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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Ayeswarya, S. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in the digital world has become a challenging and difficult task. The username-password combination is no longer reliable and the tools that support authentication mechanism are all breakable. In addition to authentication, to avoid bots, CAPTCHA is used which increases the complexity of the system. Thus the research is going towards making use of human brain which is so unique and powerful in the authentication process. This will also eliminate the need for CAPTCHA since human is computing the password. To make it feasible, the function that is computed by the human should be simple enough. The existing method trusts the computer to store the public images and if there is a key logger installed in the system, the adversary will be able to get the password. This again tends to be a fixed password. Instead, if it is based on a challenge from the server, the password will be dynamic and secure. In this paper a human computable password protocol is suggested. The experimental results prove that the proposed technique is efficient and is less prone to attacks. © 2024 IEEE.

关键词： Authentication

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Model of Sentiment Analysis in Different Platforms on Social Media 4

Model of Sentiment Analysis in Different Platforms on Social...

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

作者： Vasanthi, P. Madhu Viswanatham, V. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

Reviews provide useful information for making decisions. Online stores allow customers to express their opinionson goods and services. Many of these opinions are available tothe public as reviews. Customer reviews are very interesting to manufacturers, retailers, and customers. Such reviews are valuable to consumers since they help them choose which brand or product to purchase. These subjective reviews can help manufacturers identify areas where their product needs improvement. Because there are so many reviews available online foranalysis, manually reading them is not cost effective. An automated system that offers a summary of user sentiments is required to optimize this laborious task. Sentiment analysis is a branch of study that examines how reviews and opinionated literature reflect the sentiments or opinions of readers. In the process of sentiment analysis, machine learning, deep learning, and optimized algorithms are used to assess sentiments and emotions and generate a distilled output for use in decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time Network Optimization in 6G minimizing End-to-End Delay Using SARSA Algorithm

Real-Time Network Optimization in 6G minimizing End-to-End D...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Chauhan, Tania Renjith, P.N. Vellore Institute of Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348460

As 6G networks take off, there is an increasing need for effective system optimization strategies that can decrease end-to-end (e2e) delay and improve the general efficiency of the network. The SARSA (State-Action-Reward-State-Action) algorithm, which can learn from the network ecosystem and optimize network parameters accordingly, is the foundation of the unique real-time network optimization approach that is suggested in this research. With the aid of a simulation environment that replicates the actual 6G network environment, we assess the suggested approach. Our findings show that, the SARSA algorithm may substantially decrease e2e delay and improve network performance. In general, this research offers an intriguing plan for network optimization in 6G networks and emphasizes the SARSA algorithm's potential for improving real-time network reliability. © 2024 IEEE.

关键词： 6G e2e delay Network Optimization SARSA algorithm

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Grid Power 2.0: Artificial Intelligence (AI) and Internet of Things (IoT) Optimizations in Smart Power Grids 3

Grid Power 2.0: Artificial Intelligence (AI) and Internet of...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Thenmozhi, M. Edward, B. Jaison Rohith, V.S. Sachin, V.P. Krishna, Chethan Adit, S. Gopi Vellore Institute of Technology School of Social Sciences and Languages Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

To ensure low-cost energy supply, efficient monitoring of energy usage is crucial. Addressing the challenges of physical monitoring, we introduce a meter designed to compute and analyze total energy consumption and the optimal energy demand for each household. This meter leverages the IoT cloud platform to relay optimal energy demand and supply information to users and suppliers. Notifications are sent to users and suppliers when thresholds are crossed. Additionally, we propose integrating AI with deep learning algorithms to predict, learn, and adapt to energy usage patterns in households. These algorithms will be trained on extensive publicly available datasets,facilitating easy adaptation. Our proposed meter offers a solution for streamlined issues such as oversupply, undersupply, and user notifications. It has the potential to revolutionize the approach to low-cost and efficient energy supply and monitoring, leading to an even more efficient meter. It's worth noting that while this meter is a promising theoretical concept, practical implementation is pending. © 2024 IEEE.

