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A Study of Data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

引用

3rd International conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Deep learning Based Electric Vehicle BMS intelligent Cloud Monitoring System 22

Deep Learning Based Electric Vehicle BMS Intelligent Cloud M...

引用

3rd Asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Li, Xueguang Dumlao, Menchita F College of Computer Studies Angeles University Foundation Angeles Philippines

ISBN: (纸本)9781450395786

With the increasing shortage of fossil energy, people's requirements for the environment are becoming higher and higher. Because of its advantages of green, environmental protection and five emissions, pure electric vehicles have become the target of development. This paper mainly uses deep learning algorithm to conduct cloud monitoring on electric vehicle BMS. Firstly, the communication protocol based on CAN2.0b is designed and improved. The cloud server can interact with BMS through this protocol. Secondly, the architecture of cloud server and communication server is designed. The design can meet the requirements of multiple concurrency. Thirdly, design and improve multilayer perceptron algorithm. A multilayer perceptron algorithm was designed to estimate the SOC of lithium battery. Through the design of this paper, BMS can communicate well with cloud server. The multi-layer perceptron algorithm can also estimate SOC well. It can well adapt to the application of engineering practice. In addition, it also provides another idea for cloud monitoring of electric vehicles. It has good theoretical significance. © 2022 ACM.

关键词： Electric vehicles

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The Role of Blockchain Technology in Supply chain Management for Data Security 3

The Role of Blockchain Technology in Supply chain Management...

引用

3rd IEEE International conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Birari, Dipika R. Rathod, Vijay U. Dandavate, Aarti Shelke, Nilesh Kumar, Ranjit Kulkarni, Masira M. S Army Institute of Technology Department of Information Technology Pune India G H Raisoni College of Engineering and Management Department of Artificial Intelligent and Machine Learning Wagholi Pune India Dhole Patil College of Engineering Department of Computer Engineering Pune India Symbiosis International Deemed University Symbiosis Institute of Technology Department of Computer Science and Engineering Nagpur Campus Pune India Ajeenkya D Y Patil University Department of Computer Engineering Pune India G H Raisoni College of Engineering and Management Department of Cyber Security and Data Science Wagholi Pune India

ISBN: (纸本)9798350354348

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. Data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well. © 2024 IEEE.

关键词： Smart contract

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Deep learning based Real-time Stolen Vehicle Detection Model with Improved Precision and Reduced Look Up Time

Deep Learning based Real-time Stolen Vehicle Detection Model...

引用

intelligent Technologies (CONIT), International conference on

作者： Shaik Shafi T Pavan Sai Kumar Reddy Rohan Silla Musrath Yasmeen Dept of ECE B V Raju Institute of Technology Medak

In the recent past, the number of vehicles stolen in India has surged substantially. As per the Acko Vehicle Theft Report, in Delhi-National Capital Region (NCR), for every 12 minute a vehicle is stolen and overall Delhi-NCR accounts for more than 56% of all vehicle thefts in the country. Further, almost 3 lakh automobiles were stolen in the national capital during 2011 and 2020. While Bengaluru, India’s second-highest car theft city, accounts for 9% of vehicle theft. There exists plethora of studies for stolen vehicle tracking or detection models. The prior methods employed were time-consuming and inefficient, requiring a lot of human work to go through CCTV video recordings and trace the stolen vehicle. Some models are 96% accurate but are incapable of being employed in real time. Some are inaccurate, yet they are utilized for real-time detection. To overcome this time consuming process, a Deep learning Based Real Time Stolen Vehicle Detection Model is proposed in this paper. Our work aims to reduce the lookup time of the vehicle in CCTV footage besides the several tasks to accomplish, like vehicle and license plate detection, optical character recognition. To achieve this, a deep learning tool known as You Only Look Once (YOLO) v8 algorithm has been used for real-time object recognition in the system. In addition, TensorFlow, an open-source machine learning platform is used for vehicle number plate recognition and for image processing. Using this strategy, we can dramatically shorten the time it takes to find the stolen vehicle. Upon rigorous experiments, results showed that the proposed deep learning based real time framework is viable and can achieve decent detection.

