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Understanding the Journey to 6G Development

Understanding the Journey to 6G Development

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agrawal, Trapty Kumar, Ajit Gowrishankar, J. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The fifth-generation (5G) mobile communication technology is now being widely deployed in a number of nations, along with a significant rise in the number of 5G customers. However, it is becoming more and more clear that it is time for both academics and business to shift their emphasis towards the next generation as the mobile telecommunications market continues to change. At this point, it is crucial to provide a thorough review of the state of the art and look forward to the future of communications. This article tries to achieve this goal by providing a thorough analysis designed to outline the sixth generation (6G) system's characteristics. The motivations, use cases, use scenarios, needs, key performance indicators (KPIs), architecture, and enabling technologies that will characterize 6G are all covered in this investigation. © 2023 IEEE.

关键词： 6G AI architecture disruptive services enabling technologies generation Key Performance Indicators (KPIs) latency mobile communication mobile traffic mobility network efficiency peak data rate performance indicators technology terahertz communications use cases user-experienced data rate

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The Innovative Cloud Security Environment for Data Privacy

The Innovative Cloud Security Environment for Data Privacy

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Divya Rani, M.S. Rajesh, A. Agarwal, Ankita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

The use of online computing has grown quickly across a range of institutions and businesses. The storage of information in the clouds, which enables individuals and businesses to store their personal information largely in the cloud and not on local computers, is one of the main benefits of cloud computing. Safety of data maintained in the cloud is still a major worry, however. We provide a novel security architecture in this study that is intended to improve data confidentiality and safety. Data is separated into blocks of two bytes each, and each multi-byte bit sequence is subjected to a separate evolutionary process in the revised security framework. The end product of each genetic algorithm's operation is ciphertext, which is made up of two different sets of bits. The ciphertext for these encrypted messages is difficult to find since it is stored in many places across the cloud. Additionally, the newly developed safety architecture uses algorithms that evolve on fewer data blocks to improve data security. A capability list is also used to guarantee granular and secure access to information. © 2023 IEEE.

关键词： access control Cloud security cloud service providers (CSPs) data confidentiality data encryption data privacy data protection Genetic Algorithm (GA) Internet of Things (IoT) security architecture

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Managing Cloud Networks: A Classification of SDN-Enabled Clouds

Managing Cloud Networks: A Classification of SDN-Enabled Clo...

引用

2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Singh, Vaishali Kehkeshan Jallal, S. Das, Sunanda Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study. © 2023 IEEE.

关键词： application framework cloud computing hardware resources programmable controller resource provisioning simulation and energy modeling Software-Defined Networking (SDN)

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques 3

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

引用

3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Shreenidhi, H.S. Yadav, Dhyan Chandra Swetha, G. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka562112 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behaviour Neural Network (CBNN), Retention Decision Trees (RDT), and Long Short-Term Memory for Retention (LSTM-R). The objective is to model intricate relationships within consumer behaviours and accurately predict their retention probabilities. CBNN a key component, adeptly captures intricate patterns within consumer behaviour data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. The flowchart for CBNN showcases the training process involving forward and backward passes, underlining its deep learning approach. RDT this method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behaviour, as depicted in the flowchart. Information gain is maximized to create a robust predictive model. LSTM-R tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance. © 2023 IEEE.

关键词： Decision trees

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Utilizing Cutting-Edge Machine Vision Techniques for Swift Anomaly Detection in Suboptimal Surveillance Environments 3

Utilizing Cutting-Edge Machine Vision Techniques for Swift A...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Yadav, Rakesh Kumar Veena, C.S. Sudhamsu, Gadug Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In the realm of surveillance and anomaly detection, the proposed Adaptive Multimodal Anomaly Detection (AMAD) method stands as an innovative and potent approach. The objective of this method is to detect anomalies swiftly and accurately in suboptimal surveillance environments by amalgamating state-of-the-art machine vision techniques with multimodal data fusion. The core idea revolves around leveraging advanced deep learning architectures and statistical models, thereby enhancing the performance of anomaly detection systems. The AMAD method initiates with Algorithm 1: Multimodal Data Fusion, a process that involves integrating multimodal data streams, such as visual, thermal, and infrared information, into a unified feature space. This integration is accomplished using a weighted sum approach, allowing varying importance levels to be assigned to each modality based on weights (α, β, γ). This fusion strategy proves pivotal in capturing a holistic representation of the environment. Convolutional Neural Network (CNN) for Feature Learning follows, where a CNN architecture is employed to learn robust and discriminative features from the fused data. This study showcases that AMAD, with its multimodal integration, deep learning-based feature extraction, and efficient scoring mechanism, triumphs over traditional anomaly detection techniques. It significantly contributes to enhanced anomaly detection capabilities, emphasizing its potential for bolstering public safety and security in dynamic and challenging technological landscapes. © 2023 IEEE.

关键词： Convolutional neural networks

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The Cloud's Balancing Confidentiality and Collaboration

The Cloud's Balancing Confidentiality and Collaboration

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vivek, V. Sahoo, Girija Shankar Kehkeshan Jallal, S. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

Group data sharing in cloud settings has become more popular recently as a result of cloud computing's broad acceptance. The need for efficient and safe data exchange in such settings is currently urgent. Another particular problem in cloud-based exchange of data is finding a balance of anonymity and traceability. In order to preserve anonymity, this study focuses on providing safe and effective data exchange and storage for organizations via the cloud. In order to accommodate numerous users surreptitiously in public clouds, it suggests a unique traceable collective data sharing system that makes use of key collaboration and group signature techniques. This system ensures that member identities may be tracked when required while enabling group members to converse anonymously using group signatures. Additionally, to allow for safe data exchange and preservation among group members, a shared conference key is obtained via key agreement. Notably, the key generation process for the system uses a symmetrically balanced incomplete block architecture, greatly easing the load on participants while determining a shared conference key. The suggested approach for collective information sharing on the internet of things is shown to be secure and effective via theoretical investigation and empirical assessments. © 2023 IEEE.

