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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Chundawat, Dipendra Pal Singh Mary, Gerardine Immaculate Julian, Anitha School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Saveetha Engineering College Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331518097

A Cloud-based real-time temperature and humidity monitoring device that monitors the tobacco curing process, automates the alarm, notifies in case of any deviation from prescribed levels of temperature and humidity, transmits temperature and humidity data to the cloud, and compares the data against the specified ranges. This system predicts the future values of temperature and humidity so one can calculate the efficiency of the system. The system tackles the issue of the Barn Monitoring System using a low-cost controller. The main idea behind the Intelligent IoT-based temperature and humidity monitoring system is to maintain the temperature and humidity inside the barn in each stage of curing. The device has a sensor for temperature and humidity. If the humidity crosses the threshold value, it can alert the barn driver and farmer via a Buzzer, SMS, or e-mail. © 2024 IEEE.

关键词： Tobacco

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Early Detection of Colon Cancer using Fine Tuned Light Weight CNN Model

Early Detection of Colon Cancer using Fine Tuned Light Weigh...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Vanmathi, C. Kishore, Nanda K.V. School of Computer Science Engineering and InformationSystems Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9798350325188

There are around 1.8 million new cases of colorectal cancer diagnosed every year, making it one of the most prevalent forms of the disease. Therefore, the infection and mortality rates can be reduced with early identification of this cancer. Most diagnostic colonoscopy systems are currently incorporating artificial intelligence approaches validated for predicting advanced cancers. It is common practice to combine convolutional neural network-based patterns with image patches and preprocesses. The purpose of this paper was to develop a novel lightweight deep learning Convolutional Neural Network (CNN) that uses Adam optimizer for reliable colon cancer detection. Applying a database of publicly available histopathology images, the proposed approach is analyzed in the context of the existing best practices for colon cancer detection analysis. Based on the results, it is clear that the proposed deep neural network for colon cancer diagnosis achieves an accuracy of 98.40 percent, which is the highest accuracy achieved by any of the existing deep learning methods. © 2023 IEEE.

关键词： Adam optimizer Cancer detection CNN Colon cancer histopathological images

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Low-Cost Smart Plant Irrigation Control System Using Temperature and Distance Sensors 5th

Low-Cost Smart Plant Irrigation Control System Using Tempera...

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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Malla, Jyotsna Jayashree, J. Vijayashree, J. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789811986680

This study revolves around the idea of proposing a smart and low-cost automated irrigation control system. This system attempts to effectively provide an end-to-end solution to irrigation problems by using economical and easily accessible components. Farmers using traditional irrigation systems primarily suffer from the lack of automation tools and the mechanisms to deal with environmental problems. This paper aims to solve the limitations of traditional irrigation systems using commonly used components in a cost-efficient manner. A synergy between temperature and distance sensors is used to manage the motor and hence automate the plant irrigation system. The system is equipped with an actuator (piezo) to shut down the motor and alert the farmer in case of reduced water supply in order to prevent problems arising from depleted underground water levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Liquid crystal displays

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A Review of Modern Authentication Methods in Digital Systems

A Review of Modern Authentication Methods in Digital Systems

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Midhuna Jyothi, R. Jeyanthi, N. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350303452

In today's world, the traditional way of password based authentication is having limitations in addressing the security concerns of the digital users. There is a strong movement in favour of passwordless authentication to secure our cyber identities and digital assets. In the recent years, a lot of research outputs have been published in the field of authentication with techniques like multifactor authentication, passwordless authentication, adaptive authentication and continuous authentication. Not only the user, but also the device, the application etc. must be authenticated to access the resources, facilities and services. Even though the passwords face some serious security issues, they enjoy considerable user acceptance and hence some schemes termed as semi-passwordless authentication are also prevalent. This paper goes through existing authentication schemes, their security issues, attacks and the next step ahead. © 2023 IEEE.

关键词： Authentication

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Deep Q-Network Based Reinforcement Learning for Bitcoin Future Price Prediction 2

Deep Q-Network Based Reinforcement Learning for Bitcoin Futu...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Devi, M. Shyamala Pandian, J. Arun Umanandhini, D. Surekha, B. Sree, K. Bhavya Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350370188

Bitcoin is the very first decentralized digital money in the entire world. Bitcoin is exceptionally reliable since it uses a block chain instead of a service provider like a banking institution. As a type of digital precious metal like gold, the price of a single bitcoin seems to have been significantly increasing since the year 2010. As a result, bitcoin is extremely risky for speculators as its value varies often. Since prices may now be predicted in real time, traditional techniques to price forecasting have failed to provide sufficient information and responses. This paper recommends Deep Q-Network based Reinforcement Learning technique (DQNRL) to predict the bitcoin price more effectively. The Binance Bitcoin Futures Price Dataset from Kaggle that contains 3082284 samples and is used towards the bitcoin future cost prediction for DQNRL. The DQNRL model starts with the data exploration that portray the bitcoin price with the standard deviation and mean, log transformation of data. After data preparation, the Binance Bitcoin Futures Price dataset is fitted with the existing regression technique and proposed DQNRL model and the performance is analyzed with MAE, MSE and RMSE. The proposed DQNRL exhibits low MAE with 0.2471, low MSE with 0.061 and low RMSE of 0.2469 when compared to other existing regression models after implementation. © 2024 IEEE.

