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Utilizing Artificial Intelligence for Advance Data Science and Analysis 2

Utilizing Artificial Intelligence for Advance Data Science a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Deepak Chaudhary, Gaurav Rajput, Kapil Singhal, Amit Sharma, Tarun Kumar Srm Institute of Science and Technology Department of Computer Applications UPModinagar Delhi India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever. © 2024 IEEE.

关键词： Algorithms Correlations Fundamentals Technology Understand

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Cardiac Arrhythmia Detection and Prediction Using Deep Learning Technique 4th

Cardiac Arrhythmia Detection and Prediction Using Deep Learn...

引用

4th International Conference on Communication, Computing and Electronics Systems, ICCCES 2022

作者： Nanthini, K. Sivabalaselvamani, D. Chitra, K. Mohideen, P. Aslam Raja, R. David Department of Computer Applications Kongu Engineering College Tamil Nadu Perundurai Erode India

ISBN: (纸本)9789811977527

One of the fatal diseases in the world is heart disease. Every year, millions of people die of cardiovascular diseases. However, one can decrease the mortality rates if the heart disease was detected and treated early. Usually, people do an electrocardiogram (ECG) test to know about the well-being of their heart. Some kind of irregular functioning and illness in the heart can be found in an ECG test. When the heart malfunctions or if there is any improper beating of the heart, then it results in arrhythmia. There are several types of arrhythmia and some of them are fatal. The process to identify the correct type of arrhythmia is quite difficult and effort-taking process. Even the small changes in the ECG relate to another kind of arrhythmia. It takes experience and patience to recognize the type of arrhythmia accurately. Therefore, deep learning techniques should be employed to analyze the test. Machine learning that involves many levels of processing is known as deep learning. From computer vision to natural language processing, there’s a lot to learn. It has been used in various applications. This method is receiving more popularity because of extreme accuracy, provided the numerous amount of data. The interesting feature is that it analyses the examples and distinguishes the classes and levels automatically. This study is regarding arrhythmia prediction in ECG and the attention it deserves in deep learning community. Providing CNN model, we are going to elaborate the process of detecting cardiac arrhythmia using ECG dataset in this study. The model is executed by rendering CNN with cardiac arrhythmia recognition database. Purpose: About one-third of the world’s population is affected by arrhythmia. Hence, the development of new and successful methodologies is highly in demand in the field of arrhythmia prediction. Further, the need of a cost-effective wearable monitoring gadget to identify the condition of arrhythmia is highly recommended. It assures the trouble-

关键词： Electrocardiograms

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Soil Classification using Deep Learning Techniques 2

Soil Classification using Deep Learning Techniques

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Sivabalaselvamani, D. Rahunathan, L. Nanthini, K. Harshini, T. Hariprasath, C. Kongu Engineering College Perundurai Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798350325799

The study of soil categorization is a burgeoning field nowadays. Living animals and plants depend heavily on soil for their survival. This study is to classify the different types of soils like Clay soil, Black soil, Red soil and Alluvial soil by using CNN algorithm. Classifying soil is important in fields like farming, geology, and engineering. It helps us make smart choices about the use of land, which crops to grow, plan construction, and to take care of the environment. CNN are a type of Deep Learning technique which is used for image analysis. The model assigns weights and biases to different parts of an input image, enabling them to differentiate and identify different objects within the image. Along with that other deep learning methods can also be employed to enhance accuracy. The image datasets used in this study were obtained from Kaggle and underwent processing with various algorithms. Afterwards, performance metrics were employed to compare the results. To efficiently classify the images based on their labels, VGG19 is used, which is a subset of CNN. This study enables us to effectively classify soil images. The models are trained on a dataset of soil images that had been labeled with their corresponding soil type. This model achieves an accuracy of 94.87% on the test set. The results of this study show that CNNs can be used for effective soil classification. This could lead to the development of more efficient and cost-effective methods for soil classification. © 2023 IEEE.