关键词： (KA-ABE) access control anonymity Cloud computing data breaches data integrity fault tolerance group data sharing multiple-to-many data sharing traceability

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Reassessing Deep Learning in Time Series: Predicting Time with Foresight

Reassessing Deep Learning in Time Series: Predicting Time wi...

引用

2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Vinutha, C.B. Vivek, V. Pandeya, Megha Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Machine learning relies heavily on time series forecasting, which has a wide range of uses, including although not limited to forecasting air quality, traffic patterns, and power use. Recent developments in deep training and matrix factorization models, however, have shown competitive performance and are emerging as alternatives. However, a fundamental disadvantage of these contemporary models over more established ones is their complexity. The Gradient Boosted Regression Tree, a well-known machine learning baseline, and popular deep learning models are compared in this work. We convert the time series forecast challenge into a window-based prediction issue, which is akin to (DNN) models. In addition, we design the GBRT model's input and output structure. In order to construct a single input instance to simulate a multi-output GBRT model, we concatenate the desired values with additional characteristics for each training window before flattening them. Using nine datasets, we undertake a thorough investigation and evaluate eight cutting-edge deep learning models recently presented at prestigious conferences. The findings show that a simple GBRT model performs much better than the state-of-the-art DNN models evaluated in this research thanks to the window-based input transformation. © 2023 IEEE.

关键词： Deep Learning DNN and Temporal Regularized Matrix Factorization Feature engineering Gradient Boosted Regression Trees (GBRT) Time Series Forecasting

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Secure and Tailored Health Solutions for People with Specific Requirements Leveraging Advanced AI Techniques 3

Secure and Tailored Health Solutions for People with Specifi...

引用

3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Vaishali Veena, C.S. Raja Praveen, K.N. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

Our proposed method aims to revolutionize healthcare by leveraging advanced AI techniques to tailor health solutions for individuals with specific health requirements. This innovative approach integrates three powerful AI algorithms: Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Generative Adversarial Network (GAN). These algorithms work collaboratively to develop secure, precise, and individualized healthcare services. The DNN mimics the brain's structure to extract intricate patterns, the LSTM handles sequential data and long-term dependencies, and the GAN generates data resembling real distributions. Security and privacy are ensured through data encryption and secure communication. The tailored health solution is a result of integrating these techniques, providing enhanced healthcare services. © 2023 IEEE.

关键词： Long short-term memory

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Evolving Mobile Cloud: Enhancement Technologies and Future Approaches

Evolving Mobile Cloud: Enhancement Technologies and Future A...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mahesh, T.R. Kumar, Ritesh Kehkeshan Jallal, S. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

There are still limitations in terms of life of the battery and processing power that make it difficult to fulfill the expanding needs of mobile users, despite the fast progress in hardware capacity or the widespread usage of mobile devices. Both conventional and cutting-edge approaches have been investigated to close the distance between customer demands and the constraints of mobile devices. In the context of the mobile cloud, current studies have concentrated on boosting the performance for mobile devices using augmentation approaches. Our emphasis is on the methods themselves, as opposed to current taxonomies in this area, with the aim of establishing a whole mobile computing cloud system. With the goal of ensuring dependable mobile cloud services, this survey may be used as a reference for implementing the augmentation approaches that are now accessible in portable cloud computing systems. We also talk about the unresolved issues and prospective directions for further study in this area. © 2023 IEEE.

关键词： augmentation approaches context sensitivity data interoperability Mobile cloud mobile storage augmentation parallel execution privacy and security and incentive mechanisms reliability resource amplification

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Fostering Smart Agriculture: Using Vision-Based AI for Livestock Managing

Fostering Smart Agriculture: Using Vision-Based AI for Lives...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Shukla, Gaurav Gowri, K. Sudhamsu, Gadug Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Jain University Faculty of Engineering and Technology Department of Computer Science Engineering India

ISBN: (纸本)9798350344387

The swine industry constitutes a significant segment within livestock farming. However, conventional methods employed for monitoring pig health and welfare are laborious and time-intensive. Relying on these traditional monitoring techniques becomes increasingly taxing for both pigs and farmers, particularly in the face of the expanding scale of hog farming. This stress, in turn, impacts pig welfare and overall economic productivity. Integrating artificial intelligence (AI) has emerged as a fundamental component in the realm of intelligent pig farming. Advanced pig farming systems leverage AI integration to continuously monitor biometric data encompassing pig vocalizations and behavior. This data, acquired through sensors such as cameras and RF ID devices, is transformed into pivotal indicators of pig health and welfare. Analyzing these essential variables enables the early identification of health issues affecting pigs, facilitating prompt interventions and treatments that significantly enhance production efficiency and financial outcomes in pig farming. This paper presents a comprehensive overview, highlighting the challenges and opportunities within precision pig husbandry. The fusion of AI technologies with traditional farming practices presents novel prospects and hurdles, shaping the landscape of modern pig farming. Through this evaluation, the paper aims to underscore the transformative potential and the evolving landscape of precision-based pig farming. © 2023 IEEE.

关键词： Artificial intelligence (AI) Livestock management Pig farming Pig health Precision farming and Behavioral analysis Sensors Smart agriculture Vision-based AI Welfare monitoring

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