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Aggravation of Cancer,Heart Diseases and Diabetes Subsequent to COVID-19 Lockdown via Mathematical Modeling

computer Modeling in engineering & Sciences 2024年第7期140卷 485-512页

作者： Fatma Nese Efil Sania Qureshi Nezihal Gokbulut Kamyar Hosseini Evren Hincal Amanullah Soomro Near East University Department of MathematicsNicosia99010North Cyprus Mehran University of Engineering and Technology Department of Basic Sciences and Related StudiesJamshoro76090Pakistan Department of Computer Science and Mathematics Lebanese American UniversityP.O.Box 13-5053BeirutLebanon Near East University Mathematics Research CenterNicosia99010North Cyprus

The global populationhas beenandwill continue to be severely impacted by theCOVID-19 *** primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer,heart disease,and ***,using ordinary differential equations(ODEs),two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart *** that,we highlight the stability assessments that can be applied to these *** analysis is used to examine how changes in certain factors impact different aspects of *** sensitivity analysis showed that many people are still nervous about seeing a doctor due to COVID-19,which could result in a dramatic increase in the diagnosis of various ailments in the years to *** correlation between diabetes and cardiovascular illness is also illustrated *** effects of smoking and obesity are also found to be significant in disease *** fitting is also provided for interpreting the relationship between real data and the results of *** people,in particular,need tomonitor their health conditions closely and practice heart health *** with heart diseases should undergo regular checks so that they can protect themselves from diabetes and take some precautions including suitable *** main purpose of this study is to emphasize the importance of regular checks,to warn people about the effects of COVID-19(including avoiding healthcare centers and doctors because of the spread of infectious diseases)and to indicate the importance of family history of cancer,heart diseases and *** provision of the recommendations requires an increase in public consciousness.

关键词： COVID-19 mathematical modeling cancer diabetes heart diseases sensitivity analysis

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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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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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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...

引用

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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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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Brain Tumor Identification and Classification Using Enhanced Convolution Neural Network 5

Brain Tumor Identification and Classification Using Enhanced...

引用

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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Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1121-1131页

作者： Li, Wenting Deka, Deepjyoti Wang, Ren Paternina, Mario R. Arrieta LANL Applied Mathematics and Plasmas Physics Division Los AlamosNM87544 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States National Autonomous University of Mexico Department of Electrical Engineering Mexico City04510 Mexico

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness of natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This article proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids and then using the descriptive sets during adversarial training. Compared to state-of-the-art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness. © 2023 IEEE.

关键词： Neural networks

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