An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images by Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He

“…To address these limitations, we propose a Transformer-based unsupervised anomaly detection method for IVCM images, capable of identifying corneal abnormalities without prior knowledge of specific disease features.MethodsOur method consists of three submodules: an EfficientNet network, a Multi-Scale Feature Fusion Network, and a Transformer Network. …”
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Cell‐free epigenomes enhanced fragmentomics‐based model for early detection of lung cancer by Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song, Fei Zhao, Tiantian Gu, Zhe Feng, Haibo Wang, Bowen Li, Daoyun Wang, Bin Zhou, Chao Guo, Yuan Xu, Yang Song, Zhibo Zheng, Zhongxing Bing, Haochen Li, Xiaoqing Yu, Ka Luk Fung, Heqing Xu, Jianhong Shi, Meng Chen, Shuai Hong, Haoxuan Jin, Shiyuan Tong, Sibo Zhu, Chen Zhu, Jinlei Song, Jing Liu, Shanqing Li, Hefei Li, Xueguang Sun, Naixin Liang

“…This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model. …”
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A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification by Amoakoh Gyasi-Agyei

“…Machine learning (ML) models trained on disease datasets are more effective in detection and classification than the conventional methods with hand-crafted features highly dependent on pathologists’ expertise. …”
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Scrotal Ultrasonography Features of Testicular Adrenal Rest Tumors in Male Congenital Adrenal Hyperplasia Patients: A Systematic Review by Epifani A. Chandra, Agustini Utari

“…Male CAH patients diagnosed by clinical and hormonal examination or genetic analysis with at least one of the features of TART in scrotal ultrasonography were included. …”
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Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering by Mohammad Abboush, Christoph Knieke, Andreas Rausch

“…Furthermore, an adopting density-based clustering method, i.e., DBSCAN, has been proposed to group the detected faults based on representative features extracted from DAE. …”
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A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels by Xiaoguang Ma, Zhaolong Sun, Runxiang Jiang, Xinquan Yue, Qi Liu

“…To address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. …”
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OVALYTICS: Enhancing Offensive Video Detection with YouTube Transcriptions and Advanced Language Models by Sneha Chinivar, Roopa M.S., Arunalatha J.S., Venugopal K.R.

“…In response, this work presents OVALYTICS (Offensive Video Analysis Leveraging YouTube Transcriptions with Intelligent Classification System), a comprehensive framework that introduces novel integrations of advanced technologies for offensive video detection. …”
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ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios by Baofeng Ye, Baofeng Ye, Renzheng Xue, Renzheng Xue, Haiqiang Xu, Haiqiang Xu

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Self-Supervised Multi-Task Learning for the Detection and Classification of RHD-Induced Valvular Pathology by Lorna Mugambi, Ciira wa Maina, Liesl Zühlke

“…Embedding visualisations, using both Uniform Manifold Approximation Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE), revealed distinct clusters for all tasks in both models, indicating the effective capture of the discriminative features of the echocardiograms. This study demonstrates the potential of using self-supervised multi-task learning for automated echocardiogram analysis, offering a scalable and efficient approach to improving RHD diagnosis, especially in resource-limited settings.…”
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AI-Powered System for an Efficient and Effective Cyber Incidents Detection and Response in Cloud Environments by Mohammed Ashfaaq M. Farzaan, Mohamed Chahine Ghanem, Ayman El-Hajjar, Deepthi N. Ratnayake

“…Unlike conventional methods, our system employs advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide accurate, scalable, and seamless integration with platforms like Google Cloud and Microsoft Azure. Key features include an automated pipeline that integrates Network Traffic Classification, Web Intrusion Detection, and Post-Incident Malware Analysis into a cohesive framework implemented via a Flask application. …”
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Diagnostic Methods Used in Detecting Multiple Myeloma in Paleopathological Research—A Narrative Review by Kinga Brawańska-Maśluch, Cyprian Olchowy, Grzegorz Mikita, Marta Wanat, Ada Świątko, Michał Krotliński, Martyna Byrska, Joanna Grzelak, Krzysztof Data, Paweł Dąbrowski

“…The diagnostic process is shaped by factors such as preservation, context, and access to technology; despite these variables, characteristic features of lesions were consistently recognized. Conclusion: This review highlights how macroscopic analysis remains central to diagnosis in paleopathology, with radiological and microscopic methods increasingly enhancing accuracy and interpretive depth. …”
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Hollow-core PCF for terahertz sensing: A new approach for ethanol and benzene detection. by Md Golam Sadeque, Suchana Aktar Tithi, Md Safiul Islam, A H M Iftekharul Ferdous, Diponkar Kundu, Md Galib Hasan, Md Zakirul Islam Sarker

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GEOLOGICAL FEATURES IDENTIFIED FROM FIELD OBSERVATIONS AND REMOTE SENSING DATA ON THE UM TAGHIR AREA, EASTERN DESERT, EGYPT by H. A. Awad, I. A. El-Leil, M. Kamel, A. Tolba, A. V. Nastavkin, R. M. El-Wardany

“…The current study presents the integration between field observations and remotely sensed data for detection and extraction of geological structural features using Sentinel-2A and Aster DEM images. …”
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Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features by Guozhen Yang, Yuanheng Huang, Huiguo Chen, Weibin Wu, Yonghui Wu, Kai Zhang, Xiaojun Li, Jiannan Xu, Jian Zhang

“…ABSTRACT Background With the widespread adoption of low‐dose CT screening, the detection of pulmonary ground‐glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). …”
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DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments by Ke Xiong, Zhonghao Wu,  Xuzhong Jia

“…DeepContainer implements a multi-layered detection approach, combining feature engineering techniques with optimized deep learning models to identify security anomalies across diverse container workloads. …”
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A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning by Priyanshu Sinha, Dinesh Sahu, Shiv Prakash, Tiansheng Yang, Rajkumar Singh Rathore, Vivek Kumar Pandey

“…In addition, the model has 90.2% accuracy in conditions of adversarial attack proving that the model is robust and can be used for practical purposes. Based on feature importance analysis using SHAP, the work finds that packet size, connection duration, and protocol type should be the possible indicators for threat detection. …”
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Detection of anthropogenic objects of aero-images based on geometric parameters of the edge of homogeneous areas by A. G. Shauchuk, V. Yu. Tsviatkou

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Validation of a Novel Data-Driven Algorithm to Detect Atypical Prescriptions in Radiation Therapy by Connor Thropp, MS, Jaroslaw Hepel, MD, Timothy Leech, Eric E. Klein, PhD, Qiongge Li, PhD

“…In that study, prototype analysis was conducted within a single institution with a single treatment site. …”
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Detecting the fractal physical activity pattern in aged adults with cerebral small vessel disease by Hóngyi Zhào, Hóngyi Zhào, Wei Wei, Fang Lv, Jie Shen, Yonghua Huang

“…Furthermore, these MRI markers were summed in a score of 0–4, representing all cSVD features combined. Detrended fluctuation analysis (DFA) was used to evaluate the fractal physical activity fluctuations at multiple time scales. …”
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Study of component features of essential oils of <i>Rosmarinus officinalis</i> and <i>Salvia sclarea</i> of Crimean origin by R. E. Ermachenkov, A. L. Markov, M. M. Agaev, E. V. Burtseva, I. I. Terninko

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