Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding by Joe Benganga, Tshepo Kukuni, Ben Kotze, Lepekola Lenkoe

“…As a result of the opportunities that artificial intelligence presents to different sectors by optimally performing tasks with less error compared to humans or traditional models, the use of AI in artefact detection is being investigated. This research paper thus presents a comparative model efficiency analysis based on dissimilar algorithms, namely CNN, VGG16, Inception_V3, and ResNet_50. …”
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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

“…Second, there are complex spatial and temporal features in industrial sensor data. The full extraction of such features plays a key role in improving detection performance. …”
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Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network by Liming Li, Shubin Zheng, Chenxi Wang, Shuguang Zhao, Xiaodong Chai, Lele Peng, Qianqian Tong, Ji Wang

“…This work presents a new method for sleeper crack identification based on cascade convolutional neural network (CNN) to address the problem of low efficiency and poor accuracy in the traditional detection method of sleeper crack identification. The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). …”
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Leveraging stacking machine learning models and optimization for improved cyberattack detection by Neha Pramanick, Jimson Mathew, Shitharth Selvarajan, Mayank Agarwal

“…Abstract The ever-growing number of complex cyber attacks requires the need for high-level intrusion detection systems (IDS). While the available research deals with traditional, hybrid, and ensemble methods for network data analysis, serious challenges are still being met in terms of producing robust and highly accurate detection systems. …”
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Lightweight Detection of Train Underframe Bolts Based on SFCA-YOLOv8s by Zixiao Li, Jinjin Li, Chuanlong Zhang, Huajun Dong

“…By combining the C2f module with ScConv lightweight convolution and replacing the Bottleneck structure with the Faster_Block structure, the SFC2f module is designed for feature extraction to improve detection accuracy and speed. …”
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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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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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YOLOv9-GSSA model for efficient soybean seedlings and weeds detection by Baihe Liang, Liangchen Hu, Guangxing Liu, Peng Hu, Shaosheng Xu, Biao Jie

“…Challenges include the small size and morphological similarity of seedlings and weeds, complicating conventional detection methods. To tackle these issues, we propose a real-time detection algorithm called YOLOv9-GSSA. …”
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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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Proposal of a Methodology Based on Using a Wavelet Transform as a Convolution Operation in a Convolutional Neural Network for Feature Extraction Purposes by Nora Isabel Pérez-Quezadas, Héctor Benítez-Pérez, Adrián Durán-Chavesti

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Automatic titration detection method of organic matter content based on machine vision by Bingjie Zhang, Meng Li, Qing Song, Lujian Xu

“…First, by analysing the colour change characteristics during the titration process, machine learning techniques are used to classify the titration speed, and a titration experiment state recognition model is constructed to divide the titration speed into four categories and improve titration efficiency; Second, through a large number of titration experiments to collect relevant data and extract key feature parameters, an efficient titration algorithm based on histogram similarity was designed to accurately identify titration endpoints and improve detection accuracy. …”
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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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Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective by Minghua Wang, Lianru Gao, Longfei Ren, Xian Sun, Jocelyn Chanussot

“…Inspired by spatial–spectral gradient domain-based constraint, HyADD removes additive noises and preserves advantageous image smoothness information to improve intermediate detection performances in the iteration loop. Conversely, with the assistance of the antinoise dictionary conduction and the subspace domain-based low-rankness, the identification of anomalies with different features from the background can provide effective feedback to the denoising process. …”
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Blockchain-Enabled Deep Recurrent Neural Network Model for Clickbait Detection by Abdul Razaque, Bandar Alotaibi, Munif Alotaibi, Fathi Amsaad, Ansagan Manasov, Salim Hariri, Banu B. Yergaliyeva, Aziz Alotaibi

“…To achieve the clickbait analysis and source rating, the detection of blocklisted/allowlisted source and source rating check algorithms are introduced. …”
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Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu, Yong He

“…Meanwhile, optimized feature selection algorithms identified effective wavelengths (EWs) and vegetation indices (VIs) for yield estimation. …”
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Underground personnel detection and tracking using improved YOLOv7 and DeepSORT by Weiqiang FAN, Xuejin WANG, Yinghui ZHANG, Xiaoyu LI

“…Based on this, an improved YOLOv7 and DeepSORT underground personnel detection and tracking algorithm is proposed. First, in order to be able to extract more critical underground personnel image features and improve the model's adaptability in the complex scene of coal mine underground, the SimAM attention mechanism is incorporated into the Neck module of YOLOv7, and the improved YOLOv7 model is used to detect underground personnel targets. …”
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Detection and Geolocation of Peat Fires Using Thermal Infrared Cameras on Drones by Temitope Sam-Odusina, Petrisly Perkasa, Carl Chalmers, Paul Fergus, Steven N. Longmore, Serge A. Wich

“…Finally, a geolocation algorithm is presented to identify the GNSS location of these fires once they are detected in an image to aid fire-fighting responses. …”
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Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema by Yu Fang, Jianwei Lin, Peiwen Xie, Huishan Zhu, Tsz Kin Ng, Guihua Zhang

“…Conclusion This study established an ensemble ML algorithm based on 3D-OCT images and clinical features for automatic detection of treatment responses to anti-VEGF treatment in DME patients to predict the efficacy of anti-VEGF treatment in DME patients and assist clinicians in optimal treatment decisions.…”
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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks by Omair Ahmad, Muhammad Sohail Khan, Salman Jan, Inayat Khan

“…Therefore, developing effective algorithms to distinguish fake audio from real audio is critical to preventing such frauds. …”
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Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm by Amany M. Sarhan, Hesham A. Ali, Shady Yasser, Mohamed Gobara, Ahmed A. Kandil, Ghada Sherif, Esraa Moustafa

“…Abstract Skin cancer, particularly melanoma, poses a critical global health challenge due to its high mortality rate. Early and precise detection is vital for effective treatment and better prognosis. …”
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