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Blockchain Fundraising and Charity Platform 2

Blockchain Fundraising and Charity Platform

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Govindarajan, Sreevatsan Rajkumar, Sujeeth Sundarajan Mayan, Albert Posonia, A.Mary Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350347982

Charity is regarded as a moral imperative throughout the world, and enormous sums of money are spent in its name. In most situations, the charity collection systems are not clear, and owing to this, charitable organisations struggle to acquire contributors' confidence and attention. This study introduces a crowdfunding platform built on the blockchain to increase the transparency of the fundraising process and grant makers' expenses and to overcome the drawbacks that exist in the present distribution system. Donations can only be withdrawn by the people or organizations that organized the charity event, enabling registered charities to post their identities for investor verification. Since no single entity is in charge of the blockchain for charities and fundraising, blockchain technology can help prevent data breaches and promote openness. The platform serves as a gateway for fundraising and charitable work. © 2023 IEEE.

关键词： Transparency

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Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Liu, Zhao-Qing Ge, Xiaohua Han, Qing-Long Wang, Yu-Long Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia School of Mechatronic Engineering and Automation Shanghai University Shanghai China

This paper deals with the problem of cooperative target tracking control for multiple networked unmanned surface vehicles under data falsification attacks. First, a data falsification attack model is constructed to account for unknown falsified control commands. Next, a networked cooperative target tracking model is developed by integrating the concurrent impacts of data falsification attacks and network-induced delays. Then, a reinforcement learning-based compensation mechanism, consisting of an actor neural network and an adaptive critic function, is developed to offset the adverse effects caused by the unknown dynamics and external disturbances. Meanwhile, to cope with data falsification attacks and reduce the neural network approximation errors, a secure control input is subtly developed. Based on the developed compensation mechanism and secure control input, a dynamic control law is constructed to regulate the action of involved unmanned surface vehicles. Furthermore, a resilient cooperative target tracking control scheme is proposed to achieve the expected cooperative target tracking control objectives. At last, two case studies are provided to validate the efficacy of the designed resilient cooperative target tracking control scheme. IEEE

关键词： Reinforcement learning

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Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection 4

Enhanced Route Optimization: Incorporating Road Safety Facto...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai India Gitam School of Technology Gandhi Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences. © 2024 IEEE.

关键词： Travel time

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Future prediction for precautionary measures associated with heart-related issues based on IoT prototype

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Multimedia Tools and Applications 2024年第23期83卷 63723-63753页

作者： Yenurkar, Ganesh Keshaorao Mal, Sandip Wakulkar, Advait Umbarkar, Kartik Bhat, Aniruddha Bhasharkar, Akash Pathade, Aniket Yeshwantrao Chavan College of Engineering Wanadongri Nagpur India School of Computing Science & amp Engineering VIT Bhopal University Bhopal India Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Wardha India

Cardiovascular disease (CVD) has become a significant cause of death around the world, with heart-related problems being a major contributor to this trend. It’s crucial to identify the signs and symptoms of potential health problems before they become severe. To address this issue, an IoT-based prototype has been proposed. This prototype utilizes an ML-based prediction model to provide precautionary measures for heart-related issues. It includes an IoT device that gathers real-time data from the user’s body, such as heart rate and ECG. This data is utilized to create an ML-based prediction model for heart-related problems. The model can alert the user of any potential heart-related risks. The proposed prototype is designed to offer a practical solution for the early detection of heart-related issues, therefore achieving a greater accuracy of 98.3% in lowering the death rate from heart disease than current methods. The results demonstrate that the suggested IoT-based prototype is useful for detecting various heart diseases and predicting future mortality rates compared to other existing machine-learning methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain

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Frontiers of Computer science 2024年第3期18卷 183-197页

作者： Xingxing CHEN Qingfeng CHENG Weidong YANG Xiangyang LUO State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of TechnologyZhengzhou 450001China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou Institute of Information Science and TechnologyZhengzhou 450001China

With the widespread use of network infrastructures such as 5G and low-power wide-area networks,a large number of the Internet of Things(IoT)device nodes are connected to the network,generating massive amounts of ***,it is a great challenge to achieve anonymous authentication of IoT nodes and secure data *** present,blockchain technology is widely used in authentication and s data storage due to its decentralization and ***,Fan et *** a secure and efficient blockchain-based IoT authentication and data sharing *** studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT *** overcome these security flaws,this paper proposes an enhanced authentication and data transmission scheme,which is verified by formal security proofs and informal security ***,Scyther is applied to prove the security of the proposed ***,it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.

