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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Ushashree, P. Naik, Archana Gurav, Siddarth Kumar, Ankit Chethan, S.R. Madhumala, B.S. Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350398465

This paper uses capabilities of deep learning algorithms Long Short-Term Memory (LSTM) models for the detection of hoax news. In today's world Fake news has become a major problem, as it can spread quickly and have a significant impact on public opinion. Traditional methods for detecting fake news have relied on fact-checking and manual verification, which are time-consuming and not always effective. With the increasing availability of news articles and social media posts, there is a need for automated methods for detecting fake news. One of the effective ways used to eradicate the fake news is adopting LSTM models that have shown considerable results in a variety of natural language processing tasks, including text classification and sentiment analysis. This paper describes how to use LSTM for fake news detection and evaluates its performance on a news article dataset. © 2023 IEEE.

关键词： Long short-term memory

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A Comprehensive Exploration of Noise Models, Blur Models, Filters and Restoration Techniques

A Comprehensive Exploration of Noise Models, Blur Models, Fi...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Malathy, S. Prasanna Devi, S. Rajasekar, V. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

With an objective of restoring a clear image from a distorted version, image restoration is necessary for sophisticated image processing. It employs a mathematical model to address degradation during image acquisition. Noise plays a substantial part in the deterioration of images during their prisoner or transmission. To attack this issue, there exists a wide array of algorithms and filtering ways, each carrying its own set of hypotheticals, advantages, and limitations, all depending on previous knowledge of the noise. Image smoothing is a dynamic and massively retained process in replica processing. The ideal of the conventional deed is to present a compact overview of the most efficacious restoration miniatures attainable on the inquiry. In this study, we will explore and assess the pros and cons linked to different image restoration styles. © 2023 IEEE.

关键词： Blurring Techniques Degradation model Filters Noise Models Restoration Techniques

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PEGASIS - LEACH: Energy Efficient Protocols with Route Optimised Techniques 2

PEGASIS - LEACH: Energy Efficient Protocols with Route Optim...

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Chauhan, Sandeep Nene, Manisha J. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

The ad-hoc deployments of Wireless Sensor Network (WSN) in military operations is to gather information in critical areas where human interaction is limited. To improve the capability of WSN model there is a need to improve the power efficiency of the models. Power Efficient Gathering in Sensor Information Systems (PEGASIS) is tree based whereas Low-Energy Adaptive Clustering Hierarchy (LEACH) is clustering-based protocols which are used to improve the overall efficiency of ad-hoc WSN deployment and also increases the operational capability of the WSN *** paper aims to compare the results of PEGASIS and LEACH which is optimized with Particle swarm optimization (PSO) making it LEACH-PSO protocol. LEACH is optimized with PSO as it works on clustering as these techniques helps it to find the correct cluster head (CH) which will make it more efficient and also improves the overall deployment of the WSN architecture. PSO is stochastic optimizer technique based on the intelligence and movement of swarms therefore it assist in finding the correct CH. PEGASIS works on chain-based flow of information therefore it can be clubbed with ACO which will help in increasing the overall efficiency by identifying the correct leader Head (LH). PEGASIS AND LEACH when clubbed with route optimizing techniques such as Ant Colony Optimizer, PSO and Grey Wolf Optimizer has improved and effective WSN deployment. Simulation is done in MATLAB. © 2023 IEEE.

关键词： Wireless sensor networks

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RLR: Joint Reinforcement Learning and Attraction Reward for Mobile Charger in Wireless Rechargeable Sensor Networks

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IEEE Internet of Things Journal 2023年第18期10卷 16107-16120页

作者： Shang, Cuijuan Chang, Chih-Yung Liao, Wen-Hwa Roy, Diptendu Sinha Chuzhou University School of Computer and Information Engineering Chuzhou239000 China Tamkang University Department of Computer Science and Information Engineering Taipei25137 Taiwan National Taipei University of Business Institute of Information and Decision Sciences Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

