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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Harish Sree Prasanna Kumar, D. Malardhas, Julia Punitha Devapriya, I. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

The neurodevelopmental illness known as autism spectrum disorder is a lifelong condition marked by a persistent or limited development of social-communication skills, cognitive abilities, activities, and behavior. The symptoms of autism are easier to see and identify in children between the ages of two and three. A prediction system based on traditional machine learning techniques, such as support vector machines, random forests, multiple layer perceptrons, naive Bayes, convolution neural networks, and deep neural networks, accounts for most of the research on autism spectrum disorder. Accuracy, precision, and recall are among the performance indicators used to verify the proposed models. This study investigated and evaluated the prediction of autism spectrum disorders using standard criteria including application type, simulation method, comparison approach, and input data. The purpose of this work is to offer a consolidated framework for autism spectrum disorder prediction to researchers. With the best results, the random forest method performed better than traditional machine learning algorithms. 97.8% accuracy percentage was achieved. Readers can better comprehend the underlying mechanisms and structures of the investigated frameworks with the aid of these work flow representations. © 2024 IEEE.

关键词： Support vector machines

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Enhanced air Quality Prediction Using Optimized Sarimax Model for Coimbatore Region

Enhanced Air Quality Prediction Using Optimized Sarimax Mode...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Revanth, Bande Manoranjitham, R. Division of Computer Science and Engineering Karunnya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

This research delves into examining the forecast of air quality in Coimbatore, India, according to data gleaned from the Central Pollution Control Board (CPCB) specific to the area. The gathered information underwent a preprocessing step, using interpolation to ensure uniformity and completeness. Forecasting methods such as Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models were utilized to predict air quality levels. The SARIMAX model was fine-tuned through a grid search process, while the LSTM underwent hyperparameter adjustments to improve air quality predictions. Performance evaluation of these models involved metrics like R-squared, Mean absolute error, and Root mean square error. Based on the selected evaluation criteria, the outcome shows that SARIMAX outperformed LSTM in Coimbatore when it came to predicting the air quality. This analysis improves our understanding of air quality patterns in Coimbatore. Highlights SARIMAX's effectiveness as a modeling tool for atmospheric science applications. © 2024 IEEE.

关键词： Long short-term memory

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Food Oil Quality and Usage Detection Using ai 10

Food Oil Quality and Usage Detection Using AI

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sanjay, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353068

The proposed system revolutionizes food oil quality assessment through an intelligent integration of Artificial Intelligence (ai) and Convolutional Neural Networks (CNNs). Overcoming the limitations of traditional methods, the system employs a robust CNN model trained on a diverse dataset of labeled food oil images. This innovative approach enables swift and reliable quality classification, addressing the challenges of real-time monitoring in food production and distribution. The system's architecture emphasizes user-friendliness, offering a seamless interface for diverse stakeholders, including food manufacturers and regulatory bodies. Its adaptability and scalability make it applicable across various food oil types and production settings. By leveraging the efficiency of CNNs, the proposed system heralds a paradigm shift in food oil quality assessment, fostering rapid and accurate decision-making in the ever-evolving landscape of the food industry. © 2024 IEEE.

关键词： Convolutional neural networks

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A Lightweight Security Algorithm for Authenticating Mobile Transactions using Wearable Devices and ECC Based Signcryption 10

A Lightweight Security Algorithm for Authenticating Mobile T...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Harrish, A. David, Shibin Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

Internet of Things (IoT) devices are intelligent and act as the core component for capturing transactional data, initiating communication with wearable devices. Wearable devices such as smartwatches or smart wristbands serve as the user's payment interface. It aligns with near field communication (NFC) technology which gets connected with payment terminals at a closer range. As these terminals have secure payment gateways like Unified Payment Interface (UPI) and other intermediaries, transactions between the user's bank account and the merchant will be easier. To prove authorization in the transaction, an additional layer of security called vein pattern authentication is invoked. Alongside, elliptic curve cryptography (ECC) based signcryption ensures secure transactions by encrypting and digitally signing the payment data which guarantees confidentiality and non-repudiation. Therefore, a system that adds additional security to the mobile transactions by appending vein pattern and ECC-based signcryption technique. As a result, confidentiality, authorization, and robustness in the transaction is achieved at a higher rate by inculcating forward secrecy, verifiability and non-repudiation. © 2024 IEEE.

