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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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Fake Product Identification using Blockchain 14

Fake Product Identification using Blockchain

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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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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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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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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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Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks 5

Deception Detection from Linguistic and Physiological Data S...

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5th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Li, Panfeng Abouelenien, Mohamed Mihalcea, Rada Ding, Zhicheng Yang, Qikai Zhou, Yiming University of Michigan Department of Electrical and Computer Engineering Ann Arbor United States University of Michigan Department of Computer Science Ann Arbor United States Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Saarland University of Applied Science Department of Engineer Sciences Saarland Germany

ISBN: (纸本)9798350352719

Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic. In particular, we make three main contributions. First, we extract linguistic and physiological features from this data to train and construct the neural network models. Second, we propose a fused convolutional neural network model using both modalities in order to achieve an improved overall performance. Third, we compare our new approach with earlier methods designed for multimodal deception detection. We find that our system outperforms regular classification methods;our results indicate the feasibility of using neural networks for deception detection even in the presence of limited amounts of data. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Brain Tumor Identification and Classification Using Enhanced Convolution Neural Network 5

Brain Tumor Identification and Classification Using Enhanced...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Raju, J. Anand, Krteen Krishna Kumar, D. Sivaganesan, D. Psg Institue of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

Brain tumor identification and classification play a crucial role in medical imaging. Accurately identifying tumors is a challenging task that often requires highly skilled and experienced doctors. Manual identification is prone to errors, leading to the need for automated solutions. This study aims to utilize advancements in Machine Learning (ML) and Artificial Intelligence (AI) to develop an automated brain tumor identification and classification system. The primary objective is to predict the type of tumor accurately. This study employs a deep neural network architectures specifically Convolutional Neural Network (CNN) and other pre trained deep learning models to find the optimal solution that provides a high degree of accuracy in detecting cancerous cells in the brain. In this study, advanced ML and AI techniques are used to perform brain tumor classification. The study utilizes various deep CNN architectures and methods like random cropping to improve training accuracies. Proposed CNN 7x7 model demonstrates remarkable performance in accurately classifying brain tumors. It achieves a high degree of accuracy in detecting the presence of cancerous cells, surpassing manual identification methods and the accuracies of other pre trained models. The automated brain tumor classification system presented in this study has the potential to streamline the diagnosis and treatment process, significantly reducing the reliance on healthcare systems. By reducing the cost and time required for brain tumor diagnosis, it improves patient access to care. Future research can further enhance the proposed methodology and its applications in medical diagnosis. © 2023 IEEE.

关键词： Medical imaging

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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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