关键词： Internet of things

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An AI-Enabled Smart Healthcare Application for Remote Patient Monitoring System 2

An AI-Enabled Smart Healthcare Application for Remote Patien...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Abdullah, A Sheik Pooja, A. Poovithazhan, K. Anjali, K. Kavya, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350366570

This research presents a groundbreaking healthcare application aimed at revolutionizing patient care and improving the overall healthcare experience. The application is developed through an extensive research and development process that leverages cutting-edge technologies to address critical challenges faced by healthcare providers and patients alike. By seamlessly integrating advanced features and functionalities, this application holds the potential to revolutionise the way healthcare is given and received. This, outlines a health care application that aims to improve patient care, facilitate interaction between the healthcare professionals and patients and enhance overall healthcare management. The application provides a comprehensive platform for patients, doctors and healthcare providers to access and manage medical records, schedule appointments, receive personalized health recommendations, and securely communicate in real-time. It incorporates advanced features such as AI-powered symptom analysis, medication reminders, and telemedicine capabilities. The application's user-friendly interface, combined with robust data privacy and security measures, ensures a seamless and confidential healthcare experience for all users. By leveraging technology, this health care application strives to revolutionize the way healthcare is delivered and accessed, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Electronic health record

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Survival Analysis of Superficial Bladder Cancer patients using DeepSurv and Cox models 2

Survival Analysis of Superficial Bladder Cancer patients usi...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Supriya, K. Anitha, A. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

The advancements in technology help in analyzing and predicting the disease of human life using automation. Out of various technologies, Machine Learning (ML) and Deep learning (DL) provide some promising results to help humankind. Automated technologies help improve the prediction process of the healthcare system, especially in carcinoma detection based on past information. Thus, the proposed model made a survival analysis of the patients who are affected by bladder cancer (BC) based on their past health records. The tumor in the urinary bladder indicates that cancer among patients, when not treated can be spread to the other parts of the human body and cause death. However, the health records provide some linear combinations of the data to analyze the survival rate on treatments such as Placebo and Thiotepa. Thus, the proposed work utilized DeepMLPSurv and CoxPH survival model to make an analysis on the tumour recurrence and predicts the survival rate among the bladder cancer patients. © 2024 IEEE.

关键词： Diseases

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Enhancing User Authentication through Keystroke Dynamics Analysis using Isolation Forest algorithm 2

Enhancing User Authentication through Keystroke Dynamics Ana...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Iyapparaja, M. Karunanithi, Isvarya Sahana Bhat, U. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in modern era, has evolved significantly to address the increasing complexity and security challenges of our digital world. Traditional methods of authentication, such as passwords and PINS have proven to be vulnerable to various forms of attacks. As a result there is a growing need for innovative and robust authentication techniques. Keystroke dynamics involves the analysis of user typing patterns, including the time interval between keystrokes and the key pressed duration, to establish a unique biometric profile for each individual. Unlike traditional authentication methods keystroke dynamics offer several advantages, such as continuous authentication without the need for additional hardware and the ability to adapt to a user's changing typing behavior. This model will additionally analyze users typing patterns have the capability to identify individuals using the system by considering their typing speed. The first premise involves developing a system that can capture the keystroke dynamics of a user during a training phase and create a unique profile for that user. The second premise involves implementing Mahalanobis distance for Dynamic keystroke Authentication with various machine learning algorithms. This model can maximize the strengths of both methods to improve the accuracy and robustness of the authentication system. Authentication using keystroke dynamics is secured and convenient method and can be used wherever there is requirement of authentication such as baking, e-commerce and secured access control system. From the experiment performed below we can attained that the model functions exceptionally well using Isolation Forest with an accuracy of 90%. © 2024 IEEE.

