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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Big Data Mining and Analytics 2023年第1期6卷 32-43页

作者： Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi Department of Computer Science and Engineering Parala Maharaja Engineering CollegeBerhampur 761003India Amity School of Engineering and Technology Amity UniversityUttar Pradesh 201303India Department of Computer Applications Sikkim Manipal Institute of TechnologySikkim Manipal UniversitySikkim 737102India Directorate of Research Sikkim Manipal UniversityGangtokSikkim 737102India

As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time ***,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical *** this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud *** is trained marvelously on cloud images with an impressive amount of prediction *** time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset *** satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its *** proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

关键词： satellite images satellite image classification cyclone prediction Deep Convolutional Neural Network(DCNN) features layers down-sampling process

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Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning 8th

Predicting Chronic Kidney Disease Progression Using Classifi...

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Valli Mayil, V. Ratha Jeyalakshmi, T. Gupta, Rishu Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of MCA Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9789819732418

A serious medical problem that impacts millions of people globally is chronic kidney disease, or CKIDD. Accurate CKIDD progression prediction is essential for disease management and early intervention. Machine learning (ML) has shown great potential in predicting the course of CKIDD by employing a variety of medical, laboratory, and demographic factors. In our research, we suggested an ensemble approach for the increased level of accuracy. We contrast the outcome with well-known machine learning models such as neural networks, Dec-Trees, random forest, and support vector machines. On a sizable dataset of CKIDD patients, where good progression outcomes are identified, our model is trained. Our model is trained on a big dataset of CKIDD patients and proposed to find the result that the patient is affected or not. The chronic kidney disease dataset from Kaggle is utilized to test the model. Standard metrics including accuracy, precision, recall, F1-score, and area under receiver operating characteristic curve (AUC-ROC) are considered for proving the capability of the proposed work. In comparison to the conventional method, our model's experiment results demonstrate 100% accuracy in pattern prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Optimization of Network Mapping for Screening and Intrusion Sensing Devices 1

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Atri, Haritima Sharma, Amisha Mehrotra, Tushar Saxena, Sandeep Department of Computer Science and Applications School of Engineering and Technology Sharda University UP Greater Noida India Uttar Pradesh Greater Noida India

ISBN: (数字)9789819706419

ISBN: (纸本)9789819706402

Many of today’s cyberattacks heavily rely on screening addresses over the World Wide Web to find susceptible hardware and software. By keeping track of this scanning activity, you may assess the present situation with regard to several vulnerabilities and their exploitation. Studies examining scanning activity to this point have relied on uninvited traffic obtained from darknets and have concentrated on random screens of the address space. We suggest a method for identifying network scanning threats from both inside and outside the network. Our approach operates on the presumption that every legitimate connection between two computers must typically be accompanied by a DNS resolution;hence, any transfer that is not followed by a DNS inquiry is considered to be an inspection, unless it is permitted by the network’s security protocol. Effective port inspection and sniffing of packet technologies are a must for ensuring strong security. Wiremap, a Python-based port monitoring and packet sniffing programme that makes use of the Scapy library, is introduced in this study. Network administrators, security experts, and penetration testers may evaluate the security posture of their networks using Wiremap’s complete solution. Wiremap’s main goal is to give users a simple, effective tool that lets them scan a target IP address to look for accessible ports and record network data for study. The application takes advantage of the Scapy library’s robust sniffing and packet-dissection capabilities, giving users insights into transmission patterns and possible weaknesses in their networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Hybrid Approach for the Classification and Detection of Brain Tumor 2

Hybrid Approach for the Classification and Detection of Brai...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Rekha, S. Geetha, K. Gireesh Gokulavasan, B. Patil, Harshal Maranan, Ramya Psg College of Arts and Science Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore India Mohan Babu University Department of Electronics and Communication Engineering Tirupati India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

Brain tumors (BT) are a leading cause of global mortality, affecting all ages, with early detection being crucial for successful treatment. Traditional biopsy-based categorization is invasive and often occurs post-surgery, highlighting the need for non-invasive diagnostic methods. This study introduces a hybrid machine learning (ML) model combining Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB) to classify glioma, meningioma, pituitary tumors, and normal brain tissue from MRI scans. Trained on MRI images from Kaggle, the hybrid model's performance was compared to standalone models (SVM, GNB, Linear Discriminant Analysis, and Random Forest), achieving superior accuracy (96.19%), TPR (95.83%), TNR (96.53%), precision (96.06%), and F1-score (98.50%). BTs are notoriously aggressive, and misdiagnosis can significantly reduce patient lifespans. The hybrid model addresses the limitations of traditional ML models, enhancing diagnostic accuracy, reducing unnecessary surgeries, and expediting treatment initiation, thereby improving patient outcomes. © 2024 IEEE.