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Efficient Brain Tumour Classification Using Pre-Trained Xception Model

Efficient Brain Tumour Classification Using Pre-Trained Xcep...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： D. Raghava Kumari Ch.Rajendra Prasad Srinivas Samala Department of ECE SR University Warangal Telangana Department of ECE Sumathi Reddy Institute of Technology for Women Warangal Telangana Dept. of ECE SR University Warangal India Dept.of ECE SR University Warangal India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

A brain tumour is among the most lethal tumours worldwide. It exhibits the lowest survival rate and presents various varieties based on location, texture, and form. Thus, accurately determining the type and grade in the early stages is crucial to selecting an appropriate treatment strategy. The substantial volume of data and the diverse forms of brain tumors render the manual procedure time consuming and prone to human error. This research study presents a brain tumour classification using the Xception transfer learning model. The proposed Xception model is trained and tested using the Brain Tumour Image Dataset collected from Kaggle. The raw data are preprocessed before applying to transfer learning model to match its parameters. The proposed model provides a testing accuracy of 0.98 and a loss of 0.02 and shows exceptional efficiency contrasted to the existing models. In real-time, this model assists the doctor in identifying the particular brain tumor.

关键词： Training Accuracy Transfer learning Brain tumors Medical services Manuals Brain modeling Data models Real-time systems Testing

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Optimal Management of Multi-Area System Incorporating Wind Power Across Varied Climatic Conditions using Firefly optimization Methodology

Optimal Management of Multi-Area System Incorporating Wind P...

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Energy, Power and Electrical Engineering (EPEE), International conference on

作者： Prince Kumar Kunal Kumar Aashish Kumar Bohre Nabanita Adhikary Electrical Engg. Department NIT Silchar Assam India Electrical Engg. Department NIT Durgapur WB India Electrical Engg. Department NIT Rourkela Odisha India

Increased randomness in climatic condition due to global warming has stressed researchers to prepare the technology for harsh weather condition. In this paper wind power plant under different climatic conditions is considered for testing the resiliency of proposed power network with proposed controller. An intelligent controller hybridized with firefly optimization technique has been proposed in this paper which is helpful in enhancing the robustness of system in tackling the unknown random weather on proposed hybrid power system. In this paper, a uniquely designed fitness function i.e. ITMWAE (Integral of Time multiplied Magnified Weighted Absolute Error) is proposed for evaluating the performance of proposed power system with wind power plant. Proposed work in the current paper has been processed with the help of MATLAB/SIMULINK tool.

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Advanced Mango Leaf Disease Detection and Severity Analysis with Federated learning and CNN

Advanced Mango Leaf Disease Detection and Severity Analysis ...

引用

intelligent Technologies (CONIT), International conference on

作者： Shiva Mehta Vinay Kukreja Rishika Yadav Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India

Worldwide mango production is threatened by mango leaf diseases, resulting in considerable financial losses. Effective illness management and reduction depend on early identification and severity level categorization. This research provides a brand-new FL-CNN model for categorizing mango leaf diseases into four severity categories. The FL-CNN model allows training to be distributed across several clients without compromising data privacy. The suggested model consistently outperforms all clients in identifying the four severity levels of mango leaf diseases. The model's efficiency across customers with various data distributions is shown using macro, weighted, and micro-averaging techniques. Client 4 had the greatest macro average (96.11), weighted average (96.09), and micro average (96.08) values, while Client 1 had the lowest macro average (93.92), weighted average (93.96), and micro average (93.96) values. These findings show that the FL-CNN model successfully recognizes and categorizes various degrees of mango leaf disease severity, making it an invaluable tool for practical agricultural applications. Utilizing federated learning provides data privacy while offering a scalable and safe method for managing plant diseases. Additionally, federated learning preserves data privacy and lessens the need for data centralization, offering a solid foundation for cooperation between many stakeholders. Future studies should focus on enhancing the model's effectiveness and accuracy and investigating whether it can predict additional plant diseases and agricultural situations.