关键词： Internet of Things blockchain authentication data transmission

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Computer Aided Diagnosis Multi-model System using Late Fuison and Ensemble Learning

Computer Aided Diagnosis Multi-model System using Late Fuiso...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Priya, M.Kani Aruru, Gunabhiram Vanam, Nikhil Suganiya, M. Salomi, M. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India

ISBN: (纸本)9798331508685

Lung Cancer and brain tumors are the well-known causes of cancer deaths universal. Therefore, appropriate and accurate diagnosis is an important issue that affects better and more reliable treatment and the patient's chance of survival. Present diagnostic strategies are mostly based on a single piece of information. This often leads to missing necessary information, which can lead to Misidentification of condition or delay in timely treatment. This study presents a novel Computer-Aided-Diagnosis system designed to enhance the detection of lung cancer and brain tumors by integrating multimodal data, including medical images (CT and MRI scans) and clinical information. The proposed approach employs late fusion and ensemble learning techniques, utilizing CNN for processing CT images, VGG19 for analyzing MRI scans, and Random Forest classifiers for evaluating clinical data. By separately training these models, the system capitalizes on the distinct characteristics of each data modality, overcoming the limitations of traditional CAD systems that typically rely on a single data source. This comprehensive analysis not only improves diagnostic accuracy but also provides reliable predictions, ultimately facilitating better clinical decision-making for lung cancer and brain tumor detection. © 2025 IEEE.

关键词： Lung cancer

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NLP Techniques Cyberbullying Text Analysis on Twitter 10

NLP Techniques Cyberbullying Text Analysis on Twitter

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rishi, R. Mohammed Irfan, M.A. Balamurugan, G. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350353068

With the pervasive nature of social media, the rise of cyberbullying has become a critical concern, necessitating advanced methodologies for timely identification and analysis. This research delves into the application of Natural Language Processing (NLP) mechanisms to mention the challenges of cyberbullying within the dynamic and fast-paced environment of Twitter. The study explores the utilization of state-of-the-art NLP algorithms and methodologies for the classification and analysis of cyberbullying text. Techniques such as sentiment analysis, text categorization, and semantic understanding are employed to discern the nuanced and context-dependent nature of cyberbullying content on the Twitter platform. We present a comprehensive examination of various machine learning models, including but not limited to recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer methods like BERT, for their effectiveness in accurately classifying cyberbullying text. Additionally, attention is given to feature engineering and pre-processing strategies tailored to the unique characteristics of Twitter data. © 2024 IEEE.

关键词： Sentiment analysis

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A new multi-stream approach using acoustic and visual features for robust speech recognition system

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Materials Today: Proceedings 2022年 62卷 4916-4924页

作者： Radha, N. Shahina, A. Nayeemulla Khan, A. Velusami, Jansi Rani Sella Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Chennai India School of Computing Science and Engineering Vellore Institute of Technology Chennai India Department of Computing Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Building a robust Automatic Speech Recognition (ASR) system and improving recognition accuracy in adverse conditions is still a challenging task. One way to improve the robustness of an ASR system is combining information from multiple sources (streams). A multi-stream approach which handles the multiple inputs at the model level is the key contribution of our work. Standard mic (Sm), Throat mic (Tm), and Lip reading (Lr) are the various source streams that have been used. This work explores a static weighted two stream HMM (TSH) and multi-stream HMM (MSH) model for the bimodal and multimodal systems. A syllabic units of the Hindi language database categorized into three categories – Vowel, Place of Articulation (POA), and Manner of Articulation (MOA) are used for training and testing. In this study, four types of TSH are proposed for the combination of bimodal ((Sm+Tm), (Tm+Lr), (Sm+Lr), (Lm+Lm)) systems and one type MSH is proposed for multimodal (Sm+Tm+Lr) system in a synchronous and asynchronous manner. Mel Frequency Cepstral Coefficient (MFCC) features are used for Sm and Tm signals. Combined pixel-motion based features (DCT/DWT-MHI) are used for Lr signals. Among these two features, DWT outperforms than DCT and used as a feature for visual speech. Experiments were conducted for bimodal and multimodal system. The proposed MSH approach shows improvements of 1.36%, 6.21%, and 5.8% in recognition accuracy for Vowel, POA and MOA category respectively, as compared to bimodal systems. © 2022

关键词： Discrete wavelet transforms

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A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

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Recent Advances in Computer science and Communications 2023年第5期16卷 82-92页

作者： Reddy, Pundru Chandra Shaker Sucharitha, Yadala School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods: A unique energy-saving Distance-Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusion: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN. © 2023 Bentham science Publishers.

关键词： Cognitive radio

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