关键词： Support vector machines

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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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5G in Smart Cities: Enhancing Urban Futures for Sustainable Growth

5G in Smart Cities: Enhancing Urban Futures for Sustainable ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rajvanshi, Anita Mahajan, Prerna Singh, Alka Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

This essay delves into the concept of 'Smart Cities,' outlining its numerous benefits. It elucidates the diverse applications and services essential for transforming a city into a Smart City, underscoring the pivotal role of Information and Communication Technology (ICT) in their realization. Furthermore, it scrutinizes the challenges posed by the deployment of Smart Cities for contemporary ICT systems. The study investigates the promised attributes of 5G technology and its potential as a linchpin for the efficient implementation of urban smart cities. Additionally, it provides a catalog of 5G-enabled Smart City use cases, emphasizing how 5G will serve as the bedrock for the Internet of Things (IoT), fostering the evolution of smart cities. © 2023 IEEE.

关键词： 5G technology ICT infrastructure Information and Communication Technology Internet of Things Quality of Life Smart Cities Smart ICT infrastructure Sustainable City Urban Transit and City Governance

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Deep Learning-Based Self-Supervised Transfer Learning for Medical Image Classification 2

Deep Learning-Based Self-Supervised Transfer Learning for Me...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Shaikh, M.Z. Singh, Samender Varshney, Neeraj Saraswat, Birendra Kumar Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

Self-supervised switch getting to know is a practical approach for the scientific image type. It includes using unlabeled statistics (from non-clinical images) to teach models to categorize medical photos with low attempts and excessive accuracy. This approach has been utilized in numerous duties, including clinical photo segmentation, computer-aided prognosis, and disease category. To summarize the research studies carried out in this discipline, a complete survey of self-supervised switch learning for scientific image type was performed in 2020. The survey evaluations numerous transfer mastering techniques often used in healthcare, which include area adaptation, multitasking studying, and zero/one-shot mastering. It additionally gives an in-intensity analysis of the modern-day challenges and capability solutions. © 2024 IEEE.

关键词： Medical Outperforms Results Unlabeled Utilizes

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A Novel Approach to Optimize EV's Dealing and Charging

A Novel Approach to Optimize EV's Dealing and Charging

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vijay, Sandip Gobi, N. Sharma, Ambrish Kumar Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Computer Applications Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

It's an interesting idea to investigate the energy market as a potential means of reducing the power costs related to charging electric cars at public charging stations. Through intraday energy markets., several nations allow electricity trading until just before the delivery time. This research utilizes the market as an illustration to investigate the possibility of lowering power prices via intraday market trading. After conducting extensive simulations., the results show that charging schedule optimization combined with intraday trading in the markets may result in cost savings of around 9% when compared to obtaining all needed energy from the energy provider. A further 2% or more of cost savings may be realized by giving the charging station manager the ability to resell energy to the everyday electricity market. The research explores how the intraday electricity market's lead time and trading unit affect total expenses in addition to any possible cost savings. Lead time is shown to have a major impact on feasible power prices., whereas each trading unit has a negligible impact on the total cost dynamics. © 2023 IEEE.

关键词： charging schedule Electric vehicles (EV's) electricity costs mixed-integer linear programming trading

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Implementation of Ensemble Predictive Models for Parkinson’s Disease Detection 5th

Implementation of Ensemble Predictive Models for Parkinson’...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Ruth, J. Anitha Mahesh, Vijayalakshmi G. V. Uma, R. Department of Computer Science and Applications SRM Institute of Science and Technology Vadapalani Campus Tamil Nadu Chennai India Department of Electronics and Communication BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819761029

A framework based on ensemble classifier models is presented in this study, which may be used to predict the presence of disease and to increase the accuracy of the prediction. Machine learning's ensemble technique pools several different classification algorithms and an extensive number of classifier models to get more precise predictions. In this present study SVM and DT are employed as base estimators, which takes use of well-known ensemble techniques, such as bagging and boosting, to construct ensemble classifiers. The proposed technique is tested on a Parkinson's database taken from machine learning repository and is found to be effective. Classification accuracy (CA), precision, recall, area under curve (AUC), Cohen kappa score (CKS), Mathews correlation coefficient (MCC), and F1-measure obtained from receiver operating characteristics (ROC), as well as the MCC and F1-measure indicate that the ensemble classifier model has enhanced prediction performance with the least possible errors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neurodegenerative diseases

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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2024 International Conference on Computational Intelligence and Computing applications, ICCICA 2024

作者： Jain, Shweta Gandhi, Anju Bhandari Mehla, Stuti Aggarwal, Rishit Kwatra, Tanu Panipat Institute Of Engineering And Technology Department Of Artificial Intelligence And Machine Learning Panipat India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat India Infosys Limited Pune India D.A.V Centenary College Department Of Computer Science And Applications Faridabad India

ISBN: (纸本)9798350306446

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future. © 2024 IEEE.

关键词： Virtual reality

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