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Breast Cancer Classification Model Using Principal Component Analysis and Deep Neural Network 1

引用

World Conference on Information Systems for Business Management, ISBM 2022

作者： Sindhuja, M. Poonkuzhali, S. Vigneshwaran, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai602105 India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai602105 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India

ISBN: (数字)9789811974472

ISBN: (纸本)9789811974465

Life-threatening cancer is prevalent over the globe. According to statistics, most people are diagnosed with cancer in the later stages, even though cancer can be prevented and cured in early stages. The goal of this research is to diagnose a breast cancer is either benign or malignant, as well as to forecast the likelihood of a cancer recurrence even after a course of therapy has been completed. Despite the fact that many machine learning algorithms have resulted in strong predictions, the accuracy in the early stages of categorization is not up to the expected level. Deep learning (DL), a higher degree of machine learning, can forecast breast cancer types and recurrences. Classifiers were built using a deep neural network (DNN) that used Principal Component Analysis (PCA) to choose features. Different machine learning techniques are compared with the proposed system’s accuracy. Early-stage breast cancer prediction is more accurate with the DNN-based method. The clinical management system will benefit from the proposed system since it will aid in identification of cancer at an early stage and the subsequent provision of appropriate therapy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A Review of Earth or land surface use and cover Classification using the Landsat8 Remote Sensing data for Dehradun Region

A Review of Earth or land surface use and cover Classificati...

引用

2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Sachin Memoria, Minakshi Kumar, Rajiv Joshi, Kapil Anandaram, Harishchander Gupta, Amit Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Centre for Computational Engineering Networking Amrita School of Artificial Intelligence Coimbatore India Graphic Era Hill University Deptt. of Cse Dehradun India

ISBN: (纸本)9798350323917

In accordance with economic and industrial development, the number of land use or land-cover or (ESU/ESC) deviations has increased tremendously with uncontrolled population growth, particularly in emerging nations in the late 20th and primary 21st periods. In the best efficient way to understand the impact of ESU/ESC and control the effects of land change, ESU/ESC change has become a major concern for ecologists and policymakers to understand how different machine learning classifiers significantly support the estimation of ESU/ESC maps from remote sensing data. Prime purpose of the study is to categorize ESU/ESC applying different machine learning algorithms on the Google Earth Engine platform, specifically using the Random Forest, Support Vector Machine, Classification and Regression Trees to compare the effectiveness of each algorithm using accuracy estimation. Future sustainable development and a knowledge of the dynamics of our changing environment will undoubtedly benefit from ESU/ESC categorization study. © 2023 IEEE.

关键词： Neural networks

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Survey on Security Based Approach to Prevent Attacks in SDN Using IoT Framework 2

Survey on Security Based Approach to Prevent Attacks in SDN ...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Prabhu, V. Balamurugan, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Tamilnadu Chengalpattu603 203 India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798350347579

Network evolution trendincludes Internet of Things (IoT) and Software-Defined networking (SDN). By separating the data plane from the control plane, the software defined networking architectural framework is used and it will makes the network admins works more easily. SDN mainly focuses on network management and controls at the higher levels to monitoring and sharing the connection between the devices with the help of network connectivity in real time using the Internet of Things concepts. Almost in all the fields, IoT devices used so have to improve the security of IoT devices. The SDN has the centralized control logic, if the number of network traffic increases then centralized controller may not be handle all the network traffic requests at the same time, because the SDN controller is a single point of failure, its failure can bring the entire network down. The Software Defined Network (SDN) is easily exposed to various attacks mostly to DDoS attacks. Majority of the existing research is concentrated only on sensing of HR-DDoS attacks occurring over the control layer and not focusing on LR-DDoS attack occurring over the data layer because the accuracy of attacks on the LR-DDoS attacks are very low. This study mainly focusing on how to design the multi controller SDN architecture to improve the scalability, how to increase the detection accuracy of low-rate DDoS attacks on the SDN data layer and how to detect the attacks using machine learning algorithms. © 2023 IEEE.

关键词： Internet of things

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Particle Swarm Optimization for Efficient Brain Tumor Classification Using InceptionV3 Deep Learning Model 9

Particle Swarm Optimization for Efficient Brain Tumor Classi...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Akhila, P. Prabaharan, G. Pandiiyan, K. Swarna, S.L. Hussain, A. Sakthivelu, U. B V Raju Institute of Technology Department of Artificial Intelligence and Machine Learning Telangana Narsapur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Srm Arts and Science College Department of Computer Science with Artificial Intelligence Kattankulathur Chengalpattu India Sona College of Technology Department of Computer Science Science and Engineering Junction Main Road Tamil Nadu Salem India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350377972

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Symmetric TDMA Mechanism to Optimize the Performance of the Body Sensor Network for Sports Application 5

A Symmetric TDMA Mechanism to Optimize the Performance of th...

引用

5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Mustafa, Mohammed M. Khalifa, Ahmed A. Cengiz, Korhan Sri Krishna College of Engineering & Technology Department of Information Technology Coimbatore India College of Information Technology University of Fujairah Department of Networking and Security Fujairah United Arab Emirates National Telecommunication Institute Switching and Traffic Department Cairo Egypt Istinye University Department of Computer Engineering Zeytinburnu Istanbul Turkey

ISBN: (纸本)9798350337525

The body sensor network plays a vital role in the analysis of the gait analysis of human, sensing the various events happening in the human body. The communication that takes place between the sensed device and the processing system is very important in which the nodes worn on the human body communicate with the sink node placed in the center of the human body. A wireless communication mechanism TDMA was used and the results gave around 60 percent reliability among the nodes. The dynamic TDMA gave a reliability of 90 percent and retransmission mechanism of 95 percent. Our research work focused on to improve the reliability between the nodes and to develop an application for the users to retrieve the data from the sink node. A Symmetric TDMA algorithm was used in which the reliability was increased up to 2 percent resulted in 97 percent. Also, a Bayesian model was developed to identify the probability of nodes initiating to transmit at the same time. The model proved that the chances of nodes transmitting at the same time are 8 percent when comparing to all other techniques. The reliability of the network was also increased. Further, the work will be developed to bring the security concepts into the transmission of data packets, so that the loss can be minimized. © 2023 IEEE.

