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Leveraging Blockchain Technology for Enhanced Government Services, Transparency, and Administrative Efficiency 2

Leveraging Blockchain Technology for Enhanced Government Ser...

引用

2nd international conference on advanced computing and Communication Technologies, ICACCTech 2024

作者： Dewangan, Shweta Manoharan, Geetha Sharma, Animesh Kumar Kumar, Sanjeev Talukder, Mohammad Badruddoza ICFAI University Department of Commerce Raipur India School of Business SR University Warangal India ICFAI University Department of Science Raipur India School of Hotel Management and Tourism Lovely Professional University Punjab India College of Tourism and Hospitality Management International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (纸本)9798331519056

Blockchain technology prevents the corruption of documents. Property rights, land titles, company registrations, and official permits are all secured by blockchain. Increased transparency, reduced fraud, and increased public trust. Blockchain technologies help streamline government operations. Procurement, licensing, and regulatory compliance are made simpler by digitalizing and safely storing papers and data on the blockchain. Digitization of documents and blockchain smart contracts streamline bureaucratic procedures, automating procurement, licensing, and regulatory compliance, reducing paperwork and processing time. This lessens the amount of paperwork, manual mistakes, and service delivery. Government networks are in danger from cyber-attacks and data breaches. The cryptography and decentralization of blockchain improve government cyber security. The blockchain protects citizen data. Blockchain-based systems improve the sharing of government information. Citizen Service is made more accessible as a result. Blockchain securely distributes medical records amongst doctors, cutting down on redundant testing, improving patient care, and speeding up administration. Blockchain can help with resource distribution and allocation in government. Monitoring and tracking public money ensure resource distribution while reducing corruption. intelligent contracts built on blockchains enforce financial standards. Transparent and immutable records confidence and the maintenance of citizen identification are made secure and decentralized via blockchain. Blockchain technology offers governments the potential for enhanced services, transparency, and administrative efficiency across sectors like property management, citizen services, and financial accountability. Thanks to blockchain's cryptographic features, people can maintain their identification information and grant access to governmental entities and service providers. Centralized identity databases protect orivacy. © 2024 ieee

关键词： Efficiency

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PPM: A Privacy-Preserving Framework for intelligent Medical Diagnosis systems 17

PPM: A Privacy-Preserving Framework for Intelligent Medical ...

引用

17th ieee international conference on Anti-Counterfeiting, Security, and Identification, ASID 2023

作者： Xia, Ruidan Huang, Chaolong Liu, Yining Peng, Ming Huang, Pingxiang Hefei University of Technology School of Microelectronics Hefei China

ISBN: (纸本)9798350319613

The extensive utilization of deep learning techniques has significantly boosted the efficacy of intelligent Computer-Aided Diagnosis (CAD) using medical data from multiple modalities. Deep learning techniques generally suffer from high computational complexity and cloud computing can effectively empower deep learning techniques by optimized software (e.g. distributed training/inference) and specific hardware (e.g. GPUs, FPGAs and TPUs). However, cloud computing also increases the risk of medical data leakage, which greatly hurts patients' privacy. There has been several techniques including homomorphic encryption approach proposed to preserve patients' privacy. However, none of them can be directly applied to deep neural networks to improve medical CAD applications via multi-modal data fusion. In this paper, we propose PPM, a framework integrating semantic level multi-modal data fusion neural network with homomorphic encryption mechanism, in order to preserve patients' privacy while maintaining the diagnostic performance. We conduct experiments on multi-source and multi-modal medical data for Upper Gastrointestinal (UGI) cancer screening. © 2023 ieee.

关键词： Learning systems

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Wearable Smart Health Monitoring Transmitter and Portable Receiver systems for Enhanced Wellness Insights 2

Wearable Smart Health Monitoring Transmitter and Portable Re...

引用

2nd international conference on Automation, computing and Renewable systems, ICACRS 2023

作者： Sivaprasad, T.C. Surendhar, R. Heiner, A.J. Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

This study presents the development and evaluation of a smart wearable belt designed for comprehensive health monitoring and fall detection. The wearable integrates advanced sensors, including vibration and heartbeat, to give users real-time insights into their well-being. The vibration sensor, meticulously calibrated for precise fall detection, exhibited high sensitivity and responsiveness, distinguishing between daily activities and potential accidents. Simultaneously, the heartbeat sensor demonstrated accurate and consistent heart rate monitoring, contributing to proactive cardiovascular health management. The seamless integration of these sensors, supported by MQTT connectivity and secure data transmission, ensures a user-friendly experience. Cloud integration enhances data processing and analytics, empowering users with a comprehensive health dashboard. Results indicate the reliability of the fall detection system and the accuracy of heart rate monitoring. This intelligent wearable belt represents a promising advancement in health technology, offering a holistic solution for users seeking real-time health insights and proactive fall prevention. Ongoing research focuses on refining algorithms and incorporating user feedback for continuous improvement and broader applications in personalizedhealth care. © 2023 ieee.

