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Role of Big data and Machine learning on Marketing and Digital Transformation 3

Role of Big Data and Machine Learning on Marketing and Digit...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mittal, Shikha Kumar, K. Suresh Thakur, Gaurav Naredla, Suman Ibrahim, Waleed Khalid Alazzam, Malik Bader Rajkumar Goel Institute of Technology Department of Management Ghaziabad India Panimalar Engineering College Mba Department Chennai600123 India Uttaranchal Institute of Technology Uttaranchal University India School of Business Sr University Telangana Warangal India Medical Technical College Al-Farahidi University Baghdad10021 Iraq Information Technology College Ajloun National University Jordan

ISBN: (纸本)9798350343274

The article discusses the impact of big data and machine learning (ML) on the information technology of the marketing industry as well as the challenges it faces with regard to management of information and data. The analysis found aspects of the sector's digital transformation that have not already been completely covered by consensus academic studies in order to achieve this. Papers spanning five databases have been obtained. Website of Science, Scopus, Research-gate, Emerald, and ProQuest are some examples of collections. 69 papers were chosen for this reviewed literature after 214 articles were screened. Five topics that are highly applicable to the technological age in marketing were highlighted as study gaps in the current literature. Five other prospective areas of further studies in the use of big data and ML-driven solutions in the marketing sector were also suggested. By highlighting areas that require actual investigations, systematisation, follow-up research, affirmation, or the collection of experimental observations, an exploration of the potentials of digitization in branding could be helpful to university scholars as well as practitioner's alike working in the fields of information technologies (IT), information systems (IS), business, & advertising. © 2023 IEEE.

关键词： Big data

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Research on Computer Information System Based on Ant Colony intelligent Neural Network

Research on Computer Information System Based on Ant Colony ...

引用

Electrical engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Xinxin Zhang College of Energy and Mining Engineering Shandong University of Science and Technology Qingdao China

In this project, ant algorithm and neural network technology are organically integrated to study a new fault diagnosis method. Then the ant algorithm is used to optimize the weight of the neural network, and the optimal rule set is used to train the neural network to obtain the fault intelligent diagnosis model. The model is applied to an example, which proves that the algorithm is feasible.

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Particle Swarm Optimization for Efficient data Dissemination in VANETs 1

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3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Kumar, Arvind Dixit, Prashant Tyagi, S.S. Manav Rachna International Institute of Research and Studies Faridabad India IIMT College of Engineering Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Vehicular Ad-Hoc Networks (VANETs) are wireless networks that use vehicles as nodes, and they have become increasingly important due to growing concerns about urban traffic congestion. Reliable and safe data dissemination in VANET networks requires effective optimization methods that balance competing performance metrics. The meta-heuristic technique known as Particle Swarm Optimization (PSO) can achieve efficient optimization by creating multiple paths through Time delay-based Multipath Routing (TMR) and finding the best routes. In a recent study, PSO was compared to other optimization methods, including Ant Colony Optimization (ACO) and Firefly Optimization (FFO), and demonstrated considerable enhancements in various metrics, including increased throughput, decreased packet-loss ratio, reduced end to end delay, and lower routing overhead-ratio. These results highlights potential of PSO-based approaches to improve data dissemination in VANETs and address the challenges of urban traffic congestion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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An Efficiency way to analyse Diabetic Retinopathy Detection and Classification using Deep learning Techniques 3

An Efficiency way to analyse Diabetic Retinopathy Detection ...

引用

3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Reddy, K Sudhakar Narayanan, M. Malla Reddy University School of Engineering Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350399264

Lesions develop on the retina of the eyes as a result of the progressive eye condition known as diabetic retinopathy (DR), which is brought on by type-2 diabetes. In especially for the working-age population in sustainable nations, it is thought that Diabetes Retinopathy is the main cause of blindness in diabetes patients. The aim of the treatment appears to be to maintain the patient's degree of eyesight because the issue is chronic. Diabetic retinopathy must be accurately identified in order to fully protect the patient's vision. The major issue with DR detection is manual diagnosis, which is time-consuming, expensive, and labor-intensive. A retinal scan of the patient's eyes must also be evaluated by an ophthalmologist as part of the treatment. The latter also appears to be more challenging, particularly in the early phases of the ailment when sickness indications are less obvious in the photos. Early detection of diabetic retinopathy has become easier because of deep learning algorithms, and images of the retinal fundus (DR) may now be analysed using machine learning. There are various stages of diabetic retinopathy, and the early stages are symptomless. Ophthalmologists can spot some retinal issues, but they can't always determine their root causes or stages of development. Ophthalmologists advise retina specialists to treat disease as a result. Bayesian neural networks (BNNs) had been used to benchmark the binary categorization of diabetic retinopathy as referable or non-referable in the current system. We suggest developing a Convolution neural network (CNN) and data analysis method to categorise diabetic retinopathy based on clinical data, predicting whether the patient is diabetic or not and identifying its stage with estimation, employing measurements are needed to maximise the intended performance measure with different datasets and clinical lesion images. © 2023 IEEE.

