Case-Based Approach to Detect Cancer in Women with Curative Intent at Beginning by Imran Majeed, Ahmad Imran, Mazhar Hussain, Mubahir Ali Syed, Tuba Zainab, Hamza Waheed, Tayyaba Dawood, Iqra Danish, Maryam Zeb, Kashif Hameed, Mehreen Farooq, Fatima Amjad, Razia Faqeer, Maria Abdullah

“…Objective: To Increase the detection rate of mammography for detection of early breast cancer. …”
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Intelligent Detection and Recognition of Marine Plankton by Digital Holography and Deep Learning by Xianfeng Xu, Weilong Luo, Zhanhong Ren, Xinjiu Song

“…In this paper, an intelligent method designed with digital holography and deep learning algorithms is proposed to detect and recognize marine plankton (IDRMP). …”
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Review of Visual Defect Detection Technology for Micro Coaxial Cable Harnesses by Jin Li, Zhipeng Guo, Lei Ye, Hailin Wang

“…Subsequently, we discussed the application of traditional image processing methods, machine learning methods, and deep learning methods to defect detection technology for high-precision components, and summarized the algorithmic performance, advantages and limitations of the various methods. …”
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Leveraging AI for Intrusion Detection in IoT Ecosystems: A Comprehensive Study by Shashi Bhushan Sharma, Amit Kumar Bairwa

“…The examination comprehensively analyzes current state-of-the-art AI methodologies utilized in IoT-based IDS, including machine learning, deep learning, and anomaly detection algorithms. The paper delves into IoT systems’ distinctive characteristics and vulnerabilities that demand specialized intrusion detection mechanisms. …”
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Optimized DINO model for accurate object detection of sesame seedlings and weeds by Yong Wang, ShunFa Xu, ZhenYuan Ye, KongHao Cheng

“…However, in environments where the target and the surrounding morphology are highly similar, such as distinguishing sesame seedlings from weeds, the problem essentially becomes one of optimizing edge detection algorithms for similar targets. To address this issue in agricultural object detection, we developed a custom dataset containing 1,300 images of sesame seedlings and weeds. …”
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Iterative PolInSAR Target Decomposition for Scattering Characterization and Building Detection by Di Zhuang, Lamei Zhang, Bin Zou

“…In densely rotated built-up areas, restricted by interactive and complex scatterings, most polarimetric synthetic aperture radar scattering analyses and unsupervised building detection algorithms have failed, especially under conditions of large radar look angles. …”
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Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU by Laura López-Viñas, Jose L. Ayala, Francisco Javier Pardo Moreno

“…Developing interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases. …”
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Deep Learning Model-Based Detection of Anemia from Conjunctiva Images by Najmus Sehar, Nirmala Krishnamoorthi, C. Vinoth Kumar

“…This study offers a comprehensive analysis of non-invasive anemia detection using conjunctiva images processed through various machine learning and deep learning models. …”
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A New Approach for Brain Tumor Detection Using Machine Learning by Elsadek Hussien Ibrahim, Shaaban Ebrahim Abo-Youssef, Khaled El-Bahnasy, Khaled Ahmed Mohamed Fathy

“…Methods: Researchers have developed algorithms for detecting and classifying brain tumors and prioritizing accuracy and efficiency. …”
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A Machine Vision Method for Detecting Pineapple Fruit Mechanical Damage by Jiahao Li, Baofeng Mai, Tianhu Liu, Zicheng Liu, Zhaozheng Liang, Shuyang Liu

“…To address these challenges, this paper proposes a pineapple mechanical damage detection method based on machine vision, which segments the damaged region and calculates its area using multiple image processing algorithms. …”
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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. …”
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Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…Analysis of hyperspectral data identified that spectral regions (410–503 nm, 510–615 nm, and 630–690 nm) and vegetation indices like PRI and ARI2 were highly correlated with DI, flavonoids, and anthocyanins, providing potential spectral indicators for disease assessment and early detection. Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R² of 0.857, 0.910, and 0.963, respectively. …”
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Artificial Intelligence in Wind Turbine Fault Detection and Diagnosis: Advances and Perspectives by Nejad Alagha, Anis Salwa Mohd Khairuddin, Zineddine N. Haitaamar, Obada Al-Khatib, Jeevan Kanesan

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Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR by Maoyuan Zhang, Xiaojuan Wei, Guojun Liu, Mengxu Chen, Chunxia Zhao, Yingxiao Liu, Zhikang Bao, Yunfeng Guo, Run An, Pengcheng Zhao

“…However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. …”
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Contextual information based anomaly detection for multi-scene aerial videos by Girisha S, Ujjwal Verma, Manohara M M. Pai, Radhika M. Pai

“…Further, the lack of standard UAV-based anomaly detection datasets has restricted the development of novel algorithms. …”
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Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches by Naveen Kumar Vaegae, Kranthi Kumar Pulluri, Kalapraveen Bagadi, Olutayo O Oyerinde

“…In this paper, an efficient distracted driver detection scheme (DDDS) has been proposed using two robust deep learning architectures, mainly visual geometric groups (VGG-16) and residual networks (ResNet-50). …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…To address these limitations, this paper proposes a novel hybrid super learner ensemble model named Phish-Jam, a mobile application specifically designed for phishing detection on mobile devices. Phish-Jam utilizes a super learner ensemble that combines predictions from diverse Machine Learning (ML) algorithms to classify legitimate and phishing websites. …”
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Deep Transfer Learning Approach in Smartwatch-Based Fall Detection Systems by Alessandro Leone, Andrea Manni, Gabriele Rescio, Pietro Siciliano, Andrea Caroppo

“…This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…First, most detection models usually consider centralized detection. …”
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