Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging by Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel

“…Generative Artificial Intelligence (AI) leverages this attribute to synthesize images that accurately reflect these normative patterns. This capability enables the AI allowing them to effectively detect and correct anomalies in new, unseen pathological data without the need for expert labeling. …”
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Luciferase-mediated assay to detect the PAMP-triggered gene expression in transgenic Nicotiana benthamiana by Ola Barakat, Fushuang Zhang, Mengzhu Zeng, Yuanchao Wang

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Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation by Haiwoong Park, Hyeryung Jang

“…In contrast, image anomaly detection has leveraged advancements in technologies for analyzing spatial patterns and visual features, achieving high accuracy and promoting research aimed at improving efficiency. …”
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Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks by Kaqian Zeng, Zhao Li, Xiujuan Wang

“…Subsequently, seven sentiment-based features are extracted to quantify statistical disparities in emotional expression patterns between bot and human accounts. An attention gating mechanism is further designed to dynamically fuse these sentiment features with user description, tweet content, numerical attributes, and categorical features. …”
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Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning by Berk Özel, Muhammad Shahab Alam, Muhammad Umer Khan

“…Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.…”
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A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM by Guoli Song, Zheng Huang, Yiwen Zhao, Xingang Zhao, Yunhui Liu, Min Bao, Jianda Han, Peng Li

“…First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

“…Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. …”
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Generative AI in Intrusion Detection Systems for Internet of Things: A Systematic Literature Review by Zhe Deng, Ants Torim, Sadok Ben Yahia, Hayretdin Bahsi

“…Generative Artificial Intelligence (GenAI) demonstrates strong capabilities in generating synthetic data based on realistic distributions and learning complex patterns from high-dimensional data. By harnessing the capabilities of generative AI, it is feasible to augment intrusion detection models, allowing for more robust and adaptive security solutions in IoT environments. …”
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Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction by Wei Bai, Lan Xiong, Yubei Liao, Zhengyang Tan, Jingang Wang, Zhanlong Zhang

“…The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. …”
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Image-Based Face Recognition Techniques Used in Disease Detection Approaches: A Survey by Ghalib Ahmed Salman, Inas Jawad Kadhim, Ahmed Husham Al-Badri

“…These alterations in facial patterns can serve as potential indicators for corresponding diseases, particularly in the fields of endocrinology and metabolism, Muscles-Nervous disorders, Chromosomes, and Genetic disorders, among others. …”
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Novel Organic-Inorganic Hybrid Polystyrene Nanoparticles with Trichromatic Luminescence for the Detection of Latent Fingerprints by Xiao Wang, Tao Liao, Haiyan Wang, Hongxia Hao, Qinglai Yang, Hong Zhou, Yu Ma, Minjie Zhi, Jiahao Wang, Ruihang Fan

“…The developed fingerprints with excellent clarity of ridges and contrast could be viewed, and the digital magnification of fluorescence-developed fingerprints provided more minutiae details about some regional patterns. The colorimetric and fluorescent trichromatic light could provide complementary signals without the background interference from fluorescent substrates and/or complex multicolor surfaces, which improved the applicability of fluorescent nanoparticles for fingerprints development. …”
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Experimental Results of Diver Detection in Harbor Environments Using Single Acoustic Vector Sensor by Saier MAHMOUD, Louay SALEH, Ibrahim CHOUAIB

“…Experimental data from sea trials demonstrate the capability of all three methods to reconstruct the waveform of the acoustic diver signal and highlight the periodic breathing patterns. The diver’s breathing rate and corresponding power are estimated using the fast Fourier transform (FFT) of the power signal, therefore serving as a key signature for diver detection. …”
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Detecting cyber attacks in vehicle networks using improved LSTM based optimization methodology by C. Jayasri, V. Balaji, C. M. Nalayini, S. Pradeep

“…Feature extraction is performed using the Discrete Fourier Transform (DFT), capturing frequency-domain patterns indicative of anomalies. Detection is executed through an Improved Long Short-Term Memory (ILSTM) model, whose parameters are optimized using the Crocodile Optimization Algorithm (COA), aiming to maximize classification accuracy. …”
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Artificial Intelligence for Smoking Detection: A Review of Machine Learning and Deep Learning Approaches by Mohammed Al-Hayali, Fawziya Ramo

“…Recent advances in deep learning, machine learning, Artificial Intelligence (AI), big data analytics, and computer vision have greatly enhanced smoking detection. These technologies enable the analysis of diverse datasets to identify patterns that indicate smoking behavior By enhancing the effectiveness of smart smoking detection systems And so we can better protect public health and reduce exposure to secondhand smoke in public places. …”
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Characterising Payload Entropy in Packet Flows—Baseline Entropy Analysis for Network Anomaly Detection by Anthony Kenyon, Lipika Deka, David Elizondo

“…A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. …”
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Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods by M. Hafidz Ariansyah, Sri Winarno

“…Researchers get detection results using a branch of AI technology, namely machine learning to find new knowledge from data and find patterns to make diagnoses. …”
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Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection by Giacomo Patrucco, Francesco Setragno, Antonia Spanò

“…Architectural heritage conservation increasingly relies on innovative tools for detecting and monitoring degradation. The study presented in the current paper explores the use of synthetic datasets—namely, rendered images derived from photogrammetric models—to train convolutional neural networks (CNNs) for the automated detection of deterioration in historical reinforced concrete structures. …”
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Biological sensors and bio-inspired technologies: the role of insects in advanced detection systems and robotics by Ebrahim Abbasi

“…The findings reveal that insect olfactory receptors, mechanosensory systems, and locomotion patterns can be effectively mimicked in bio-hybrid robots and bio-inspired materials. …”
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Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review by Marcelo Leiva-Bianchi, Nicolas Castillo, César A. Astudillo, Francisco Ahumada-Méndez

“…Multilayer Perceptron (MLP) demonstrated the highest accuracy (ACC=92%, p<0.001) and precision (P=81%, p<0.001), excelling in capturing complex, nonlinear patterns essential for analyzing nuanced online interactions. …”
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Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Introduction: Detecting water contamination in community housing is crucial for protecting public health. …”
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