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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Hemkiran, S. Prakash Ramanathan, R.M. War, Mohammad Mudasir Aadhithiyan, K.S. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

Manufacture and sale of counterfeit goods has been plaguing several business sectors for many years. The production of counterfeit goods are increasing, this has an impact on the sales and earnings of the businesses. By stifling revenues, economic growth, and consumer health, it has an impact on governments, global trade, businesses, and consumers. The current technology used for combating counterfeit goods rely on a centralised authority. Issues with this design include single point processing, failure, and storage. Blockchain technology has can be used as an alternative to address all these issues for these *** code can be used along with a blockchain hash code to uniquely identify every single product that ever enters the supply chain, hence no fake product can flood the supply chain. Ethereum blockchain is utilized and smart contracts are deployed to store the data about each and every product. Each products whole history can be tracked by using its QR code. © 2023 IEEE.

关键词： Smart contract

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Toddler Activity Analysis Using Machine Learning

Toddler Activity Analysis Using Machine Learning

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Kala, I. Bhuvaneswari, P. Dharun, R. Dharshini, S. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Toddler activity recognition aims in finding out the activities of toddler and also toddlers based on the video captured by camera. The purpose of activity recognition is to monitor a new employee of the company (maybe a software or even a restaurant) whether employees are working properly or do we need to give more training. This system can also be used by the parents to monitor the activity of the kids who are alone at home. This system can also be implemented in hospitals to monitor the patients and to check their improvement. Toddler activity recognition system is developed using CNN and LSTM. To predict the activity the path of the video file is given as input. The predicted result by the model is checked for accuracy. To make the result available for the end user (who needs to monitor the activity) and can access the result through two ways. In the First method user can directly view on the system where the model is predicting the result. In the second method user is out of station, so the predicted result will be updated on the cloud on fixed time intervals and user can view the result from the working place itself. © 2023 IEEE.

关键词： Personnel training

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Online Legal Literacy Platform 1

Online Legal Literacy Platform

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Kala, I. Hariharan, K. Kavisri, S. Kaviyarasu, D. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371185

The Motor Vehicle Act is a complex legal framework that regulates various aspects of motor vehicle usage in India. However, many individuals lack knowledge and understanding of the Act, which can lead to unsafe driving practices, non-compliance with regulations, and legal violations. Furthermore, the Act is subject to frequent amendments, making it challenging for individuals to stay up to date on the latest regulations. To address these issues, this project proposes an Online Legal Information Platform for the Motor Vehicle Act. The platform utilizes advanced technologies such as BERT fine-tuning and natural language processing techniques to match user input scenarios with relevant sections of the Act. Users can input their scenarios and the platform suggests the ideal law that can be utilized in the given situation. The system displays matching laws with their title and description and generates a summary for the selected law to facilitate easy comprehension. Additionally, voice-to-text feature is incorporated in order to assist people with visual impairments. The platform aims to bridge the gap between legal language and everyday usage, promoting better compliance with road safety laws. The system will be user-friendly and accessible to individuals with limited legal knowledge, making it easier for them to navigate the legal system. Future work includes expanding the platform to include other regional languages as well to increase its availability to people all around the nation. © 2023 IEEE.

关键词： User interfaces

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Pattern Recognition Algorithm to Detect Suspicious Activities

Pattern Recognition Algorithm to Detect Suspicious Activitie...

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2023 International Conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Mahavishnu, V.C. Vikhas, S.G. Shyamganesh, T. Roopakumar, S. Kavin, S.V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333602

Detecting suspicious activities in public places with higher people gathering and interaction has turned out to be an act with growing interest due to the increasing number of crime scenes and causalities happening in these days. Surveying and tracking of human activities are increasingly difficult owing to the random nature of human movements and actions. The reliability is greatly affected due to this randomness. Also a human operator cannot continuously monitor multiple screens efficiently in a consequent manner so an automated surveillance system deployment becomes a necessity. Currently, tracking individuals may be done remotely, and the analysis of the recorded images can be automated using object detection models, with the help of high resolution cameras and the development of machine learning techniques. This proposed system aims in identifying threats that are probable to occur in a public gathering or space which may be an explosion, accident or possession of armoury, etc. This proposed model takes advantage of the information from the image data to learn complex patterns and develop pattern recognition technique to identify the anomalies using high resolution camera and alert the monitoring authority in order to take the necessary actions. This proposed work compares various object detection techniques of machine learning algorithms and suggests the best model based on its performance metrics. © 2023 IEEE.

