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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

science China(information sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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Deep Learning for Automated Classification of Kidney Lesions in Chronic Kidney Disease Patients 2

Deep Learning for Automated Classification of Kidney Lesions...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Lakshmi, B. Seetha Anand, Vijay Omotunde, Habeeb Birundadevi, M. Mahalakshmi, S. Brahmam, Madala Guru Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Namakkal India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Department of Information Systems Riyadh Saudi Arabia Sri Sairam Siddha Medical College and Research Centre Department of Maruthuvam Chennai India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data - specifically quantitative and qualitative data - to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care. © 2024 IEEE.

关键词： Automated Classification Chronic Kidney Disease Deep Learning Diagnostic Accuracy Kidney Lesions Medical Image Analysis

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A Study of Deep Learning Algorithms in Sentiment Analysis of Diverse Domains 1

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International Health Informatics Conference, IHIC 2022

作者： Tanmaiyee, Jutla Vasamsetti, Ramya Sri Sushma Pallavi, Vangala Sai Jagdish, Mukta Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9789811990908

ISBN: (纸本)9789811990892

Analysis of sentiment and opinion mining is the space of examination that investigates considerations, discernments, appraisals, practices, and feelings of individuals from the composed language on products, administration associations, human issues, occasions, points, and different traits. The main agenda of this paper is to depict the sentiments of people using deep learning algorithms and tools by classification methodology in categorizing polarity into positive, negative, and neutral. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

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computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

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A Performance Analysis of Boosting Algorithms for the Identification of Card Fraud 21

A Performance Analysis of Boosting Algorithms for the Identi...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Han, Sai Saw Wai, Khaing Khaing Faculty of Information Science Myeik Myanmar University of Computer Studies Yangon Department of Information Technology Support and Maintenance Yangon Myanmar

ISBN: (纸本)9789998601673

A cashless society is becoming more and more of a reality because of the quick development of technology. This has made it easier for fraudsters to commit their crimes, to a certain extent. To solve this issue, fraud classification systems are used to identify fraudulent activity after it has already occurred. In this experiment, the performance of classification models for this fraud detection analysis was built and compared using five deep boosting algorithms: Adaboost, CatBoost, Gradient Boosting, XGBoost, and LightGBM (LGBM). On the IEEE-CIS Fraud Detection Dataset, which Vesta Corporation provided, this study was carried out. The models were then evaluated using various performance metrics, including the Accuracy measure, F1 Score, and Area Under the Receiver Operating Characteristic Curve (AUC). The research goal is to identify the best boosting algorithm that exhibits high classification performance and high AUC and accuracy scores. An analysis of the boosting ensemble algorithms was conducted in order to accomplish this goal. For the experiment, the dataset was preprocessed using feature selection and scaling methods. The metric comparison showed that LGBM is ranked second and XGBoost has the best classification. Since lowering the false negative rate (FNR) is the main objective of a fraud detection system, XGBoost obtains the highest fraud detection score. Overall, XGBoost achieves the highest predictive accuracy when compared to the other methods. © 2024 IEEE.

关键词： Crime

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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E - Voting Using Blockchain 2

E - Voting Using Blockchain

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Suganthi, N. Gokul, S. Shravanth, E. Veena, K. Computer Science Department Kumaraguru College of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9798350306811

In general, building an online voting system that complies with the laws set forth by legislators has always been a difficult task. An innovative development in the field of information technology is distributed ledger technology. Blockchain technology provide a limitless number of sharing economy-friendly applications. This research attempts to assess how well distributed online voting systems may be implemented using blockchain technology. The project explains the specifications for developing online voting systems and outlines the technical constraints of using blockchain to implement such systems. A few of the well-familiar frameworks that uses blockchain as a service are first evaluated for the project. Then put forth a cutting-edge blockchain-based online voting system that fixes all the issues found. In a broader sense, this project assesses the possibilities of distributed ledger technologies by telling a case study, specifically the conduct of an election and the execution of a blockchain built application that improves privacy and reduces the cost of holding national elections. © 2023 IEEE.

关键词： Blockchain

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An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT

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Journal of Intelligent and Fuzzy Systems 2023年第2期44卷 2441-2454页

作者： Sathish, S. Kavitha, K. Poongodi, J. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electronics and Communication Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. © 2023 - IOS Press. All

关键词： Internet of things

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JEDI: Joint and Effective Privacy Preserving Outsourced Set Intersection and Data Integration Protocols

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IEEE Transactions on information Forensics and Security 2023年 18卷 4504-4514页

作者： Chen, Yu-Chi Huang, Kuan-Chun National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan Telecom Technology Center New Taipei City220 Taiwan

In the era of big data with an ever-increasing amount of data, the demand for data processing is increasing, and various computing service agreements have also been created. While data are widely used, data providers worry that their privacy will be infringed, so the security of the data has also received more attention. Private set intersection (PSI) allows two data-owning parties to jointly compute the intersection of private datasets. The party cannot obtain any other information except for the intersection results. However, Bloom filter (BF) is a naive solution that can be used to potentially obtain PSI, but the verifiability of using BF is usually a concern for effectiveness. Hence, garbled Bloom filter is indeed expected to overcome this concern in two-party setting. We aim for the outsourced scenario where there are many data-owning parties in distributed setting. These parties would like to jointly obtain the set intersection in a private manner supported by an outsourced cloud, and further to obtain data integration. The main challenges are that the previous solutions cannot be directly transformed into an outsourced scenario. In this paper, the proposed joint and effective privacy preserving outsourced set intersection and data integration protocols are called JEDI. They are generic constructions. We start by introducing a basic outsourced PSI protocol from the cryptographic building blocks and new BF and GBF techniques. Then, we modify the basic protocol and propose the full-fledged scheme to achieve an outsourced multi-party PSI protocol. Finally, we analyze the security and execution efficiency aspects to ensure usability. © 2023 IEEE.

关键词： Data integration

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A Secure Watermarking Based Image Integrity Verification in IoMT 4

A Secure Watermarking Based Image Integrity Verification in ...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sulfi, Saeeda Neena Raj, N.R. College of Engineering Trivandrum Department of Computer Science and Engineering Thiruvananthapuram India Mar Baselios College of Engineering and Technology Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350306118

The Internet of Medical Things in healthcare necessitates a secure way to transfer medical images due to the rise of telemedicine. Security breaches in public networks lead to falsification of medical images and further incorrect diagnoses. To address this issue, we propose a multiwatermarking scheme in which logo is embedded in region of interest and text data is concealed in regions of non-interest for providing authentication and integrity verification. Fractal encoding technique is used to ensure the secure transmission of watermarked medical image. Experimental results demonstrate that proposed scheme ensures high visual quality, lossless recovery of text data and high security. The proposed scheme provides better watermarked image quality as compared with state-of-The-Art schemes. © 2023 IEEE.

关键词： Authentication

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