Fermi-level-managed multi-barrier heterojunction diodes for terahertz detection by Iñigo Belio-Apaolaza, James Seddon, Cyril C. Renaud

“…Abstract Terahertz heterodyne receivers are essential for enabling coherent, high-sensitivity signal detection. At room temperature, GaAs Schottky barrier diodes remain the leading technology but present limitations, particularly in terms of high local oscillator power requirements and contact reproducibility. …”
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Detection of Malicious Clients in Federated Learning Using Graph Neural Network by Anee Sharma, Ningrinla Marchang

“…The framework detects malicious clients with high accuracy by representing FL local models as directed graphs and capturing layer-wise statistical features. …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…The study demonstrated the superiority of ensemble learning techniques in enhancing detection accuracy and robustness. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to elucidate model predictions, improving transparency and trustworthiness. …”
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The Use of BEREP4 Immunohistochemistry Staining for Detection of Basal Cell Carcinoma by Anthony Paulo Sunjaya, Angela Felicia Sunjaya, Sukmawati Tansil Tan

“…The American Cancer Society reported that 8 out of 10 patients with skin cancer are suffering from BCC with over 2 million new cases each year. BCC needs to be detected at the early stages to prevent local destruction causing disabilities to patients and increasing treatment costs. …”
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Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows by Rui Guo, Yongqiang Dai

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Molecular Detection of Feline Panleukopenia Virus From Clinical Cases in India by P. Saranraj, M. Manigandan, R. Nisha, L. Yuvalakshmi

“…This study was planned for molecular detection of Feline panleukopenia virus from the clinical cases in India. …”
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Adversarial detection based on feature invariant in license plate recognition systems by ZHU Xiaoyu, TANG Peng, ZHANG Haochen, QIU Weidong, HUANG Zheng

“…FIAD utilized neural network invariants and local intrinsic dimensionality invariants for effective sample detection. …”
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Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou, Jun Zhao

“…The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. …”
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The Detection of Soybean Bacterial Blight Based on Polarization Spectral Imaging Techniques by Jia Yi, Huilin Jiang, Yong Tan

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LPFFNet: Lightweight Prior Feature Fusion Network for SAR Ship Detection by Xiaozhen Ren, Peiyuan Zhou, Xiaqiong Fan, Chengguo Feng, Peng Li

“…To address the issue, a lightweight prior feature fusion network (LPFFNet) is proposed to better improve the performance of SAR ship detection. A perception lightweight backbone network (PLBNet) is designed to reduce model complexity, and a multi-channel feature enhancement module (MFEM) is introduced to enhance the SAR ship localization capability. …”
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Islanding Detection Methods and Challenges for Distribution Generation: A Technological Review by Farshad Aslani, Shuhui Li

“…This paper presents a structured and comprehensive review of local islanding detection methods (IDMs), which are categorized into three principal classes: passive, active, and hybrid approaches, with the associated challenges. …”
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Insurance claims estimation and fraud detection with optimized deep learning techniques by P. Anand Kumar, S. Sountharrajan

“…Abstract Estimation and fraud detection in the case of insurance claims play a cardinal role in the insurance sector. …”
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LBP-Based Edge Detection Method for Depth Images With Low Resolutions by Xinyu Wang, Jie Cao, Qun Hao, Kaiyu Zhang, Zihan Wang, Saad Rizvi

“…Furthermore, conventional edge detection methods fail to handle depth images with low resolution. …”
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GSB: GNGS and SAG-BiGRU network for malware dynamic detection. by Zhanhui Hu, Guangzhong Liu, Xinyu Xiang, Yanping Li, Siqing Zhuang

“…For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks' malware to improve the detection rate of attacks' malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. …”
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Evolutionary variation of the monkeypox virus detected for the first time in Nantong, Jiangsu by Xingpei Ji, Ruyue Liang, Chao Bao, Xiuli Cai, Shiyao Chen, Lu Chen, Li Li, Xiaolei Ji

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An explainable feature selection framework for web phishing detection with machine learning by Sakib Shahriar Shafin

“…Conventional and machine learning (ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics. …”
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Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen, Qinghua Liu

“…Smart rice disease detection is a key part of intelligent agriculture. …”
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Sensor Fusion Enhances Anomaly Detection in a Flood Forecasting System by Andrew Ma, Abhir Karande, Natalie Dahlquist, Fabien Ferrero, N. Rich Nguyen

“…To ensure that incorrect readings are identified and addressed appropriately, we devise a novel method for multi-stream sensor data verification and anomaly detection. Our method uses time-series anomaly detection to identify incorrect readings. …”
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STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets by Liuwei Zhang, Zhitao Zhou, Yuyang Xi, Fanjiao Tan, Qingyu Hou

“…However, for infrared dim and small targets with low signal-to-clutter ratios (SCRs), the detection difficulty lies in the fact that their poor local spatial saliency will lead to missed detections and false alarms. …”
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Malicious code within model detection method based on model similarity by Degang WANG, Yi SUN, Chuanxin ZHOU, Qi GAO, Fan YANG

“…The privacy of user data in federated learning is mainly protected by exchanging model parameters instead of source data.However, federated learning still encounters many security challenges.Extensive research has been conducted to enhance model privacy and detect malicious model attacks.Nevertheless, the issue of risk-spreading through malicious code propagation during the frequent exchange of model data in the federated learning process has received limited attention.To address this issue, a method for detecting malicious code within models, based on model similarity, was proposed.By analyzing the iterative process of local and global models in federated learning, a model distance calculation method was introduced to quantify the similarity between models.Subsequently, the presence of a model carrying malicious code is detected based on the similarity between client models.Experimental results demonstrate the effectiveness of the proposed detection method.For a 178MB model containing 0.375MB embedded malicious code in a training set that is independent and identically distributed, the detection method achieves a true rate of 82.9% and a false positive rate of 1.8%.With 0.75MB of malicious code embedded in the model, the detection method achieves a true rate of 96.6% and a false positive rate of 0.38%.In the case of a non-independent and non-identically distributed training set, the accuracy of the detection method improves as the rate of malicious code embedding and the number of federated learning training rounds increase.Even when the malicious code is encrypted, the accuracy of the proposed detection method still achieves over 90%.In a multi-attacker scenario, the detection method maintains an accuracy of approximately 90% regardless of whether the number of attackers is known or unknown.…”
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