Advances in wireless charging technology give great new opportunities for extending the lifetime of a wireless sensor network (WSN) which is an important infrastructure of IoT. However, the existing greedy algorithms lacked learning from the experiences of energy dissipation trends. Unlike the existing studies, this article proposes a reinforcement learning approach, called reinforcement learning recharging (RLR), for mobile charger to learn the trends of WSNs, including the energy consumption of the sensors, the recharging cost as well as the coverage benefit, aiming to maximize the coverage contribution of the recharged WSN. The proposed RLR mainly consists of three modules, including sensor energy management (SEM), charger location update (CLP), and charger reinforcement learning (CRL) modules. In the SEM module, each sensor manages its energy and calculates its threshold for the recharging request in a distributed manner. The CLP module adopts the quorum system to ensure effective communication between sensors and the mobile charger. Meanwhile, the CRL module employs attraction rewards to reflect the coverage benefit and penalties of waiting time raised due to charger movement and recharging other sensors. As a result, the charger accumulates the learning experiences from the Q -Table such that it is able to execute the appropriate actions of charging or moving in a manner of state management. Performance results show that the proposed RLR outperforms the existing recharging mechanisms in terms of charging waiting time of sensors, the energy usage efficiency of the mobile charger, as well as the coverage contribution of the given sensor network. © 2014 IEEE.

关键词： Reinforcement learning

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An Innovative Embedded System based Solution for Waste Management 2

An Innovative Embedded System based Solution for Waste Manag...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Chitra, R. Guntanala, Ganesh Bille, Sairaj Moguluri, Yeshwanth Singamshetty, Johnson Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350346640

Waste management is one of the key practical issues faced nowadays. The main objective of this paper is to develop a smart garbage collection system that will help to maintain an effective waste maintenance system. The Swachh Bharat Mission serves as our inspiration for maintaining a clean and eco-friendly environment. As technology is getting smarter, Arduino is used for developing a smart garbage can that will help in keeping the environment clean. The proposed smart trash can management system incorporates an Arduino Uno, ultrasonic sensors, a servo motor, and a battery jumper cable to create an efficient solution. The microcontroller-based design with ultrasonic sensors ensures the proper maintenance of trash cans, preventing pollution and promoting a healthy environment. Neglecting the upkeep of trash cans can result in harmful pollution. The lid of the dustbin opens when someone approaches from a certain distance and stays open until the trash is put inside and the lid is closed. As far as society is concerned, it will promote cleanliness and health. As far as the economy is concerned, it is ensured that the benefits of our work are accessible and affordable to people from all walks of life, regardless of wealth. © 2023 IEEE.

关键词： Waste management

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IoT and AI-based Intelligent Agriculture Framework for Crop Prediction

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International Journal of Sensors, Wireless Communications and Control 2023年第3期13卷 145-154页

作者： Singh, Pushpa Singh, Murari Kumar Singh, Narendra Chakraverti, Ashish Department of Computer Science & Engineering GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Management GL Bajaj Institute of Technology and Management Greater Noida India

Background: Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly. Objective: In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil. Methods: Various supervised machine learning algorithms such as KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers have applied to build the proposed model. The model is continuously monitoring the field via various sensor data as a sample data for the prediction of best crops to be grown for farmers. Results: In this research, we investigated the contribution of supervised machine learning classifiers like KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers. The maximum accuracy has been observed as 99.39% of the Naïve Bayes classifier. Conclusion: In this paper an AI and IoT based model is used to recommend/predict the best crop based on environmental factors. The proposed model will collect the real time sensor data to predict the crops and plants smartly. © 2023 Bentham Science Publishers.