关键词： Support vector machines

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Explainable Artificial Intelligence (XAI) Approach to Heart Disease Prediction 3

Explainable Artificial Intelligence (XAI) Approach to Heart ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sethi, Aryan Dharmavaram, Sahiti Somasundaram, S.K. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Cardiovascular continue to stand as the leading cause of mortality globally. The World Health Organization (WHO) noted that in 2019, 32% of all fatalities were attributed to heart-related issues. In India, the Ministry of Health and Family Welfare reported that cardiovascular diseases accounted for 28.1% of the overall deaths. [1] Approximately 85% of individuals diagnosed with heart failure survive the first year after diagnosis, with rates decreasing to around 55% at two years, approximately 33% at five years, and about 35% at ten years. [2] Cardiovascular diseases, in 2019, claimed the lives of approximately 17.9 million people worldwide, making them the primary cause of death, constituting around 32% of all global deaths. [3] Underscoring the critical need for predictive models in early detection, our proposal integrates machine learning with Explainable AI (XAI) and a user interface (UI) tailored for medical professionals to address cardiovascular diseases (CVDs). We have developed a predictive model for heart disease with 96.07% accuracy, integrating factors like peak exercise ST segment slope, maximum heart rate, and exercise-induced angina. Our model prioritizes transparency, offering clear explanations for decisions to build trust in AI-driven healthcare. Emphasizing feature importance, it enables interpretable predictions, facilitating early detection and informed management. Our objective is two-fold: accurate risk identification and providing medical professionals with a user-friendly interface for transparent, reliable decision-making. This research advances healthcare by bridging AI with human understanding, enhancing outcomes in heart disease management. © 2024 IEEE.

关键词： Decision making

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A hybrid wrapper technique enabled Network Intrusion Detection System for Software defined networking based IoT networks 3

A hybrid wrapper technique enabled Network Intrusion Detecti...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramaiah, Mangayarkarasi Padma, Adla Vishnukumar, Ravula Rahamathulla, Mohemmed Yousuf Chithanuru, Vasavi Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

In the age of smart era, usage of IoT devices is inevitable. As the number of IoT devices increases, the amount of data they generate also increases, which in turn leads to security breaches. Continuous monitoring through SDN is the appropriate solution to mitigate security breaches. SDN introduces a new architecture where the control plane operates independently of the data plane. While this design allows applications in the base plane to interact with network components via the controller, it also creates security concerns. An attacker could exploit vulnerabilities in the control plane, potentially leading to significant data breaches. Amid a lot of research in various venues to address the security aspects of SDN, a simple and robust Intrusion detection system is necessary for the current context. Hence, a hybrid-wrapped enabled feature selection RF-RFE has been included in determining the highly influential features upon the derived features and a fine-tuned machine learning-based model to detect the anomalies at the earliest possible in the context of SDN has been trained and tested on SDN-IoT dataset. The tested results reveal that RF-based RF-RFE-SDNIoT-NIDS obtained 99% of its binary and multi-classification accuracy using minimal features compared to other machine learning models. © 2024 IEEE.

关键词： Intrusion detection

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Spatio-temporal patterns assisted deep learning model for PM2.5 prediction (STEEP)

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Intelligent Data Analysis 2025年第1期29卷 141-155页

作者： Sharmiladevi, S. Siva Sathya, S. Vellore Institute of Technology Chennai India Department of Computer Science Pondicherry University Pondicherry India

Air pollution is an alarming problem in many cities and countries around the globe. The ability to forecast air pollutant levels plays a crucial role in implementing necessary prevention measures to curb its effects in advance. There are many statistical, machine learning, and deep learning models available to predict air pollutant values, but only a limited number of models take into account the spatio-temporal factors that influence pollution. In this study a novel Deep Learning model that is augmented with Spatio-Temporal Co-Occurrence Patterns (STEEP) is proposed. The deep learning model uses the Closed Spatio-Temporal Co-Occurrence Pattern mining (C-STCOP) algorithm to extract non-redundant/closed patterns and the Diffusion Convolution Recurrent Neural Network (DCRNN) for time series prediction. By constructing a graph based on the co-occurrence patterns obtained from C-STCOP, the proposed model effectively addresses the spatiotemporal association among monitoring stations. Furthermore, the sequence-to-sequence encoder-decoder architecture captures the temporal dependencies within the time series data. The STEEP model is evaluated using the Delhi air pollutants dataset and shows an average improvement of 8%–13% in RMSE, MAE and MAPE metric compared to the baseline models. © 2024 – IOS Press. All rights reserved.

关键词： Prediction models

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