A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images by Touba torabipour, Abolfazl Gandomi, Mohammad Ghanimi

“…The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. …”
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Analog Circuits Fault Diagnosis Using ISM Technique and a GA-SVM Classifier Approach by Sabah Kouachi, Nacerdine Bourouba, Kamel Mebarkia, Imad Laidani

“…One of these troubleshoots faced is the lack of effective features that help to optimize fault classifier and hence improve circuit fault detection and identification. …”
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Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo, Zhi Wang

“…Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. …”
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Token Mixing for Breast Cancer Diagnosis: Pre-Trained MLP-Mixer Models on Mammograms by Hosameldin O. A. Ahmed, Asoke K. Nandi

“…Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced mammographic analysis by automating feature extraction and improving early detection. …”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…Overall, the results demonstrate that advanced ensemble learning, meticulous feature selection, and effective class imbalance handling can significantly enhance early detection in traumatic brain injury cases. …”
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Fatigue Resistance of Asphalt Concrete Pavements. Peculiarity and Assessments of Potentials by Zhongyu Li, Tingguo Liu, Jicun Shi, Uladzimir Veranko, Vitali Zankavich

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A Novel Approach for Identifying and Eliminating the Degradations in Real Time Images by S. Rajkumar, L. M. Jenila Livingston, Vansh Juneja, Shashwat Sharv, Pratik Dattatray Mahajan

“…Satellite imagery plays a crucial role in various applications, but the quality of the images can be degraded by noise. Accurate noise detection is essential for effective image enhancement and analysis. …”
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Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model by Zeqing Yang, Jiahui Zhang, Zhimeng Li, Ning Hu, Zhengpan Qi

“…Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. …”
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Dioctylsulfosuccinate Functionalized NiAl-Layered Double Hydroxide for Sensitive Fenuron Electroanalysis Using a Carbon Paste Electrode by Aude Peggy Kameni Wendji, Herve Leclerc Tcheumi, Ignas Kenfack Tonle, Emmanuel Ngameni

“…The electrochemical procedure for fenuron analysis consisted of immersing the working electrode in an electrolytic solution containing the appropriate amount of fenuron, followed by voltammetry detection without any preconcentration step. …”
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Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression by Zhao‐Yue Zhang, Zi‐Jie Sun, Dong Gao, Yu‐Duo Hao, Hao Lin, Fen Liu

“…Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. …”
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The Determination of On-Water Rowing Stroke Kinematics Using an Undecimated Wavelet Transform of a Rowing Hull-Mounted Accelerometer Signal by Daniel Geneau, Drew Commandeur, Ryan Brodie, Ming-Chang Tsai, Matt Jensen, Marc Klimstra

“…Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. …”
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Multispectral image fusion method based on edge chromatic aberration by Y. Shi, D. Qiu, R. Wu, R. Wu, W. Niu, Z. Wang

“…<br />Experiments show that the multispectral image fusion method based on edge color difference significantly improves the performance of road damage analysis on the self-built BUCEA-MS-Road-Damage dataset: the edge IoU in the detection task is increased to 80.1% (+1.3%), and the target detection accuracy is 92.3% (+3.6%); the accuracy and recall of the classification task are increased to 91.3% (+3.0%) and 89.8% (+3.0%) respectively; the Dice coefficient of the segmentation task is 83.3% (+3.0%). …”
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Fuzzy Intelligence in Physical Immersion Teaching System Based on Digital Simulation Technology by Aihui Du

“…In addition, this study proposes a new algorithm based on the morphological features of geometric images, which combines the transformation detection method of cluster analysis to realize the intelligent processing of images. …”
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CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images by Sadia Din, Muhammad Shoaib, Erchin Serpedin

“…In chest X-ray analysis, however, challenges remain in accurately segmenting and classifying organs such as the lungs, heart, diaphragm, sternum, and clavicles, as well as detecting abnormalities in the thoracic cavity. …”
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Action Recognition in Videos through a Transfer-Learning-Based Technique by Elizabeth López-Lozada, Humberto Sossa, Elsa Rubio-Espino, Jesús Yaljá Montiel-Pérez

“…The proposed method comprises four stages: (1) human detection and tracking, (2) motion estimation, (3) feature extraction, and (4) action recognition using a two-stream model. …”
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A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network by Xing Ding, Xiangrong Zhang, Chao Liang, Bo Liu, Lanjie Niu

“…The test results show that the multimode mutual attention fusion network containing a feature fusion attention mechanism has the highest detection performance and anti-interference ability. …”
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Integrating expert knowledge with machine learning for AI-based stroke identifications and treatment systems by Taddesse kassu Yimenu, Abebe Belay Adege, Sofonias Yitagesu Techan

“…The data for this study were obtained from Debre Berhan Referral Hospital through expert interviews, prescriptions, and from a public dataset in the Kaggle platform. Feature selection was performed using decision trees, Chi-Square tests, Elastic Net coefficients, and correlation analysis. …”
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Dual Attention Dual-Resolution Networks for Real-Time Semantic Segmentation of Street Scenes by Baofeng Ye, Renzheng Xue

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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. Explainability techniques such as SHAP, LIME, and Grad-CAM approaches are employed to present a complete comprehension of feature significance and local or global predictive behavior of the model over various malware categories. …”
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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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