An optimized anomaly detection framework in industrial control systems through grey wolf optimizer and autoencoder integration by Muhammad Muzamil Aslam, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong, Ali Tufail

“…The method operates in two stages: (1) Optimizing GWO for feature selection to identify relevant features and reduce feature errors, and (2) Utilizing AE for efficient anomaly detection. Experimental validation on the SWaT and WADI benchmark datasets demonstrates the superior performance of the proposed model, achieving significant improvements in accuracy, precision, recall, and F1-score over existing state-of-the-art approaches. …”
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ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection by N. I. Md. Ashafuddula, Rafiqul Islam

“…Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. …”
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The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning. by Bo Yu, Zheng Wang, Shangke Liu, Xiaomin Liu, Ruixin Gou

“…Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. …”
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A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology by Mustafa N. Abbas, David Broneske, Gunter Saake

“…Abstract Detecting protein complexes is crucial in computational biology for understanding cellular mechanisms and facilitating drug discovery. …”
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Infrared Small Target Detection Based on Approximate Background Regularization and Bimodal Slice Based Graph Constraints by Xiaoling Ge, Zipeng Fu, Xuelian Yu, Weixian Qian, Kan Ren, Minjie Wan, Guohua Gu

“…Experimental results show that the proposed method outperforms state-of-the-art approaches in both detection performance and efficiency while demonstrating greater robustness in complex scenarios.…”
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OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions by Xin Lai, Xin Lai, Min Liu, Yuqian Liu, Xiaoyan Zhu, Jiayin Wang, Jiayin Wang

“…This sensitized T2 control chart is specifically designed to accurately differentiate between the three categories of chromatin states: open, partially open, and closed. Experimental results demonstrate that under this framework, the model exhibits not only excellent performance in terms of three-class classification, but also higher accuracy and sensitivity in binary classification compared to the state-of-the-art models currently available.…”
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Clinical Significance of Rotational Thromboelastometry (ROTEM) for Detection of Early Coagulopathy in Trauma Patients: A Retrospective Study by Mohammad Asim, Ayman El-Menyar, Ruben Peralta, Suresh Arumugam, Bianca Wahlen, Khalid Ahmed, Naushad Ahmad Khan, Amani N. Alansari, Monira Mollazehi, Muhamed Ibnas, Ammar Al-Hassani, Ashok Parchani, Talat Chughtai, Sagar Galwankar, Hassan Al-Thani, Sandro Rizoli

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Detection of Multiple Small Biased GPS Spoofing Attacks on Autonomous Vehicles Using Time Series Analysis by Ahmad Mohammadi, Reza Ahmari, Vahid Hemmati, Frederick Owusu-Ambrose, Mahmoud Nabil Mahmoud, Parham Kebria, Abdollah Homaifar

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The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022. by Maggie S J McCarter, Stella Self, Kyndall C Dye-Braumuller, Christopher Lee, Huixuan Li, Melissa S Nolan

“…Arboviruses (arthropod-borne-viruses) are an emerging global health threat that are rapidly spreading as climate change, international business transport, and landscape fragmentation impact local ecologies. Since its initial detection in 1999, West Nile virus has shifted from being a novel to an established arbovirus in the United States of America. …”
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Multimodal LLM for enhanced Alzheimer’s Disease diagnosis: Interpretable feature extraction from Mini-Mental State Examination data by Meiwei Zhang, Yuwei Pan, Qiushi Cui, Yang Lü, Weihua Yu

“…Alzheimer’s Disease (AD) poses a considerable global health challenge, necessitating early and accurate diagnostics. The Mini-Mental State Examination (MMSE) is widely used for initial screening, but its traditional application often underutilizes the rich multimodal data it generates, such as videos, images, and speech. …”
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A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection by Rodrigo Colnago Contreras, Monique Simplicio Viana, Victor José Souza Bernardino, Francisco Lledo dos Santos, Önsen Toygar, Rodrigo Capobianco Guido

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The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity by Nikita Pospelov, Alina Tetereva, Olga Martynova, Konstantin Anokhin

“…However, patterns of this resting-state (RS) activity can undergo long-term alterations following exposure to preceding meaningful stimuli. …”
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An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification by Xin Liu, Qiao Meng, Xin Li, Zhijie Wang, Siyuan Kong, Bingyu Li

“…Performance evaluation using fixed‐point road monitoring data indicates that the proposed model outperforms traditional traffic state detection models, with a performance improvement of approximately 4.7% to 9.5%. …”
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Ultra-sensitive urinary lipoarabinomannan (LAM) immunoassay for tuberculosis detection: a performance evaluationResearch in context by Qisheng Jiang, Carolyn Duncan, Harisha Ramachandraiah, Ige A. George, Sumanth Gandra, Marcos Perez, Lorraine Lillis, David S. Boyle, Scott Crick, Morten Ruhwald, Srikanth Singamaneni

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Evaluation of real-time polymerase chain reaction for detection of Strongyloides stercoralis in immunocompromised patients in North India by Dr Abhishek Mewara, Ms. Nikita Sharma, Dr Gaurav Prakash, Dr Vignesh Pandiarajan, Dr Sahajal Dhooria, Dr Varun Dhir, Dr Surjit Singh, Dr Ritesh Agarwal

“…In this study, we evaluated real-time polymerase chain reaction (RT-PCR) for detection of Strongyloides stercoralis in immunocompromised patients in comparison with serology, microscopy and culture Methods. …”
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An AI explained systematic modular approach for enhanced Electricity Theft Detection for urbanized Smart Grid environment by Muhammad Ammar, Nadeem Javaid, Ali Arishi

“…Electricity theft is a major issue that disrupts utilities and compromises the stability of urbanized Smart Grids (SG), leading to significant financial losses, operational disruptions, and compromised system stability. Traditional detection systems often face challenges like severe data discrepancies, high class imbalance, and volatile consumption patterns, which hinder their ability to detect abnormal movements and limit system dependability. …”
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Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery by Yue Wang, Tian Ye

“…The distress detection on pavement and roads and delivering healthcare and medical services need to be monitored through state-of-the-art technology, i.e., drone technology. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot be generalized well because of the lack of anomalous datasets. …”
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An automatic acne detection, severity, and assessment framework using generative adversarial network with deep neural network by Umara Khalid, Li Chen, Abdullah Ayub Khan, Faisal Mehmood

“…However, the simulation shows that the technological collaboration achieves promising results and elaborates on the accuracy and efficiency while the detection of acne as compared to other state-of-the-art baseline methods.…”
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An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal by Jalil Manafian, Mehdi Fazli, Onur Ilhan, Subhiya Zeynalli, Sukaina Tuama Ghafel

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