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检索条件"机构=Division of Computer Science and Engineering Karunya Institute of Technology and Sciences"

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Forecasting Bitcoin Price Trends: Integrating Sentiment Analysis and Machine Learning 3

Forecasting Bitcoin Price Trends: Integrating Sentiment Anal...

引用

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

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

ISBN: (纸本)9798350375190

With the increasing scrutiny of Bitcoin and other cryptocurrencies in recent years, there is a growing significance in studying methods to predict their values, including sentiment analysis on Twitter-collected data. The primary objective is to perform sentiment analysis on Bitcoin prices and examine the predictive accuracy of various machine learning algorithms. The approach cleans up Twitter data that is relevant to Bitcoin. Followed by quantifying people's opinions on Bitcoin using sentiment analysis. With this data and Bitcoin's price history, a binary target variable for price movement is built to specify the price variations. In order to predict the changes in Bitcoin price, several machine learning models are developed and evaluated. Among these models are Long Short-Term Memory networks, Decision Trees, Random Forests, and Linear Discriminant Analysis are mostly applied. The proposed approach is the enhanced Random Forest base classifier to improve the prediction accuracy. With an F1-score of 64% and 70% accuracy, the Enhanced Random Forest model performs better than the existing models. This model illustrates how sentiment analysis and machine learning may be combined to forecast cryptocurrency prices. © 2024 IEEE.

关键词： Machine learning

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Al-Proctored Exam Portal with Mobile Companion Application 3

Al-Proctored Exam Portal with Mobile Companion Application

引用

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

作者： Daniel, Ronald C Caleb Andrew, H. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

Upholding academic integrity during exams has always been difficult in the world of online learning. We provide a novel approach to solve this problem: an AI-proctored exam Portal combined with a mobile companion app. This solution uses artificial intelligence for proctoring and enables instructors to prepare and conduct exams safely. The site includes elements like multiple-choice exams, written assessments with a question bank, and integrated course modules with quizzes and videos. With the use of AI algorithms, the exam process is tracked in real time, and each student receives a thorough report. Additionally, the mobile companion app improves the assessment process's integrity by acting as an additional monitoring tool and giving a side view of the student during the test. This article describes the architecture, functionality, and use of the AI-Proctored Exam Portal and its accompanying mobile application, emphasizing the platform's effectiveness in guaranteeing transparent and equitable testing in the online learning environment. © 2024 IEEE.

关键词： E-learning

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Enhancing Retinal Image Analysis through Deep Learning-Based Blood Vessel Segmentation 10

Enhancing Retinal Image Analysis through Deep Learning-Based...

引用

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

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

ISBN: (纸本)9798350353068

Blood vessel segmentation is the process of identifying and describing blood vessels from retinal pictures, which is required for quantitative analysis and anomaly detection. However, the accuracy and efficiency of the current blood vessel segmentation approach are frequently inadequate. By using deep learning techniques for blood vessel segmentation, it aims to increase retinal analysis's effectiveness and precision. Based on our findings, UNet outperforms FCN (94.02%) and SegNet (95.89%) in terms of accuracy, reaching a whopping 98.89%. UNet is well-suited for biomedical image segmentation tasks because to its symmetric encoder-decoder structure and skip connections, which allow for good feature extraction and spatial preservation. A user interface is used to provide the segmented image when a retinal picture is injected. It helps in the creation of computer-aided diagnostic tools for retinal diseases and showing in the application by providing a trustworthy method for correct blood vessel segmentation, which is essential for early detection and monitoring of various ocular pathologies. © 2024 IEEE.

关键词： User interfaces

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Smart Waste Bins using DCNN and Internet of Things 2

Smart Waste Bins using DCNN and Internet of Things

引用

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

作者： Ande, Aaron Sundar, G. Naveen Thomas, Rino Nair, Varun Narmadha Karunya Institute of Technology and Sciences Division of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350328202

Waste sorting poses significant challenges because of several factors, including a lack of awareness and education about proper disposal, inadequate infrastructure and collection systems, cultural practices that discourage segregation, economic factors without financial incentives, confusion due to the absence of clear guidelines and regulations for waste sorting, and time and convenience constraints. These challenges give rise to various health issues. Improper waste disposal can result in the spread of diseases through pests and rodents. Burning or burying mixed waste releases pollutants that can lead to respiratory problems. Hazardous waste, when not correctly separated, can contaminate groundwater, and unsorted waste may pose injury risks for waste workers and scavengers, including sharp objects and hazardous materials. In our study, we present a smart waste segregation system and describe the development of an intelligent waste bin designed to categorize deposited waste into various groups according to its nature, spanning from household-level waste to municipal-level waste.. This smart waste bin employs Deep Convolutional Neural Networks (DCNN) to sort the waste and incorporates ultrasonic sensors, moisture sensors, and rotary actuators. This integration enables the efficient sorting and segregation of waste placed into the smart waste bin. Combining the DCNN with actuators and sensors enhances the effectiveness of waste sorting. The implementation of these sensors will be carried out using Arduino UNO R3 and the ESP32 Camera Module © 2024 IEEE.

