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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： C. P. Shirley S.V. Evangelin Sonia V. Sathya N. Manikandan M.K. Vidhyalakshmi Ch. Venkata Krishna Reddy Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Software Engineering Periyaar Maniammai Institute of Science and Technology Thanjavur Tamil Nadu India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur Tamil Nadu India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

The use of Electrical Vehicles (EVs) is increasing rapidly nowadays. This results in the efficient utilization of electrical energy. This leads to making the grid more economically reliable. Thus the smart charging of electric vehicles is done through blockchain technology with deep learning to meet the demand side management. This helps in the determination of the charging system with higher accuracy the systems. This indudes real-time implementation with higher precision control. This helps to obtain the exact optimal solution for dynamic functions. The deep earning helps to obtain real-time implementation in the system. This also helps to obtain the optimum charging cost consumed at a specific period. The charging station energy management system is implemented based on blockchain technology. This helps to meet the demand side management in the system. They are accompanied by centralized and decentralized energy models in the architecture. This also helps in the reduction of peer-to-peer transactions proceeding from the electricity grid to electric vehicles.

关键词： Deep learning Demand side management Electric vehicles Real-time systems Power grids Blockchains Security

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning

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A Tree-Guided CNN for Image Super-Resolution

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Song, Mingjian Fan, Xiaopeng Zheng, Xiangtao Zhang, Bob Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Northwestern Polytechnical University School of Software Xi’an710129 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China University of Macau Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science Macau999078 China School of Science and Engineering Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks can extract more accurate structural information via deep architectures to obtain good performance in image super-resolution. However, it is not easy to find effect of important layers in a single network architecture to decrease performance of super-resolution. In this paper, we design a tree-guided CNN for image super-resolution (TSRNet). It uses a tree architecture to guide a deep network to enhance effect of key nodes to amplify the relation of hierarchical information for improving the ability of recovering images. To prevent insufficiency of the obtained structural information, cosine transform techniques in the TSRNet are used to extract cross-domain information to improve the performance of image super-resolution. Adaptive Nesterov momentum optimizer (Adan) is applied to optimize parameters to boost effectiveness of training a super-resolution model. Extended experiments can verify superiority of the proposed TSRNet for restoring high-quality images. © 1975-2011 IEEE.

关键词： Cosine transforms

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Cybersecurity in Healthcare: Safeguarding Patient Data in Telemedicine Platforms

Cybersecurity in Healthcare: Safeguarding Patient Data in Te...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Chandravadhana S Narendran M Banu S Rahmaan K Anurag Reddy Ekkati Venkateswara Rao Jallepalli Department of Mechatronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Information Technology KCG College of Technology Chennai India Department of Computer Science and Engineering KCG College of Technology Chennai India Principal Software Engineer Palo Alto Networks Inc Santa Clara California Department of Pharmacy Practice MB School of Pharmaceutical Sciences Mohan Babu University Tirupathi Andhra Pradesh

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Telemedicine systems are exponentially increasing and, therefore, high-quality cybersecurity is inevitable for implementing such systems to secure the private information of the patient from threats that continue to change. In this paper, a new security architecture based on Hybrid Encryption Standard AES-256 and Quantum Key Distribution QKD for telemedicine applications is proposed along with biometric authentication and blockchain technology to ensure integrity of the data. IDS designed in this proposal utilized models based on the Random Forest and SVM approaches for anomaly detection. It obtained an F1 score of 97.1% and an accuracy of 97.3%. The designed framework is particularly suitable for real-time healthcare systems because it offers superior security performance compared to traditional methods while simultaneously reducing latency and increasing precision. The solution ensures the security of data transmission and the control of access while further developing trust in care with patients and matching pace for data protection regulations. It resolves substantial vulnerabilities in telemedicine systems.

关键词： Accuracy Telemedicine Data security Intrusion detection Medical services Biometric authentication Real-time systems Encryption Blockchains Quantum key distribution

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Multi-label Garbage Image Classification Based on Deep Learning

Multi-label Garbage Image Classification Based on Deep Learn...

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第十九届分布式计算及其应用国际学术研讨会

作者： Kang Yan Wenyu Si Jin Hang Hong Zhou Quanyin Zhu Faculty of Computer & Software Engineering Huaiyin Institute of Technology

In recent years, with the development of deep learning technology, the accuracy of image recognition has been significantly improved. Deep learning has been widely used in the recognition of single-label images. This project aims to intelligently classify domestic garbage images as application scenarios based on depth. Learn to carry out multi-label classification research on images containing multiple visual objects, and design and build a multi-label garbage image classification model to improve recognition accuracy and speed as the main research goal to conduct classification research on multi-label garbage images.

