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An Improved Network Segmentation Performance in Lesion Segmentation based on Mask R-CNN 6

An Improved Network Segmentation Performance in Lesion Segme...

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Thivya, T. Kalaivanan, C. Juliet, N. Vengadapathiraj, M. Mary Valantina, G. Mohanavel, V. Velammal Engineering College Department of Information Technology Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (纸本)9781665482714

Computational intelligence has grown increasingly common in the analysis of healthcare data in recent years. The deep learning algorithms, in instance, have such a number of fruitful implications in the analysis of medical images. This research study has presented a double awareness deep convolution network-based system for end-to-end healthcare lesion classification. Several fully supervised and poorly supervised extracted features are included in this framework. The issue of the high cost of pixel-level labels with lesions in the medical pictures is alleviated by the weakly supervised segmentation module's use of bounding-box labels of lesion regions, which allows for correct lesion segmentation to be accomplished. In addition to this, a dual attention mechanism has been included for the purpose of improving the network's capacity for visual feature learning. The dual attention mechanism, which consists of both channel and spatial attention, may assist in drawing the network's attention to the extraction of features from significant locations. It is possible for it to considerably minimize the gaps between ground-truth labels and pseudo labels, in comparison to the current standard approach of weakly supervised segmentation using pseudo labels. Final results show that our proposed approach beat the competitors on to an oral disease collection, while our approach was extended to incorporate dermatological disease categorization in the ultimate analysis. © 2022 IEEE.

关键词： Intelligent systems

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Prognosticate Trending Days of Youtube Videos Tags Using K-Nearest Neighbor Algorithm

Prognosticate Trending Days of Youtube Videos Tags Using K-N...

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Information technology for Education and Development (ITED)

作者： S. Olugbenga Olukumoro Cecilia Ajowho Adenusi Emmanuel Izuchukwu Ofoegbunam Oguns Yetunde Josephine Opakunle Victor Abayomi Department of Computer Technology Yaba College of Technology Lagos State Nigeria Linux Professional Institute Nigeria Master Affiliate UI Ibadan Oyo State Nigeria Department of Computer Science Federal School of Surveying Oyo Oyo State Nigeria Department of Computer Science The Polytechnic Ibadan Ibadan Oyo State Nigeria Federal University of Agriculture Abeokuta Ogun State Nigeria

YouTube is a video-sharing website where users may publish, watch, share, and comment on videos and other media. The proliferation of technological gadgets, combined with rapid advancements in technology, has resulted in an increase in trending videos on the platform, where videos and content receive hundreds of thousands, if not millions, of views within minutes of being uploaded and continue to trend throughout the day. This study uses the US YouTube Trending dataset, which includes 130591 occurrences and was acquired from the kaggle repository between August 11, 2020 to May 14, 2022. This study used qualitative and quantitative methods to analyze the YouTube videos dataset, and then performed a predictive analysis on the trending video tags, predicting how a particular video on YouTube might trend in the next two to eight days by predicting the trending of such videos for the next two to eight days and showing their accuracy results using the K-nearest neighbor algorithm (KNN). The model that was utilized to perform the prediction analysis has an accuracy of around 98 percent.

关键词： Video on demand Education Predictive models Media Prediction algorithms Market research Predictive analytics

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Enhanced Particle Swarm Optimization based Node Localization Scheme in Wireless Sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Singh, Shashank Poonkuzhali, R. Nithya, G. Kumar, R. Anil Kartigeyan, J. Ramya, S. Department of Computer Science and Engineering Integral University Kurshi Road Uttar Pradesh Lucknow India Department of Information Technology K.S.Rangasamy College of Technology Tamilnadu Tiruchengode India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Telangana Secunderabad India Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Telangana Hyderabad India Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu Thalavapalayam India

ISBN: (纸本)9781665489621

Wireless sensor networks (WSNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WSNs generally must be known. WSN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. Some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle Swarm Optimization based Node Localization Scheme (EPSONLS) in WSN. The presented EPSONLS technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPSONLS model applies the PSO algorithm to identify the node position. In addition, the EPSONLS model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPSONLS model over recent approaches. © 2022 IEEE.