关键词： Actuators

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Swarm Based Searching Method for Effective Sinkhole Attack Detection in Internet of Things 1

Swarm Based Searching Method for Effective Sinkhole Attack D...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mona, R. Manoranjitham, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

Over the last several decades, the IoT and WSN have seen a fast proliferation, which has resulted in astonishing innovation and integrated possibilities. Since the developments in networking technology and nanodevices, the transition from IPv4 to IPv6 has further increased connection. Due to this change, it becomes easier to connect a greater variety of devices to servers. The growing interconnectedness has resulted in challenges in effectively managing and evaluating the massively generated data across all levels of IoT. Considering the massive IoT devices and their tiny size, the demand of comprehensive security management is especially significant for these devices. In IoT layered design, the network layer is of utmost significance in terms of assuring security. This layer is responsible for storing routing information and carrying out choices that match to specific routing information. Among the several security assaults that are being handled, the sinkhole attack is one that is commonly observed and poses a considerable problem. In this study, the ramifications of the sinkhole attack on IoT are investigated in detail, and the effect of the attack is meticulously analyzed. In addition to this, it introduces a unique mitigation method that is specially built to effectively combat threats of this kind. To conduct comprehensive simulations, the study makes use of MATLAB v9. This has made it possible to evaluate the throughput of the network as well as the End-to-End delay and network lifetime. © 2024 IEEE.

关键词： MATLAB

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Treatment Prediction using Dual Adaptive Sequential Networks 3

Treatment Prediction using Dual Adaptive Sequential Networks

引用

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

作者： Benedict, Salva Sachin Reyana, A. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The application of advanced forecasting techniques has attracted major interest in the area of medicinal study and treatment as a method of increasing quiet care. This research study introduces a new method for predicting the best course of treatment based on individual persistent profiles, called Double Adaptive Sequential Networks (DASN). The DASN enhances the accuracy and strength of treatment predictions by combining the control of successive neural systems with adaptable learning components. The system prepares and fine-tunes two parallel successive systems using verifiable persistent information, including genomic data, treatment outcomes, and restorative records. While the moment arrange focuses on encoding flexible designs that evolve over time as patients respond to various medications, the primary arrange records different conditions inside persistent information. DASN's adaptable character is then achieved. DASN's adaptability is accomplished by a method of active learning that continually adjust the network's weights in response to ongoing data from responses. This enables the display to detect slight modifications to uniform responses and adjust predictions accordingly. Furthermore, DASN is integrated with a fortification learning component, which enables it to iteratively improve treatment recommendations by simulating possible persistent reactions was carried out using various runs of understanding cases and treatment scenarios in order to validate the viability of DASN. The results demonstrate that, in terms of treatment precision and flexibility, DASN consistently outperforms current predictive models. The model has the potential to revolutionize personalized medication due to its ability to adapt to changing quiet elements and treatment patterns. © 2024 IEEE.

关键词： Forecasting

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Deep Learning Approaches in Anticipating Diabetic Retinopathy Progression 10

Deep Learning Approaches in Anticipating Diabetic Retinopath...

引用

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

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

ISBN: (纸本)9798350353068

One of the most prevalent complications caused by diabetes that still causes loss of vision and blindness worldwide is diabetic retinopathy. For treatments to be effective, early and correct identification is essential. Despite advancements in medicine, a complex automated solution is still required to address the impending threat of blindness due to the difficult early diagnosis of diabetes-related retinopathy, a key contributor to visual impairment. Using a well-selected diabetic retinopathy dataset, our novel approach applies transfer learning techniques using EfficientNetB3, ResNet50, and DenseNet169. Our model is strengthened by a new hybrid oversampling method that addresses class imbalance, and its training incorporates cutting-edge components like differential regularization, adaptive dropout, and Adamax optimization for increased *** results demonstrate the remarkable sensitivity, specificity, accuracy, and F1 score of our model at various degrees of retinopathy severity, establishing a new standard for diagnostic dependability in the identification of diabetic retinopathy. By utilizing cutting-edge deep learning architectures, our method establishes a strong basis upon which future developments will be built. These developments will include adaptive optimization strategies, ensemble learning to improve resilience, and self-supervised pre-training, all of which will accelerate the ongoing advancement of diabetic retinopathy detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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Analysis of Cancer Category using Bidirectional LSTM from Medical Records 3

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

引用

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

A Lightweight Security Algorithm for Authenticating Mobile T...

引用

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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Machine learning Approaches for Autism Spectrum Disorder Prediction 10

Machine learning Approaches for Autism Spectrum Disorder Pre...

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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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