关键词： Reliability

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Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture 3

Enabling an On-demand Access to Community Sentiments using L...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Prabakaran, N. Anbarasi, A. Deepa, N. Pandiaraja, P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Department of computing Technologies School of Computing SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Computer Science and Engineering M.Kumarasamy college of Engineering Karur639113 India

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users' digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results. © 2023 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Breathero: Not Another Slow Breathing Game - Exploring Faster-Paced VR Breathing Exercise Games

Breathero: Not Another Slow Breathing Game - Exploring Faste...

引用

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Wang, Shun-Yu Cheng, Chia-Yu Huang, Shu-Meng Chan, Weng Io Huang, Yu-Hsuan Lin, Jhih-Wei >National Taiwan University Graduate Institute of Networking and Multimedia Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan National Taiwan University of Science and Technology Department of Design Taiwan

ISBN: (纸本)9781450394222

BreatHero is a faster-paced Virtual Reality (VR) action game that explores the potential of combining breathing techniques learning with the combat elements of action games. BreatHero uses breathing techniques as various skills for attacking and defending against the enemies, so players can do breathing exercises while enjoying defeating the in-game enemies. We choose three breathing techniques: Kapalabhati, Box Breathing, and Full Yogic Breathing, which are all beneficial for physical and mental health. To enhance breathing techniques learning and memorization in action games, we present two design concepts: Imitative Breathing Feedback and Associate Learning on Breathing Techniques. Results from our study indicated that players found the game to be more engaging and were willing to play it repeatedly, suggesting that breathing exercises can be effectively integrated into faster-paced action games. © 2023 Owner/Author.

关键词： Virtual reality

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Function Clustering to Optimize Resource Utilization on Container Platform 29

Function Clustering to Optimize Resource Utilization on Cont...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lee, Chao-Yu Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan Academia Sinica Institute of Information Science Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan National Taiwan University The Graduate Institute of Networking and Multimedia Taipei Taiwan

ISBN: (纸本)9798350330717

In recent years, container technology has gained significant attention in the software industry, with many businesses opting for its elasticity, cost-effectiveness, and ease of implementation. The most common way to ensure a seamless user experience is to keep a substantial number of containers active throughout the day, which causes resource over-provision. Conversely, closing the container right after handling the requests can reduce memory consumption but generate a cold start whenever the request arrives. Cold start occurrence and resource usage is a trade-off and presents a significant challenge on the container *** address this challenge, we observe that serving consecutive requests with the same container can notably decrease the number of cold starts. We propose TAC, a Temporal Adjacency Function Clustering algorithm, to meet the challenge. TAC selects the functions with time adjacency requests into a cluster from the historical data. TAC packs functions serving time adjacency requests into a cluster to reduce cold starts and enable efficient resource utilization. The experiment result shows that TAC reduces 8% cold start occurrences and 53% memory usage with the real-world traces compared to the state-of-the-art methods, e.g., Defuse [1] and Hybrid histogram policy [2]. © 2023 IEEE.

关键词： cold-start container platform Function-as-a-Service serverless

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Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques 22

Investigating Positive and Negative Qualities of Human-in-th...

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2022 CHI Conference on Human Factors in Computing Systems, CHI 2022

作者： Chan, Liwei Liao, Yi-Chi Mo, George B Dudley, John J Cheng, Chun-Lien Kristensson, Per Ola Oulasvirta, Antti Department of Computer Science National Yang Ming Chiao Tung University Taiwan Department of Communications and Networking Aalto University Finland Department of Engineering University of Cambridge United Kingdom

ISBN: (纸本)9781450391573

Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical. © 2022 ACM.

关键词： Multiobjective optimization

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Improved Security for Cloud Storage Using Elgamal Algorithms Authentication Key Validation 2

Improved Security for Cloud Storage Using Elgamal Algorithms...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Narayanan, K. Lakshmi Naresh, R. SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering Chennai Tamilnadu Chengalpattu603 203 India SRM Institute of Science and Technology Kattankulathur Department of Networking and Communications Chennai Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9781665475174

Cloud computing is a newly developing concept in the area of computer technology. In one data centre, the data has traditionally been kept and managed. However, cloud computing nowadays offers computation as a service. It has the benefits of optimization, dependability, superior efficiency, and being a comparatively low-cost feasible solution compared to dedicated infrastructures. It expected to grow in the future. In this case, the system administrator will first encrypt the usable index using the ElGamal cryptosystem, which will then use to protect the data. Therefore, depending on the service, extensive data transferred over the network and this enormous amount of data retained in cloud service centers. The result introduces a new method of encrypting data known as batch key generation, which is a unique idea since it enables certain operations to done on the encrypted files, it is used to analysis of security and in which two parties exchange a large number of keys to enhance security. ElGamal variants divided into two kinds based on the method of creating signatures used in producing them. Afterwards, we build a general process that converts a collection of ElGamal variations into their threshold versions, and we demonstrate that the schemes we construct are unforgeable. In this article, we present a key encryption method for enhancing the privacy and security of information kept in the cloud while also reducing storage costs. © 2023 IEEE.

关键词： Cloud storage

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