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检索条件"机构=Electronics and Computer Engineering Technology"

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An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive Learning Methodology

An Effective Customer Segmentation Approach to Improve E-Com...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suresh, G.R. Vinoparkavi, D. Govindasamy, Suresh Rohini, S. Giri, Jayant Sabri, M. Omar Dhanalakshmi Srinivasan College of Engineering and Technology Department of Ece Mamallapuram Chennai India Nandha Engineering College Department of Cse Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India R.M.K Engineering College Department of Computer Science Engineering Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification Learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts. © 2024 IEEE.

关键词： Sales

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Resource Allocation Optimization in 5G Networks Using Variational Quantum Regressor 1

Resource Allocation Optimization in 5G Networks Using Variat...

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Pathak, Param Oad, Vidhi Prajapati, Aditya Innan, Nouhaila Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering India Vishwakarma Government Engineering College Dept. of Electronics and Communication India Dhirubhai Ambani Institute of Information and Communication Technology Dept. of Information and Communication Technology India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M'sick Hassan II University of Casablanca Morocco

ISBN: (纸本)9798350366778

Quantum Machine Learning (QML) is a powerful tool for addressing complex telecommunications challenges. As 5G technology continues to evolve, our research provides a roadmap for integrating QML into the resource allocation framework, potentially improving the efficiency and performance of 5G networks. Through the application of the Variational Quantum Regressor (VQR), our study demonstrates the potential for quantum models to enhance predictive accuracy in resource allocation scenarios. Our VQR accurately aligns predicted quantum states with their intended targets, thereby reducing discrepancies. Such precision in regression analysis, particularly in the context of 5G networks, represents a significant breakthrough. A key highlight of our algorithm is its ability to substantially reduce the mean squared error to an impressively low value of 0.0081, indicating near-perfect prediction accuracy. This innovative approach is set against the backdrop of the evolving landscape of communication networks and computational systems, where the challenge of resource allocation is increasingly pronounced due to the rising demand for bandwidth-intensive and computation-heavy services. © 2024 IEEE.

关键词： Mean square error

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Investigation of Different Techniques on Digital Actual Frameworks Toward Distributed Denial of Services Attack 15

Investigation of Different Techniques on Digital Actual Fram...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mandale, Prashant Modi, Shabina Jadhav, Makarand Mohan Khawate, Swati Shailesh Borate, Vishal Kisan Mali, Yogesh Kisan Department of Information Technology Pune India Karmaveer Bhaurao Patil College of Engineering Satara India Dept of Electronics & Telecommunication Engg NBN SinhgadTechnical Institutes Campus Pune India Ajeenkya D Y Patil School of Engineering Dept Electronics and Telecommunication Pune India Dr D Y Patil College of Engineering & Innovation Department of Computer Engineering Talegaon Pune India G.H Raisoni College of Engineering and Management Department of Artificial Intelligence Wagholi Pune India

ISBN: (纸本)9798350370249

The arising development of the Web of Things (IoT) offers different adaptability in information move processes, specialized strategies, and capacity. Distributed storage is used by limit of the populace to use their information for simple access. Then again, business utilization of distributed storage additionally expanding. The Cloud control is produced through a brought together organization. The arising development gives an approach to outsider access in normally associated networks. Digital actual frameworks(CPS) are applied in many computerized networks. The investigation of different difficulties in digital actual frameworks and their effect on information security are examined in different academic articles. The arising development of AI innovation offers solid advantages for digital actual frameworks security. The introduced concentrate on led far reaching research on difficulties in digital actual frameworks and planned an idea for future improvement. © 2024 IEEE.

关键词： Cloud security

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Multi-Sensor Data Fusion for Detection of Sensor Drifts in a Landslide Monitoring System 7

Multi-Sensor Data Fusion for Detection of Sensor Drifts in a...

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7th International Conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, electronics, computer and Telecommunications engineering, NCON 2022

作者： Smunyahirun, Nathnaree Karnjana, Jessada Laitrakun, Seksan Nishio, Takayuki Deepaisarn, Somrudee Sirindhorn International Institute of Technology School of ICT Thailand NSTDA National Electronics and Computer Technology Center Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Japan

ISBN: (纸本)9781665495103

This paper aims to extend the lifetime of polymer dielectric-based humidity sensors used in landslide monitoring systems by mathematically adjusting the sensor's drifting value as time passes. Therefore, a strategy based on nested Kalman filters, cosine similarity, and Euclidean distance was proposed in this paper. An outer Kalman filter is used top redict humidity values, with the prediction based on an inner Kalman filter that fuses data from surrounding sensors. To adjust the process noise covariance of the nested Kalman filter, cosine similarity and Euclidean distance are used. When the sensor is in normal conditions, the process noise covariance remains constant;however, when the sensor is drifting, the process noise covariance changes depending on the angle and distance between the value from surrounding sensors and the drift. The proposed method could reduce the drifting value error by 14.85 percent, according to the experimental results. © 2022 IEEE.

