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A Comparative Analysis of Deep learning Algorithms for Predicting Parkinson's Disease, Chronic Kidney Disease, and Liver Disease 7

A Comparative Analysis of Deep Learning Algorithms for Predi...

引用

7th international conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Sukate, Sameeksha Bhalerao, Satyam Mit Adt University Computer Science and Engineering Pune India

ISBN: (纸本)9798350305005

Chronic kidney disease (CKD), liver disease, and Parkinson's disease are significant health challenges that affect millions of people worldwide. Early detection and accurate prediction of these diseases can facilitate timely interventions and improve patient outcomes. Approximately 10per of the Indian populace experiences CKD and Liver disease is rapidly spreading in India, resembling an epidemic, with one in every five adults being affected. According to health experts, India would see a whopping 200-300 per increase in Parkinson's disease over the next two to three decades. In this study, we aim to develop and evaluate deep learning models for the prediction of diseases as mentioned earlier using publicly available datasets from the UCI Machine learning Repository. Using this dataset, we design and implement deep learning models, including K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random forest architectures, to capture complex relationships between input features. Using these deep learning algorithms, we train and evaluate predictive models for each disease. Furthermore, we conduct comparative analyses to determine the relative performance of different algorithms across the three diseases. Our models leverage the ability of deep learning algorithms to automatically extract relevant features from raw data, thereby enabling accurate disease prediction. We conduct rigorous evaluations of the deep learning models using appropriate performance metrics and comparative analysis. The results demonstrate the effectiveness of the proposed models in predicting CKD, liver disease, and Parkinson's disease, surpassing the performance of traditional machine learning methods. This study contributes to the ongoing research on the application of deep learning algorithms in healthcare and highlights their potential in predicting CKD, liver disease, and Parkinson's disease. The developed models hold promise for integration into clinical decision support systems, en

关键词： Support vector machines

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Aadhar UID Mask Detecting Tool Using CNN With Verhoeff Algorithm 9

Aadhar UID Mask Detecting Tool Using CNN With Verhoeff Algor...

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9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Syed Musthafa, A. Monisha, B. Roshni, P. Sowmiyha, N. Sri Dharshini, K.R. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur India

ISBN: (纸本)9798350397376

Aadhaar UID Mask Detecting Tool is a computer vision system that uses convolutional neural networks (CNNs) and the Verhoeff algorithm to detect and extract masked Aadhaar UID numbers in digital images. The tool is designed to address the growing concern over the protection of personal information and privacy in the era of digital communication. The CNNs are trained to recognize patterns in the Aadhaar UID numbers and the Verhoeff algorithm is used to validate the extracted numbers. This tool can be used in various applications such as document scanning and verification, data protection and privacy, and information security. The use of a combination of machine learning and validation algorithms makes this tool robust and reliable for detecting masked Aadhaar UID numbers in images. © 2023 IEEE.

关键词： learning algorithms

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Detection of epileptic seizure using hybrid machine learning algorithms 12

Detection of epileptic seizure using hybrid machine learning...

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12th IEEE international conference on Advanced Computing, ICoAC 2023

作者： Velvizhy, P. Len, Ria Bas Rajeshwari, N. Kanimozhi, K. Anna University Department of Computer Science and Engineering Chennai25 India

ISBN: (纸本)9798350318210

A brain condition known as epilepsy is characterized by frequent seizures. A seizure is an abrupt change in behavior brought on by a brief disturbance in the electrical activity of the brain. For those who are experiencing seizures, abrupt and synchronized electrical energy bursts that impact their consciousness, movements, and sensations disturb the usual electrical pattern. Even now, it is simple to detect an epileptic seizure automatically because the existing detection techniques are time-consuming and inaccurate. In order to effectively identify seizures, machine learning and deep learning algorithms are integrated. Using features retrieved from EEG data where the MLP+CNN+SVM model was used, we present a method in this study for detecting epileptic seizures from EEG signals using three hybrid machine learning classification networks, namely SVM+CNN, MLP+CNN, and SVM+MLP+CNN. © 2023 IEEE.

关键词： Brain

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Towards Precise and Equitable Organ Allocation in Transplant with Machine learning 11

Towards Precise and Equitable Organ Allocation in Transplant...

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11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Ding, Sirui Texas A&m University Department of Computer Science and Engineering College Station United States

ISBN: (纸本)9798350302639

Organ transplant has become an important therapy for many end-stage and severe diseases, e.g., hepatic failure. There is an essential but challenging step in organ transplant which is organ allocation. However, the number of donor organs is scarce compared to the patients who need them. So the allocation of this kind of scarce medical resource remains a challenge. The allocation of organs needs to consider both precision and fairness, which is very hard to balance for the human expert and traditional allocation methods. My Ph.D. research aims to tackle this problem from the computational perspective, especially with machine learning methods. © 2023 IEEE.

关键词： Machine learning

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Segment-Based, User-Generated Image Styling with Neural Style Transfer 2

Segment-Based, User-Generated Image Styling with Neural Styl...

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2nd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Krishna, C. Vamshi Venkatesh, Karthik Nibin, Sooraj Vishakh, H. Nair, Manjula G. Amrita Vishwa Vidhyapeetam Department of Electrical and Electronics Engineering Amritapuri India

ISBN: (纸本)9798350347456

With the recent improvement in machine learning and deep learning technologies due to an increase in computation power, its uses for image processing and computer vision have also increased. The style of the image, through orthodox methods, has been changed with the help of filters that are not capable of applying the style from only a few sets of options, with no consideration for semantic or contextual data. The transfer of style is also limited to the entire image, with no provisions for applying style to only a selected portion of it. Through advanced deep learning technologies, style transfer can be achieved with semantic and contextual accuracy while also providing the ability to apply to a selected portion of the image. The style can also be generated through the use of images generated for the text using deep learning. © 2023 IEEE.

