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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

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Business Intelligence: Security and Privacy-Related Concerns with Cloud Computing

Business Intelligence: Security and Privacy-Related Concerns...

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2024 international conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Hamid, Nagham Al-Tahee, Mustafa Al-Hameed, Mazin Riyadh Ahmed, Wasan Saad Fouad, Rami Al-Chilibi, Haider Hasson, Ahmed Rasol Habelalmateen, Mohammed I. College of Business Informatics University of Information Technology and Communications Baghdad Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al Mustaqbal University Babil Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Internet technology is used in Business Intelligence (BI) and continues to provide centralized control assistance with knowledge from many structured and unstructured information sources. Business intelligence's interrelated limits are complicated, pricey, and restrictive. Resources from cloud computing with cost-effective performance and scalability can be used to upgrade existing BI systems and applications to address these issues. Due to rigorous confidentiality, reliability, interoperability requirements, and many dependencies with the other applications, BI systems are often barred from migrating to cloud applications. The paper addresses the obstacles to implementing BI within a cloud infrastructure. It provides the Security and Privacy-related Concerns in Business Intelligence (SPC-BI) framework, which assists decision-makers in considering the impact and privacy, security, costs, and effectiveness of BI structure migrating to network infrastructure. © 2024 IEEE.

关键词： Cloud security

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Enhancing Supply Chain Efficiency with IoT and Data Analytics

Enhancing Supply Chain Efficiency with IoT and Data Analytic...

引用

2024 IEEE international conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Jayalakshmi, D. Vimal, V.R. Loganayagi, S. Narayanan, Lakshmi Kanthan Hemavathi, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

This study aims at exploring the use of internet of things technology and data analytics in the field of supply chain management. Using a range of software tools, including Apache Kafka, Apache NiFi, Python libraries such as Pandas, and Scikit-learn, the study accumulated, assimilated, and quantified data from IoT sensors, supply chain systems, and qualitative interviews. The primary data sources are primary supply chain data history and IoT sensor data while secondary data includes academic and industrial journals, and reports. This paper presents a number of basic quantitative measures showing the general performance indicators: the average percentage of order fulfilment as 92.5% and the inventory turnover ratio equalling 6.2. When using linear regression, random forest, and LSTM neural network, the models reveal the mean absolute errors of demand forecasting between 80 and 120 units. The results suggest that IoT and data analysis offer significant opportunities to create value through the integration of SC, cost cutting, and higher customer satisfaction. It has now become essential for organizations to effectively apply data trends to enable competitive gains within the complex business setting to foster operational efficacy and stability. © 2024 IEEE.

关键词： Customer satisfaction

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Combining Fuzzy Logic and Reinforcement Learning for Resource Management in Edge Computing 5

Combining Fuzzy Logic and Reinforcement Learning for Resourc...

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Prasath, S. Ram Selvi, M. Krishnan, R. Santhana Nithila, Ezhil E. Malar, P. Stella Rose Sankar, G. Ram Scad College of Engineering and Technology Computer Science and Engineering Cheranmahadevi India Rajarajeswari College of Engineering Computer Science and Engineering Bangalore India Scad College of Engineering and Technology Electronics and Communication Engineering Chranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Electronics and Communication Engineering Chennai Avadi India J.P. College of Engineering Electronics and Communication Engineering Thenkasi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350379945

With the wide spread of Internet of Things (loT) devices, edge computing plays a decisive part in meeting the computational demands of loT applications. Efficient resource allocation in such networks is essential to ensure optimal utilization of resources and meet the quality of service (QoS) necessities of loT applications. This research addresses the challenge of dynamic resource allocation in edge computing networks for loT applications through a novel hybrid AI approach, 'FledegeRL.' By integrating fuzzy logic and reinforcement learning, FledegeRL aims to optimize resource utilization and enhance system performance. The proposed framework is evaluated through simulation analysis and compared with existing algorithms, including Q-Iearning, Fuzzy Logic Control, and Ant Colony Optimization, using metrics such as resource allocation accuracy, average throughput, and network overhead. The results demonstrate that FledegeRL consistently outperforms these algorithms, achieving higher accuracy, greater throughput, and lower network overhead. This highlights the potential of FledegeRL as an effective solution for dynamic resource allocation in edge computing environments. © 2024 IEEE.