关键词： Kalman filters

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An Analysis of Web Security Measures for Protecting Wireless Networks from Malicious Attack 3

An Analysis of Web Security Measures for Protecting Wireless...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Balaji, N.V. Senthamilselvan, K. Dhabliya, Dharmesh Mishra, Dinesh Saxena, Manish Akhtar, Md. Amir Khusru Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Mangalayatan University Department of Computer Science Jabalpur India Himalayan University Department of Computer Science Itanagar India Usha Martin University Department of Coputing & Information Technology Ranchi India

ISBN: (纸本)9798350319125

This technical abstract is an analysis of internet security features for shielding Wi-Fi networks from malicious assaults. The main objective of this evaluation is to perceive the best security features for defensive a wireless community, and to determine which of the net protection alternatives are the best and within your means. The evaluation makes use of each quantitative and qualitative studies methods to degree different factors which includes cost, inconvenience, implementation complexity, and safety warranty. After an extensive assessment of the present net protection alternatives, its miles concluded that a combination of encryption, authentication, firewalls, access control lists, and antivirus/anti-malware software is the maximum efficient approach for preserving a wireless community secure from malicious assaults. Furthermore, the most excellent security features depend upon the size, complexity, and price range of the corporation, and ought to be tailor-made to fulfill the precise needs of the employer. © 2023 IEEE.

关键词： community corporation determine evaluation maximum

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Rice quality Prediction using Convolution Neural Network 2

Rice quality Prediction using Convolution Neural Network

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2023

作者： Rekha, V. Shiny, R.M. Ramya, J. Sakthivel, B. Pratheep, V.G. Kumar, Ashok AgurchandManmull Jain College Department of Bachelors's in Computer Application Chennai India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Thiruvallur India St. Joseph's College of Engineering Department of Computer Science Engineering Chennai India Vivekanandha College of Engineering for Women Department of Electronics and Communication Engineering Namakkal India Kongu Engineering College Department of Mechatronics Engineering Perundurai India Banasthali Vidyapith Department of Computer Science Banasthali India

ISBN: (纸本)9798350347456

Rice is the most widely grown crop in Asia. In Asia, 60% of the population relies on rice, China, Indonesia especially India consumes the majority of it. Food is the single most direct way for an individual to become infected with any ailment. Predicting the quality of rice is a crucial aspect as it is one of the most consumed foods in Asia. In this study, the quality of the rice is predicted using a Deep Learning (DL) model to predict the quality more precisely. The dataset required for this study is collected from the Kaggle website composed of a total of 5000 images of 5 different rice varieties. The collected raw dataset is then pre-processed using several methods to make them compatible with the DL model. The pre-proceed images are then subjected to the prediction of the quality of the rice using the Convolutional Neural Network (CNN), a DL model. Performance metrics such as accuracy, loss, and time are calculated to validate the precision of the model. The experimental result showed that the model has the highest accuracy of about 98.6% when compared to the other existing models. The model demonstrated a very minimal loss percentage of only about 1.24% and the model predicts the result in about 25 microseconds. The proposed DL model can be utilized for better and more objectively evaluate the quality of the rice at trading points inside the rice marketing chain due to its fairly high precision. © 2023 IEEE.

关键词： Convolutional neural networks

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Leveraging Machine Learning for Constructing Robust Automated Real-Time Data Analysis Systems

Leveraging Machine Learning for Constructing Robust Automate...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mishra, Awakash Mukuntharaj, C. Sathi, G. Dhabliya, Dharmesh Kharbas, Vikash Kumar Muthukumar, B. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Animation and Virtual Reality Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper provides a technique to leverage system-study strategies to develop robust automatic real-time information evaluation structures. In particular, it covers the usage of supervised and reinforcement learning to pressure the strong automated characteristic engineering of a massive variety of data analytics tasks, from time collection forecasting to anomaly detection. The paper additionally proposes an architecture that applies deep knowledge of algorithms to reap statistics from uncooked and aggregated statistics streams. The proposed architecture improves upon current fashions by introducing a hierarchical statistics shape to constitute the enter information, supervised gaining knowledge of strategies to initialize the version, and reinforcement learning to refine the styles based totally on the statistics move's dynamics. Checks conducted on a public dataset exhibit the effectiveness of the proposed framework. This method has potential applications across numerous domains, such as financial services, telecoms, and analytics inside the net of things. © 2023 IEEE.