关键词： Metadata

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Early Fire and Smoke Detection Using Deep Learning - InceptionV3 with Attention Mechanism Through a Real-Time Surveillance

Early Fire and Smoke Detection Using Deep Learning - Incepti...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Sangaraju, Sai Harsha Varma Jose, Jasmin T. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350313796

Efficient and accurate fire and smoke detection systems are essential to prevent potential catastrophes, save lives, and minimize property damage. This research paper introduces an innovative solution that combines deep learning principles, specifically leveraging the InceptionV3 convolutional neural network architecture, with real-time surveillance for a groundbreaking approach to early fire and smoke detection. The proposed methodology incorporates an attention mechanism to enhance the model's discriminative ability by focusing on salient features within visual data. By harnessing deep learning's capability to extract intricate patterns from extensive datasets, the InceptionV3 model identifies subtle visual cues indicative of fire and smoke instances. Augmented with the attention mechanism, the model effectively prioritizes relevant regions of interest, resulting in elevated detection accuracy. Real-time surveillance further accelerates the system's effectiveness through continuous environmental monitoring, expediting the detection-to-response timeline. In experiments, the proposed architecture achieves remarkable training and validation accuracies of 99.64% and 97.44%, respectively. Comparative assessments against baseline models confirm the superiority of the proposed approach. This research contributes to the advancement of fire safety by integrating cutting-edge technologies, fostering safer environments, and enhancing disaster management strategies. © 2023 IEEE.

关键词： Smoke

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Mutual Funds Investment Risk Analysis Using Machine Learning 2

Mutual Funds Investment Risk Analysis Using Machine Learning

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Dasgupta, Anupam Ravuri, Nandhika Jhansi Nallakaruppan, Musiri Kailasanathan Gite, Shilpi Natesan, Deepa Balusamy, Balamurugan Vellore Institute of Technology School of Electronics and Communication Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Information Technology and Engineering Vellore India Symbiosis Institute of Technology Aiml Department Pune India Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur Chennai India Shiv Nadar Institution of Eminence Noida India

ISBN: (纸本)9798350384277

The involvement of machine learning (ML) in the finance sector has ushered in a new era of investment management, profoundly influencing the mutual funds landscape. It has redefined strategies and optimized financial decision-making. This paper provides a comparative analysis of ML technologies such as AdaBoost Regressor, Support Vector Machines, Gradient Boosting, and Random Forest. The positive results indicated by the paper highlight the pivotal role of ML in revolutionizing mutual funds management in India, reflecting the nation's aspirations for financial innovation and inclusion aligning it with global levels of efficiency in investment strategies. The efficiency of the models was tested with performance metrics such as Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Performance Error (MAPE). Our evaluation concluded Random Forest was the best performer with an R2 Score of 0.925, MSE of 0.246, RMSE of 0.496, MAE of 0.271, and MAPE of 0.096. © 2024 IEEE.

关键词： Decentralized finance

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Clinical Text Classification Using Sequential Forward Selection 2

Clinical Text Classification Using Sequential Forward Select...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Rajasekaran, Rajkumar Sanghavi, Veer Solanki, Rashi Masih, Jolly Vellore Institute of Technology Department of Analytics Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Bml Munjal University Department of Business Analytics Gurugram India

ISBN: (纸本)9798350306415

Clinical text classification is a critical undertaking within the realm of Natural Language Processing, holding significant implications for healthcare applications. In this NLP project, our primary objective is the categorization of medical transcripts into their corresponding medical conditions. To accomplish this, we harness the power of Sequential Forward Selection (SFS), a feature selection technique meticulously chosen for its capacity to streamline data dimensions. By employing SFS, our project aspires to not only enhance classification performance but also elevate the efficiency of pattern recognition, ensuring both speed and accuracy in disease identification. This research contribution underscores the pivotal role of Clinical Text Classification, with a specific focus on optimizing the process using SFS. © 2024 IEEE.

关键词： Clinical Text Classification Medical transcripts Sequential Forward Selection

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Link Prediction in Social Networks using Machine Learning

Link Prediction in Social Networks using Machine Learning

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Govinda, K. Rajasekaran, Rajkumar Krishna Varun, V.S.R.P. Venkata Vuyyuru, Davya Thirumaleshwari Devi, Battula Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632101 India

ISBN: (纸本)9781665462631

Prediction in social networks is an intriguing topic which has recently received a lot of attention. Given a snapshot of a dynamic social network, represented as a directed graph of nodes and edges (where nodes represent users and edges represent followers), the problem can be phrased as follows: - followed relations) Can we forecast which new relationships (links) will emerge in the near future? Such predictions can be used to recommend 'friend suggestions' on Facebook, 'fresh subjects' on Twitter, 'interesting movies' and other tailored Netflix recommendations, among other things. The link prediction problem was proposed in this research for a dataset acquired from Twitch, an online video streaming platform. © 2023 IEEE.

关键词： Directed graphs

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Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

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SN computer science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

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