关键词： Sensor nodes

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A Novel Approach to Identify the Anomaly Detection in Electricity usage based on Machine Learning Algorithms and Big Data 3

A Novel Approach to Identify the Anomaly Detection in Electr...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Panneerselvam, M. Neela, K. Rajeshwari, R. Vengadapathiraj, M. Sobitha, S. Mohanavel, V. Sona College of Technology Department of Electrical and Electronics Engineering Salem India Sri Sairam Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Comprehension mistakes and frauds may both be detected via the analysis of advanced metering information. By means of on-site examinations or even other techniques, the energy suppliers will be made aware of any suspect usage patterns by this study. The analysis of usage patterns using Machine Learning (ML) techniques may reveal errors, measuring interruptions from assaults, or fine motor control with utility companies. Another of the basic instances of an anomaly detection is fraud detection since it is difficult to categories transactional or utilization information. Additionally, scams vary in type, making it difficult to consistently learn from them. This research study presents an unsupervised ML method to identify out-of-the-ordinary qualities in time series, set a threshold for the proportion of out-of-the-ordinary readings towards the overall measurements, and then classify the moving average as questionable or not. Initially, two different methods are provided to find the differences in time-series based data streaming: Spectral Residual-Convolutional Neural Network and anomaly trained model based on martingales. The properly established information is then subjected to Fisher Linear Discriminant analysis and Two-Class Boosted Decision Tree applications. © 2022 IEEE.

关键词： Large dataset

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Design and Implementation of Business Intelligence Dashboard for Project Control at the Port Harbor-Company 12th

Design and Implementation of Business Intelligence Dashboard...

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12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020

作者： Lumban Gaol, Ford Syahir, Arsyan Matsuo, Tokuro Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Information System Bina Nusantara University Jakarta Indonesia Advanced Institute of Industry Technology Tokyo Japan

ISBN: (纸本)9789811533792

This study aims to assist resource management and the project controller at Port Harbor Company. Port Harbor Company is an IT Consulting. Problems that often occur in IT Consulting are related to the project cost, resource management and control of each project. Problems that occur at the Port Harbor Company are poor resource management, spending costs that exceed a predetermined budget and how to see the status of ongoing projects. Using Business Intelligence and the dashboard as a front end will help the project controller and resource management manage resources, manage project costs and monitor ongoing projects. In this study, the development and implementation of Business Intelligence and dashboards using the ASAP method are expected to be more structured and can solve the Port Harbor Company problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Project management

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Innovations in Image Forensics: Designing a Smart Learning Based Methodology for Forgery Detection over Digital Images 1

Innovations in Image Forensics: Designing a Smart Learning B...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Ramkumar, G. Pithamber, K. Srilakshmi, C.H. Rathika, S. Anni Princy, B. Kandaswamy, Vijay Anand Saveetha University Department of ECE Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad Dundigal India R.M.D. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350365016

Detecting forgery in digital images is crucial for several reasons. Firstly, with the widespread use and accessibility of image editing tools, individuals can easily manipulate digital images, raising concerns about the authenticity and integrity of visual information. This study introduces a novel approach, termed SFVGG, for detecting forgery in digital images by leveraging a smart learning methodology. The SFVGG method integrates the robust features of SIFT, the clustering capabilities of Fuzzy C-Mean clustering, and the deep learning process of VGG19. Through a synergistic combination of these techniques, the proposed forgery detection system achieves a comprehensive and effective solution. The methodology was implemented using Python, showcasing its practical applicability. Remarkably, the SFVGG framework demonstrates a high accuracy of 94.5%, underscoring its effectiveness in identifying forged content within digital images. This research represents a significant advancement in digital image forgery detection, offering both a sophisticated methodology and practical implementation. © 2024 IEEE.

