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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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Secure and Transparent Medical Record Management System Using Python and Blockchain

arXiv

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arXiv 2024年

作者： Sathya, S. Naidu, Chitikela Atchiyya Rao, Mareedu Lakshmi Hanumanth Krishna, Vadla Vamsi Dept.of Computer science and Engineering SRMIST Ramapuram Chennai India

In recent years, blockchain technology has emerged as a transformative innovation across various industries, offering unique advantages such as decentralization, immutability, transparency, and enhanced security. One sector that stands to benefit significantly from blockchain integration is healthcare, where the need for secure and efficient management of patient health records is paramount. In this paper, we propose a robust health record storage and management system built on blockchain technology to address the challenges faced by traditional healthcare record *** primary advantage of employing blockchain in healthcare record management is its ability to provide a secure and decentralized platform. Unlike traditional centralized databases, where a single point of failure can compromise data integrity and security, blockchain distributes data across a network of nodes, ensuring redundancy and resilience against cyber-attacks. This distributed nature of blockchain enhances data security and privacy, crucial considerations when dealing with sensitive health information. Central to our proposed system is the utilization of smart contracts, which are self-executing contracts with predefined rules and conditions. Smart contracts automate processes related to health record management, such as data access, sharing, and updating, based on predefined permissions and protocols. This automation not only streamlines administrative tasks but also reduces the risk of human errors and ensures data accuracy and consistency. Furthermore, our system prioritizes patient empowerment by granting individuals complete control over their health records. Patients can securely access and manage their data using cryptographic keys, granting permission to healthcare providers or other authorized entities as needed. Overall, our proposed health record storage and management system on the blockchain offer significant advantages over traditional systems, including enhanced security, dat

关键词： Electronic health record

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Privacy-Preserving Deep Learning on Big Data in Cloud

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China Communications 2023年第11期20卷 176-186页

作者： Yongkai Fan Wanyu Zhang Jianrong Bai Xia Lei Kuanching Li State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing 100024China Industrial and Commercial Bank of China Zhuhai Branch Zhuhai 519000China Dept.of Computer Science and Information Engineering Providence UniversityTaichung 43301China

In the analysis of big data,deep learn-ing is a crucial *** data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage ***-ertheless,there is a contradiction between the open nature of the cloud and the demand that data own-ers maintain their *** use cloud resources for privacy-preserving data training,a viable method must be found.A privacy-preserving deep learning model(PPDLM)is suggested in this research to ad-dress this preserving *** preserve data privacy,we first encrypted the data using homomorphic en-cryption(HE)***,the deep learn-ing algorithm’s activation function—the sigmoid func-tion—uses the least-squares method to process non-addition and non-multiplication operations that are not allowed by ***,experimental re-sults show that PPDLM has a significant effect on the protection of data privacy *** with Non-Privacy Preserving Deep Learning Model(NPPDLM),PPDLM has higher computational effi-ciency.

关键词： big data cloud computing deep learning homomorphic encryption privacy-preserving

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Space Robotics-The Influence of Advanced Technology

Space Robotics-The Influence of Advanced Technology

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Deepa Kumari Akshara Chauhan Ishika Chauhan Akansh Srivastava Nishant Anupam Dept.of Electrical Engineering Chandigarh University Mohali India Dept.of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Space robotics is a rapidly evolving field with profound implications for space exploration and technological advancement. This paper provides a comprehensive overview of the history, challenges, current scenario, and future opportunities in space robotics. Beginning with a historical retrospective, the paper highlights key milestones in space robotics, from the pioneering launch of Sputnik to the recent successes of Mars rovers and beyond. Challenges in both land-based and space environments are discussed, including environmental conditions, obstacle avoidance, energy efficiency, communication lag, and autonomy Additionally, advancements in humanoid robots and the growing space robotics market are highlighted. Looking to the future, the paper explores various opportunities for space robotics, including planetary exploration, asteroid mining, satellite lite servicing, space debris cleanup, infrastructure construction, deep space exploration, space tourism support, and interplanetary communication. Specific future missions, such as Mars Sample Return mission of NASA and ESA's ExoMars Rover, are discussed in detail, illustrating the breadth and depth of possibilities in space robotics. [1] Overall, this paper underscores the transformative potential of space robotics in expanding human presence beyond Earth and unlocking the mysteries of the cosmos.