关键词： Convolutional Neural Network Garbage classification

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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Application of Artificial Intelligence technology in Power System Stability Assessment

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Procedia computer Science 2023年 228卷 79-88页

作者： Siyu Xiang Cao zhen Jian Peng Linghao Zhang Zhengguo Pu State Grid Sichuan Electric Power Research Institute Chengdu Sichuan 610095 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611700China College of Computer Science Sichuan University Chengdu 610065 China Anhui Jiyuan Software Co. Ltd. Hefei 230088 China

In order to realize power system stability assessment, the author studies a deep Convolutional neural network algorithm for power system operation stability assessment. Combining short-term simulation with neural network prediction to reduce the time required for transient stability analysis can be used in various simulation analysis scenarios. A deep Convolutional neural network algorithm is used to improve the performance of the security system. A deep Convolutional Neural Network (CNN) is used to construct a multi-layer and multi-column neural network to evaluate the stability of electrical networks. The system significantly improves the efficiency of evaluating specific faults, particularly enhancing the specificity of evaluation judgments, and reducing the additional maintenance workload caused by providing stability warnings for the operation status of faultless power grids. The experimental results indicate that, the system has shown certain advantages in terms of sensitivity to the judgment of various types of faults mentioned above, especially improving the sensitivity to other faults with unknown causes, which is 10.8 percentage points higher than the previous system. The smallest difference in the improvement of sensitivity indicators is the sensitivity to ground faults. The previous system reached 95.4%, while this system increased by 3.9 percentage points to 99.3%. It is proved that the deep CNN is applicable to the task of power system operation stability assessment.

关键词： Deep Convolutional neural network Power system operation Stability assessment Algorithm Innovation

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Efficient reversible data hiding via two layers of double-peak embedding

arXiv

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arXiv 2022年

作者： Wu, Fuhu Sun, Jian Zhang, Shun Chen, Zhili School of Computer Science and Technology Anhui University Hefei230601 China Software Engineering Institute East China Normal University Shanghai200062 China

Reversible data hiding continues to attract significant attention in recent years. In particular, an increasing number of authors focus on the higher significant bit (HSB) plane of an image which can yield more redundant space. On the other hand, the lower significant bit plane is often discarded in existing schemes due to their harm to the embedding rate. This paper proposes an efficient reversible data hiding scheme via a double-peak two-layer embedding (DTLE) strategy with prediction error expansion. The higher six-bit planes of the image are assigned as the HSB plane, and double prediction error peaks are applied in either embedding layer. This makes fuller use of the redundancy space of images compared with the one error peak strategy. Moreover, we carry out the median-edge detector pre-processing for complex images to reduce the size of the auxiliary information. A series of experimental results show that our DTLE approach achieves up to 83% higher embedding rate on real-world data while providing better image quality. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Power load prediction of smart grid based on deep learning

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Procedia computer Science 2023年 228卷 762-773页

作者： Siyu Xiang Cao Zhen Jian Peng Linghao Zhang Zhengguo Pu State Grid Sichuan Electric Power Research Institute Chengdu Sichuan 610095 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611700 China College of Computer Science Sichuan University Chengdu 610065 China Anhui Jiyuan Software Co. Ltd. Hefei 230088 China

Real electricity costs, weather, and historical load data are added as new reference data when analyzing the characteristics of the load change policy to improve the forecasting model. A load forecasting model for smart grid based on convolutional neural network(CNN) is proposed. The model first analyzes the characteristics of the variable model and determines the forecast model inputs using two methods of correlation analysis to understand the impact of actual energy costs, weather and other factors on the changes. Second, since Feedforward neural network is deficient in processing the correlation information between loads, the prediction model developed by the author was studied as CNN. After completing the training, input two sets of real datasets for final data analysis and comparison, and the results show that, LSTM, BP, SVR[UNK]MAPE[UNK]RMSE respectively 0.49%, 0.57%, 0.83%, 1.44% and 106.08 MW, 113.82MW, 169.77 MW, 285.61 MW. It can be seen that the prediction model proposed by the author has the highest accuracy and the best fit with the trend of actual load changes. It is proved that the CNN model has certain advantages when dealing with load forecasting problems related to time series.

关键词： Real-time electricity price CNN Input features Power load forecasting

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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