关键词： Reinforcement learning

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A Novel Dynamic Watermarking for Secure Data Protection from Cyber Theft Based on Artificial Intelligence Supervision 8

A Novel Dynamic Watermarking for Secure Data Protection from...

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8th IEEE International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Madala, Ravikiran Monica, R. Malwade, Sulakshana Sagar Selvakumaran, S. Budhavale, Shilpa Jivan Sathyabalaji, N. University of Cumberlands Information Technology DurhamNC United States Nandha Engineering College Department of Electronics and Communication Engineering Tamilnadu Erode52 India Dr.Vishwanath Karad School of Polytechnic and Skill Development Department of Computer Engineering Maharashtra Pune India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Dr. Vishwanath Karad School of Polytechnic and Skill Development Department of Computer Engineering Maharashtra Pune India Jkk Munirajah College of Technology Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350347791

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security"in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence ***, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint. © 2023 IEEE.

关键词： Artificial intelligence

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Image reconstruction for the artificial compound eye based on deep learning 4

Image reconstruction for the artificial compound eye based o...

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AI and Optical Data Sciences IV 2023

作者： Lee, Jioh Kim, Cheolsun Choi, Youngin Lee, Heung-No Artificial Intelligence Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Smart Electronics Research Center Korea Electronics Technology Institute Jeollabuk-do Jeonju-si54853 Korea Republic of

ISBN: (纸本)9781510659810

The visual system of arthropods, called the compound eye, has distinctive features such as a wide field of view, high-speed motion detection, and infinite depth of field. These features have attracted researchers to build artificial compound eyes. However, the compound eye is limited in spatial resolution by its structural constraints such as the number and size of ommatidia that compose the compound eye. These constraints also can be found in the existing artificial compound eye. In previous work, a design method overcame these limitations and achieved resolution improvements by increasing the acceptance angle of ommatidia and using numerical optimization based on compressive sensing (CS). However, the limitation is that prior information such as a sparsifying basis is needed to solve the numerical optimization problem, and obtaining the solution to this problem is computationally time-consuming. In this paper, we propose a deep learning-based artificial compound eye. The deep learning architecture takes a measurement from the compound eye as input and learns how to reconstruct the original image. The experimental result demonstrates that the proposed deep learning approach provides improved performance in image reconstruction for the artificial compound eye. © 2023 SPIE.

关键词： Image reconstruction

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Predictive Analytics For Groundwater Resources Using SVM And Deep Radial Basis Approaches In Smart City Planning For Feature Extraction And Prediction 2

Predictive Analytics For Groundwater Resources Using SVM And...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Padmavathi, N. Rafee, Shaik Mohammad Ahmed, Salman Kanagavalli, N. Gangwar, Prashant Kumar Meenakshi, S. Rmd Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu Chennai India Sasi Institute of Technology & Engineering Department of Ai&ml Andhra Pradesh Tadepalligudem India Aligarh Muslim University Interdisciplinary Department of Remote Sensing & Gis Applications Uttar Pradesh Aligarh India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Woldia University Department of Construction Technology & Management Woldia Town Ethiopia Tamilnadu Kavarapettai India

ISBN: (纸本)9798350371055

The growth of smart cities is happening at the same time that there is an increasing demand for water resources. Controlling groundwater in a responsible manner is absolutely necessary in order to ensure sustainability. When it comes to groundwater forecast, traditional methods are frequently wrong, which in turn leads to inefficient use of resources and bad planning. Specifically, the research employs a two technique, which combines Support Vector Machines (SVMs) for efficient feature extraction with DRB Approaches (DRBAs) for accurate prediction. SVM is excellent at extracting significant properties from complicated datasets, whereas DRB takes use of neural network capabilities to produce variable predictions. DRB employs neural network capabilities. When compared to methods that are considered to be more conventional, the findings indicate a significant improvement in the accuracy of this prediction. It is exciting to see that the combined support vector machine and DRB model are producing positive results when it comes to estimating groundwater resources. © 2024 IEEE.

关键词： Deep Radial Basis Groundwater Resources Predictive Analytics Smart City SVM

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