TFKAN: Transformer based on Kolmogorov–Arnold Networks for Intrusion Detection in IoT environment by Ibrahim A. Fares, Mohamed Abd Elaziz, Ahmad O. Aseeri, Hamed Shawky Zied, Ahmed G. Abdellatif

“…The RT-IoT2022, IoT23, and CICIoT2023 datasets were used in the evaluation process. The proposed TFKAN Transformer outperforms and obtains higher accuracy scores of 99.96%, 98.43%, and 99.27% on the RT-IoT2022, IoT23, and CICIoT2023 datasets, respectively. …”
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Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system by M. Nuthal Srinivasan, Mohamed Yacin Sikkandar, Maryam Alhashim, M. Chinnadurai

“…CapsNets’ inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes. …”
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BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection by Long Guo, Lubin Wang, Qiang Yu, Xiaolan Xie

“…In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. …”
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Method on intrusion detection for industrial internet based on light gradient boosting machine by Xiangdong HU, Lingling TANG

“…Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.…”
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Green Synthesis of Silver Nanoclusters for Sensitive and Selective Detection of Toxic Metal Ions by Sayed M. Saleh, Shahad Altaiyah, Reham Ali

“…The coffee extract was employed in the synthesis process to stabilize and enhance the quantity of AgNCs generated. …”
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Rolling window for detecting multiple Chan signatures to diagnose excessive water production by Ahmed MohamedSalih Musa Hamdoon, A. P. Mohammed Abdalla Ayoub Mohammed, A. P. Khaled Abdalla Elraies

“…A successful interactive model with the rolling window feature was developed to track slope changes in Chan signatures, resulting in a 7–10% improvement in pattern detection accuracy compared to static features. Throughout, an iterative optimization process, window size was determined as seven points, considering pattern duration. …”
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Antarctic Sea ice distribution detection based on improved ant colony algorithm by Xingdong Wang, Xingdong Wang, Xingdong Wang, Zehao Sun, Zehao Sun

“…The ant colony algorithm adopts a positive feedback mechanism to continuously converge the search process and ultimately approaches the optimal solution, making it easy to find the optimal segmentation threshold for detecting the sea ice distribution. …”
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Cross-lingual hate speech detection using domain-specific word embeddings. by Ayme Arango Monnar, Jorge Perez Rojas, Barbara Polete Labra

“…However, a large portion of web users around the world speak different languages, creating an important need for efficient multilingual hate speech detection approaches. In particular, such approaches should be able to leverage the limited cross-lingual resources currently existing in their learning process. …”
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Extracting Target Detection Knowledge Based on Spatiotemporal Information in Wireless Sensor Networks by Tian Wang, Zhen Peng, Cheng Wang, Yiqiao Cai, Yonghong Chen, Hui Tian, Junbin Liang, Bineng Zhong

“…Wireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. …”
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Application of metal nanoparticle-loaded nanocomposites for extraction, removal, and detection of mycotoxins in foods by Mehdi Maham, Reza Abedi-Firoozjah, Mohammad Hadi Moradiyan, Maryam Azizi Lalabadi, Elham Assadpour, Hamed Ahari, Seid Mahdi Jafari

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Recent Advances on Rapid Detection Methods of Steroid Hormones in Animal Origin Foods by Yaohui Xue, Jinhua Li, Ming Ma, Pan Fu, Sihua Qian, Chao Han, Yuhui Wang

“…The judicious application of steroid hormones in the breeding process can serve multiple purposes, including growth promotion, weight gain, and anti-inflammatory effects, among others. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…However, during the growth process, apples are prone to various diseases that not only affect the quality of the fruit but also significantly reduce the yield, impacting farmers' economic benefits and the stability of market supply. …”
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Pesticide Residue Detection in Broccoli Based on Hyperspectral Technology and Convolutional Neural Network by Dan WANG, Yuqing LUAN, Zuojun TAN, Wei WEI

“…The detection of pesticide residues in agricultural products is an important step in ensuring the food safety of agricultural products, while traditional detection methods are cumbersome and costly. …”
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Height-Adaptive Deformable Multi-Modal Fusion for 3D Object Detection by Jiahao Li, Lingshan Chen, Zhen Li

“…In this paper, we propose a novel framework for 3D object detection, called Height-Adaptive Deformable Multi-Modal Fusion, which leverages Deformable Attention to enhance the fusion process. …”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…The detection methods are then categorized into three main groups: traditional image processing, machine learning, and deep learning, with their principles, case studies, limitations, and future development directions analyzed. …”
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Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms by Adil Gaouar, Souaad Hamza Cherif, Abdellatif Rahmoun, Mostafa El Habib Daho

“…Despite their solid performance, these models are often considered ”black boxes” due to their lack of transparency in the decision-making process, which poses significant challenges in medical applications and fields where human life is at stake. …”
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A survey on detection and localisation of false data injection attacks in smart grids by Muhammad Irfan, Alireza Sadighian, Adeen Tanveer, Shaikha J. Al‐Naimi, Gabriele Oligeri

“…FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. …”
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Multilingual hope speech detection from tweets using transfer learning models by Muhammad Ahmad, Iqra Ameer, Wareesa Sharif, Sardar Usman, Muhammad Muzamil, Ameer Hamza, Muhammad Jalal, Ildar Batyrshin, Grigori Sidorov

“…Our observations indicate that a rigorous process for annotator selection, along with detailed annotation guidelines, significantly improved the quality of the dataset. …”
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CenterNet-Elite: A Small Object Detection Model for Driving Scenario by Lingling Wang, Xiang Li, Xiaoyan Chen, Bin Zhou

“…Furthermore, we introduce the content-aware reassembly of features (CARAFE) to replace deconvolution, refining the upsampling process to enhance the quality of feature maps. A series of comparative experiments and ablation studies demonstrate the effectiveness of our method in small object detection. …”
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Robust SAR Change Detection Using Hierarchical Clustering With Adaptive Parameter Tuning by Ahidjo Abdoulaye, Alejandro C. Frery, Mingyang Ma, Shaohui Mei

“…The method automatically identifies clusters of varying densities while filtering out noise, ensuring a more precise change detection process. A new hyperparameter optimization strategy is introduced to enhance clustering performance by tuning key parameters based on the silhouette index. …”
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