An analytics-driven model for identifying autism spectrum disorder using eye tracking by Deblina Mazumder Setu

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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

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Progressive multi-scale attention neural network for pneumonia classification in chest X-rays by Mohammad Reza Mahdiani

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Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey by Ahmed Sharafeldeen, Justin Keowen, Ahmed Shaffie

“…This survey paper provides a comprehensive review of the current literature on the application of ML and DL techniques for AD detection through the analysis of a patient’s speech signal, utilizing various acoustic and textual features. …”
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CSDNet: Context-Aware Segmentation of Disaster Aerial Imagery Using Detection-Guided Features and Lightweight Transformers by Ahcene Zetout, Mohand Saïd Allili

“…Additionally, we introduce a detection-guided feature fusion mechanism that integrates outputs from auxiliary detection tasks to mitigate class imbalance and improve discrimination of visually similar categories. …”
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Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features. by Rida Nayyab, Asim Waris, Iqra Zaheer, Muhammad Jawad Khan, Fawwaz Hazzazi, Muhammad Adeel Ijaz, Hassan Ashraf, Syed Omer Gilani

“…The R-peaks of the clean signal were used to detect the subsequent morphological features, i.e., P-QRS-T intervals and amplitudes. …”
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Anomaly Detection in Industrial Machine Sounds Using High-Frequency Features and Gate Recurrent Unit Networks by Thi-Thu-Huong Le, Andro Aprila Adiputra, Jiwon Yun, Howon Kim

“…Detecting anomalies in industrial sound is critical for maintaining operational efficiency, preventing costly equipment failures, and ensuring workplace safety. …”
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Robust scale fusion and edge-aware feature attention network for remote sensing UAV road detection under harsh weather by Jialang Liu, Jialei Zhan, Jiehua Zhang, Jiangming Chen, Yan Song, Lixing Tang, Le Zhou, Chengsi Du, Yingmei Wei, Yanming Guo

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Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson’s disease classification by Sameer Bhat, Piotr Szczuko

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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). …”
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Research on a Bone Stick Fragment Stitching Method Based on Improved Corner Detection by Jiahuan Liu, Huiqin Wang, Rui Liu, Ke Wang, Zhan Wang, Ting Wang

“…First, the maximum outer contour of the bone stick fragment is obtained using connectivity analysis. Then, the broken edge features are enhanced by combining multi-scale and multi-stage corner detection, as well as a double screening mechanism of local template and corner density analysis. …”
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Detection of wheat, chickpea, and sea foam in black pepper using hyperspectral imaging technique by Mohammad Hossein Nargesi, Jafar Amiri Parian, Kamran Kheiralipour

“…After feature extraction, sequential feature selection method was used to find efficient features and then artificial neural network was used for classification and prediction. …”
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MFA-SCDNet: A Semantic Change Detection Network for Visible and Infrared Image Pairs by Xingyu Li, Jiulu Gong, Jianxiong Wen, Zepeng Wang

“…To address this issue, we propose a Modal Feature Analysis Semantic Change Detection Network (MFA-SCDNet), a novel framework that analyzes cross-modal features for change identification. …”
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Enhanced detection of malaria infected red blood cells through phase driven classification by Charlotte Kyeremah, Aditya S. Paul, Daniel Haehn, Manoj T. Duraisingh, Chandra S. Yelleswarapu

“…The parasitemia detection rate improved from 48% (feature-specific method) to 61% (phase-based features method) on the same sample set, demonstrating enhanced detection accuracy. …”
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Early detection of Alzheimer’s disease in structural and functional MRI by Rudrani Maity, Vellupillai Mariappan Raja Sankari, Umapathy Snekhalatha, Umapathy Snekhalatha, Shubashini Velu, Tahani Jaser Alahmadi, Zaid Ali Alhababi, Hend Khalid Alkahtani

“…Among some DL techniques, VGG-16-RF classifier has given better accuracy of 96.87%.ConclusionThe novelty of this work lies in the seamless integration of advanced segmentation techniques with hybrid classifiers, offering a robust and scalable framework for early AD detection. The proposed study demonstrates a significant advancement in the early detection of Alzheimer’s disease by integrating state-of-the-art deep learning models and comprehensive functional connectivity analysis. …”
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Multi-Domain Feature Analysis and Application Research of GPR Aliased Signals by Chuan Li, Yawei Wang, Qibing Ma, Xiaorong Wan

“…The Hilbert transform effectively extracts instantaneous phase inversions, and STFT provides an intuitive time–frequency distribution, facilitating the extraction and analysis of signal features. Additionally, this study includes the design and implementation of an aliasing peak point extraction algorithm with a relative error of less than 10% in practical applications.…”
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A Feature-Selective Independent Component Analysis Method for Functional MRI by Yi-Ou Li, Tülay Adali, Vince D. Calhoun

“…We perform ICA estimation of artificial activations superimposed into a resting state fMRI dataset to show that the feature-selective scheme improves the detection of injected activation from the independent component estimated by ICA. …”
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Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang, Yueqian Shen

“…Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. …”
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Structural and functional features of endometrium in patients undergoing in - vitro fertilization by Y. A. Kovalenko, L. M. Chuprinenko

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A critical review of technical case studies for electricity theft detection in smart grids: A new paradigm based transformative approach by Muhammad Sajid Iqbal, Shoaib Munawar, Muhammad Adnan, Ali Raza, Muhammad Ali Akbar, Amine Bermak

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