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International Conference on Communications and Signal Processing

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

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

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.

关键词： Osteoporosis Machine learning algorithms Lung cancer Medical services Stroke (medical condition) Predictive models Random forests

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Malaria Detection using Multi Model Algorithms

Malaria Detection using Multi Model Algorithms

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Aluri John Abhishek A Reyana Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Malaria is a major global health concern, services, novel for malaria detection using a combination of multiple machine learning algorithms. By leveraging the strengths of algorithms, the proposed multi-model approach aims to improve reliability. The proposed method first preprocesses the dataset to extract relevant features and then apply different machine learning to classify malaria-infected and uninfected blood samples. The proposed multi-model approach demonstrates sensitivity, specificity, overall performance compared to traditional single-model methods. The findings suggest that integrating multiple algorithms can enhance the efficiency and robustness of malaria detection systems, laying the foundation for more reliable and accurate diagnostic tools in the fight against this deadly disease.

关键词： Pervasive computing Machine learning algorithms Sensitivity Social networking (online) Malaria Machine learning Feature extraction

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Quorum-based Tier Approach: Integrating PBFT and PoS in a Hybrid Consensus Mechanism

Quorum-based Tier Approach: Integrating PBFT and PoS in a Hy...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Abinivesh S Deva Priya Isravel Julia Punitha Malar Dhas Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Blockchain technology is an advanced system centered around the concept of a shared and immutable ledger, emphasizing decentralization, security, and transparency. Transactions on a blockchain are securely linked through cryptographic hashes, forming blocks. A fundamental component of blockchain is the consensus mechanism, which comes in various types, each with its own advantages and limitations. For instance, Proof of Work (PoW) faces challenges related to energy consumption, while Proof of Stake (PoS) is susceptible to certain attacks. This paper proposes a hybrid consensus mechanism that integrates Practical Byzantine Fault Tolerance (PBFT) and Proof of Stake (PoS) using a quorum-based tier approach. This new hybrid model combines the refined features of both PoS and PBFT to create a hierarchical structure for reaching consensus. Nodes are categorized into high-tier and low-tier groups, with the consensus process primarily involving high-tier nodes. Low-tier nodes serve as backups in situations like network issues or node failures. Through experimentation it is evident that the hybrid consensus mechanism achieves a balance between decentralization and energy efficiency. The results demonstrate that this newly developed mechanism outperforms several existing consensus algorithms in terms of throughput and latency.

关键词： Fault tolerance Adaptation models Proof of stake Computational modeling Scalability Simulation Fault tolerant systems Throughput Energy efficiency Consensus protocol

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Stroke Rehabilitation using Virtual Reality

Stroke Rehabilitation using Virtual Reality

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Nived Melvin John R. Sangeetha Division of Computer Science and Engineering Karunya Institute Of Technology and Sciences Coimbatore India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

A specialized virtual reality platform, designed for desktop use, has emerged to assist in the recovery of stroke patients’ impaired limbs. The proposed system integrates a mixed reality headset for computational power, enabling users to interact within a virtual environment. Inside this setting, two separate rehab protocols have been carefully developed, addressing various facets of hand mobility, including reach, velocity, and power. Through the implementation of performance-based target levels, the proposed system aims to enhance patient motivation and tailor exercises to individual requirements. Initial clinical trials involved the monitoring of movement using these systems, revealing issues related to patient motivation. However, objective evaluations demonstrated notable improvements in the majority of hand parameters for each patient over the course of the training period, corroborated by positive subjective feedback from the patients. This research study offers detailed perspectives on the creation and implementation of this state-of-the-art VR rehabilitation innovation.

关键词： Training Headphones Technological innovation Protocols Virtual environments Mixed reality Stroke (medical condition)

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Automatic Kidney Stone Detection using XResNet 152 Model for CT Images

Automatic Kidney Stone Detection using XResNet 152 Model for...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： S. M. Manudhas R. Manoranjitham Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

This study provides an automatic kidney stone identification using computed tomography (CT) scan images and cutting-edge deep learning techniques. The identification and categorization of diverse kidney disorders, such as stones and normal structures, are the main goals of this study. Our research intends to transform the diagnosis process by offering an accurate and effective automated system for renal health evaluation using the resilience of the XResNet152 architecture. The methodology includes a well-selected dataset with a variety of kidney diseases, which makes thorough model training, validation and assessment possible. The model used augmentation approaches and preprocessing processes specific to medical imaging data to improve its capacity to identify complex patterns and features that correspond to various kidney abnormalities. The model demonstrated remarkable precision in categorization, demonstrating encouraging outcomes in discerning and differentiating various renal ailments. The model demonstrated remarkable accuracy in distinguishing between stones and normal structures, and it demonstrated resilience in intricate medical imaging situations. In this study, we present a thorough explanation of our approach, dataset preparation, model architecture, and in-depth performance analysis. We critically assess the advantages and disadvantages of our methodology and suggest directions for further development. By advancing the field of medical image analysis, this research opens the door to more advanced precision medicine and better healthcare diagnostics.

