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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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An Evaluation of Interactive communication Media Algorithms and Their Usage for Networking Applications 5th

An Evaluation of Interactive Communication Media Algorithms ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kudari, Jayashree M. Yadav, Rakesh Kumar Yadav, Surendra Kapse, Vinod Mansiram Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper evaluates using interactive conversation media algorithms inside the context of networking applications. Exceptional forms of algorithms are mentioned, which includes media movement algorithms, interactive conversation protocols, recording algorithms, and video compression protocols. The overall performance of those algorithms is classified and evaluated thru a quantitative analysis that includes measurements of common latency and packet loss. Additionally, the paper investigates using interactive communication media algorithms for one of kind programs together with video conferencing, on line gaming, and voice over IP offerings. Sooner or later, a contrast of various algorithms is made, with hints for the fine answers primarily based on the evaluated performance and application necessities.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video conferencing

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The Role of Modern Machine Intelligence in Decoding Activity Patterns from Visual Data of Various Organisms

The Role of Modern Machine Intelligence in Decoding Activity...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Gowrishankar, J. Kumar, Ritesh Srivastava, Dharmesh Kumar 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 Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

The integration of modern machine intelligence presents a transformative paradigm in the study of organisms' behaviors through visual data analysis. Leveraging deep learning algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this approach automates the extraction of intricate patterns from images and videos, enabling comprehensive insights into diverse organisms. Object detection algorithms further facilitate precise tracking. By surpassing traditional methods, it enhances accuracy and scalability. Ethical considerations guide responsible research. This fusion of technology and ecology fosters a deeper understanding of natural ecosystems, paving the way for sustainable coexistence with the environment and the preservation of biodiversity. © 2023 IEEE.

关键词： Data handling

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Translational Deep Learning-Based Diagnostics: Enhancing Pathology With AI

Translational Deep Learning-Based Diagnostics: Enhancing Pat...

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Ashwini, B. Sinha, Deepak Kumar Chaudhary, Chetan 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

Transformative innovation in healthcare is led by the constantly changing field of deep learning techniques, especially designed for cellular imaging in diagnostic pathology. This paper provides a thorough examination of several deep learning approaches in the field of pathology, highlighting new uses and tackling important issues that are essential to the field's progress. The successful deployment of AI in clinical settings depends on the seamless integration of these apps into processes in digital pathology. This all-inclusive diagnostic method not only simplifies processes but also provides pathologists with cutting-edge instruments that accelerate work, improve diagnosis accuracy, and lower mistakes. The US has recently approved a limited number of manufacturers' digital pathology products for diagnostic usage, which offers a strong basis for the creation of useful AI solutions. Researchers, physicians, business executives, government agencies, and patient advocacy organizations working together will make it much easier for these cutting-edge technologies to spread and become widely used by medical professionals. These technologies usher in a new age in healthcare, distinguished by their exceptional performance, faster processing rates, cost effectiveness, increased accuracy, and strict safety regulations. AI and computational pathology will continue to develop in harmony, opening the door to a new age of improved healthcare delivery. © 2023 IEEE.

关键词： Artificial Intelligence Cellular Imaging Deep Learning Diagnostic Imaging and Machine Intelligence Diagnostics Digital Pathology Healthcare Pathology

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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Natya, S. Vivek, V. Yadav, Dhananjay Kumar 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 and Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS). © 2023 IEEE.

关键词： Cloud computing Hypervisor Infrastructure-as-a-Service (IaaS) Multitenancy Platform-as-a-Service (PaaS) Security issues Service-driven cloud computing Software-as-a-Service (SaaS) and Denial of Service (DoS) Virtualization

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Cloud Deployment Unveiled: A Security Model Focused on Industry

Cloud Deployment Unveiled: A Security Model Focused on Indus...

