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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Active-Darknet: An Iterative Learning Approach for Darknet Traffic Detection and Categorization by Sidra Abbas, Imen Bouazzi, Gabriel Avelino Sampedro, Shtwai Alsubai, Ahmad S. Almadhor, Abdullah Al Hejaili, Natalia Kryvinska

“…The majority of models exhibited encouraging outcomes; however, the models that utilised active learning, specifically the Random Forest (RF) and Decision Tree (DT) models, attained promising accuracy levels of 87%, rendering them the most efficient in detecting darknet traffic. Large traffic analysis is greatly enhanced by this method, which also increases the detection process’s robustness and effectiveness.…”
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Design of ELISA-based diagnostic system for detection of enterohaemorrhagic Escherichia coli by Mohammad Javad Rezaei, Maryam Eidi, Seyed Ali Mirhosseini, Rouhollah Kazemi, Mohammad Javad Motamedi, Soghra Khani, Jafar Amani

“…EspA, Intimin and Tir proteins (EIT) are the most important bacterial features in the process of binding. These antigens can be very useful in detecting these bacteria. …”
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Implementation for Lightweight Deep Learning for Anomaly Detection and Denoising on Gravitational Waves by R. K. Mohith Niranjen, C. Yogesh, Anirudh Vinodh, Tharun Sureshkumar, S. Vatchala

“…Our study demonstrates the development of state-of-the-art deep learning methods to surmount the specific obstacles concerning gravitational wave detection, paving the way for real-time processing of astrophysical data and an improved understanding of the Universe.…”
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Unveiling gastric precancerous stages: metabolomic insights for early detection and intervention by Xiaoyue Zhang, Ziming Lin, Boyan Xu, Chenyu Ma, Bowen Jiang, Yan Geng, Yingyue Sheng, Yuanyuan Dai, Yuzheng Xue, Yilin Ren

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Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection by Shakeel Ahmad, Muhammad Zaman, Ahmad Sami Al-Shamayleh, Rahiel Ahmad, Shafi'I Muhammad Abdulhamid, Ismail Ergen, Adnan Akhunzada

“…The novelty of this research lies in providing a roadmap for researchers, practitioners, and cybersecurity experts to navigate the landscape of machine learning (ML) and deep learning (DL) models for phishing detection. The study reviews traditional phishing detection methods, ML and DL models, phishing datasets, and the step-by-step phishing process. …”
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GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced Understanding by Faheem Shaukat, Naveed Ejaz, Zeeshan Ashraf, Mrim M. Alnfiai, Mona Mohammed Alnahari, Reemiah Muneer Alotaibi

“…Automatic movie genre detection is vital for improving content recommendations, user experiences, and organization. …”
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Detecting the left atrial appendage in CT localizers using deep learning by Aydin Demircioğlu, Denise Bos, Anton S. Quinsten, Lale Umutlu, Oliver Bruder, Michael Forsting, Kai Nassenstein

“…To guide the imaging process, technologists first perform a localizer scan, which is a preliminary image used to identify the region of interest. …”
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Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals by Yinian Liang, Yan Wang, Fangjiong Chen, Hua Yu, Fei Ji, Yankun Chen

“…In the ocean environment, passive acoustic monitoring (PAM) is an important technique for the surveillance of cetacean species. Manual detection for a large amount of PAM data is inefficient and time-consuming. …”
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A Hybrid CNN Framework DLI-Net for Acne Detection with XAI by Shaila Sharmin, Fahmid Al Farid, Md. Jihad, Shakila Rahman, Jia Uddin, Rayhan Kabir Rafi, Radia Hossan, Hezerul Abdul Karim

“…To enhance its interpretability further, Grad-CAM (Gradient-Weighted Class Activation Mapping) is utilized to visualize the regions of the image that the model focuses on during predictions, providing transparent insights into the decision-making process. This study underscores the transformative potential of AI in dermatology, offering a robust solution for acne detection and classification, which can significantly improve clinical decision making and patient outcomes.…”
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Violence Detection From Industrial Surveillance Videos Using Deep Learning by Hamza Khan, Xiaohong Yuan, Letu Qingge, Kaushik Roy

“…In this paper, we propose a three-stage deep learning-based end-to-end framework for violence detection. The lightweight convolutional neural network (CNN) model initially identifies individuals in the video stream to minimize the processing of irrelevant frames. …”
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Detection of Adulteration in Food Using Recurrent Neural Network with Internet of Things by Vishwesh Nagamalla, B. Muthu Kumar, Neha Janu, Anusha Preetham, Syam Machinathu Parambil Gangadharan, Mejdal A. Alqahtani, Rajnish Ratna

“…In the proposed project, the fractional-order element would be investigated for its potential use in the detection of milk adulteration. With this fractional-order element-based impedance sensor, you can distinguish between different types of contaminated milk and different types of faking it, which is quite useful in the detection and differentiation of fake and real milk. …”
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Dead or Alive? Identification of Postmortem Blood Through Detection of D-Dimer by Amy N. Brodeur, Tai-Hua Tsai, Gulnaz T. Javan, Dakota Bell, Christian Stadler, Gabriela Roca, Sara C. Zapico

“…Fibrinolysis is the natural process that breaks down blood clots after healing a vascular injury. …”
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Enhancing Lung Cancer Detection through Dual Imaging Modality Integration by Costin Teodor STREBA, Mircea-Sebatian ŞERBĂNESCU, Liliana STREBA, Alin Dragoş DEMETRIAN, Andreea-Georgiana GHEORGHE, Mădălin MĂMULEANU, Daniel-Nicolae PIRICI

“…Three convolutional neural network (CNN) models—AlexNet, GoogLeNet, and ResNet—originally trained on ImageNet, were either fine-tuned using only pCLE images (confocal TL) or underwent a dual TL process, where they were first trained on histological images before adapting to pCLE data. …”
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Efficient Anomaly Detection for Edge Clouds: Mitigating Data and Resource Constraints by Javad Forough, Hamed Haddadi, Monowar Bhuyan, Erik Elmroth

“…Additionally, we utilize knowledge distillation to distill the knowledge from the previously mentioned high-capacity model, known as the teacher model, into a more compact student model. This distillation process enhances the student model’s computational efficiency while retaining its detection power. …”
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scFocus: Detecting branching probabilities in single-cell data with SAC by Chunlin Chen, Zeyu Fu, Jiajia Yang, Huaqing Chen, Jiabao Huang, Shitian Qin, Chuhuai Wang, Xiaoqian Hu

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Detecting Simulated Nosocomial Disease Outbreaks with Sequential Monte Carlo Methods by Dr Conor Rosato

“…Using ROC curves and the AUC metric, we assess the performance of the proposed method of disease outbreak detection. We show that algorithm can detect true outbreaks with minimal false alarms. …”
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Detecting faults in electronic combustion engine control systems by acoustic parameters by V. D. Gabidulin, V. N. Dobromirov

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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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An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection by Mohammad Rifat Ahmmad Rashid, Md. AL Ehtesum Korim, Mahamudul Hasan, Md Sawkat Ali, Mohammad Manzurul Islam, Taskeed Jabid, Raihan Ul Islam, Maheen Islam

“…The early and accurate detection of plant diseases is critical for sustainable agriculture, ensuring crop health, reducing losses, and supporting food security. …”
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