关键词： Quality of service

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Empirical Studies of Resampling Strategies in Noisy Evolutionary multi-Objective Optimization

Empirical Studies of Resampling Strategies in Noisy Evolutio...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Shasha Li, Ke College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350337020

Optimization problems are ubiquitous in real-world engineering scenarios where the goals are to enhance interested aspects such as efficiency, productivity, and profitability. However, solving practical optimization problems could be non-trivial, partly due to the presence of a wide range of noises, including environmental noises, model biases, time-domain variations, measurement uncertainties and many other uncontrolled variables. In this paper, we empirically study the effect of noise range, sample size and resampling type on the solution quality of MOEAs when noise is added to decision variables. Our empirical results, conducted on three commonly used multi-Objective Optimization Problems (MOEAs), i.e. NSGA-II, MOEA/D and IBEA, demonstrate that noise range has more significant impact on the robustness of optimization algorithms compared to sample size and resampling type. In addition, we introduce the concept of bad point, which is able to illustrate how noise affects the performance of different MOEAs. © 2023 IEEE.

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Hybrid Particle Swarm Optimization Algorithm with Deep Learning for Hyperspectral Images in Crops 2

Hybrid Particle Swarm Optimization Algorithm with Deep Learn...

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2nd IEEE international conference on Data science and Network Security, ICDSNS 2024

作者： Natarajan, Vignesh Prasanna Adnan, Myasar Mundher Shalini, G. Chanti, Yerrolla Shanmugam, Sathiya Priya Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India Panimalar Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350373110

Mapping is a crucial part of using remote sensing to monitor agricultural resources. Crop maps provide essential baseline data in image categorization for a range of agricultural monitoring and decision support applications. For this reason, the research employs a Hybrid Particle Swarm Optimization-Deep Learning-Driven Crop Type Classification technique (HPSODL-CTC) in Hyper Spectral Image (HSI). The proposed HPSODL-CTC objective is to classify the different crop types on HSI. Initially, the suggested model preprocesses images to increase quality of images. Furthermore, the model under description builds a dilated Convolutional Neural Network (CNN) for extracting the features. The dilated CNN algorithm for hyperparameters are adjusted using the HPSO approach. Furthermore, the HPSODL-CTC model that is being described Extreme Learning Machine (ELM) method for crop categorization. The results showed that the proposed method performed better in terms of accuracy of (99.81%), precision of (99.63%), F1-measure of (99.71%), and recall of (99.86%) than the existing models, such as multi-Objective Binary Chimp Optimization Algorithm (MOBChOA), multi-Objective Binary Grey Wolf Optimizer (MOBGWO), and Adaptive Fick's Law technique - SCNN (AFLA-SCNN). © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A novel compact Chipless RFID Tag based on notched antenna 12

A novel compact Chipless RFID Tag based on notched antenna

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12th IEEE international conference on computer science and Network Technology, ICCSNT 2024

作者： Zhang, Jufang Wang, Zipu Xi'An University of Technology Faculty of Printing Packing Engineering and Digital Media Technology Xi'an China Northwestern Polytechnical University School of Materials Science and Engineering Xi'an China

ISBN: (纸本)9798350350791

This paper presents a novel compact chipless radio frequency identification (RFID) tag using dual mode loop resonator (DMLR), open-loop resonator (OLR) and dual band-notched UWB antennas. 3N codes can be obtained by coupling N resonators of this kind with the microstrip. Through introducing the two band-notches on the UWB antenna, two more code bits can be performed without increasing the size of tag. Band notches on the antennas are designed in the lower frequency for high space efficiency. The simulated results are given and discussed. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Design and Testing of Unmanned Surface Vehicle with Water Sampling Mechanism for measuring Water Quality

Design and Testing of Unmanned Surface Vehicle with Water Sa...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sai Shiva Tej, T. Vishnu, N. Pal, Joyita Krupa, A Jaba Deva Monika, R. Dhanalakshmi, Samiappan Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulatur603023 India