关键词： Space vehicles Location awareness Mars Satellites Space missions Space debris NASA Humanoid robots History Intelligent systems

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BlogGen- A Blog Generation Application Using Llama-2

BlogGen- A Blog Generation Application Using Llama-2

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Amitasha Anand Verma Deshik Kurupudi Sathyalakshmi S Dept.of Computer Science and Engineering Hindustan Institute Of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In recent years, Large Language Models (LLMs) have emerged as transformative tools in natural language processing (NLP) and artificial intelligence (AI) *** excel in generating coherent and contextually relevant text across a wide range of applications one such segment focusing on content generation for websites, blogs and creative writing platforms. In such cases,with an open-source LLM like Llama-2, any person or business can use it for their means without having to pay licensing fees. This includes deploying the LLM to their own infrastructure and fine-tuning it to fit their own needs eliminating the exposure of data to third party interfaces, also making it task specific. This paper introduces a unique model to generate customised blogs by fine-tuning Llama-2 model and perform a comparison with GPT-3.5 and BERT.

关键词： Computational modeling Blogs Focusing Writing Data engineering Natural language processing Data models

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Unforgettable Password Generation Using LoRA Fine-Tuned Large Language Model

Unforgettable Password Generation Using LoRA Fine-Tuned Larg...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Mohammed Sameer K Sudharsan A Benazir Begum Dept.of Computer Science & Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In response to the challenges posed by complex and hard-to-remember passwords in traditional authentication systems, this research introduces an innovative approach leveraging Low Rank Adaption (LoRA) fine-tuned Large Language Models (LLMs) to generate natural language-like strong passwords. Focused on striking a balance between usability and security, proposed system methodology employs smaller-sized pre-trained LLMs, adapted using LoRA fine-tuning, to produce passwords that are both strong and memorable. By addressing the limitations of current password generation practices, proposed system framework aims to encourage user adoption of secure practices, contributing to a positive societal impact through improved cybersecurity hygiene. The proposed solution involves deploying a fine-tuned LLM and serving them through an frontend web UI which emphasizes resource efficiency scalability of deployment of such model, and ease of using the generated passwords in real life authentication purposes fostering good cybersecurity practices among non-technical users.

关键词： Computational modeling Scalability Authentication Passwords Data engineering Data models Usability

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Fuzzy Logic-based Energy Management for WSNs in Smart Grid Applications 4

Fuzzy Logic-based Energy Management for WSNs in Smart Grid A...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Vengatesan, K. Vijayalakshmi, R. Samee, Sayyad Chopade, Neha Mahalle, Shilpa Bashkaran, K. Department of AIDS Sanjivani University Kopargaon India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Dept.of Computer Science Al Sadara School Al Ain Abu Dhabi United Arab Emirates SIES College of Management Studies India Kongunadu College of Engineering and Technology Thottiam India

ISBN: (纸本)9798331515720

Fuzzy Logic-Based Energy Management (FLBEM) is an innovative approach for optimizing energy consumption in Wireless Sensor Networks (WSNs) utilized in Smart Grid applications. This technique employs fuzzy logic principles to dynamically adjust network parameters, such as transmission power and data sampling rates, to enhance energy efficiency while maintaining reliable communication. By intelligently adapting to network conditions and environmental factors, FLBEM extends the operational lifetime of sensor nodes and ensures the stability of the WSN, thus facilitating real-Time monitoring and control of Smart Grid systems. The proposed FLBEM approach encompasses a comprehensive framework that includes the design of fuzzy logic controllers, the development of energy-Aware routing protocols, and the integration of renewable energy sources for powering sensor nodes. Simulation results demonstrate significant improvements in energy efficiency, network lifetime, and overall performance, validating the effectiveness of the FLBEM strategy in the context of Smart Grid applications. As energy management remains a critical concern for WSNs, the implementation of FLBEM offers a promising solution to address the challenges of energy conservation and sustainability in modern power systems. © 2024 IEEE.

