ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions by Zohaib Khan, Yue Shen, Hui Liu

“…This comprehensive review synthesizes object detection methodologies, tracing their evolution from traditional feature-based approaches to cutting-edge deep learning architectures. …”
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Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations by Yunlong Sun, Dehan Luo, Hui Li, Chuchu Zhu, Ou Xu, Hamid Gholam Hosseini

“…We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. …”
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SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Mobile Devices by Chao Wang, Zhizhong Wu, Xi Li, Xuehai Zhou, Aili Wang, Patrick C. K. Hung

“…The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server’s main task is to detect anomalies using state-of-art detection algorithms. …”
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Evaluation of Post Hoc Uncertainty Quantification Approaches for Flood Detection From SAR Imagery by Jakob Ludwig, Ronny Hansch

“…In particular when these predictions are used by human decision makers in high stake scenarios, e.g., during detection and monitoring of natural disasters, trustworthiness is a necessary feature. …”
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Advances and Challenges in Deep Learning for Automated Welding Defect Detection: A Technical Survey by Abdulrahim Mohammed, Muhammad Hussain

“…The study also highlights the integration of advanced preprocessing techniques, such as noise reduction and contrast enhancement, within DL workflows to improve feature extraction and detection accuracy. Persistent challenges, such as the scarcity of large, labeled datasets, lack of real-time applicability, and limited model interpretability, are explored in depth. …”
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Type 2 Diabetes Detection With Light CNN From Single Raw PPG Wave by Serena Zanelli, Mounim A. El Yacoubi, Magid Hallab, Mehdi Ammi

“…Recent studies have demonstrated the utility of PPG analysis for carrying out large-scale screening to prevent and detect diabetes. …”
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An improve fraud detection framework via dynamic representations and adaptive frequency response filter by Juncheng Yang, Shuxia Li, Zijun Huang, Junhang Wu

“…Consequently, we introduce the Dynamic Pattern with Adaptive Filter Graph Learning framework for telecom fraud detection. With the sequential network, we capture users’ dynamic behavioral features for LSN input. …”
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A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment by Yuezhong Wu, Xuehao Shen, Qiang Liu, Falong Xiao, Changyun Li

“…Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. …”
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Combined Thermal Index Development for Urban Heat Island Detection in Area of Split, Croatia by Majda Ćesić, Katarina Rogulj, Andrija Krtalić

“…This research compares ground-based and sensor-based temperatures, and their analysis results in the proposal of a new index: the Combined Thermal Index. …”
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Enhancing Software Quality with AI: A Transformer-Based Approach for Code Smell Detection by Israr Ali, Syed Sajjad Hussain Rizvi, Syed Hasan Adil

“…Traditional machine-learning techniques, such as gradient boosting and support vector machines (SVM), have demonstrated effectiveness in code smell detection but require extensive feature engineering and struggle to capture intricate semantic dependencies in software structures. …”
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Machine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems by Enrico Crotti, Andrea Colagrossi

“…Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. …”
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A Comprehensive Framework for Out-of-Distribution Detection and Open-Set Recognition in SAR Targets by Fei Gao, Heqing Huang, Jun Wang, Jinping Sun, Amir Hussain, Huiyu Zhou

“…The rejection of outlier data in synthetic aperture radar (SAR) image analysis presents a significant challenge, particularly in the scenarios of out-of-distribution (OOD) detection and open set recognition (OSR). …”
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Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB by Hang Shi, Hang Shi, Changxi Liu, Changxi Liu, Miao Wu, Miao Wu, Hui Zhang, Hui Zhang, Hang Song, Hang Song, Hao Sun, Hao Sun, Yufei Li, Yufei Li, Jun Hu, Jun Hu

“…The model’s outputs are visualized using a heat map, and an Average Temperature Weight (ATW) metric is introduced to quantify the heat map’s effectiveness.Results and discussionComparative analysis reveals that YOLO11-CGB outperforms established object detection models like Faster R-CNN, YOLOv4, YOLOv5, YOLOv8 and the original YOLO11 in detecting Chinese cabbage seedlings across varied heights, angles, and complex settings. …”
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Cyberattack Detection Systems in Industrial Internet of Things (IIoT) Networks in Big Data Environments by Abdullah Orman

“…Second, while existing studies frequently favor hybrid models, findings from this study reveal that the standalone MLP model outperforms other architectures, achieving the highest detection accuracy of 99.99%. This outcome highlights the critical role of dataset-specific feature distributions in determining model effectiveness and calls for a more nuanced approach when selecting detection models for IIoT cybersecurity applications. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…Consequently, enhancing both the quantity and quality of detection data, along with refining the feature extraction approach for key risk indicators, is anticipated to further boost the accuracy of the model. …”
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Driver drowsiness shield (DDSH): a real-time driver drowsiness detection system by Archita Bhanja, Dibyajyoti Parhi, Dipankar Gajendra, Kreetish Sinha, Arup Kumar Sahoo

“…This paper aims to develop an advanced real-time drowsiness detection system using deep learning algorithms. For this purpose, we utilized an eye image dataset from the MRL Eye Dataset and performed extensive feature engineering and preprocessing to prepare the data for analysis. …”
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Action unit based micro-expression recognition framework for driver emotional state detection by Parul Malik, Jaiteg Singh, Farman Ali, Sukhjit Singh Sehra, Daehan Kwak

“…The model was trained and evaluated on two benchmark datasets: SAMM and KMU-FED, achieving recognition accuracies of 96.38% and 95.96%, respectively. Furthermore, case analysis was carried out to detect driver emotional state using the proposed framework, obtaining an accuracy of 91.00%. …”
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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

“…Machine Learning (ML) and Deep learning (DL) algorithms have emerged as powerful feature extraction and classification tools in EEG signal analysis. …”
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Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning by Vânia Guimarães, Inês Sousa, Miguel Velhote Correia

“…Conclusions Our results suggest that gait analysis can be a useful tool for detecting cognitive impairment in patients with cerebrovascular disease, serving as a suitable alternative or complement to MoCA in the screening for cognitive impairment.…”
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PlantNet: Scalable Convolutional Neural Network for Image-Based Plant Disease Detection by Sinha Anupa, Kumaraswamy Balasubramaniam

“…By leveraging deep learning techniques, PlantNet processes large-scale image datasets to detect disease symptoms with high precision. The model employs transfer learning, utilizing pre-trained networks on vast image repositories before fine-tuning on a specialized plant disease dataset, thereby enhancing feature extraction while minimizing computational complexity. …”
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