关键词： data analytics Patient monitoring Smart belt Wearable Sensor

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Secure MAC-Based Two Way Authentication System (MTWAS) for data Communication in Internet of Vehicle 1

Secure MAC-Based Two Way Authentication System (MTWAS) for D...

引用

1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Singh, Dhananjay Kumar Bhardwaj, Diwakar Mathura India

ISBN: (纸本)9798350353648

The Internet of Vehicles (IoV) is a rising area within the perspective of the intelligent Transportation System (ITS) that has gained research emphasis in recent years. It enables the people involved in the network to make collaborative decisions. Huge-scale information monitoring, accumulating, and exchanging among automobiles, roadside units, and other intelligent components in the environment are all made possible by IoV, enabling dynamic and impromptu communication. Nevertheless, there are still many obstacles to overcome in terms of confidentiality and security for these IoV connections, making them more vulnerable to security breaches. The present research proposes an encrypted communication mechanism between automobiles and various other IoV smart components, along with a reliable and affordable Secure MAC -based Two Way Authentication System (MTWAS) in (/oV). Leveraging the BAN logic, we propose a strict verification of reliability and authenticity for the suggested (MTW AS) approach. The research culminates with both quantitative and qualitative assessments showing that the proposed approach outperforms than other recent state-of-the-art systems. © 2024 ieee.

关键词： IoV security MAC - based verification security two way authentications in IoV vehicle authentication

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Research on intelligent data Processing and Signal Processing Algorithm in UAV Power Inspection 7

Research on Intelligent Data Processing and Signal Processin...

引用

7th ieee international conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Zhiwei, Luo Jing, Fang Haitao, Cheng Yuxi, Du Wind Power Co. LTD Fujian Fuzhou China State Grid Power Space Technology Co. LTD China University of Mining and Technology Beijing China State Grid Power Space Technology Co. LTD Beijing China

ISBN: (纸本)9798350350760

In this article, an advanced data handling technique integrating Principal Component Analysis (PCA), the YOLOv5 methodology, and Fourier transformation is introduced for image manipulation and signal scrutiny in UAV power audits. Initially, PCA is employed to condense the image dataset acquired during UAV surveillance, eliminate superfluous details, and isolate essential characteristics. Subsequently, the YOLOv5 technique is utilized to pinpoint power infrastructure components, execute object localization and categorization, and discern possible malfunction patterns. Ultimately, the sensor readings are examined in the frequency domain via Fourier transformation to pinpoint irregular vibration frequencies and other anomalous signals. This approach can efficiently process and scrutinize extensive inspection datasets, as well as promptly and accurately spot latent defects in power installations, thereby enhancing the operational efficacy and safety of the power grid. ©2024 ieee.

关键词： data integration

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Research on Precision Optimization of Deep Learning Method based on VAT-LSTM and Transformer Encoder in Financial data Prediction 1

Research on Precision Optimization of Deep Learning Method b...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： You, Yue Shanghai University School of Management Shanghai China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This article addresses the challenge of improving prediction accuracy for financial data using two advanced deep learning methods: VAT-LSTM and Transformer Encoder. Financial time series data often present significant challenges for traditional econometric models due to their complex characteristics, such as high noise levels, low signal-to-noise ratios, nonlinearity, and strong volatility. In order to overcome these problems, we propose two innovative models: incorporating VAT standardization into LSTM and constructing a VAT-LSTM model to enhance the stability of the model and reduce the generalization error. Through empirical analysis of the data of the Shanghai Composite Index (2015-2022), VAT-LSTM has short term, medium term, in the long term forecast, LSTM, RNN, GRU, BP, SVR, it is better than the traditional model (such as arima), the second model based on transformer encoder and attention. In particular, the prediction accuracy of various time ranges is improved. Comparison with traditional models shows that the Transformer Encoder provides the best performance across different time dimensions. Overall, the proposed models excel in forecasting financial data, with promising results in short-, medium-, and long-term prediction. However, there is potential for further optimization in long-term forecasting. This research not only contributes new insights for financial data prediction but also has wide application potential in the field. © 2025 ieee.

关键词： Prediction models

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data acquisition System for Hydroponic Culture: A Comprehensive Study and Implementation of Scale Model 3

Data Acquisition System for Hydroponic Culture: A Comprehens...