关键词： Learning systems

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Addressing Food Waste: Survey of Approaches, Technological Innovations and Sustainable Solutions 11

Addressing Food Waste: Survey of Approaches, Technological I...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Nandeesh, Talari Rakhra, Manik Chowdary, Pullela Harish Amanpreetsingh Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Lovely Professional University Department of Computer Applications Punjab Phagwara India

ISBN: (纸本)9798350350357

Food waste management in our local is to high for solution. This review article critically examines the complex issue of food waste, analyzing root causes and examining environmental and social impacts. The essay delves into the issue of waste across various phases of the food supply system, specifically concentrating on production, processing, distribution, retail, and consumption stages. Environmental impacts, particularly greenhouse gas emissions and resource scarcity, are scrutinized alongside social consequences such as increased food poverty and hunger. The review evaluates existing strategies and technologies to reduce food waste and proposes comprehensive, long-term solutions. © 2024 IEEE.

关键词： Greenhouse gases

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Detection Of Breast Cancer By Machine Learning Algorithms

Detection Of Breast Cancer By Machine Learning Algorithms

引用

2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Damini, D.H. Shailaja, K.P. Pushpa, T.S. B.M.S. College of Engineering Department of Computer Applications Karnataka India

ISBN: (纸本)9798350317060

According to an NHS survey, 10 women among 80 are diagnosed with breast cancer in their lives. Nowadays, breast cancer is becoming more common and is detected in women between the ages of 29 and 50. In a very rare case, even men can be tested and diagnosed with breast cancer. Multiple tests and procedures are frequently used in traditional cancer diagnosis systems, which puts patients under physical and emotional strain. Machine learning (ML) algorithms have become potential methods for precise and effective breast cancer diagnosis in an effort to lessen this burden. In the initial stage, with very few symptoms, it can be detected through mammographic screening, and from this, it can be classified as benign or malignant. This can increase the rate of survival. Most of the cancer detection system, which takes a lot of time for detection with physical and mental stress, will involve many tests that can cause mental and physical breakdowns by considering all the aspects. The detection of cancerous and noncancerous cells can be done using machine learning (ML) algorithms like logistic regression, random forest, and K-nearest neighbour (KNN) algorithms. These ML algorithms may be used to analyse mammographic data to measure the efficacy and accuracy of breast cancer diagnosis. By examining the patterns and traits seen in mammograms, these algorithms can help identify possible anomalies suggestive of breast cancer. Healthcare workers may speed up the detection process and cut down on the time and stress involved with conventional procedures by utilising the power of *** Applying these algorithms to the mammogram data, we can detect the accuracy and efficiency of breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Vulnerability Classification for Detecting Threats in Cloud Environments Against DDoS Attacks 13

Vulnerability Classification for Detecting Threats in Cloud ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jyothi, V. Esther Chowdary, N. Sampreeth Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350305463

In order to secure data, infrastructure, and services from potential attacks, cloud security is a wide issue that includes any combination of policies, technologies, and procedures. The cloud has substantially different security needs than traditional settings. Because of its dynamic nature and low client infrastructure ownership, the cloud has decreased traditional security architecture. Internet connectivity and distant servers are used in cloud computing to manage data and applications. It provides users with on-demand software, bandwidth, and dynamic virtualized resources through the internet and ensures that its adapters will share in various financial advantages. The utilization of hardware, software licenses, and system maintenance is decreased for the benefit of the customers. The protocol known as SOAP or Simple Object Access Protocol, it enables communication between various web-based services. Extensible Mark-up Language (XML) and Hyper Text Transport Protocol (HTTP) are both used to develop SOAP communications. A cloud web service that uses HTTP and XML might be bringing down by the new type of Distributed Denial of Service (DDoS) attacks. HTTP and XML Denial of Service (DoS) attacks provide a serious security risk to cloud computing. The Hypertext Extensible-Denial of Service (HX-DoS) attack, which causes the cloud service provider's communication channel to get blocked and go down, is made up of purposefully transmitted HTTP and XML messages. Differentiating between suitable and improper communications is necessary to solve the issue of HX-DoS attacks against cloud web services. To decide and place the packets on the side of the victim, the reconstruct and drop technique is utilized. Detection rate, accuracy, and precision were the performance parameters used to evaluated the model's performance. © 2024 IEEE.

