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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： E. Gothai R.R. Rajalaxmi R. Thamilselvan Sridhar K Department of Computer Technology Kongu Engineering College Perundurai Tamilnadu India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%.

关键词： Support vector machines Renewable energy sources Accuracy Machine learning algorithms Supervised learning Artificial neural networks Nearest neighbor methods Brain modeling Vectors Biological neural networks

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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Computer systems Science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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Automated Identification of Security-Relevant Configuration Settings Using NLP 22

Automated Identification of Security-Relevant Configuration ...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Patrick Stöckle Theresa Wasserer Bernd Grobauer Alexander Pretschner School of Computation Information and Technology / Chair of Software and Systems Engineering Technical University of Munich Germany T CST Siemens AG Germany

ISBN: (纸本)9781450394758

To secure computer infrastructure, we need to configure all security-relevant settings. We need security experts to identify security-relevant settings, but this process is time-consuming and expensive. Our proposed solution uses state-of-the-art natural language processing to classify settings as security-relevant based on their description. Our evaluation shows that our trained classifiers do not perform well enough to replace the human security experts but can help them classify the settings. By publishing our labeled data sets and the code of our trained model, we want to help security experts analyze configuration settings and enable further research in this area.

关键词： Security Configuration Hardening Natural Language Processing

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Baby Bump: A Monitoring System for Pregnant Mothers and Babies

Baby Bump: A Monitoring System for Pregnant Mothers and Babi...

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International Conference for Convergence of technology (I2CT)

作者： Dhanuka Balasooriya Sasini Perera Dinuka R. Wijendra Sahani Rathnayaka Rajendra Kishan Karthiga Rajendran Markandu Thirukumar Department of Computer Systems and Software Engineering Sri Lankan Institute of Information Technology Malabe Sri Lanka Department of Obstetrics & Gynaecology Teaching Hospital Batticaloa Sri Lanka

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In contemporary times, nations like Sri Lanka are actively enhancing their efforts to improve the life expectancy of their citizens, with a strong focus on public health. The relationship between health and life expectancy is pivotal. Poor health conditions tend to diminish life expectancy, while robust health measures tend to extend it. Within the context of health, birth and death events are of paramount importance. Birth represents the genesis of life, enabling individuals to contribute to future generations and the overall population's expansion. It's the cornerstone of life on our planet. However, modern times have witnessed challenges in pregnancy and childbirth, with some infants not surviving the prenatal period or being born with health complications. Consequently, there is a pressing need to present a solution that can contribute to the betterment of maternal and infant health, ultimately augmenting Sri Lanka's life expectancy. The proposed solution is the development of a mobile application, encompassing features such as a Nutrition Predictor, Medicine Effect Predictor, AI chatbot, and Baby Status Predictor. This innovative application aims to address the complex issues surrounding pregnancy and childbirth, with the overarching goal of improving the nation's life expectancy.

关键词： Pregnancy Pediatrics Planets Pressing Extraterrestrial measurements Chatbots Mobile applications

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UAV navigation in high dynamic environments:A deep reinforcement learning approach

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Chinese Journal of Aeronautics 2021年第2期34卷 479-489页

作者： Tong GUO Nan JIANG Biyue LI Xi ZHU Ya WANG Wenbo DU School of Electronic and Information Engineering Beihang UniversityBejing 100083China Key Laboratory of Advanced Technology of Near Space Information System(Beihang University) Ministry of Industry and Information Technology of ChinaBeijing 100083China Research Institute of Frontier Science Beihang UniversityBeijing 100083China College of Software Beihang UniversityBeijing 100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China

Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning(DRL) methods have shown promise for addressing the UAV navigation problem,but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving *** this work, we propose an improved DRL-based method to tackle these fundamental *** be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory(LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.

