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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Abul Ala Walid Maloy Kumar Devnath Bakhtiar Muiz Md Mehedi Hassan Melon Pronob Kumar Barman Pronoy Kumar Barman Department of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Department of Information Systems University of Maryland Baltimore County MD USA Department of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Department of Computer in Data Science Pacific States University Los Angeles CA USA Department of Statistics Jagannath University Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

In this study, we explore the efficacy of using a graph-based intrusion detection system (GBIDS) for Controller Area Network (CAN) bus security with minimal training data. Traditional machine learning models often require extensive data for training, typically around 70%, to achieve high accuracy and reliability. However, our approach demonstrates that even with significantly reduced training data—specifically, using just 30% of the available 70% of data (21% of total data)—we can maintain high levels of accuracy, precision, recall, and F1 score. This minimal data training is facilitated by on-the-fly training capabilities akin to edge computing, allowing for real-time adaptation and learning. Our results indicate that it is not always necessary to rely on the conventional 70% training data benchmark to ensure robust CAN bus security. By leveraging advanced graph-based algorithms and edge computing techniques, we achieve efficient and effective intrusion detection with reduced data requirements, thus offering a promising solution for real-time CAN bus security in resource-constrained environments.

关键词： Training Accuracy Training data Intrusion detection Machine learning Real-time systems Controller area networks Security Reliability Edge computing

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Blockchain Based Voting System for Secure and Transparent Electoral Processes

Blockchain Based Voting System for Secure and Transparent El...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Sumathy S S. Karthick Murugan J. Latha G. Mageshwari Allen Joseph Gilbert Andrews M Hema Kumar PG Department of Computer Science Sir Theagaraya College-Shift II Chennai Tamilnadu India Department of Computer Science and Engineering R. M. D. Engineering College Thiruvallur Tamilnadu India Department of Computer Science and Engineering R. M. K. Engineering College Tiruvallur Tamilnadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tiruvallur Tamilnadu India Specialist leader Deloitte consulting LLP 200 Sterling road Mechanicsburg PA USA Department of ECE Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This study describes an innovative approach to blockchain-based voting systems. The proposed method guarantees the integrity, security, and resistance to manipulation of elections conducted on a decentralized network through the utilization of smart contracts and cryptographic principles. Emphasizing participant confidentiality, vote integrity, and real-time auditability, the experimental outcomes demonstrate the system's scalability, dependability, and security. Within the domain of theoretical democratic discourse, presents a prospective scenario in which voting methods based on blockchain technology. The results indicate that the approach can be easily implemented on a large scale, representing a significant milestone in the struggle for democratic, secure, and transparent voting procedures. With highly efficient, boasting a transaction speed of 500 TPS, scalability to accommodate over a thousand users, and a block confirmation time of less than 10s. Encryption efficiency and digital signature dependability are indicators of the system's high level of security. Equipped with an accuracy rate that is 97.2%.

关键词： Resistance Electric potential Voting Scalability Smart contracts Systems architecture Transforms Real-time systems Blockchains Encryption

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IoT-Based Smart Home Energy Management System (SHEMS) Using Networking and Automation

IoT-Based Smart Home Energy Management System (SHEMS) Using ...

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data science and Business Systems (ICDSBS), International Conference on