关键词： Object detection

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Machine Learning Framework for Analyzing Disaster-Tweets

Machine Learning Framework for Analyzing Disaster-Tweets

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Manimegalai, R. Kavisri, S. Vasundhra, M. Grace, R. Kingsy PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

During natural disasters and catastrophes, Twitter is becoming a more popular source of information exchange. It is primarily used to share the status of disaster recovery efforts initiated by humanitarian and disaster relief organizations, to report and request or provide volunteer services, and to update on the scope of geographic phenomena. This paper supports the creation of future automated crisis management systems as well as the planning and preparation of effective disaster responses by teams working on disaster mitigation. This work focuses on developing a comprehensive framework for text processing and analysis on tweets posted in Twitter during natural catastrophes using natural language processing techniques. Disaster-related tweets are categorized into precautionary tweets, educational tweets, and recovery tweets. The algorithms which are used to develop the framework are Naive Bayes based on Bayes theorem, Logistic Regression based on Sigmoid function, Random Forest based on decision trees, Extreme Gradient Boosting is based on bagging and boosting, Support Vector Machine is based on hyperplane. Five performance metrics, namely, accuracy, precision, recall, F1-score, and time, are calculated to assess how well the algorithms perform. The data set is split into training set and testing set as 75:25, 63:37, and 50:50. This comparison is to provide insights about the performance of algorithms in terms of efficiency with time bound actions and reactions. © 2023 IEEE.

关键词： Text processing

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ARWeather: Weather Forecasting and Visualization using Augmented Reality

ARWeather: Weather Forecasting and Visualization using Augme...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Manimegalai, R. Arawind, S. Jegan, G V Sri Rajiv Gomathi, B. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Weather Forecasting is the application of AI to predict the state of the atmosphere for a given location. Earlier, weather forecasting methods usually relied on observed patterns of events. Our ancestors predicted the next day weather based on the happenings of the previous day evening. However, those intuitive methods and predictions are not reliable. This paper depicts the design and implementation of an application for weather forecasting and visualization using Augmented Reality (AR), which can forecast climatic conditions, namely, such as rain, snow, sun, wind, and hail. This work deals with various real-world weather types and how they could be simulated using a mobile augmented reality system. Users can move freely inside the real world without limitations, experiencing the developed augmented objects. A visual change of the augmented reality weather conditions can be used as a supplement to train the simulations for search and rescue teams during catastrophic disasters. © 2023 IEEE.

关键词： Visualization

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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

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Mathematical Biosciences and engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and Applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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PharmaSafe-Blockchain-Based Counterfeit Detection in the Pharmaceutical Sector

PharmaSafe-Blockchain-Based Counterfeit Detection in the Pha...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Sivaganesan, D. Kareshmmaa, A.S. Sharan Shankar, A.K. Anitha Petricia, B. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

The pharmaceutical industry's crucial task is to look after the medicines while they are being transported, by ensuring ideal storage conditions and keeping track of the complete medications sent to prevent risky scenarios. These three classifications are used by WHO: falsified, substandard, and unregistered/unlicensed. The need for sophisticated tools to identify fabricated pharmaceuticals will rise as criminals grow craftier. To identify phoney and substandard drugs, new technologies are continually being developed. A substantial lack of transparency in the current system makes it difficult for patients to understand the cost of acquired medications, making counterfeit drugs a prime issue. Moreover, if there are accusations of illegal or unethical behaviours, it could be difficult to look into supply chain tampering. The blockchain technology was employed. Two parties' transactions can be efficiently and permanently recorded using an open, distributed ledger like blockchain. Blockchains are transparent, decentralized, distributed, and unchangeable, which makes them an ideal solution for the problem of fake medications. This study proposes a mechanism for the safe distribution of medications throughout the whole supply chain. Each product in the chain can be moved between chain associates who have been confirmed using an event request-response approach. A product may be traced back to its source thanks to smart contracts, which are used to record all transactions between entities onto the blockchain. A DApp is made using the React Framework. On a local blockchain that Ganache provided, the smart contracts were put into action. The blockchain connection between the DApp and *** is made possible by the Truffle framework. The outcomes of the studies show that our method is practical and marginally more secure than existing solutions. © 2023 IEEE.

关键词： Smart contract

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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