A Hybrid Graph-Based Risk Assessment and Attack Path Detection Model for IoT Systems by Ferhat Arat, Sedat Akleylek, Zaliha Yuce Tok

“…Our proposed approach targets high-vulnerability components within the system through risk-weighted backtracking, thereby facilitating more efficient attack path detection and filtering. The developed method is evaluated in a border security case scenario, with comparisons made in terms of algorithmic and asymptotic complexity. …”
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. …”
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Infrared Small Target Detection Based on Compound Eye Structural Feature Weighting and Regularized Tensor by Linhan Li, Xiaoyu Wang, Shijing Hao, Yang Yu, Sili Gao, Juan Yue

“…Furthermore, to improve detection speed, the structural tensor of the effective imaging region in infrared compound eye images is reconstructed, and the Representative Coefficient Total Variation method is employed to avoid complex singular value decomposition and regularization optimization computations. …”
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DFC-Net: Dual-Branch Collaborative Feature Enhancement for Cloud Detection in Remote Sensing Images by Wanting Zhou, Yan Mo, Qiaofeng Ou, Shaowei Bai

“…Although deep learning-based methods have achieved remarkable results, challenges remain in the fine-grained segmentation of cloud boundaries and the detection of thin clouds over complex land surfaces. …”
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Radar Coherent Detection for Maneuvering Target Based on Product-Scaled Integrated Cubic Phase Function by Jinyang Chen, Ke Jin, Shangjiang Yu, Tao Lai, Yongjun Zhao

“…Analysis demonstrates that the SNUFFT has the same computational complexity with nonuniform fast Fourier transform (NUFFT), and thus the PSICPF could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT), and NUFFT. …”
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Image processing‐based noise‐resilient insulator defect detection using YOLOv8x by Shagor Hasan, Md. Abdur Rahman, Md. Rashidul Islam, Animesh Sarkar Tusher

“…This paper proposes a novel framework based on the YOLOv8x object detection scheme, addressing the challenge of detecting small defects in complex aerial images and providing a noise mitigation scheme. …”
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Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network by Chunyao Hou, Zhihui Liu, Fan Xia, Xun Zhou, Yuhang Shui, Yonglong Li

“…The improved U-Net model exhibits higher detection accuracy in corridor crack segmentation tasks, effectively extracting feature information from irregular shapes, and provides a superior solution for crack defect detection in a plunge pool corridor, thereby enhancing the safety and efficient maintenance of hydropower stations.…”
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Development of a New Indirect ELISA Test for the Detection of Anti-Feline Coronavirus Antibodies in Cats by Irene Ferrero, Sarah Dewilde, Paolo Poletti, Barbara Canepa, Enrica Giachino, Paola Dall’Ara, Joel Filipe

“…The diagnosis of FIP is complex and requires different examinations. Among serological tests, the indirect immunofluorescent antibody test (IFAT), considered the gold standard, and the enzyme-linked immunosorbent assay (ELISA) are the most widely used to detect FCoV antibodies. …”
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CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo by Muhammad Usama Zahid, Muhammad Usman Akram, Muhammad Danish Nisar, Fahd Maqsood, Syed Usman Ali, Muhammad Montaha

“…This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals.…”
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Deep learning-based automatic detection and grading of disk herniation in lumbar magnetic resonance images by Yan Guo, Xiaoxiang Huang, Wei Chen, Ichiro Nakamoto, Weiqing Zhuang, Hua Chen, Jie Feng, Jianfeng Wu

“…The greatest challenges today are the repetitive and time-consuming process of interpreting these complex MR images and the problem of unequal diagnostic results from physicians with different levels of experience. …”
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PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things by Auwal Sani Iliyasu, Abdul Jabbar Siddiqui, Houbing Song, Fahad Jibrin Abdu

“…To address this pressing issue, we propose PNet-IDS, a novel lightweight convolutional neural network (CNN)-based method to reduce computational complexity and optimize on-device resource usage for real-time intrusion detection. …”
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Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction by Abdelmoghit Zaarane, Ibtissam Slimani, Abdellatif Hamdoun, Issam Atouf

“…Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. …”
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An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment by Julian R. Rice, G. Andrew Fricker, Jonathan Ventura

“…We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. …”
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A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…It efficiently and intelligently detects and manages security data in digital library network. …”
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FEVT-SAR: Multicategory Oriented SAR Ship Detection Based on Feature Enhancement Vision Transformer by Minding Fang, Yu Gu, Dongliang Peng

“…Issues such as complex noise interference and the long-tail distribution of data present many challenges to the multicategory ship detection task in synthetic aperture radar (SAR) images. …”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…Further, research is needed to create robust, efficient techniques that generalize datasets and detect anomalies in complex industrial images. …”
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Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits by Yun Zhao, Yang Li, Xing Xu

“…We also introduced a non-maximal suppression algorithm, Cluster-NMF, which accelerates inference speed through matrix parallel computation and merges multiple high-quality target detection frames to generate an optimal detection frame, enhancing the efficiency of blueberry canopy fruit detection without compromising inference speed.…”
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K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm by Ali Al-Hafiz, Adnan Jabir, Shamala Subramaniam

“… Phishing detection is considered a critical problem in cybersecurity, and utilising machine learning with an efficient feature selection method for precisely identifying malicious websites is deemed the most critical challenge. …”
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Enhanced BDS four-frequency cycle slip detection and repair using fuzzy clustering analysis by Jinfeng Yuan, Xiaoning Su, Yuzhao Li

“…It is worth noting that during the repair process, the coefficients of the combined observation values are integers, preserving the integer cycle characteristic of the observation values, which allows cycle slips to be fixed directly, eliminating the need for complex searching procedures. …”
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Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences by K. Thenmozhi, M. Pyingkodi, V. S. Prakash, Kripa Josten, S. Manju Priya, J. Vennila

“…Abstract The Adaptive Fuzzy C-Means with Logit Boost Distributed Clustering (AFC-LBDC) technique is introduced to enhance cancer detection promptly. The various conventional techniques often struggle to improve cancer detection due to their high complexity effectively. …”
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