作者： Arokia Martin N Kirubakaran N Senthil Kumar G Selvaganesan C Jegadeeshwari P Vijayaraja V Department of Computer Science and Business Systems Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Dr. MGR Educational and Research Institute (Deemed to be University) Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Physical energy meter reading concept is outdated and it is not efficient, leading to lots of wastage of manpower and errors, btaining accurate real-time readings is challenging and complex to achieve consistently. Current implementations of IoT technology allow real-time monitoring and energy management capabilities with the integration of IoT ecosystems, Digital Communication Technology networks, and Machine Learning algorithms to further analyze data as well as enhance user convenience. However, despite their promising potential, these systems face ongoing challenges, including adaptability, scalability, interoperability and enhanced cyber security resilience, particularly in large-scale deployments. This review examines present SHEMS and their capabilities, from strengths such as real-time tracking of energy usage, optimization and consumption of energy at offpeak hours by the application of predictive analytics using Machine Learning. This also elucidates areas for improvement, such as an improved integration of renewable energy technologies, simplification of the complexities surrounding large-scale deployment and sustainability over the long term. Addressing these deficits, this paper further promotes advancements in the design of SHEMS that are more secure, scalable and inter-operable, increasing communication across IoT ecosystems and by using smart grid technology, which could improve effective management of energy and a potential wider uptake. These suggestions are aimed at fine-tuning existing technologies to be easily integrated into modern, automated energy systems for better energy management.

关键词： Renewable energy sources Machine learning algorithms Automation Smart homes Machine learning Predictive models Real-time systems Smart grids Predictive analytics Optimization

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Assessing the Efficiency of AI for Multivariate data Analysis

Assessing the Efficiency of AI for Multivariate Data Analysi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Jambi Ratna Raja Kumar Mukesh Chaturvedi D. ANandhasilambarasan V. Ashok Ashwini R. Nawadkar Manoj Va Department of Computer Engineering Genba Sopanrao Moze College of Engineering Pune Maharashtra India Faculty Of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Computer Science Mangalayatan University Aligarh India

This study aimed to evaluate the efficiency of synthetic Intelligence (AI) for multivariate records analysis. Multivariate statistics evaluation is a complicated venture that historically requires considerable manual labor and information in information science and machine mastering. AI can offer an extra green and accurate method to the venture. Consequently, this study employed a ramification of AI-based total methods to identify patterns and generate insights from the dataset. Traditional and ML algorithms were used to generate models based on the records. The models have been evaluated based on their metrics—accuracy, precision, and keep in mind. In the end, the effects from the models were analyzed to identify trends and institutions among the variables. The findings concluded that AI may want to provide a robust and accurate technique to perform multivariate records analysis. However, further research is needed to develop novel tactics to utilize AI for multivariate statistics evaluation with increased accuracy and precision.

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Synergistic Integration of Master data Management and Expert System for Maximizing Knowledge Efficiency and Decision-Making Capabilities

Synergistic Integration of Master Data Management and Expert...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Ronak Ravjibhai Pansara Ashish Kumar Mourya Shah Imran Alam Naved Alam Nikhitha Yathiraju Pawan Whig Master Data Specialist USA Tesla USA Computer Science GNIOT Institute of Professional Studies Greater Noida Department of computer science and engineering Jamia hamdard New Delhi India University of Cumberlands USA Research Scientist Threws VIPS-TC New Delhi India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The paper explores the intricate relationship between Master data Management (MDM) and Expert Systems (ES) and their combined impact on modern enterprises. It elucidates MDM’s role in consolidating and harmonizing data across disparate sources to establish a unified and authoritative data foundation. Additionally, it delves into the functionalities of ES, emphasizing their capacity to replicate human expertise and domain-specific knowledge for intelligent decision-making. Highlighting their synergy, the research demonstrates how the fusion of MDM and ES optimizes data governance, enhances data quality, and facilitates access to invaluable insights from organizational data repositories. Through real-world case studies, it illustrates how this integration empowers businesses to streamline operations, innovate, and make informed decisions, ultimately positioning this amalgamation as a strategic driver for digital transformation and heightened competitive advantage within today’s data-driven landscape.

关键词： Technological innovation Digital transformation data integrity Decision making data governance Expert systems Business

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IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

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computer, Communication and Control (IC4), International Conference on

作者： M D Ashfaqul Hasan K. Balasubadra G. Vadivel N. Arunfred M.V Ishwarya S. Murugan Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Information Technology University of Technology and Applied Science Salalah Dhofar Oman Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics’ influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management.

