bryson A Ndenga, Francis M Mutuku, Kelsey Ripp, Elizabeth Liu, Carren M Bosire, Claire Heath, Philip Chebii, Priscilla Watiri Maina, Zainab Jembe, Said Lipi Malumbo, Jael Sagina Amugongo, Charles Ronga, Victoria Okuta, Noah Mutai, Nzaro G Makenzi, Kennedy A Litunda, Dunstan Mukoko, Charles H King, A Desiree LaBeaud

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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…Traditional YOLO-series algorithms encounter challenges such as poor robustness in small object detection and significant interference from complex backgrounds. …”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…The proposed approach combines Modified Whale Transfer and Sine-Cosine algorithms along with feature selection techniques such as ANOVA, RFE, and RFA to detect malicious communications accurately. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. …”
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CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals by Ugur Ince, Yunus Talu, Aleyna Duz, Suat Tas, Dahiru Tanko, Irem Tasci, Sengul Dogan, Abdul Hafeez Baig, Emrah Aydemir, Turker Tuncer

“…Classification results were obtained using the cumulative weighted iterative neighborhood component analysis (CWINCA) feature selector and the t-algorithm-based k-nearest neighbors (tkNN) classifier. …”
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Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer Models by Yunbin Ma, Zuyue Shang, Jie Zheng, Yichen Zhang, Guangyuan Weng, Shu Zhao, Cheng Bi

“…This paper introduces a novel leakage detection and localization algorithm for oil and gas pipelines, integrating wavelet denoising with a Long Short-Term Memory (LSTM)-Transformer model. …”
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A Novel Long Short-Term Memory-Based Approach for Microgrid Fault Detection and Classification Using the Wavelet Scattering Transform by Naema M. Mansour, Abdelazeem A. Abdelsalam, Ibrahim A. Awaad

“…During islanded operation, a common mode in microgrids, fault currents are often reduced, making fault detection and isolation even more difficult. These limitations underscore the urgent need for intelligent, adaptive, and fast-responding fault detection and classification algorithms tailored specifically to the nature of microgrids. …”
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