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Descriptive Predictive Model for Parkinson’s Disease Analysis 1

引用

2nd International Conference on Information technology, InCITe 2022

作者： Ali, Akbar Rout, Ranjeet Kumar Umer, Saiyed Computer Science and Engineering Aliah University Kolkata India Computer Science and Engineering National Institute of Technology Jammu and Kashmir Srinagar India

ISBN: (数字)9789811973468

ISBN: (纸本)9789811973451

The popularity of machine learning applications is increasing day by day. Uses of prediction model are in so many fields, for example weather forecast, industry, medical, technology, and data science also. In this paper, we are going to analyze a very common disease in human known as Parkinson disease. It is mostly found in older people but some time also reflects its presence in younger people. Model objective is to predict according to the past information. It is better to have high accuracy of the proposed model. The goal of Parkinson disease predicting model is detecting Parkinson’s disease in humans according to the data we will provide. We have used two types of audio dataset in this model, namely dysphonia and dysarthria. We have used four machine learning algorithms namely support vector machine, logistic regression, K_nearest neighbor, and random forest. First dataset has 24 feature-set and other have 756 feature-set, so we have used PCA also to provide feature-set according to need. We divided the dataset into four modules in first module we have used only five feature-set, and then in second module, we have used ten feature-set and then 15 and 20 feature-set, respectively, and then all features at a time. We have feeded these module to all algorithms and noted which one is best for which case. So we have four algorithm and we have tested these module one by one to these algorithm carefully and noted down there result, and then we analyzed that for dysphonia if we use random forest, then it gives better result for ten feature-set while for dysarthria it is support vector machine that gives better performance for ten feature-set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Weather forecasting

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A Deep Learning Based Approach for Detecting Mango Leaf Diseases 6

A Deep Learning Based Approach for Detecting Mango Leaf Dise...

引用

6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Pradhan, Astik Kumar Rout, Jitendra Kumar Ghosh, Debanjan Sinha, Shailendra Kumar Arka Jain University School of computer Science and IT Jamshedpur India National Institute of Technology School of computer Science and Engineering Raipur India

ISBN: (纸本)9798331516796

The mango industry faces substantial annual economic losses due to diseases and pests. Farmers encounter difficulties in differentiating between various mango diseases, as their symptoms can closely resemble each other and even occur simultaneously. By making it possible to quickly and accurately detect and identify mango leaf illnesses, this study aims to offer a remedy. To accomplish this, the research employs state-of-the-art Convolutional Neural Networks (CNNs), which are advanced learning algorithms capable of automatically extracting and understanding complex features from raw images. Specifically, the study utilizes EfficientNet, a model based on CNNs, to facilitate the early identification and classification of mango leaf diseases. Additionally, advanced data augmentation techniques such as rotation, reflection, translation, and scaling are applied to enhance the collected dataset. This enhanced data is then used to train the EfficientNet model, which achieves an amazing 98% accuracy rate in identifying and categorizing mango leaf diseases. These findings underscore the CNN method's effectiveness in detecting a wide range of mango leaf diseases, positioning it as an invaluable and practical tool for both farmers and agricultural experts. © 2024 IEEE.

关键词： Fruits

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Enhancing Arecanut Farming Profits through Technological Advancements: A CNN-based Approach for Efficient Grading and Sorting 5

Enhancing Arecanut Farming Profits through Technological Adv...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Dhanush Ghate, D. Pallavi, K.N. Poojari, Asmita NMAM Institute of Technology Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350395235

Technological advancements can have powerful impact on the economic growth of agriculture in India. Areca nut farming is one such terrain of agriculture where investment in technology and automation can elevate the profit from farming by bringing down production cost. Areca nut is the major crop of Karnataka having roots in fifteen different districts. The grading and sorting of areca nuts is an important step in the production of areca nuts and is traditionally done by man power. People are trained to identify good and bad areca nuts, but the time taken for sorting areca nuts is more and it is not feasible for a large quantity of areca nuts. In order to minimize the time consumed, this method is introduced which classify the areca nuts into four different grades based on their quality. © 2024 IEEE.

关键词： Sorting

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Optimal Key Generation using Support Vector Machine for Health Records

Optimal Key Generation using Support Vector Machine for Heal...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Himanshu Punhani, Akash SRM Institute of Science and Technology Department of Computer Science and Engineering Delhi-NCR Campus Ghaziabad201204 India

ISBN: (纸本)9798350376470

As the digital age continues to progress, the demand for advanced data privacy techniques has become increasingly vital. With the rise of large-scale collection of data and the proliferation of machine learning algorithms, ensuring privacy while extracting valuable insights has become a challenging situation. This research paper proposes an approach that leverages machine learning-based techniques to achieve scalable privacy preservation, while also providing an optimal key generation mechanism for data encryption and decryption. The proposed approach is evaluated on a real-world healthcare dataset, and the experimental results demonstrate that the proposed approach provides a significant improvement in privacy preservation, while maintaining the performance and accuracy of the system. Healthcare data is among most sensitive one for this scenario as this data encompasses a vast array of information related to patient health, medical conditions, treatments, outcomes, and more. Leveraging this data in a responsible and privacy-preserving manner holds tremendous potential for improving healthcare outcomes, advancing medical research, and enhancing the overall quality of patient care. © 2024 IEEE.