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An Interpretable Model for Large-Scale Smart Contract Vulnerability Detection

SSRN

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SSRN 2023年

作者： Feng, Xia Liu, Hai-Yang Wang, Liang-Min Zhu, Hui-Juan Sheng, Victor S. Faculty of Data Science City University of Macao China School of Mathematical Sciences Jiangsu University Zhenjiang China School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Computer Science Texas Tech University LubbockTX United States

Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lacking interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features thereby reducing the scales of features. The learning speed could be accelerated significantly because of the reduction of the feature size. Experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each is higher than 93\% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerabilities is reduced by nearly 80\% compared with its original. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer

arXiv

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arXiv 2024年

作者： Yuan, Jinhui Lu, Shan Duan, Peibo He, Jieyue School of Computer Science and Engineering Southeast University Nanjing China Nanjing FiberHome Tiandi Co. Ltd. Nanjing China Department of Data Science and Artificial Intelligent Faculty of Information Technology Monash University Suzhou China

Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level. However, few existing studies have delved into the utilization of node attributes in heterogeneous information networks (HINs). In this paper, we investigate the impact of inter-node attribute disparities on HGNNs performance within the benchmark task, i.e., node classification, and empirically find that typical models exhibit significant performance decline when classifying nodes whose attributes markedly differ from their neighbors. To alleviate this issue, we propose a novel Attribute-Guided heterogeneous Information Networks representation learning model with Transformer (AGHINT), which allows a more effective aggregation of neighbor node information under the guidance of attributes. Specifically, AGHINT transcends the constraints of the original graph structure by directly integrating higher-order similar neighbor features into the learning process and modifies the message-passing mechanism between nodes based on their attribute disparities. Extensive experimental results on three real-world heterogeneous graph benchmarks with target node attributes demonstrate that AGHINT outperforms the state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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FUNCTIONAL-LEVEL UNCERTAINTY QUANTIFICATION FOR CALIBRATED FINE-TUNING ON LLMS

arXiv

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arXiv 2024年

作者： Niu, Ruijia Wu, Dongxia Yu, Rose Ma, Yi-An Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States

Accurate uncertainty quantification of large language models (LLMs) provides credibility measure over their outputs. However, fine-tuned LLMs often struggle with overconfidence in uncertain predictions due to the limitations in the models’ ability to generalize with limited data. Existing parameter efficient fine-tuning (PEFT) uncertainty quantification methods for LLMs focus on post fine-tuning stage and fall short of calibrating epistemic uncertainty. To address these limitations, we propose Functional-Level Uncertainty Quantification for Calibrated Fine-Tuning (UQ4CT), which captures and calibrates epistemic uncertainty over the space of functions that map input prompts to outputs. We implement UQ4CT during the fine-tuning stage via a mixture-of-experts framework that hierarchically decomposes the functional space. We demonstrate that UQ4CT reduces Expected Calibration Error (ECE) by more than 25% while maintaining high accuracy across 5 benchmarks. Even under distribution shift, UQ4CT maintains superior ECE performance with high accuracy, showcasing improved generalizability. © 2024, CC BY.

关键词： Modeling languages

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VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional data

arXiv

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arXiv 2023年

作者： Le, Van-Duc Bui, Cuong-Tien Li, Wen-Syan Department of Electrical and Computer Engineering Seoul National University Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of

An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system dedicated to end-to-end ML Lifecycle. Our system tackles several crucial problems that other systems have not solved. First, we address the high cost of building an ML lifecycle, especially for large-scale and high-dimensional dataset. We solve this problem by proposing to transfer the lifecycle of similar datasets managed in our system to the new training data. We design an algorithm based on the core set to compute similarity for large-scale, high-dimensional data efficiently. Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild. Our system helps to detect this mismatch without getting labeled data from testing data and rebuild the ML lifecycle for a new data version. To demonstrate our contributions, we conduct experiments on real-world, large-scale datasets of driving images and spatiotemporal sensor data and show promising results. © 2023, CC BY-NC-ND.

关键词： Machine learning

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