Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs by Søren Pedersen, Sanyam Jain, Mikkel Chavez, Viktor Ladehoff, Bruna Neves de Freitas, Ruben Pauwels

“…This paper presents the development of a generative adversarial network (GAN) for the generation of synthetic dental panoramic radiographs. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

“…The intricacy of environmental conditions and the lack of access to trustworthy data make it difficult to anticipate crop yields and evaluate soil health accurately. The goal of this research is • to make sophisticated models for precise crop production forecasting and thorough evaluation of soil health, • to improve sustainability by optimize farming methods, and • to assist farmers in making well-informed decisions.Iterative Partitioning-Ensemble Filter (IP-EF) is a technique used for feature selection, enhancing model performance by iteratively partitioning data and refining feature subsets. …”
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A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection by ZHANG, M., HU, H., LI, Z.

“…To address the issue, we propose an effective sparse pooling adversarial learning framework (SPLF) for anomaly event detection, which integrates self-attention and pyramid features into a unified architecture. Specifically, the network takes video frames as input and employs an efficient U-Net to predict unknown frames. …”
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Towards edge-collaborative, lightweight and privacy-preserving classification framework by Jinbo XIONG, Yongjie ZHOU, Renwan BI, Liang WAN, Youliang TIAN

“…Aiming at the problems of data leakage of perceptual image and computational inefficiency of privacy-preserving classification framework in edge-side computing environment, a lightweight and privacy-preserving classification framework (PPCF) was proposed to supports encryption feature extraction and classification, and achieve the goal of data transmission and computing security under the collaborative classification process of edge nodes.Firstly, a series of secure computing protocols were designed based on additive secret sharing.Furthermore, two non-collusive edge servers were used to perform secure convolution, secure batch normalization, secure activation, secure pooling and other deep neural network computing layers to realize PPCF.Theoretical and security analysis indicate that PPCF has excellent accuracy and proved to be security.Actual performance evaluation show that PPCF can achieve the same classification accuracy as plaintext environment.At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPCF has obvious advantages in terms of computational cost and communication overhead.…”
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Detection of Apple Trees in Orchard Using Monocular Camera by Stephanie Nix, Airi Sato, Hirokazu Madokoro, Satoshi Yamamoto, Yo Nishimura, Kazuhito Sato

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Development of approach to an automated acquisition of static street view images using transformer architecture for analysis of Building characteristics by Seunghyeon Wang

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Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements by Shuangling Ma, Zijie Situ, Xiaobo Peng, Zhangyang Li, Ying Huang

“…A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. …”
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SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces by Zhenis Otarbay, Zhenis Otarbay, Abzal Kyzyrkanov

“…Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. …”
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A parallel CNN architecture for sport activity recognition based on minimal movement data by Huipeng Zhao

“…This is achieved through the parallel implementation of Convolutional Neural Networks (CNN) and machine learning methods. The methodology being proposed consists of four fundamental phases. …”
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Modified SLAM for Agricultural Robot Navigation by N. V. Gapon, V. V. Voronin, D. V. Rudoy, M. M. Zhdanova

“…To evaluate the impact on SLAM performance, the Stereo Multi-State Constraint Kalman Filter (S-MSCKF) algorithm was employed. …”
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Depth based: No Reference Stereoscopic Image Quality Assessment by S. Kiruthika, J. Joshan Athanesious, D. Rushma

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Enhancing water saturation predictions from conventional well logs in a carbonate gas reservoir with a hybrid CNN-LSTM model by Ali Gohari Nezhad, Mohammad Emami Niri

“…A total of 10,674 data points were collected from four wells in the South Pars gas field, where well logging evaluations and core measurements were available. The methodology includes data pre-processing to de-noise and transform data into a proper format, feature engineering followed by feature selection based on domain knowledge, models’ training, testing, and optimizing. …”
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Multi-Modal Dynamic Fusion for Defect Detection in Electronic Products: A Novel Approach Based on Energy and Deep Learning by Yulin Liu, Yang Gao

“…Specifically, Transformer architectures are employed for sensor data analysis, Convolutional Neural Networks (CNNs) are applied to process image data, and Multi-Layer Perceptrons (MLPs) are used to represent part-level features. …”
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Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images by Qinggang Wu, Yonglei Wu, Yang Li, Wei Huang

“…This article presents an improved YOLOv5s network-based technique for remote sensing object recognition to overcome these difficulties. …”
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Partial contrastive point cloud self-supervised representation learning by Zijun Cheng, Yiguo Wang

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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…Method The proposed deep learning framework used convolutional neural networks, and the image database totaled 1,056 3D-DICOM CT images. …”
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Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations by Constantinos Halkiopoulos, Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou

“…<b>Methods</b>: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. …”
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Collaborative filtering based on GNN with attribute fusion and broad attention by MingXue Liu, Min Wang, Baolei Li, Qi Zhong

“…In recent years, graph neural networks (GNN) based CF models have effectively addressed the limitations of nonlinearity and higher-order feature interactions in traditional recommendation methods, such as matrix decomposition-based methods and factorization machine approaches, achieving excellent recommendation performance. …”
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IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution by Athanasios Tragakis, Chaitanya Kaul, Kevin J. Mitchell, Hang Dai, Roderick Murray-Smith, Daniele Faccio

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WWTR1-AS1 LncRNA as a Novel Potential Diagnostic and Prognostic Biomarker in Breast Cancer by Mohadese Safabakhsh, Sohrab Boozarpour, Kamran Ghaedi, Mina Lashkarboloki, Shaaban Ghalandarayeshi

“…Proteomic analysis recognized 1149 differential expressed proteins in breast cancer with regulatory agents consisting of 76 TFs, 61 kinases, 366 miRNAs, and 162 lncRNAs. A multi-regulatory network with 1811 nodes and 4022 edges was constructed based on differentially expressed proteins and their associated elements. …”
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