Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography by Oleksandr Kuznetsov, Emanuele Frontoni, Kyrylo Chernov, Kateryna Kuznetsova, Ruslan Shevchuk, Mikolaj Karpinski

“…This paper presents an extensive investigation into the application of artificial intelligence, specifically Convolutional Neural Networks (CNNs), in image steganography detection. We initially evaluated the state-of-the-art steganalysis model, SRNet, on various image steganography techniques, including WOW, HILL, S-UNIWARD, and the innovative Spread Spectrum Image Steganography (SSIS). …”
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Revolutionizing Pneumonia Diagnosis: AI-Driven Deep Learning Framework for Automated Detection From Chest X-Rays by N. Shilpa, W. Ayeesha Banu, Prakash B. Metre

“…Timely and accurate detection is paramount for initiating prompt intervention and improving patient prognoses. …”
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Detection of Salivary Biomarkers Osteocalcin and Osteonectin of Bone Turnover among smokers Periodontitis Patients in Erbil City-Iraq by Azheen A. Abdullah, Heba F. Hamodat

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A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos. by Shagun Sharma, Kalpna Guleria, Ayush Dogra, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…These outcomes demonstrate that the model is highly optimized and generalizes the improved outcomes when compared to the state-of-the-art models.…”
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Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging. by Doaa Mousa, Nourhan Zayed, Inas A Yassine

“…Alzheimer's disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. …”
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State-of-Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy Features and Fusion Interpretable Deep Learning Framework by Bohan Shao, Jun Zhong, Jie Tian, Yan Li, Xiyu Chen, Weilin Dou, Qiangqiang Liao, Chunyan Lai, Taolin Lu, Jingying Xie

“…Monitoring and accurately predicting the state of health (SOH) of lithium-ion batteries (LIBs) is essential for ensuring safety, particularly in detecting early signs of potential failures such as overheating and incorrect charging and discharging practices. …”
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Hypoxemia detection and oxygen therapy practices in neonatal and pediatric wards across seven district and referral hospitals in Rwanda by Hyacinthe Mushumbamwiza, Harriet H. Webster, Christine Kayitesi, Jasmine Miller, Nang’andu Chizyuka, Felix Musabirema, Alida Ngwije, Brenda Kateera, Sanctus Musafiri, Lisine Tuyisenge, Hamish R. Graham, Felix Lam, Corneille Ntihabose

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Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN by Burak Duran, Saeed Eftekhar Azam, Masoud Sanayei

“…Then, this acceleration time-history series was transformed into grayscale images, serving as inputs for a Convolutional Neural Network developed to detect and classify structural damage states. Initially, it was trained and tested on datasets derived from a Single-Source Domain structure, achieving perfect accuracy (1.0) in a ten-label multi-class classification task. …”
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Closing Gaps in Chronic Kidney Disease Detection: Evaluating At-Home Targeted Testing in a Safety-Net Population by Megan Schultz, Katelyn Laue, Nicole Bryer, Andrew Bzowyckyj, Anuja Java, Leslie Lake, Elizabeth Talbot-Montgomery, Brit Sovic, Bri’Anna Watson, Joseph A. Vassalotti

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Review of the implementation seven-year screening program for early detection of breast cancer in the Khanty-Mansi autonomous okrug – Yugra by Yu. A. Belaya, N. A. Zakharova

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Criteria for Detecting Turn-To-Turn Short Circuit in Stator Windings Using Vector Analysis of Electric Motor Phase Currents by I. V. Zhezhelenko, V. E. Kryvonosov, S. V. Vasilenko

“…The power series of asynchronous motors was established, for which the sensitivity of detecting the initial moment of the turn-to-turn short circuit is maximum. …”
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Altered spontaneous brain activity in primary open angle glaucoma: a resting-state functional magnetic resonance imaging study. by Yinwei Song, Ketao Mu, Junming Wang, Fuchun Lin, Zhiqi Chen, Xiaoqin Yan, Yonghong Hao, Wenzhen Zhu, Hong Zhang

“…Spontaneous brain activity may play a role in POAG initiation and progression.…”
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Analysis of observability and detectability for CSTR model of biochemical processes under uncertain system dynamics and various sets of measured outputs by Rafał Łangowski, Mateusz Czyżniewski

“…The method of indistinguishable state trajectories (indistinguishable dynamics) and tools based on the Lyapunov second method were used to investigate the observability and detectability properties. …”
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How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics by Lukas Roell, Stephan Wunderlich, David Roell, Florian Raabe, Elias Wagner, Zhuanghua Shi, Andrea Schmitt, Peter Falkai, Sophia Stoecklein, Daniel Keeser

“…We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research. …”
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Method of Diagnostics of Operation Modes of Individual Heat Supply Units, Allowing to Detect Pre-Emergency Situations at an Early Stage by Dvortsevoy A.I., Borush O.V., Khoreva V.A., Yakovina I.N.

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You Only Look Once v5 and Multi-Template Matching for Small-Crack Defect Detection on Metal Surfaces by Pallavi Dubey, Seth Miller, Elif Elçin Günay, John Jackman, Gül E. Kremer, Paul A. Kremer

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Search for Optimal Parameters for Gas Chromatographic Complex When Determining Sarin in Water Samples by Flame Photometric Detection Method by V. А. Ignatyev, L. V. Petrakova, М. А. Ponsov, А. А. Rodionov

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Forest fire detection based on temporal and spatial correction of background brightness temperature using GF-4 PMI data by P. Cheng, H. Sui, J. Wang, L. Hua, J. Liu, Q. Zhou, Y. Zeng

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VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection by Sanam Salman Kazi, Bhakti Palkar, Dhirendra Mishra

“…It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.…”
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CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection by Tianjiao Zeng, Tianwen Zhang, Zikang Shao, Xiaowo Xu, Wensi Zhang, Jun Shi, Shunjun Wei, Xiaoling Zhang

“…This strategic fusion within our framework markedly improves the detection of small and inshore ships. Evaluated on the large-scale SAR ship detection dataset-v1.0, our framework demonstrates superior performance, surpassing 20 state-of-the-art models. …”
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