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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vignesh, S. Prabhu, S. Sampathkumar, R. Rani, M. Thillai Rejina Parvin, J. Maheswari, G. Uma Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India KPR Institute of Engineering and Technology Department of Mechatronics Engineering Tamil Nadu Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Pest attacks pose a serious threat to the production of jute and other significant crops. Jute farmers often utilize their visual sense and hands-on expertise to distinguish between multiple diseases that seem to be identical. The intelligent model we built for the identification of jute pests was based on a deep convolutional neural network (DCNN). This practical difficulty led to the development of this concept. The proposed DCNN model can automatically identify jute pest concerns with high speed and accuracy based on image recognition. In addition, images of the four most common jute bugs are included in an organized image graphics collection. Our model yields a DCNN accuracy is 96.72% for the four most significant jute pest kinds. Further proof of the model's effectiveness is provided by the accuracy, recall, F1-score, and confusion matrix metrics. © 2024 IEEE.

关键词： Convolutional neural networks

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GTDDA: Graph Convolutional Network and Graph Transformer Structure for Drug Repositioning

GTDDA: Graph Convolutional Network and Graph Transformer Str...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tang, Jialan Chen, Weilin Zeng, Xiaoting Liu, Chuan-Ming Li, Pingkang Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University Medical School Shenzhen University Institute of Biological Therapy Shenzhen China National Taipei University of Technology Computer Science and Information Engineering Taipei Taiwan Shenzhen University Medical School Shenzhen University School of Biomedical Engineering Shenzhen China

ISBN: (纸本)9798350337488

Finding new uses for current drugs can be accomplished with the help of computational drug repositioning. Many deep learning-based techniques have been developed recently to find possible drug-disease associations (DDAs). However, effectively utilizing the connections between biological elements to capture biological interactions to improve DDAs prediction is still a difficult task. To address the above issues, we propose a hybrid graph representation learning method (i.e., GTDDA). Specifically, we replace the self-attention mechanism in the Transformer with graph attention layer and design a new graph Transformer encoder (i.e., GATT) for generating graph embedding. First, drug-drug and disease-disease similarity networks are used to construct the graphs. Secondly, a hybrid graph embedding model is shown to train features for drugs and diseases sequentially at the same time, by combining graph convolutional network (GCN) with GATT. Lastly, predictive models that identify DDAs are constructed by concatenating the learnt features. A series of experiment results show that GTDDA outperforms several advanced DDAs prediction methods, which can provide effective predictions for the discovery of new indications for drugs and new treatments for diseases. © 2023 IEEE.

关键词： DDA Prediction Graph Convolutional Network Graph Transformer

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ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Al Amin Kamrul Hasan Sharif Ullah M. Shamim Hossain Department of Electrical & Computer Engineering College of Engineering Tennessee State University TN USA Department of Computer Science & Engineering College of Science and Engineering University of Central Arkansas Conway AR USA Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks.

关键词： Data privacy computer vision Image analysis Accuracy Artificial neural networks Transformers Robustness Encryption Biomedical imaging

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One Gray Code Fits All: Optimizing Access Time with Bi-Directional Programming for QLC SSDs

One Gray Code Fits All: Optimizing Access Time with Bi-Direc...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Li, Shaoqi Wang, Tianyu Zhu, Yongbiao Ma, Chenlin Wang, Yi Shen, Zhaoyan Shao, Zili College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9783982674100

Gray code, a voltage-level-to-data-bit translation scheme, is widely used in QLC SSDs. However, it causes the four data bits in QLC to exhibit significantly different read and write performance with up to 8 × latency variation, severely impacting the worst-case performance of QLC SSDs. This paper presents BDP, a novel Bi-Directional Programming scheme. Based on a fixed Gray code, BDP combines both the normal (forward) and reverse programming directions to enable runtime programming direction arbitration. Experimental results show that BDP can effectively improve the read and write performance of SSD compared to representative schemes. © 2025 EDAA.

关键词： Gray Code QLC SSD Two-Step Programming

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Jamming-aided Maritime Physical Layer Encrypted Dual-UAVs Communications Exploiting Collaborative Beamforming

Jamming-aided Maritime Physical Layer Encrypted Dual-UAVs Co...

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International Conference on computer Supported Cooperative Work in Design

作者： Jiawei Huang Aimin Wang Geng Sun Jiahui Li College of Software Engineering and College of Computer Science and Technology Jilin University Changchun China

Unmanned aerial vehicles (UAVs) are widely used as relay platforms to assist in maritime wireless communications. However, due to the open channel of UAV communications, security issue needs to be paid extra attention. This paper proposes a dual-UAVs jamming-aided system to implement physical layer encryption in maritime wireless communication. Specifically, one UAV acts as a relay to transmit data to the legitimate vessel and a set of UAVs can form a maritime UAV-enabled virtual antenna array (MUVAA) as a jammer to send jamming signals to the eavesdropper. In this system, an encrypted and energy-efficient maritime communication multi-objective optimization problem (EEMCMOP) is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the overall flight energy consumption of UAVs. Since the EEMCMOP is proven to be NP-hard, we propose an enhanced evolutionary computation algorithm with some upgraded characteristics to solve the problem. Simulation results indicate that the performance of the proposed algorithm is superior to other benchmark algorithms.