关键词： Agriculture artificial intelligence blockchain crops ICT internet of things machine learning

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Decentralized Blockchain based Online Voting System with Biometric Authentication 8

Decentralized Blockchain based Online Voting System with Bio...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Waniya, Joel Sanjeev Palmer, Matthew Kathrine, G Jaspher W. Basil Xavier, S. Aarthi, Shilpa Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

Online voting system using blockchain with integration of biometric authentication have emerged as a promising solution to address the challenges of online voting such as preventing fraud and maintaining the integrity of the voting results. Blockchain technology provides a high level of security and transparency, making it difficult for hackers to manipulate or alter the voting results. Biometric authentication, on the other hand, ensures that only eligible voters can cast their votes, preventing double voting and other forms of fraud. Despite the potential benefits of using blockchain technology and biometric authentication for online voting, there are still challenges that need to be addressed. These challenges include ensuring that the system is accessible and user-friendly to all voters, protecting against cyberattacks, ensuring compliance with legal and regulatory requirements, and maintaining voter privacy. The objective of this proposed online voting system using blockchain with biometric authentication is to create a secure and reliable voting system that is accessible and user-friendly to all voters, while maintaining the privacy and integrity of the voting process. The system will use blockchain technology to create a tamper-proof and auditable record of the votes, and biometric authentication to ensure that only eligible voters can cast their votes. The system will also incorporate additional security measures such as encryption and multifactor authentication to protect against cyberattacks and ensure compliance with legal and regulatory requirements. By addressing these challenges, this proposed online voting system using blockchain with biometric authentication has the potential to increase voter confidence and trust in the election process. © 2023 IEEE.

关键词： Cryptography

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Security Framework for Electronic Health Record in the Cloud Based on Public key Encryption 9

Security Framework for Electronic Health Record in the Cloud...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Aruna, A. Sellam, V. Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350383201

In the modern world, medical data is managed and stored online so that anybody can access it at any time. For maintaining medical records and sharing with other healthcare providers, the majority of hospitals already use EHR (Electronic Health Records) with cloud storage. Medical data confidentiality, authentication, integrity, and access control are just a few of the security and privacy challenges that have arisen as a result of the growth of healthcare technology and the shift to Cloud computing. In this article, a Medi-locker has been introduced to keep all of the patient's medical records where Both patient and medical practitioners can access healthcare documents at any time, from any location. To maintain the confidentiality and improve the security of the medical data Fully Homomorphic Encryption (FHE), which enables operations to be performed on data that has been encrypted without even decrypting it. The proposed scheme has a high resistance to brute-force attacks when it is compared to other cryptosystem. © 2023 IEEE.

关键词： Data Analytics

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Analysis of Performance Metrics for Classification of Punjabi Poetry using Machine Learning Techniques

Analysis of Performance Metrics for Classification of Punjab...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kaushal, Ayush Dutta, Kamlesh National Institute of Technology Hamirpur Computer Science & Engineering Department Hamirpur India

ISBN: (纸本)9798350322309

The poetic text has always been a challenge from the computational linguistics perspective. Literature classification has always been a tedious task in itself owing to so much diversity we have. We have so much information associated with a poem that it can be classified on basis of its writing schemes, on basis of poets, on the grounds of emotions, on basis of an era, etc. In this work, we developed a dataset of 401 poems in total and they were classified into four categories related to different genres. Considering the lexical features of poems, we performed several pre-processing stages to segregate the useful information in form of tokens from poems. These tokens were modeled using Latent Dirichlet Allocation and evaluated using the Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF- IDF) weighting techniques. SVM, Random Forest, Naive Bayes, and Logistic Regression experimented with lexical features in the poem and it was found that SVM proved to be the best- performing algorithm with the highest accuracy of 82.17%. © 2023 IEEE.

关键词： Support vector machines

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones 6

Quantum Generative Adversarial Network for Autonomous Drone ...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Swarnalatha, Anumula Archana, Nalluri Mukkapati, Naveen Selvakumar, Subramanian Tamir, Eneyachew Manikandaprabu, P. Department of Aeronautical and Aerospace Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh 522213 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh 522502 India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (纸本)9798331522667

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. The RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising;this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results. © 2025 IEEE.

关键词： Generative adversarial networks

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