关键词： Recurrent neural networks

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Transformative Storage Framework for 6G Smart City Deployments 3

Transformative Storage Framework for 6G Smart City Deploymen...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Maheswaran, N. Bose, S. Shruthi, T. Logeswari, G. Anitha, T. Prabhu, D. Anna University Department of Cse Ceg Chennai India Ramco Institute of Technology Department of Cse Rajapalayam India VIT-Chennai School of Computer Science Engineering India Sathyabama Institute of Science Technology Department of Cse Chennai India Anna University Regional Campus Department of Cse Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

The advent of 6G networks is poised to revolutionize the digital landscape by offering unprecedented data transfer speeds, ultra-low latency, and enhanced connectivity capabilities. This paradigm shift necessitates the development of robust and efficient storage solutions to handle the massive influx of data generated by ubiquitous IoT devices, immersive applications, and intelligent systems. In this context, we propose a multilevel storage Algorithm (LRU + OPTIMAL) designed specifically for the 6G networking environment in smart cities. Our algorithm synergizes the Last Recently Used (LRU) and Optimal algorithms to dynamically manage storage resources, thereby ensuring optimal data retrieval and minimizing latency. The deployment of 6G networks introduces complex data storage challenges, driven by the exponential growth of data traffic and the real-time processing is required. By integrating LRU and Optimal algorithms, our multi-level storage framework can effectively prioritize data caching and retrieval operations, thereby enhancing system efficiency and responsiveness. This approach leverages the predictive capabilities of the optimal algorithm to anticipate future data needs, while the LRU algorithm ensures that frequently accessed data remains readily available. Compared to 5G networks, 6G offers significantly higher bandwidth, reduced latency, and increased device density, all of which exacerbate storage demands. Our algorithm addresses these challenges by optimizing storage utilization and ensuring seamless data flow across distributed network nodes. The comparative analysis underscores the superiority of 6G in handling complex, data-intensive applications, thus necessitating advanced storage solutions such as our proposed algorithm. In the Multi-Level Storage Algorithm (LRU + OPTIMAL), a scalable and efficient framework for managing optimal storage space in 6G networks, ensuring that the storage infrastructure can keep pace with the rapid advancements in 6G

关键词： 5G mobile communication systems

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Improving Sign Language Recognition for Deaf and Hard of Hearing Individuals using the VGG16 Model 3

Improving Sign Language Recognition for Deaf and Hard of Hea...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Benjamin, Minu Inba Shanthini Watson Patan, Yaseen Kumar, Ansilin S.M. Reddy, Pocharam Nithyanandh Ahamad, Syed Faheez Puneeth, Muvvala Sai Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331532420

Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greater research to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. Recognizing sign language using VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach 3

Interpreting the Evidence on Life Cycle to Improve Education...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Madumidha, S. Tripathi, Anju Srinivasan, P. Wategaonkar, Somnath Raghunath Chauhan, Amit Suganthi, D. Sri Krishna College of Technology Department of Information Technology Coimbatore India Mangalmay Institute of Management and Technology Dehi NCR Gr.Noida India Sona College of Technology Department of Ece Tamilnadu Salem India Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Navi Mumbai India School of Sciences Department of Life Sciences Karnataka Bengaluru India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India

ISBN: (纸本)9798350381931

Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. © 2024 IEEE.

关键词： Feature extraction

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Enhancing the Detection of Botnet Attacks in the Internet of Things Networks Through the Utilization of Hybrid Feature Selection 3

Enhancing the Detection of Botnet Attacks in the Internet of...

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3rd FORTEI-international conference on Electrical engineering, FORTEI-ICEE 2024

作者： Kurniawan, Hartanto Samsudiat Nugroho, Cahyono Saputra, Andri Nursyahbani, Aditya Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Jakarta Indonesia Center of Data and Information National Research and Innovation Agency Jakarta Indonesia

ISBN: (纸本)9798331542207

The proliferation of Internet of Things (IoT) technology is leading to an escalation in cyber risks. The Intrusion Detection System (IDS) is crucial in countering cyber threats, with botnets being the most commonly detected attack in IoT networks. Anomaly-based IDS is necessary to enhance their capability in identifying cyber threat attributes through the use of supervised machine learning. This work investigates an enhanced supervised machine learning algorithm technique that combines filter-based feature selection methods, specifically Low Variance and Pearson Correlation Coefficient, to detect a new type of botnet attack. The CICIoT2023 dataset, an updated benchmark intrusion dataset specifically designed for IoT networks, is employed to assess the model’s performance using several algorithms including Decision Tree, Random Forest, Logistic Regression, and Naive Bayes. The results demonstrate that the model effectively enhances the identification of botnet attacks by reducing the number of features from 46 to 14. This optimization leads to improved performance, including increased accuracy level and reduced computation time. © 2024 IEEE.

关键词： Bot (Internet)

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 1

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3rd international conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9783031876479

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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A Study of Various Word Embeddings in Deep learning 3

A Study of Various Word Embeddings in Deep Learning

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3rd international conference for Emerging Technology, INCET 2022

作者： Shah, Parth Shah, Smit Joshi, Saurav K.J. Somaiya College of Engineering Computer Engineering Mumbai India

ISBN: (纸本)9781665494991

With the number of users and reviews rising on the internet, and the liberty was given to post anything on it, it is still a major issue to identify the sentiment of the reviews. The traditional machine learning models are taking a lot of computational time to get desired results. The advancement in the field of deep learning has left people wondering whether it would provide better results for such applications. Word Embeddings play a huge role in the classification of text, as they represent similar meaning documents with equivalent representation. The result of the deep learning model trained on a particular set of data depends upon the sort of word embedding used. In this work, we intend to present a study of various embeddings like GloVe, BERT, Word2vec, and Fast-text using a proposed model. The research was conducted on the IMDB movie review dataset and the result displayed that GloVe embeddings produced the best result with an accuracy of 87.936 % on our proposed model. © 2022 IEEE.

关键词： Embeddings

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