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Forecasting Bitcoin Price Trends: Integrating Sentiment Analysis and Machine Learning

Forecasting Bitcoin Price Trends: Integrating Sentiment Anal...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

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

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

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.

关键词： Sentiment analysis Analytical models Accuracy Computational modeling Bitcoin Predictive models Data models

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Advancement and Enhancement of Artificial Intelligence in Diabetic Retinopathy Recognition: The Juxtaposition and Implications of Using Various Transfer Learning Tactics

SSRN

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SSRN 2024年

作者： Prasanth, S. Roshni Thanka, M. Bijolin Edwin, E. Veemaraj, Ebenezer Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

BackgroundDiabetic retinopathy, or DR, is still an important risk factor to progressive impairment of vision, especially in individuals who remain employed generation. Early detection is crucial to preventing blindness. Current deep learning techniques like convolutional neural network, deep neural network open up novel possibilities for crowdsourcing DR detection from ocular or retinal images. Identical problems like inadequate data and processing capacity can be solved with the aid of transfer learning. In order to improve DR advancement, this research assesses and compares the performance of several CNN infrastructures and transfer learning *** multiple techniques for preprocessing implemented in DR detection include color conversion, resizing, cropping, resizing, Ben Graham's preprocessing technique, and down-slicing. The various forfeiture-tuned models of transfer learning can be used, such as DenseNet, EfficientNet, GoogleNet, and others. A CNN model that has been developed specifically is used to evaluate these models' performance on a simulated dataset. The model evaluation, or performance metrics, has been evaluated using the kappa value or QWK score *** the research findings, it's been adapted Transfer learning CNNs (EfficientNetB0, B1, B2, B3, B4, B5, B6, and DenseNet121) for the DR process range. In Pre-processing techniques, Ben Graham's preprocessing technique performed well for the proposed model. Additionally, healthcare image processing has been improved. EfficientNetB1 Iteration Approach achieved good precision. The performance of each model was also compared to the outcomes of the existing system, using QWK score as the performance *** findings provide invaluable insight into selecting appropriate transfer learning models based on computing capabilities and dataset features for deep learning findings. The automated DR finding technique is advanced by this analysis, helping diabetic retinopathy suffer

关键词： Convolutional neural networks

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Lane Detection on Structured Roads using Ternaus-Net Architecture

Lane Detection on Structured Roads using Ternaus-Net Archite...

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BIT Conference (BITCON), International

作者： Rovin V Benny P Santhiya Stewart Kirubakaran S Getzi Jeba Leelipushpam Paulraj Immanuel Johnraja Jebadurai Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Lane detection is considered one of the essential elements that an autonomous driving system must provide accurate navigation over roads to ensure the safety of vehicles. Current lane detection techniques are still not good enough, and they cannot recognize lanes in structured and semi-structured environments, especially when applied in real-world driving environments. This paper introduces a U-Net-based architecture named TernausNet, which adopts an encoder-decoder based on the encoder-decoder version of VGG16, particularly fine-tuned for accuracy in segmentation tasks. This architecture is part of a deep-learning approach toward lane detection. TuSimple is a quite popular benchmark for lane detection, which covers all sorts of road conditions from around the world. The model is trained and assessed using this dataset. It can demonstrate that even in challenging circumstances, like faded lane lines, shadows, and occlusions, the results are dependable and accurate in identifying lane markings.

关键词： Measurement Image segmentation Accuracy Lane detection Navigation Roads Prediction algorithms Safety Autonomous vehicles Tuning

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Deep Learning Approaches in Anticipating Diabetic Retinopathy Progression

Deep Learning Approaches in Anticipating Diabetic Retinopath...

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International Conference on Communications and Signal Processing

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

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

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.

关键词： Training Diabetic retinopathy Adaptation models Sensitivity Visual impairment Transfer learning Blindness

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Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Adaptive Sampling of Algal Blooms Using an Autonomous Underw...

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2023 IEEE Conference on Control technology and Applications, CCTA 2023

作者： Fonseca, Joana Rocha, Alexandre Aguiar, Miguel Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures StockholmSE-100 44 Sweden

ISBN: (纸本)9798350335446

This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a concentration indicates the presence of algal blooms. The proposed method learns the kernel parameters of a Gaussian process model using satellite images of chlorophyll a from previous days. The AUV estimates the chlorophyll a concentration online using locally collected data. The algal bloom front estimate is fed to the motion control algorithm. The performance of this method is evaluated through simulations using a real dataset of an algal bloom front in the Baltic. We consider a real-world scenario with sensor and localization noise and with a detailed AUV model. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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