关键词： Image reconstruction

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A Machine learning based Facial Expression and Emotion Recognition for Human computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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StackedPhish: A Stacked Ensemble Framework for Identification of Phishing Website 9

StackedPhish: A Stacked Ensemble Framework for Identificatio...

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9th IEEE international Women in engineering (WIE) conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Murshid, Mahbub Uddin, Mohammed Nasir Hossain, Fahima Computer Science & Engineering Jagannath University Dhaka Bangladesh

ISBN: (纸本)9798350319651

The aim of phishing is to steal sensitive information such as usernames, passwords, social security numbers, credit card information, and so on from internet users. This research focuses primarily on presenting a strategy for detecting phishing websites based on machine learning. The approach takes into consideration lexical based features. Anti-phishing tools such as blacklists, whitelists, heuristics, and methods that are based on visual similarities are unable to detect newly launched websites. In addition, older methods are difficult to implement and are not appropriate for use in real-time contexts since they depend on the resources provided by a third party, such as a search engine. Therefore, one of the greatest challenges in the field of cybersecurity is the detection of recently constructed phishing web sites in an environment that operates in real-time. This work presents a lexical feature-based anti-phishing technique, which derives anti-phishing features from lexical features, as a solution to the challenges described above. The results of our experiment show that the phishing detection strategy StackedPhish using the Stacking Classifier is more effective, with higher detection accuracy of 99.39% for multi-class classification and 99.88% for binary -class classification. © 2023 IEEE.

关键词： Websites

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Plant Disease Classification Using Machine learning

Plant Disease Classification Using Machine Learning

引用

2023 international conference on Disruptive Technologies, ICDT 2023

作者： Singla, Ravi Shankar Gupta, Amit Gupta, Richa Tripathi, Vikas Naruka, Mahaveer Singh Awasthi, Shashank Graphic Era Deemed to Be University Department of Computer Science and Engineering India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Gl Bajaj Institute of Technology and Management Greater Noida India Computer Science and Engineering Gl Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798350323887

In the event of a sharp rise in global population, agriculture strives to provide food to it. In agriculture, the detection and diagnosis of diseases occurring in the plants continues to be the arduous task. That is why it is endorsed to predict the diseases when the crops are in early stage. This work is done to develop and implement a disease prediction system by using different machine learning algorithms and a convolutional neural network. The objective of the paper is to grab the attention among the organisations to employ innovative technologies to decrease the diseases that are persistent in plants. The different approaches of ML and image processing with the algorithm that will provide explicit results to recognize the leaves that are healthy and classification algorithms and techniques so that we come to a result of categorically conclusive factor that the leaf is infected by any disease or not. Firstly, the dataset of leaves is divided into directories based on some features extracted from the leaves. Now, the logistic regression estimates the probability of the leave being healthy, based on the dataset of independent variables. The same dataset is also provided to Neural Networks (NN), Support Vector Machines (SVM) and Naïve Bayes algorithms. All the models are analysed with the confusion matrix and K-Fold Cross validation techniques. The proposed model gave the accuracy of 94% using Neural Networks. © 2023 IEEE.

关键词： Support vector machines

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Bearing and distance Formation Control of Rigid Bodies in SE(3) with Bearing and distance Constraints 9

Bearing and Distance Formation Control of Rigid Bodies in SE...

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9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mansourinasab, Sara Sojoodi, Mahdi Moghadasi, S. Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran Sharif University of Technology Department of Mathematical Sciences Tehran Iran

ISBN: (纸本)9798350311402

Rigidity of the interaction graph is a fundamental condition for achieving the desired formation which can be defined in terms of distance or bearing constraints between agents. In this paper, for reaching a unique formation with the same scaling and orientation as the target formation, both distance and bearing constraints are considered for defining the desired formation. Besides, both distance and bearing measurements are also available. Each agent is able to gather the measurements with respect to other agents in its own body frame. So, the agents are coordinated-free concerning a global reference frame. On the other hand, the framework is embedded in SE(3). The control signal is designed based on a gradient descent method by introducing a cost function. Firstly, the formation problem is considered for bearing-only constraints in SE(3) configuration. Then, the formation control is expressed for the general case of both bearing and distance constraints. Furthermore, the essential conditions that guarantee reaching the desired formation is discussed. Finally, the validity of the proposed formation control is verified by numerical simulations. © 2023 IEEE.

关键词： Cost functions

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Automated Machine learning-based Farmer Support System 14

Automated Machine Learning-based Farmer Support System

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Narukulla, Charan Srujan Raju, K. Dharmiredd, Srinivasarao Computer Science and Engineering Cmr Technical Campus Hyderabad India Cybersecurity MasterCard MO United States

ISBN: (纸本)9798350335095

This research proposes an integrated farmer support system that utilises Automated Machine learning(AutoML) and the Internet of Things(IoT) to provide end-to-end support for farmers. The system aims to address the challenges faced by farmers in conventional farming methods and provide them with accurate information related to crop selection, weather prediction, plant disease detection, and market analysis. It also includes a business module that predicts future needs and provides a marketplace for purchasing chemicals and seeds from genuine companies. The proposed system is available in multiple languages, making it accessible to farmers from any state. The AutoML datasets used by the system are updated in real-time by data managers, ensuring that the information provided to farmers is always up-to-date. The system aims to increase farmers' profitability and revolutionise agriculture using advanced technologies. © 2023 IEEE.

关键词： Machine learning

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