关键词： Autonomous vehicles Deep learning Motion planning Navigation Reinforcement learning Unmanned Aerial Vehicle(UAV)

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KRATT: QBF-Assisted Removal and Structural Analysis Attack Against Logic Locking

KRATT: QBF-Assisted Removal and Structural Analysis Attack A...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Levent Aksoy Muhammad Yasin Samuel Pagliarini Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Computer and Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

This paper introduces KRATT, a removal and structural analysis attack against state-of-the-art logic locking techniques, such as single and double flip locking techniques (SFLTs and DFLTs). KRATT utilizes powerful quantified Boolean formulas (QBFs), which have not found widespread use in hardware security, to find the secret key of SFLTs for the first time. It can handle locked circuits under both oracle-less (OL) and oracle-guided (OG) threat models. It modifies the locked circuit and uses a prominent OL attack to make a strong guess under the OL threat model. It uses a structural analysis technique to identify promising protected input patterns and explores them using the oracle under the OG model. Experimental results on ISCAS'85, ITC'99, and HeLLO: CTF'22 benchmarks show that KRATT can break SFLTs using a QBF formulation in less than a minute, can decipher a large number of key inputs of SFLTs and DFLTs with high accuracy under the OL threat model, and can easily find the secret key of DFLTs under the OG threat model. It is shown that KRATT outperforms publicly available OL and OG attacks in terms of solution quality and run-time.

关键词： Threat modeling Analytical models Hardware security Circuits Benchmark testing Complexity theory Integrated circuit modeling

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Data-informativity conditions for structured linear systems with implications for dynamic networks

arXiv

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arXiv 2024年

作者： Van den Hof, Paul M.J. Shi, Shengling Fonken, Stefanie J.M. Ramaswamy, Karthik R. Hjalmarsson, Håkan Dankers, Arne G. Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Chemical Engineering Massachusetts Institute of Technology United States ASML Veldhoven Netherlands Division of Decision and Control Systems Royal Institute of Technology Stockholm Sweden Department of Electrical and Software Engineering University of Calgary Canada

When estimating models of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of frequencies. In this paper it is investigated how such a condition can be relaxed by exploiting prior structural information on the multivariable system, such as structural zero elements in the transfer matrix or entries that are a priori known and therefore not parametrized. It is shown that in particular situations the data-informativity condition can be decomposed into different MISO (multiple input single output) situations, leading to relaxed conditions for the MIMO (multiple input multiple output) model. When estimating a single module in a linear dynamic network, the data-informativity conditions can generically be formulated as path-based conditions on the graph of the network. The new relaxed conditions for data-informativity will then also lead to relaxed path-based conditions on the network graph. Additionally the new expressions are shown to be closely related to earlier derived conditions for (generic) single module identifiability. © 2024, CC BY-NC-ND.

关键词： MIMO systems

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Deep Learning Based Detection of Cardiovascular Diseases Using ECG Images

Deep Learning Based Detection of Cardiovascular Diseases Usi...

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International Conference on Communication and Electronics systems (ICCES)

作者： P Ananthi K Nirmala Devi Pradisa. T Department of Computer Technology Kongu Engineering College Perundurai TamilNadu Department of Computer Science(PG) Kristu Jayanti College Bangaluru MSc Software Systems Kongu Engineering College Perundurai TamilNadu

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

In today's world, cardiovascular disease is still a major concern. The electrocardiogram (ECG) is one of the most trustworthy non-invasive tools for identifying heart problems. ECG interpretation, however, takes a lot of effort and specialized knowledge. The quality and effectiveness of medical care are enhanced by the development of a revolutionary technique for early disease detection. Using a public dataset of ECG scans from cardiac patients, this study leveraged deep learning algorithms to predict two primary cardiac abnormalities: abnormal heartbeat and normal person classes. The low-scale pre-trained deep neural Alex Net was used to study the transfer learning process. To forecast cardiac issues, a novel Convolutional Neural Network (CNN) architecture was put out. We used the conventional deep learning methods, VGG-16, Resnet50, RNN, and LSTM, to feature-extracted data utilizing the previously mentioned pre-trained models and our suggested LSTM model. The experimental results show that the LSTM model outperforms the previous attempts in terms of performance measures.

关键词： Deep learning Transfer learning Medical services Electrocardiography Prediction algorithms Data models Convolutional neural networks Cardiovascular diseases Long short term memory Residual neural networks

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Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Sumayya Babangida-Sabo Samuel C. Avemaria Utulu Abel Ajibesin Department of Informaton Systems School of Informtaon Technology and Computing American University of Nigeria Yola Nigeria Department of Software Engineering School of Information Technology and Computing Amerucan University of Nigeria Yola Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria’s attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR.

关键词： Ethics Data security Standards organizations Data protection Collaboration Organizations Production

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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