Parkinson’s Disease Detection via Bilateral Gait Camera Sensor Fusion Using CMSA-Net and Implementation on Portable Device by Jinxuan Wang, Hua Huo, Wei Liu, Changwei Zhao, Shilu Kang, Lan Ma

“…The annual increase in the incidence of Parkinson’s disease (PD) underscores the critical need for effective detection methods and devices. Gait video features based on camera sensors, as a crucial biomarker for PD, are well-suited for detection and show promise for the development of portable devices. …”
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Hydroseismograms at Gran Sasso aquifer, central Italy, for earthquake hydrology studies by Domenico Isaya, Gaetano De Luca, Giuseppe Di Carlo, Vincenzo Guerriero, Raffaele Martorana, Marco Tallini

“…These HPD devices show distinctive features, such as deep location, high frequency sampling (20 Hz), and hosting wells intercepting the main fault network. …”
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Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars by Yuxin Xiao, Yuanning Zhai, Lei Zhou, Yiming Yin, Hengnian Qi, Chu Zhang

“…Traditional machine learning models (support vector machines and partial least squares regression) and deep learning models (convolutional neural networks, long short-term memory, and Transformer architectures) were evaluated for SSC prediction performance. …”
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Speech emotion recognition using long-term average spectrum by Huerta-Hernández Luis David, Meléndez-Acosta Nayeli Joaquinita, Troncoso-Romero David Ernesto, Ramírez-Pacheco Julio César, León-Borges Jose Antonio

“…Our framework is evaluated through a comparative study, where classifiers such as artificial neural network, K-nearest neighbours, logistic regression, Bayesian algorithms, tree-based logistics, and support vector machine were used. …”
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GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer by Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar, Glenn Van Wallendael

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Decoding skin cancer classification: perspectives, insights, and advances through researchers’ lens by Amartya Ray, Sujan Sarkar, Friedhelm Schwenker, Ram Sarkar

“…The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. …”
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Comparing Reflectivity from Space-Based and Ground-Based Radars During Detection of Rainbands in Two Tropical Cyclones by Corene J. Matyas, Stephanie E. Zick, Kimberly M. Wood

“…This study utilizes the Dual-frequency Precipitation Radar (DPR) from the Global Precipitation Measurement Mission (GPM) satellite to evaluate reflectivity obtained using four sampling methods of Weather Surveillance Radar 1988-Doppler data, including ground radars (GRs) in the GPM ground validation network and three mosaics, specifically the Multi-Radar/Multi-Sensor System plus two we created by retaining the maximum value in each grid cell (MAX) and using a distance-weighted function (DW). …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The proposed study is founded on two principal pillars: the transformation of process tabular data (generated using the Conditional Tabular Generative Adversarial Network (CTGAN)), involving the mapping of features onto a fixed grid in a heatmap structure, and the configuration of the CNN algorithm to extract complex patterns in the data that are not readily apparent in the original tabular format. …”
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Machine learning-enabled prediction of bone metastasis in esophageal cancer by Liqiang Liu, Wanshi Duan, Tao She, Shouzheng Ma, Haihui Wang, Jiakuan Chen

“…Six machine learning models were constructed: Support Vector Machine, Logistic Regression, Extreme Gradient Boosting, Neural Network, Random Forest, and k-Nearest Neighbors. Models performance was evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve. …”
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Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation by Byung-Kwan Ko, Seo-Hyun Lee, Seong-Whan Lee

“…To address these issues, we introduce a multi-receptive field convolutional neural network, designed to capture speech and idle states based on behaviorally-aligned EEG features. …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

“…The methodology is used to combine the shrimp images with different backgrounds in different phases: segmentation, dataset construction, and creating classifiers like faster recurrent convolution neural network (FRCNN) and you only look once (YOLOv7). The Enhanced Shrimp Detection algorithm generates the unique features set and also various parameters fetched from foundational classifiers. …”
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Service recommendation method based on text view and interaction view by Ting Yu, Yaqi Wang, Fangying Cheng, Tian Liang, Hongbing Liu

“…Firstly, SRTI employs graph neural network algorithms to deeply mine the historical records, extract the features of applications and services, and calculate their preferences. …”
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Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach by John Blake, Abu Saleh Musa Miah, Krzysztof Kredens, Jungpil Shin

“…The proposed model is a feature-based hybrid deep learning network that leverages part-of-speech tagging and integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with Attention modules. …”
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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…The model was trained and evaluated on a comprehensive dataset containing 37,923 samples with 13 environmental features, collected from a smart greenhouse. …”
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Morphometric analysis for understanding river basin hydrology: a case of gelda watershed, Tana Sub-Basin, Ethiopia by Dessie Wubetu Melsse, Moges Animut Tegegne, Yitbarek Andualem Mekonnen, Yilak Taye Bihon

“…Statistical methods were applied to evaluate the relationships between these parameters and various hydrological characteristics. …”
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ASSESSMENT OF THE RURAL POPULATION ECONOMIC ACTIVITY IN THE SYSTEM OF UNITED TERRITORIAL COMMUNITIES DEVELOPMENT: A CASE STUDY OF VOLYN REGION, UKRAINE by Oksana APOSTOLYUK, Tetiana SHMATKOVSKA, Inna CHYKALO1, Andrew HUSAK

“…Peculiarities of the settlement network in Volyn region are that the dominant segment of rural employment is agricultural enterprises. …”
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Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram by Qianlong Ding, Shuangquan Chen, Jinsong Shen, Borui Wang

“…We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. …”
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Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models by Mohammad Ennab, Hamid Mcheick

“…The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. …”
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Twin-roll casting of wire of magnesium alloys by Christoph Kaden, Kristina Kittner, Madlen Ullmann, Ulrich Prahl

“…Mechanical properties were evaluated through tensile testing at room temperature. …”
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Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving by Yu Cao, Bo Zhang, Xiaohui Hou, Minggang Gan, Wei Wu

“…To enhance the detection accuracy of drivers’ spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. …”
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