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HMPFL: Hypernetwork Model based Personalized Federated learning for Industrial Anomaly Detection

HMPFL: Hypernetwork Model based Personalized Federated Learn...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International conference on

作者： Zhen Zhang Weishan Zhang Zhicheng Bao Qiyuan Liu Yuru Liu Yikang Zhao Rui Zhang Wenyin Zhu College of Computer Science and Technology China University of Petroleum (East China) China Qingdao Haier Intelligent Technology RD Co China

Federated learning allows for collaborative training of distributed deep learning networks, tackling the issue of data silos while ensuring data privacy. However, in the distributed setting of industrial equipment, the collected data is dynamic, non-independent, and identically distributed(i.e., Non-IID), which negatively impacts the performance of federated learning. While the personalized solution can address the Non-IID problem, it falls short in effectively handling the dynamic environment of industrial equipment, which poses a significant challenge to model stability. Model stability enables the model to quickly transfer and share knowledge when dealing with dynamic data. To maintain model stability and generate customized models, we propose a personalized federated learning approach that leverages hypernetwork and model-oriented updates, named HMPFL, which aims to generate stable personalized models for Non-IID dynamic environments. Finally, we evaluate the effectiveness of our framework in addressing industrial equipment anomaly detection tasks using three real industrial air conditioning datasets from different factories. The results demonstrate that our method, surpasses existing personalized federated learning approaches, achieving an average accuracy improvement of 7.00%.

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Enhanced Tuberculosis Detection Using Deep learning and GAN Data Augmentation Techniques for Chest x-Ray Analysis

Enhanced Tuberculosis Detection Using Deep Learning and GAN ...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Varun Reddy Chaykam Avinash Reddy Koduru Karthick S C. Arun Department of Computational Intelligence SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

With around 1.6 million deaths yearly, primarily in low-resource environments, abstract-tuberculosis (TB) is a serious worldwide health issue. While human analysis can be error-prone and labor-intensive, early discovery using chest X-rays is vital. We used advanced machine learning methods including a mix of Histogram of Oriented Gradients (HOG) and Convolutional Neural Networks (CNN), alongside Deep Convolutional Generative Adversarial Networks (DCGANs) to create synthetic TB images, so helping to balance the dataset and reduce class imbalance. We applied explainable artificial intelligence (XAI) methods including LIME and Grad-CAM for greater interpretability in order to improve model transparency. The outcomes showed notable progress; our hybrid model obtained a stunning 99.5% diagnosis accuracy. With respective accuracy of 96.87% and 94.67%, models such as Xception and MobileNetV2 also fared rather well. Especially in resource-limited healthcare settings, this study provides a consistent and interpretable strategy for tuberculosis screening.

关键词： Deep learning Accuracy Tuberculosis Explainable AI Generative adversarial networks Data models Convolutional neural networks Reliability X-ray imaging Biomedical imaging

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Impact of machine learning in natural language processing: A review 3

Impact of machine learning in natural language processing: A...

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3rd International conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Nagarhalli, Tatwadarshi P. Vaze, Vinod Rana, N.K. Comp. Engg. Shri JJT University Rajastan India Shri JJT University Dept. of Computer Sci and Engg. Rajastan India Theem College of Engineering Mumbai India

ISBN: (纸本)9780738111834

The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine learning, Deep learning and Natural Language Processing. The new found excitement in these fields of research is the availability of high powered computing devices at a cheaper costs and the wide range of applications of these research fields. It can be safely said that in the present times there are hardly any sectors of businesses which have not been positively impacted by artificial intelligence or its subfields. Machine learning and deep learning, apart from having a positive impact on different sectors of businesses, machine learning and deep learning have also played a very important role in improving the efficiency of other subfields of artificial intelligence, including computer vision and natural language processing. Natural Language Processing is the ability of computer systems to understand human languages, which is a difficult task where these learning techniques has played a very important role in proper analysis. The paper proposes to highlight this important role played by the learning techniques in improving the efficiency of natural language processing. © 2021 IEEE.

关键词： Efficiency

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