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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Sudharsan, G. Srinivasa Vishnupriyan, J. Anand, K. Vijay Chidambarathanu, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Ece Chennai India Chennai Institute of Technology Center for Energy Research Chennai India R.M.K. Engineering College Department of Electronics and Instrumentation Engineering Chennai India R.M.K. Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

This work proposes an electronics circuit-based monitoring and control panel for wind turbines. In the wind business, wind turbine monitoring is a critical task. This work describes the process of building a monitoring and control panel system that uses LabVIEW to record and analyze various metrics such as wind speed, wind direction, tip speed ratio, energy generated, power and current power using LabVIEW and buzzer indication. The measuring equipment must function properly in order to produce the highly qualified data required for an accurate wind gust prediction. This work aims to create a promising measuring and monitoring system for wind parameter measurement. The graphical user interface makes even the most critical processes much easier to control and monitor. © 2022 IEEE.

关键词： Wind turbines

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A Study of XXE Attacks Prevention Using XML Parser Configuration

A Study of XXE Attacks Prevention Using XML Parser Configura...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Ramsha Shahid Safdar Nawaz Khan Marwat Ala Al-Fuqaha Ghassen Ben Brahim Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

ISBN: (纸本)9781665487726

Online attacks are outcomes of cyber vulnerabilities. XML (eXtensible Markup Language) is a self-descriptive markup language, and XML eXternal Entity injection (XXE) is a well-recognised web security vulnerability. XXE injection can attack web applications that several popular XML parsers are unable to withstand. Most of the available literature on XXE is based on vulnerability testing, but not much work has been done on ways for prevention of such attacks. The aim of this research is investigation of XXE attacks, and ways of prevention of such attacks. A sample vulnerable web application utilizing the standard XML parser that accompanies the Java Development Kit (JDK) has been developed in this work for executing different attacks and detection of attacks. Additionally, ways for prevention of XXE attacks are suggested as well. A virtual machine image of the developed framework is provided to perform trial and error for experimentation and representation of vulnerabilities. Different techniques and exploitation approaches for evaluating the performance of sample web application in terms of vulner-abilities are utilized to develop a platform for executing such attacks. Since the vulnerabilities of misconfigured XML parsers are depicted in this work in detail, effective methods for securing websites from XXE attacks are provided. Furthermore, this work also includes discourse on tools for developing platform for XXE attacks, and ways to eliminate vulnerabilities.

关键词： Java Codes Standards organizations XML Organizations Virtual machining Security

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Secure, Available, Verifiable, and Efficient Range Query Processing on Outsourced Datasets

Secure, Available, Verifiable, and Efficient Range Query Pro...

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IEEE International Conference on Communications (ICC)

作者： Meng Li Jianbo Gao Zijian Zhang Mauro Conti Mamoun Alazab School of Computer Science and Information Engineering Hefei University of Technology China School of Cyberspace Science and Technology Beijing Institute of Technology China Department of Mathematics HIT Center University of Padua Italy Department of Intelligent Systems Delft TU Netherlands College of Engineering IT and Environment Charles Darwin University Australia

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Range queries allow data users to outsource their data to a Cloud Server (CS) that responds to data users who submit a request with range conditions. However, security concerns hinder the wide-scale adoption. Existing works neglect item availability, fail to protect secure verification or sacrifice search accuracy for efficiency. In this paper, we propose Secure, Available, Verifiable, and Efficient (SAVE) range query processing, which has three distinctive features. (1) Secure availability checking against a malicious CS: we design a keyed index-based secure verification mechanism to check the availability of matched nodes, including validity and freshness. (2) Secure result verification: we design a targeted verification mechanism for result correctness and completeness while not compromising security. (3) Improved efficiency and accuracy: we design a lay-ered encoding method to improve search efficiency and accuracy. We formally stated and proved the security of SAVE in the random oracle model. We conducted extensive experiments over the Yelp and FourSquare dataset to validate the efficiency, e.g., a query over 10 thousand data items only needs 19.4 ms to get queried results and 3.5 ms for local verification.

关键词： Accuracy Query processing Encoding Security Servers

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Importance of Search Engine Optimization in Digitalization — A Comprehensive Analysis

Importance of Search Engine Optimization in Digitalization ...

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Advances in Computing Research on science Engineering and Technology (ACROSET), International Conference on

作者： Reshmi Tatikonda Jainath Ponnala Ramya Tatikonda Dileep Kumar Yendluri M. Kempanna A. Bhuvanesh Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderabad India Department of Information Technology University of the Cumberlands Williamsburg USA Department of Computer Science University of the Cumberlands Williamsburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This article examines the complex realm of search engine optimization and its importance in the era of digitalization. With an emphasis on different facets of search engine optimization, including optimizing on-page elements, off-page optimization, technology optimization, and optimizing content, this document thoroughly comprehends how enterprises can boost their online appearance and generate natural website visitors. Moreover, it explores the complexities of search engine optimization algorithms and advancements and reviews the available resources and methods for enhancement. Additionally, it covers recommended approaches and plans for the successful implementation of SEO. This article further explores upcoming patterns within search engine optimization, including enhancing voice search and incorporating AI and ML. Furthermore, it discusses potential hurdles and potential openings in SEO's future. Lastly, the study provides suggestions to explore further and improve optimizing search engines. It includes regional SEO, UX optimization, sound search, and socially conscious SEO practices.

关键词： Reviews Search engines Complexity theory Artificial intelligence Optimization

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Health Information Broadcast Distributed Pattern Association based on Estimated Volume

Health Information Broadcast Distributed Pattern Association...