ISBN: (纸本)9798331529833

This paper describes about the electronics system architecture of an unmanned surface vehicle (USV), which can gather data on water bodies such as lakes,ponds,rivers and *** USV robot was created to measure water contamination and assist in enhancing the techniques now employed for this purpose.a multiparameter probe or container which holds water is used to measure conductivity, turbidity, pH, and temperature in relation to water quality. The vehicle's primary functions might be mapping newly discovered water bodies, measuring the pollution levels in the water, and routinely monitoring existing water bodies. The vehicle makes use of information obtained from taking water samples in the bodies of water. This information is useful for a variety of tasks, and mapping the water bodies can be accomplished through communication between several USVs. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Concurrent and Learned Data Structures 25

Concurrent and Learned Data Structures

引用

26th international conference on Distributed Computing and Networking, ICDCN 2025

作者： Chatterjee, Bapi Peri, Sathya Department of Computer Science and Engineering IIIT Delhi Delhi India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798400710629

We explore the landscape of modern data structures that employ two powerful scaling mechanisms simultaneously: (a) concurrency to harness the power of today's multicore processors, and (b) learned queries - the operations employing machine learning inference by models trained on the underlying data. We focus on advancements, challenges, and potential future directions in this field. We delve into the motivation for incorporating learned queries in concurrent data structures. We discuss the key techniques, applications, and open research questions. On the theoretical side, we discuss the mathematical foundations for building the learned data structures in addition to the correctness of queries in a concurrent setting. On the practical side, we present an overview of techniques used for managing concurrency with learned queries that need training the models with concurrent real-time updates. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Customer Propensity Prediction in E-Commerce Loyalty Program 9

Customer Propensity Prediction in E-Commerce Loyalty Program

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9th international conference on computer science and engineering, UBMK 2024

作者： Gundogmus, Yunus Emre Kececi, Sinan Erdem, Ege Rencberoglu, Emre Growth Data Science Hepsiburada Istanbul Turkey

ISBN: (纸本)9798350365887

This paper presents a model that has been developed to predict customers';propensity to subscribe to Hepsiburada';s loyalty program, Hepsiburada Premium. Hepsiburada, a leading e-commerce platform in Turkey, aims to enhance customer loyalty by offering various benefits to its customers. In this model, data obtained from Hepsiburada';s active customers is being used to predict the likelihood of subscribing to the Premium program. This model considers demographic characteristics, shopping history, customers';website behavior and specific features of the Premium program. Based on predictions obtained using the XGBoost algorithm, different campaigns are offered to customers. This model demonstrates that machine learning can be an effective tool for identifying potential customers for loyaltyprograms and campaigns. © 2024 IEEE.

关键词： Sales

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A multi-Agent Architecture for Privacy-Preserving Natural Language Interaction with FHIR-Based Electronic Health Records 32

A Multi-Agent Architecture for Privacy-Preserving Natural La...

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32nd international conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： De Maio, Carmen Fenza, Giuseppe Furno, Domenico Grauso, Teodoro Loia, Vincenzo University of Salerno Dept. Computer Science Salerno Italy University of Salerno Dept. Management and Innovation Systems Salerno Italy

ISBN: (纸本)9789532901382

Integrating Electronic Health Records (EHRs) into clinical workflows is essential for enhancing healthcare delivery but poses significant challenges, such as improving human-machine interaction through natural language queries. This paper addresses these challenges by leveraging Large Language Models (LLMs) within a multi-agent architecture. The aim is to develop a privacy-preserving method enabling clinicians to interact with FHIR-based EHRs using natural language. The novelty of the proposed technique lies in using publicly available LLMs to construct URIs for retrieving FHIR resources and a local LLM to interpret these resources, thus safeguarding patient privacy by preventing direct exposure of sensitive data. Evaluated with the SyntheticMass dataset, the multi-agent system demonstrated superior accuracy and detail over a single-model approach while maintaining competitive response times. © 2024 University of Split, FESB.

关键词： Electronic health record

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