关键词： Contrastive Learning

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Block Chain technology for Securing IoT Devices and Data

Block Chain Technology for Securing IoT Devices and Data

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International Conference on science technology Engineering and Management (ICONSTEM)

作者： H. Mickle Aancy Shilpa Prashant Kodgire MaddulaRatna Mohitha Manimegalai V Vijay G Sunita Dhotre Department of Master of Business Administration Panimalar Engineering college Chennai Tamil Nadu India Department of Electronics and Telecommunication Engineering Maharashtra Institute of Technology Aurangabad Maharashtra India Department of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Computer Engineering Bharati Vidyapeeth(Deemed to be University) College of Engineering Pune Maharashtra India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

IoT detector data can be covered and authenticated using blockchain armature to avoid data duplication. It also enables secure device data transfer. IoT can handle scalability and trust challenges with blockchain, keeping data private and secure. Blockchain technology improves authentication, fraud resistance, and decentralized data security. It can polarize device authentication and communication, barring a central authority. The processing power demanded for blockchain mining and the vulnerability of present blockchains to mining monopolization are obstacles. Hyundai has patronized IoT security blockchain startups. Blockchain technology could secure IoT networks and sensitive data. Blockchain and IoT give a strong answer to linked device security issues. Blockchain's decentralized and inflexible tally can secure IoT bias and their data, according to this abstract. Blockchain technology allows secure, peer- to- peer connectivity and data sharing, guarding IoT bias from manipulation and unauthorised access. Blockchain's cryptographic algorithms guarantee data integrity, while smart contracts automate device relations to misbehave with security rules. Blockchain's practical counteraccusations in IoT security could alter how we guard our decreasingly linked world, as this exploration shows.

关键词： Resistance Scalability Smart contracts Authentication Mathematics Blockchains Fraud

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Machine Learning on Retail Stores for Customer Churn

Machine Learning on Retail Stores for Customer Churn

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Advanced Computing Technologies (ICoACT), International Conference on

作者： P. Brito Clinton P. Sathiya Priya S. Jagadeesan S. Nagarajan K. Mohanapriya S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The pace at which customers discontinue engaging in commercial activity with a corporation is known as customer churn, and it frequently indicates discontent or competition. Machine learning analyzes customer data to identify patterns, predict churn, and personalize retention strategies. It enables businesses to target at-risk customers and enhance engagement through tailored interventions and offers. A public dataset gathered from Kaggle is used in this research work. Exploratory data analysis is carried over to understand the patterns and trends in the churn rate. The bivariate analysis is held to find the correlation of each feature against the churn rate. With these observations, the importance of marketing strategies is exhibited. Additionally, four distinct models—Logistic Regression, Random Forest, Decision Tree, and the XGB Classifier-are used to train and assess the dataset. From the evaluation of all four models with the help of the ROC score, the XGB is found to outperform and provide better results obtaining 98% accuracy and a 1.00 ROC score.

关键词： computers Data analysis Correlation Computational modeling Market research Classification algorithms Churn Regression tree analysis Random forests Optimization

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A coherent cluster head selection algorithm based on pinnacle weight

A coherent cluster head selection algorithm based on pinnacl...

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2020 International Conference on Computation, Automation and Knowledge Management, ICCAKM 2020

作者： Gera, Anju Singh, Pawan Kumar Munjal, Deep Kiran Computer Science and Engineering G L. Bajaj Institute of Technology and Management Greater Noida India Master of Computer Application G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9781728106663

In the last two decades, there has been a rapid growth in the use of the Internet and Communication MANET consists of number of nodes in which it communicates with each other. One of the main design issues in MANET is that they are power constrained. To spare the vitality, the hubs can be assembled as groups. Cluster heads are having more duties, for example, to limit the re-connection, topology changes, and the strength of the on request organizes. Clustering is system to limit the multifaceted nature by solving the issue and to make smaller groups called clusters. To achieve this efficient cluster head should be elected. In this paper 'A coherent Cluster head selection algorithm based on pinnacle weight for MANET' is proposed from ECAM with additional parameter. © 2020 IEEE.

关键词： Mobile ad hoc networks

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Designing Robust Deep Learning Models for RealTime Data Analysis in Machine Learning

Designing Robust Deep Learning Models for RealTime Data Anal...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Monali R. Borade Mukur Gupta Ayaan Faiz Kalyan Acharjya A Rajesh P.M. Nunciya Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place.

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