DAFDM: A Discerning Deep Learning Model for Active Fire Detection Based on Landsat-8 Imagery by Xu Gao, Wenzhong Shi, Min Zhang, Lukang Wang

“…Traditional methods for detecting AFs rely on the statistical analysis of AF radiance and background features. …”
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Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

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Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection by Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li

“…By analyzing parameters such as pressure distribution, gait cycles, and gait features of the foot sole, we aim to evaluate an individual’s gait function and detect and diagnose gait-related diseases such as Parkinson’s disease. …”
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Enhanced skin cancer diagnosis: a deep feature extraction-based framework for the multi-classification of skin cancer utilizing dermoscopy images by Hadeel Alharbi, Gabriel Avelino Sampedro, Roben A. Juanatas, Se-jung Lim

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Semantic and Geometric Fusion for Object-Based 3D Change Detection in LiDAR Point Clouds by Abderrazzaq Kharroubi, Fabio Remondino, Zouhair Ballouch, Rafika Hajji, Roland Billen

“…The model is evaluated on the Urb3DCD-v2 dataset, with feature importance analysis to identify important features. …”
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Leveraging computational linguistics and machine learning for detection of ultra-high risk of mental health disorders in youths by Jordon Junyang Kho, Shangzheng Song, Samuel Ming Xuan Tan, Nur Hikmah Fitriyah, Matheus Calvin Lokadjaja, Jie Yin Yee, Zixu Yang, Eric Yu Hai Chen, Jimmy Lee, Wilson Wen Bin Goh

“…Optimized machine learning (ML) models trained on Boruta-selected features achieved a mean AUC of 0.70. Our findings highlight the potential of sentiment and linguistic analyses in speech for training ML models to aid in early detection and monitoring of mental health conditions.…”
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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. …”
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Detection and Segmentation of Glioma Tumors Using an Improved Visual Geometry Group (IVGG) Deep Learning Structure by Parameswari Alagarsamy, Vinoth Kumar Kalimuthu, Bhavani Sridharan

“…BRATS Imaging 2020 (BI20) and Kaggle Brain Imaging (KBI) are the datasets. Analysis of the performance efficiency has been done in relation to the Jaccard index, recall, precision, and detection rate.…”
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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…The significant features of healthcare are the privacy of medical details and the accuracy of disease identification. …”
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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…Long Short-Term Memory (LSTM) can capture long-range dependencies in sequential data, while Temporal Convolutional Network (TCN) efficiently models temporal patterns using convolutional layers and Transformer Networks fathom the relative importance of temporal features against one another through self-attention, thus improving their detection accuracy for anomalies that happen over a long duration. …”
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Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli, Rani Kumari, Adnan Akhunzada, Korhan Cengiz, Santosh Kumar Sharma, Rakesh Ranjan Kumar, Dinesh Kumar Sah

“…The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. …”
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GLIDE: A Game Theory and Data-Driven Mimicking Linkage Intrusion Detection for Edge Computing Networks by Qianmu Li, Jun Hou, Shunmei Meng, Huaqiu Long

“…Due to the dual constraints of the hybrid feature of edge computing networks and the uncertainty of new attack features, implementing active defence measures such as detection, evasion, trap, and control is essential for the security protection of edge computing networks with high real-time and continuity requirements. …”
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Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle by Feng Ding, Yibing Zhao, Lie Guo, Mingheng Zhang, Linhui Li

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Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches by T. Anitha Kumari, Sanket Mishra

“…In addition, principal component analysis (PCA) and partial least squares (PLS) were used to determine the most significant features. …”
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Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. …”
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Federated XAI IDS: An Explainable and Safeguarding Privacy Approach to Detect Intrusion Combining Federated Learning and SHAP by Kazi Fatema, Samrat Kumar Dey, Mehrin Anannya, Risala Tasin Khan, Mohammad Mamunur Rashid, Chunhua Su, Rashed Mazumder

“…Furthermore, UDP has been found to be the most significant feature of the network layer from the SHAP analysis. …”
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Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study by Zhai Liu, Yongjun Li, Chenguang Zhang, Hui Xu, Junlu Zhao, Chencui Huang, Xingzhi Chen, Qingyun Ren

“…Abstract Objective This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approaches from lumbar spine computed tomography (CT) images during an abdominal CT examination. …”
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A framework of partial discharge detection for gas-insulated switchgear based on LSTM and multi-head attention by YANG Zhengsheng, LIU Fang

“…ObjectivesTo achieve long-term stable operation of electrical equipment， a framework for anomaly detection and classification of partial discharge （PD） in gas insulated switchgear （GIS） based on long short-term memory （LSTM） networks and a multi-head attention mechanism was proposed.MethodsFirstly， phase resolved partial discharge （PRPD） analysis was used as the input sequence， and LSTM was employed to learn the temporal correlations in PRPD signals. …”
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A System for Efficient Detection of Forest Fires through Low Power Environmental Data Monitoring and AI by İpek Üremek, Paul Leahy, Emanuel Popovici

“…Validation of the fire detection component remains a critical future task to confirm its effectiveness and reliability.…”
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Early warning value of multiple serum indicators combined with ultrasound detection in girls with central precocious puberty by Xiaolong Song, Jianping Zhou, Tiantian Han, Zhifeng Lin, Xu Chen, Yufang Li, Yufang Li

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