关键词： Electronic health record

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Enhanced Heart Disease Diagnosis in MRI Images Using Swan Adapt Optimized Thresholding Segmentation with U-Net Model 5

Enhanced Heart Disease Diagnosis in MRI Images Using Swan Ad...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Steephan Amalraj, J. Kumaresan, T. Pravin Savaridass, M. Vaanathi, S. Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Electronics and Instrumentation Engineering Sathyamangalam India Bannari Amman Institute of Technology Atrificial Intelligence and Data Science Sathyamangalam India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

The diagnosis of heart disease in MRI images is a critical application within the area of medical imaging. Conventional segmentation algorithms have difficulty in the accurate delineation of normal and sick heart tissues due to problems such as noise sensitivity, inaccuracy in boundaries, and sensitivity to parameters. In this work, we propose a novel strategy of thresholding based on image clusters, which we term SwanAdaptOpti thresholding for the segmentation of MRI images. The proposed system also utilizes Watershed segmentation with markers refined to enhance the segmentation of diseased heart tissues. Indeed, our approach is capable of achieving satisfactory performance in delineating normal and sick heart images, avoiding issues like overfitting, underfitting, and local optima. The overall robustness of our approach is demonstrated by extensive experimentation, which reveals the potential of our approach for the accurate and reliable diagnosis of heart disease from MRI images with an accuracy of 96%. © 2024 IEEE.

关键词： Image segmentation

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IoT-based Food Quality Monitoring System Using Machine Learning 2

IoT-based Food Quality Monitoring System Using Machine Learn...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vasanthi, R. Ashika, K. Archana, D. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

One of the main considerations in preventing food waste is food safety and cleanliness. Foods should not be allowed to deteriorate or rot due to environmental elements including humidity, temperature, and darkness. The quality of the food should also be regularly checked. Deploying quality monitoring systems is beneficial due to the following reasons:. These food quality monitoring systems keep an eye on the external elements influencing food quality. Factors such as vacuum storage, refrigeration, and others later kept the items from spoiling. More specifically, the sensors in this system - which measure temperature, pH, humidity, gas, and DHT - provide the vital data required to assess the condition of the product that is being packed or kept. Leveraging Internet of Things (IoT) technology involves transmitting data to a computer system. Subsequently, this system displays a user interface that tracks the product's evolution over time. The goal of the project is to enhance food quality assessment by employing machine learning algorithms. © 2024 IEEE.

关键词： Internet of things

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An Optimized Hybrid Deep Learning Framework for Monitoring Botnet Attacks in IoT Networks 5

An Optimized Hybrid Deep Learning Framework for Monitoring B...

引用

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

作者： Manimaran, A. Sathish Kumar, P.J. Kartheesan, L. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Technical Sciences Department of Electronics and Communication Engg Chengalpattu India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Department of Computer Science and Engineering Institute of Science and Technology Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engg Sriperumudur India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The serious threat of botnet attacks in the IOT world today can be effectively addressed with deep learning (DL). However, to train the model, large and complex data sets are required, which adds cost and necessitates expensive hardware to perform intricate mathematical operations. The IoT plays a significant challenge in memory constrained by implementing the DL. The hybrid Principal Component Analysis (PCA) and Autoencoder is proposed to measures the performance to lower the IoT network traffic data and consumes bulky information. Following that, reduced datasets are examined and trained using hybrid Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) algorithms. The hybrid deep learning algorithms attain the feature to enhance the precision, accuracy, recall, and F1 scores 96.51%, 100%, 96.51%, and 98.22%, which were higher than the conventional methods. The model's performance is analyzed using extensive experiments. © 2024 IEEE.

关键词： Bot (Internet)

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Transforming Traditional Medical Appointment Booking with Advanced Technologies and User-Centric Design 3

Transforming Traditional Medical Appointment Booking with Ad...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mahanthi, Avinash Naidu Jacob, T.Prem Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350322842

Life gets too busy to go to a doctor in character and keep up with the right hospital therapy. The most important motive of this work is to offer patients quite simple and luxurious experience during the appointment with the physician, as well as to addressing the questions that the sufferers provide during the appointment. The Find-My-Doctor acts as a client, and is maintained with the aid of an internet site that acts as a server and at the same time a database containing medical records, doctors, patients, and appointment statistics. The present-day widespread working procedures for registering patients and making appointments are time-eating and extremely cumbersome. Find-My-Doctor is an internet-based application designed to streamline the appointment procedure for more than one scientific/healthcare institution. © 2023 IEEE.

关键词： Appointment Booking System Database Security Full Stack Application MongoDB NodeJS ReactJS

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A Real-time Drowsiness Tracking Framework Using the Single Shot with Mediapipe 9

A Real-time Drowsiness Tracking Framework Using the Single S...

引用

9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Mouthami, K. Yuvaraj, N. Navadharani, M. Kpr Institute of Engineering and Technology Department of Computer Science Engineering Tamilnadu Coimbatore641407 India

ISBN: (纸本)9798350384208

In the realm of automotive safety, the pivotal challenge of mitigating drowsy driving, especially prevalent during nighttime journeys, assumes critical importance. This endeavor is dedicated to the construction of an advanced Driver Drowsiness Detection system, harnessing two cornerstone algorithms: UYOLOv5, distinguished for its exceptional facial tracking precision, with an impressive 0.993 mAP@0.5, and the MediaPipe library, renowned for its meticulous eye movement detection capabilities. The fusion of these algorithms establishes a real-time drowsiness assessment framework, with a particular focus on its efficacy in the demanding context of nighttime driving. Through the seamless integration of UYOLOv5 and the MediaPipe library, the objective is to provide a comprehensive solution that transcends mere observation, equipped to proactively initiate safety measures upon the identification of signs of driver fatigue. This initiative is geared toward significantly enhancing nighttime road safety, addressing a matter of paramount concern. © 2023 IEEE.

关键词： Object detection

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Software Effort Estimation using ANN (Back Propagation) 7

Software Effort Estimation using ANN (Back Propagation)

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ravi Kumar, B.N. Suresh, Yeresime Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (纸本)9781665464086

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. This model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results. © 2023 IEEE.

关键词： Neural networks

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