关键词： Smart power grids

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Synchrosqueezed Wavelet transform Based Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid 2

Synchrosqueezed Wavelet transform Based Power Quality Distur...

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2nd International Conference on Power Electronics and Energy, ICPEE 2023

作者： Pattanaik, Debasish Swain, Sarat Chandra Samanta, Indu Sekhar Rout, Ullash Kumar Dash, Ritesh Swain, Kunjabihari Kiit Deemed to Be University School of Electrical Engineering Bhubaneswar India Iter Soa University Dept.of Computer Science and Engineering Bhubaneswar India Odisha University of Technology and Research Dept. of Electrical Engineering Bhubaneswar India Reva University Dept.of Electrical and Electronics Engineering Bangalore India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9781665470582

Microgrid monitoring has gained popularity in recent years, owing to their popularity and the power quality disturbances (PQD) caused by renewable energies. Many artificial intelligence-based PQD monitoring strategies have been proposed by researchers. This paper presents a novel method for detecting, classifying and monitoring the power quality disturbances in solar integrated microgrids. Power quality events are detected by analysing the frequency signal with the wavelet synchro-squeezing transform (WSST). The features of power quality disturbances are used to classify them. For PQD classification, the retrieved features are fed into a Convolutional neural network (CNN) classifier, using GoogLeNet and SqueezeNetfier. Thus the accuracy was found to be 99.90% and 93.76% respectively. © 2023 IEEE.

关键词： Artificial Neural Network Convolutional Neural Network GoogLeNet neural network Innovation Micro Grid Power Quality Disturbances Renewable Energy Sources Wavelet synchro-squeezing transform

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Using Multi Class SVM for Creating an Algorithm for Human Activity Recognition

Using Multi Class SVM for Creating an Algorithm for Human Ac...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Kaarthik Ganapathy S Kaarthick C R. Sethuraman Dept.of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai TamilNadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Identifying human activity is a crucial task in numerous fields involving human-computer interaction. This study introduces a innovative approach for human activity recognition using a multi-class Support Vector Machine (SVM). The methodology unfolds in three crucial steps: preprocessing, feature extraction, and classification. In the preprocessing stage, a depth map undergoes transformation into a silhouette for each frame, achieved by segregating foreground elements from the background. To mitigate the effects of variations in size and position, foreground silhouettes undergo normalization via shrinking and cropping. The process of feature extraction includes deriving spatial and temporal features from these normalized silhouettes. This involves integrating the directed gradients histogram, optical flow histogram, the motion history image, and the 2D Discrete Cosine Transform. Each frame is represented by a comprehensive feature vector obtained by concatenating these extracted features. These aggregated features are subsequently utilized for activity identification using a multi-class SVM classifier. Experimental results on three benchmark datasets validate the effectiveness of this approach, achieving commendable accuracy rates in activity recognition. A notable strength of the proposed method is its robustness to occlusions, changes in illumination, and variations in view angles, making it a compelling choice for accurate and reliable human activity identification in real- world scenarios.

关键词： Support vector machines Histograms Accuracy Transforms Feature extraction Vectors Robustness

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Smart AI-Based Delivery Robot

Smart AI-Based Delivery Robot

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Lavanya Vemulapalli Kolanti Charitha Kuralla Durga Lakshmi Harshitha Vallepu Udaya Sri Dept.of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Dept.of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Humans are responsible for the majority of delivering the packets over the area of a network. When compared to classic delivery system the Autonomous Delivery Robot(ADR's) is meant to deliver the goods instead of a human involvement. The delivery robots are a contact-less and alternative to human interaction. At their core, we design a budget-friendly prototype for an Autonomous Delivery Robot (ADR) that can deliver packages to their destinations safely. This robot uses a GPS (like a car's navigation system) to find its way around. We use a special kind of computer brain called Artificial Intelligence to understand the best way to go. Once delivery robot know the right path, it follows until it reaches to the final stop. The robot keeps packets secure by putting them in a special container that needs a secret code to open. The secret code to open the container can be sent to the customer mobile when they order something. Once the robot delivers your package, it can find its own way back to where it started.

关键词： Codes Navigation Prototypes Containers Real-time systems Information and communication technology Security Artificial intelligence Robots Global Positioning System

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