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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： R. Dhanalakshmi A.V. Kalpana J. Umamageswaran B. Praveen Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Information Technology R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering K L University Andhra Pradesh India

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. Data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery.

关键词： Histograms Neural networks Redundancy Approximation algorithms Data models Classification algorithms Data mining

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Accelerate Solving Expensive Scheduling by Leveraging Economical Auxiliary Tasks

arXiv

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arXiv 2024年

作者： Li, Minshuo Liu, Bo Xin, Bin Feng, Liang Li, Peng Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China College of Computer Science Chongqing University Chongqing400044 China The Artificial Intelligence Department Cainiao Network Group Hangzhou China

To fully leverage the multi-task optimization paradigm for accelerating the solution of expensive scheduling problems, this study has effectively tackled three vital concerns. The primary issue is identifying auxiliary tasks that closely resemble the original expensive task. We suggested a sampling strategy based on job importance, creating a compact matrix by extracting crucial rows from the entire problem specification matrix of the expensive task. This matrix serves as an economical auxiliary task. Mathematically, we proved that this economical auxiliary task bears similarity to its corresponding expensive task and preserves the essential behavior of the expensive task. The subsequent concern revolves around making auxiliary tasks more cost-effective. We determined the sampling proportions for the entire problem specification matrix through factorial design experiments, resulting in a more compact auxiliary task. With a reduced search space and shorter function evaluation time, it can rapidly furnish high-quality transferable information for the primary task. The last aspect involves designing transferable deep information from auxiliary tasks. We regarded the job priorities in the (sub-) optimal solutions to the economical auxiliary task as transferable invariants. By adopting a partial solution patching strategy, we augmented specificity knowledge onto the common knowledge to adapt to the target expensive task. The strategies devised for constructing task pairs and facilitating knowledge transfer, when incorporated into various evolutionary multitasking algorithms, were utilized to address expensive instances of permutation flow shop scheduling. Extensive experiments and statistical comparisons have validated that, with the collaborative synergy of these strategies, the performance of evolutionary multitasking algorithms is significantly enhanced in handling expensive scheduling tasks. © 2024, CC BY-SA.

关键词： Multitasking

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A Fast Blind Scheme for Color Image Authentication

A Fast Blind Scheme for Color Image Authentication

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International Conference on Pattern Analysis and Intelligent systems (PAIS)

作者： Abdallah Soualmi Adel Alti Lamri Laouamer University Center Cheikh Amod Ben Moukhtar Illizi Algeria LRSD Computer Science Department University of SETIF-1 Sétif Algeria Department of Management Information Systems College of Business Qassim University KSA

Today, information security has become a stumbling block in the development of useful multimedia applications. Since advanced applications contain sensitive data, attackers prioritized data authentication. This situation makes the transmission of sensitive and confidential data over Internet a prime target for theft and manipulation. Watermarking is a reliable and promising solution to safeguard sensitive data and ensure its authenticity. In this paper, an innovative blind image watermarking technique is presented for personal private data’s validity and secrecy. This technique consists of two major processes, embedding and extracting. The first consists of achieving the watermark embedding on DFT coefficients using A-star algorithm with a chaotic sequence. The second one extracts the watermark using the same embedding steps that succeed without any original or watermarked image. The proposed technique has been tested against a variety of attacks to show its reliability. Experimental results show that the proposed technique maintains strong robustness while producing good image quality.

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MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation

arXiv

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arXiv 2024年

作者： Cheng, Jintao Chen, Xingming Liang, Jinxin Tang, Xiaoyu Chen, Xieyuanli Li, Dachuan Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Electronic and Information Engineering South China Normal University Foshan528225 China The College of Intelligence Science and Technology National University of Defense Technology Changsha China

Effectively summarizing dense 3D point cloud data and extracting motion information of moving objects (moving object segmentation, MOS) is crucial to autonomous driving and robotics applications. How to effectively utilize motion and semantic features and avoid information loss during 3D-to-2D projection is still a key challenge. In this paper, we propose a novel multi-view MOS model (MV-MOS) by fusing motion-semantic features from different 2D representations of point clouds. To effectively exploit complementary information, the motion branches of the proposed model combines motion features from both bird's eye view (BEV) and range view (RV) representations. In addition, a semantic branch is introduced to provide supplementary semantic features of moving objects. Finally, a Mamba module is utilized to fuse the semantic features with motion features and provide effective guidance for the motion branches. We validated the effectiveness of the proposed multi-branch fusion MOS framework via comprehensive experiments, and our proposed model outperforms existing state-of-the-art models on the SemanticKITTI benchmark. The implementation codes are available at https://***/Chengjt1999/MV-MOS. Copyright © 2024, The Authors. All rights reserved.

关键词： Robot applications

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Using a Wearable Device and Patient Reported Outcome to Evaluate the Influence of Sleep on Quality of Life Among Breast and Prostate Cancer Patients 18

Using a Wearable Device and Patient Reported Outcome to Eval...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Dhar, Eshita Barsasella, Diana Srikanth, Sruthi Panja, Ajith Kumar Malwade, Shwetambara Syed-Abdul, Shabbir Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Taipei Medical University Taipei Taiwan Health Polytechnic of Health Ministry Tasikmalaya Tasikmalaya West Java Indonesia Department of Computer Science and Engineering College of Engineering Anna University Guindy Chennai India School of Gerontology Health Management College of Nursing Taipei Medical University Taiwan

ISBN: (纸本)9781643682648

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Surveys

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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