引用

3rd ieee international conference on Signal, Control and Communication, SCC 2023

作者： Kahla, Radhwen Ben Ghezail, Feiza Harbaoui, Imen Université de Tunis El Manar École Nationale d'Ingénieurs de Tunis LR11ES20 Laboratoire d'Analyse Conception et Commande des Systèmes École Supérieure Privée d'Ingénierie et de Technologie Tunis1002 Tunisia Université de Carthage Institut National des Sciences Appliquées et de Technologie LR11ES26 Laboratoire d'Informatique Pour les Systèmes Industriels Tunis Tunisia

ISBN: (纸本)9798350326390

The development of an intelligent monitoring system suitable for hydroponic greenhouses allows the control of climate parameters and nutritive ingredients, making it possible to grow many plants in urban areas all year round by using intelligent industrial techniques, such as the Internet of Things, cloud computing, big data, and artificial intelligence. In this field, we are working on the development of a fully controlled system for greenhouses, in order to provide an intelligent adjustment of climate parameters and nutrient solution ingredients based on artificial intelligence techniques. In the current paper, the aim is to evaluate the criteria for low-cost environmental parameter sensors, and to detail our scale model. Then, a description of the climate parameter sensors, the nutrient solution sensors, the acquisition board, and the data logging methodology is provided. © 2023 ieee.

关键词： Greenhouses

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Design of an intelligent Asset Recovery System Based on Deep Learning 4

Design of an Intelligent Asset Recovery System Based on Deep...

引用

4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yang, Fang Guizhou University Faculty of Public Administration Guizhou Guiyang550001 China

ISBN: (纸本)9798331528850

The increasing complexity of financial crimes poses significant challenges for traditional asset recovery methods, which often fail to effectively trace covert fund transfers and complex money laundering schemes. To address these limitations, this paper proposes and implements an intelligent asset recovery system based on deep learning, incorporating advanced computational techniques to enhance the detection and analysis of complex financial transactions. The system utilizes a hybrid CNN-LSTM model to extract both spatial and temporal features from transaction data, while a self-attention mechanism dynamically highlights critical transaction patterns, improving the model's interpretability and recognition capabilities. A multimodal data fusion approach integrates diverse sources, including bank transaction records, social network data, and public financial statements, enabling comprehensive analysis of heterogeneous data. Experimental results demonstrate that the proposed system significantly outperforms traditional methods in key metrics, achieving higher accuracy, precision, recall, and Fl scores. Moreover, it exhibits superior performance in identifying intricate fund transfer chains and adapting to diverse financial crime scenarios. The study not only provides a robust, automated tool for asset recovery but also establishes a foundation for the development of intelligent systems aimed at preventing and combating financial crimes. Future research will focus on enhancing system scalability, optimizing computational efficiency, and integrating additional data sources to further expand its application scope. © 2024 ieee.

关键词： Decentralized finance

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A Feature Selection Method Based on Hybrid Natural Inspired Algorithms 11

A Feature Selection Method Based on Hybrid Natural Inspired ...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Xia, Xiaoyu Ye, Zhiwei Sun, Shuang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

data mining is one of the fastest-growing research domains in the information industry as a result of the wide availability of numerous data. Feature selection is the important preprocessing step to affect the performance of data mining. Ttimum. Besides that, hybrid algorithms maintain the advantages of each single-mode algorithm and avoid its weakness effectively. In this work, we propose a parallel model for feature selection named THDWL, which is a combination of Differential Evolution (DE), Whale Ohe natural-inspired algorithm is one of the most effective methods for feature selection, but there are some limitations in the single-mode algorithm such as slow efficiency and local optimization Algorithm (WOA), and Lightning Attachment Procedure Optimization (LAPO). In THDWL, the operators of these algorithms are independently implemented on their subpopulations and communicate at the end of each iteration to get the global best solution. Comparisons are conducted between single-mode algorithms and THDWL to verify the performance of the proposed method. The simulation results show that THDWL effectively improves the classification accuracy and convergence speed comparing with the single-mode algorithm.

关键词： feature selection natural-inspired algorithm hybrid algorithm data mining

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Autism Detection System using Behavioral data 1

Autism Detection System using Behavioral Data

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Vijaya Shetty, S. Sowmya, P. Srujana, S.N. Shruthi Shetty, J. Mishra, Aditya Zaid, Mohd Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798331529246

This study introduces an automated autism spectrum disorder (ASD) detection system employing advanced machine learning techniques. The system integrates behavioral and physiological data from diverse sources. The score parameters of various machine learning models, such as Random Forest, Decision Tree, SVM, and AdaBoost, are compared. Pickling is performed on each model for easy storage and retrieval, and their predictions are aggregated to present a majority decision. Extensive experiments are conducted on a diverse dataset comprising toddlers, children, and adults to assess the system's performance in accurately classifying individuals as ASD or non-ASD. The system aggregates predictions from various models using a majority voting algorithm to present a majority decision, ensuring reliable and efficient early ASD identification across all age groups. The proposed system achieved an accuracy of 97%, with the majority voting algorithm enhancing reliability and efficiency in early ASD identification across all age groups. The novelty of this work lies in integrating behavioral and physiological data from diverse sources, along with the majority voting algorithm, to develop an automated ASD detection system using machine learning techniques. © 2025 ieee.

关键词： Adversarial machine learning

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