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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Neetu Srivastava, Animesh Barik, Lal Bihari Dhondiyal, Shiv Ashish College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350329186

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks. © 2023 IEEE.

关键词： Authentication

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Comparative Analysis of Machine Learning Algorithms for Medical Insurance Cost Prediction 6th

Comparative Analysis of Machine Learning Algorithms for Medi...

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Praveen, Mahadasa Manikanta, Gundu Sri Gayathri, Gella Mehrotra, Shashi Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Faculty of Engineering College of Computing Science and IT Teerthankar Mahaveer University Moradabad India

ISBN: (纸本)9789819933143

In the health industry, predicting medical insurance costs remains an issue. These days medical prices are very high, making it very difficult to pay for good treatment. The medical insurance cost amount must be included in financial budgets. Experts use various approaches to anticipate annual medical premium costs in the insurance industry. Usually, the erroneous prediction has a negative impact on a business's success as a whole. The paper presents a comparative analysis of machine learning techniques, multiple linear regression, random forest, and XGBoost for predicting health insurance prices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Intelligent Predictive Model for Hepatitis C 3

Intelligent Predictive Model for Hepatitis C

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Shahzadi, Mehreen Bukhari, Faisal Shafi, Numan University of the Punjab Faculty of Computing & Information Technology Department of Data Science Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350322125

Hepatitis C is the liver's festering that can lead to severe liver damage, usually caused by the hepatitis C virus. Hepatitis C has different stages. It is tough to cure in it's last stages;at the same time, it is expensive and painful process. The current research, however, is an alternative precaution to this issue. Hepatitis C can be predicted early by using multiple factors. The dataset related to hepatitis C was not publicly available. To overcome this challenge, the healthy and HCV effected samples were collected from different hospitals in Punjab. A questionnaire based survey was taken including different HCV related factor i.e. gender, weight loss, hives/ rashes, swelling, jaundice, drug addiction history, hepatic encephalopa-thy (drowsiness, slurred speech), Ascites (fluid buildup in belly/ abdomen), spider angiomas (Spiderlike blood vessels), shared syringe usage, medical history, and severeness. Different cleaning, scaling, and feature selection techniques were applied to collect the best feature data. After selection, various machine learning algorithms were applied. Random forest, KNN, Decision Tree, SVC, and MLP were used, but MLP yielded optimal results in all classification algorithms. We have gained 95.9 % accuracy when tested on unknown data based on the MLP model. As the predictions' results were satisfactory, it would be helpful for the people and act as a critical awareness. © 2023 IEEE.

关键词： Decision trees

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear Systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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Success Factors of Requirement Elicitation in the Field of Software engineering 4

Success Factors of Requirement Elicitation in the Field of S...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Attanayaka, Buddhima Nawinna, Dasuni Manathunga, Kalpani Abeygunawardhana, Pradeep K.W. Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors. © 2022 IEEE.

关键词： Requirements engineering

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Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain 5

Secure Goods Storage and Anti-Theft Approach using Ethereum ...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy 12

Algorithmic Trading Model for Stock Price Forecasting Integr...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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