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Enhancing students' learning achievements, self-efficacy, and motivation using mobile augmented reality

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Journal of computer Assisted Learning 2024年第4期40卷 1823-1837页

作者： Aldeeb, Fatimah H. Sallabi, Omar M. Elaish, Monther M. Hwang, Gwo-Jen Computer Network College of Computer Technology Benghazi Libya Computer Science The Libyan Academy Benghazi Libya Software Engineering Information Technology Faculty University of Benghazi Benghazi Libya Computer Science Information Technology Faculty University of Benghazi Benghazi Libya Graduate Institute of Educational Information and Measurement National Taichung University of Education Taichung City Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei City Taiwan Yuan Ze University Taoyuan City Taiwan

Background: This paper examines the use of augmented reality (AR) as a concept-association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR-enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR-assisted concept-association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self-efficacy and learning motivation among primary school students. Methods: A quasi-experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR-assisted concept-association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self-efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology. © 2024 John Wiley & Sons Ltd.

关键词： augmented reality biology education cognitive load learning motivation mobile learning self-efficacy

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on Computing, Robotics and System sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Anh, Dang Van Chehri, Abdellah Quy, Nguyen Minh Hue, Chu Thi Minh Nguyen, Dinh C. Quy, Vu Khanh Hung Yen University of Technology and Education Faculty of Information Technology Hungyen Viet Nam Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada Fpt University Faculty of Software Engineering Hanoi Viet Nam The University of Alabama Department of Electrical and Computer Engineering HuntsvilleAL United States

ISBN: (纸本)9798350351255

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems' service response times across various workloads and traffic. © 2024 IEEE.

关键词： Electronic health record

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A Complementary Fused Method Using Gru and Xgboost Models for Long-Term Solar Energy Hourly Forecasting

SSRN

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SSRN 2024年

作者： Xu, Yaojian Zheng, Shaifeng Zhu, Qingling Wong, Ka-Chun Wang, Xu Lin, Qiuzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

Solar photovoltaic (PV) energy plays a crucial role in global renewable energy generation and has received extensive attention from the academic and industrial communities in recent decades. However, the inherent volatility and stochastic nature of solar energy generation present challenges for the integration of solar energy with smart grids. Accurate and reliable long-term forecasting of solar energy is the key to improving energy scheduling, planning, and intelligent decision-making. In this paper, we propose a complementary fusion of GRU and XGBoost models to improve the performance of long-term solar energy hourly forecasting. The proposed method includes data preprocessing, feature engineering, training of GRU and XGBoost models, and fusion of the forecasting results. Specifically, based on historical solar energy, solar irradiance and numerical weather prediction (NWP) data (e.g. temperature, wind direction, wind speed, etc.) are used as input data to forecast the hourly solar energy for the next ten days. In the UNiLAB Algorithm Competition Track 3: Forecasting of Generated Renewable Energy, held by Tianchi in 2022, our proposed approach won the first rank in the rematch among 513 participating teams, thereby confirming its dominance in the competition. The experimental results showed that the proposed method achieved highly competitive performance in terms of forecasting accuracy, which outperformed six state-of-the-art long-time series forecasting models DLinear using four different forecasting horizons from both the competition dataset and the GEFCom2014 dataset. © 2024, The Authors. All rights reserved.

关键词： Solar energy

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PASTD: Progressive Augmentation and Spatiotemporal Decoupling Contrastive Learning for Skeleton-Based Action Recognition

PASTD: Progressive Augmentation and Spatiotemporal Decouplin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qian Huang Weiwen Qian Chang Li Gongyou Xu Zhongqi Chen College of Computer Science and Software Engineering Hohai University Nanjing China Faculty of Science and Technology United International College BNU-HKBU Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Contrastive learning has achieved significant progress in the field of self-supervised skeleton-based action recognition. However, existing methods often apply strong augmentations directly to skeleton data, which can distort or even lose the semantic of the skeletons. Additionally, most methods focus on unified feature extraction through spatiotemporal modeling, leading to spatiotemporal entanglement that hinders model’s interpretability and degrades performance. To address these issues, we propose a Progressive Augmentation and SpatioTemporal Decoupling contrastive learning model (PASTD). Specifically, we propose progressive augmentation to enhance the model’s generalization capability, which generates multiple distinct positive sample pairs through multiple branches and uses inter-branch constraints to maintain consistent motion patterns. Moreover, we propose spatiotemporal decoupling module to separate the feature’s spatial and temporal information, using a dual-path self-attention module combined with an intra-branch cross-domain learning strategy to facilitate information exchange between domains. Extensive experiments on the four public datasets NTU-RGB+D (60&120) and PKU-MMD (I&II) demonstrate the effectiveness of these components. Moreover, PASTD outperforms state-of-the-art methods across various evaluation metrics.

关键词： Measurement Three-dimensional displays Semantics Contrastive learning Speech recognition Signal processing Skeleton Spatiotemporal phenomena Speech processing Information exchange

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