关键词： Gateways (computer networks)

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Analysis of Cancer Category using Bidirectional LSTM from Medical Records 3

Analysis of Cancer Category using Bidirectional LSTM from Me...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAaiC 2024

作者： Dhinakar, Yalla Reyana, A. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

Analyzing medical data effectively is critical for making educated decisions and providing patient care in this age of quickly improving healthcare technology. Natural language processing methods, particularly text classification and sentiment analysis in cancer patients' medical records is the primary objective of the proposed work. The goal is to help healthcare professionals diagnose and treat patients more effectively by extracting meaningful insights from textual data. Logistic Regression is used as separate text analysis method in the research. In order to get a full picture of patients' histories, these models are trained and tested to see how well they categorize medical information pertaining to cancer into certain predetermined groups. To make sure the input data is good, data preparation is essential. Remove stop words, special letters, and any unnecessary information from the text to make it cleaner. In addition, the dataset is examined to learn about its properties and distribution which understanding of the data that lie underneath. The suggested models are successful, according to the experimental data;the Bidirectional LSTM model had Healthcare practitioners must pay close attention to these results because they pave the way for improved decision support via the accurate and rapid identification of pertinent medical information. This study highlights the value of natural language processing methods in analyzing cancer patients' medical records, which is a contribution to healthcare analytics as a whole. In addition, it highlights how Bidirectional LSTM models may improve medical record classification, which in turn improves patient care and healthcare decision-making. © 2024 IEEE.

关键词： Sentiment analysis

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Machine Learning Approach To Predict Multiple Diseases Based On Symptoms 10

Machine Learning Approach To Predict Multiple Diseases Based...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Raj, Mu.Krishna Malardhas, Julia Punitha Devapriya, I. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

Healthcare is being altered by machine learning for early sickness prediction and diagnosis. Based on patient symptoms, Random Forest, an ensemble machine learning algorithm, will predict diabetes, heart attack, cancer, TB, arthritis, lung disease, stroke, asthma, brain tumor, osteoporosis, COPD, and oral disease. A comprehensive dataset of patient records, symptoms, and sickness outcomes is curated to start the research. We manage missing data, encode category variables, and normalize numerical features. For accurate sickness prediction, feature selection methods identify the most essential symptoms and clinical traits. On the preprocessed data, a Random Forest classifier is trained to handle complex feature interactions without overfitting. Model performance is improved via hyperparameter tuning, and many measures are employed to ensure forecast accuracy. In addition to sickness prediction, the research analyzes feature importance to identify key symptoms and covariates. This may assist doctors understand disease start and progression. Health care providers utilize the final model to predict illnesses in real time using patient symptoms. We will regularly evaluate and update the model to incorporate new medical knowledge and data. Patient data privacy, ethics, and compliance with healthcare data laws and standards are paramount. The effort seeks unbiased, transparent, and prejudice-free disease prediction to ensure equitable healthcare results. © 2024 IEEE.

关键词： Forecasting

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Delving Into Multi-Modal Multi-Task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-25页

作者： Luo, Sheng Chen, Wei Tian, Wanxin Liu, Rui Hou, Luanxuan Zhang, Xiubao Shen, Haifeng Wu, Ruiqi Geng, Shuyi Zhou, Yi Shao, Ling Yang, Yi Gao, Bojun Li, Qun Wu, Guobin School of Computer Science and Engineering Southeast University Nanjing China DiDi Chuxing Beijing China UCAS-Terminus AI Lab University of Chinese Academy of Sciences Beijing China

Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems. In the context of intelligent vehicles, leveraging the power of foundation models has proven to be transformative, offering notable advancements in visual understanding. Equipped with multi-modal and multi-task learning capabilities, multi-modal multi-task visual understanding foundation models (MM-VUFMs) effectively process and fuse data from diverse modalities and simultaneously handle various driving-related tasks with powerful adaptability, contributing to a more holistic understanding of the surrounding scene. In this survey, we present a systematic analysis of MM-VUFMs specifically designed for road scenes. Our objective is not only to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques, but also to highlight their advanced capabilities in diverse learning paradigms. These paradigms include open-world understanding, efficient transfer for road scenes, continual learning, interactive and generative capability. Moreover, we provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, low-resource conditions, embodied driving agents, and world models. To facilitate researchers in staying abreast of the latest developments in MM-VUFMs for road scenes, we have established a continuously updated repository at https://***/rolsheng/MM-VUFM4DS. IEEE

关键词： Roads and streets

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Green Vision: Plant Care Monitoring System 1

Green Vision: Plant Care Monitoring System

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Nithish, M. Denisha, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

The entire human development process is dependent on plants, however caring for them can be quite difficult. The occurrence of different type of diseases that are difficult to identify have blended into these plants. This research study has recognized the usage of technology such as mobile applications and Convolutional Neural Networks [CNN] to examine the afflicted plant and to lessen the human efforts whenever scientists or doctors are unable to travel to different locations to examine them. This study has developed an Android smartphone application, which allows users to remotely analyze their plants and determine which diseases they may have entrenched in them and provide cure for it. All it takes is taking a picture of the plant with a camera or importing it from the gallery to receive diagnosis reports and the cures and needs for it with a simple user-interface, which will help all users to use it easily. © 2024 IEEE.

关键词： Diagnosis

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Motion planning

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