关键词： Denial-of-service attack

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A Survey on Data Augmentation Techniques 7

A Survey on Data Augmentation Techniques

引用

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

作者： Nanthini, K. Sivabalaselvamani, D. Chitra, K. Gokul, P. KavinKumar, S. Kishore, S. Department of Computer Applications Kongu Engineering College Perundurai Tamil Nadu Erode India

ISBN: (纸本)9781665464086

Data augmentation is a technique used to generate new data sets from existing ones and artificially increase the size of a dataset. By providing the model with more training data, it is possible to enhance the performance of the proposed model. Various methods, such as image rotating, cropping, and flipping, and adding noise to audio signals can be used to enhance the data. When the original dataset is limited or unbalanced, the use of data augmentation can result in better generalization and increased performance for unknown data. This also reduces the over-fitting by increasing the number of training data included in the machine learning model. However, it is crucial to select the data augmentation methods that are used to be aware of how these methods will affect the models' performance on actual data. To deal with data scarcity and lack of diversity, computer vision and natural language processing (NLP) models employ data augmentation strategies. Accuracy of machine learning models can be improved further by employing AI/ML approaches like data augmentation. According to an experiment, deep learning model performs better by reducing the training loss and validation loss than a model without augmentation for image classification. This study has briefly discussed about the data augmentation techniques that are used to process the image, text and signal data by increasing the volume and variety of training data and set it as the primary goal for a machine learning model to perform better when it comes to generalization. By employing these techniques, over-fitting can be eliminated and the robustness and accuracy of the model can also be improved. The main objective of this study is to implement data augmentation as a solution for the problem of data scarcity. Data augmentation refers to a group of methods that are used to improve the amount and quality of training datasets so that more effective machine learning models may be constructed to process the data. The ap

关键词： Deep learning

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Future Database Technologies for Big Data Analytics

Future Database Technologies for Big Data Analytics

引用

2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Rani, Geeta Sharma, Taruna Sharma, Avinash Chitkara University Institute of Engineering and Technology Dept of Computer Applications Punjab India Maharishi Markandeshwar Engineering College Department of Computer Science and Engineering Mullana India

ISBN: (纸本)9798350335835

Data is valuable asset for organizations but it cannot be used directly for business decision making. It needs to be processed in a form that is easily understandable and can to be used by decision makers. The database technology is continuously changing as the data is growing at a rapid date and business requirements change with time. With the advent of IOT, social media and other technology advancements, data become more complex in terms of volume, variety and veracity. Traditional technology can't be used for handling this data due to its complex and dynamic nature of Big Data. So more advanced and sophisticated tools are essential to handle complex nature of data. In this paper the various database tools are discussed and analysed by different vendors which are suitable for big data Analytics. © 2023 IEEE.

关键词： Big data

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Recognition of Bean Leaf Diseases Using Neural Network and Machine Learning Techniques 3

Recognition of Bean Leaf Diseases Using Neural Network and M...

引用

3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Rahunathan, L. Sivabalaselvamani, D. Elakkiya, E.S. Madhumitha, M. Kumaresh, K. Kongu Engineering College Department of Computer Applications Tamil Nadu Erode Perundurai India

ISBN: (纸本)9781665464871

Plant leaf diseases have increased in prevalence recently, making it necessary to conduct accurate research. Bean leaf diseases can be prevented from spreading by earlier detection and accurate identification. Diseases of bean leaves have negatively affected bean yield and quality. There are two types of diseases predicted in bean leaves: angular leaf spots and rust. This work includes a contrast between deep learning algorithms and machine learning algorithms-based approaches such as CNN (Convolutional Neural Networks/ConvNet) and its predefined models, K-Nearest Neighbor in short KNN, Support Vector Machine can also have written as SVM, Multinomial Logistic Regression that automate the identification of leaf diseases in bean plant species. As far as known, no one has offered a comparison study for identifying bean leaf disease. © 2023 IEEE.

关键词： Radial basis function networks

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

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