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Yadav, Rakesh Kumar Natya, S. Mahesh, T.R. 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 Cloud Compute Adoption Architecture (CCAF), a unique architecture designed to improve safety of the business cloud installations, is introduced in this research article. Using router, authentication and authorization, and encryption methods embedded in Industrial File Sync/Share systems, CCAF aims to provide a multi-layered privacy strategy. Our paper describes the creation of this security A system, examines relevant literature, and explains the reasoning behind CCAF. Through testing and thorough analysis of the key technologies, the study assesses the effectiveness of the CCAF security layered approach. The intricate security system in place at CCAF proved to be an outstanding tool during penetration testing, detecting and eliminating 99.95% of worms including trojans, and even deactivating over 85% of them after 100 hours of continuous attack. Of particular note was the operating system's lightning-fast reaction time of less than 0.012 seconds per trojan and virus, which allowed for the early detection and mitigation of threats. Moreover, each try at SQL injection was successfully thwarted by the CCAF's extensive security defenses, guaranteeing strong security of data. The article highlights the usefulness of CCAF by exploring how its multi-layered security is seamlessly integrated into current regulations, services, and corporate processes. The research highlights how important CCAF's complex security is for Big Data applications running in the cloud, especially when it comes to scalability, speed, and veracity. The study's conclusions highlight CCAF's legitimacy and significant contributions to the field, demonstrating its applicability and effectiveness in protecting the cloud. © 2023 IEEE.

关键词： Cloud Compute Adoption Framework (CCAF) encryption Enterprise File Sync firewall identity management multilayered security approach penetration testing and SQL injection assessment security technologies Share solutions

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Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

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

作者： Kale, Kiran Raja Praveen, K.N. Savita 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

Fifth-generation wireless networks are ready to handle the increasing volume of mobile traffic and provide critical technological solutions to suit various service needs. Utilizing the millimeter-wave (mm-wave) spectrum is a crucial strategy for achieving large data speeds. Additionally, analogue radio-over-fiber (ARoF) has a wide range of benefits, such as affordability, little power usage, and exceptional spectrum efficiency. As a result, mm-wave ARoF becomes a strong candidate for providing becomes the standard waveform for 5G, it is crucial that mm-wave ARoF systems support it. However, in mm-wave OFDM ARoF systems, an existence of phase interference represents a substantial performance-limiting constraint. Through a painstakingly constructed test setup, we perform a thorough examination of phase noise in this study. We are able to systematically modify the system's overall last phase noise level thanks to this configuration. In addition, we provide a fresh and unique approach for OFDM transmitter phase noise compensation. Our testing confirms the algorithm's effectiveness under these circumstances and highlights the potential advantages of mm-wave OFDM ARoF for 5G to meet by evaluating how well it works over a range of period noise values and subcarrier spacings. © 2023 IEEE.

关键词： Analogue Radio-over-Fiber Beyond 5G Distortion Frequency-Selective Channels Investigation Millimeter-Wave MM-Wave OFDM Phases Spectral Efficiency and Signal-to-Noise Ratio Subcarrier Spacings

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Visualizing Wellness: The Impact of Deep Learning on Healthcare Image Analysis

Visualizing Wellness: The Impact of Deep Learning on Healthc...

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Sinha, Deepak Kumar Naval, Preeti Fiza, Samreen 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 Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

Deep convolution networks have quickly established itself as the main method for deciphering medical pictures due to their widespread use. This extensive study examined almost 300 recent papers, mostly from the preceding year, that summarized the basic artificial intelligence ideas related to medical picture analysis. Applications of deep learning were explored in detail in a number of disciplines, including object recognition, picture grouping, classification, and registering. This book presents a succinct summary of studies conducted in the fields of neurological disorders, retinal cells, asthma, malignant scans, breasts, cardiovascular and cardiac pelvic, and nerve imaging. Study conducted concludes our assessment with a review of the state-of-the-art at this time, a thorough investigation of outstanding problems, and suggestions for further research. All areas, including computerized pathology, musculoskeletal, pulmonary, the heart, breasts, muscles and tendons, retinal, and neurology imaging, are summarized in a summary of current studies. This thorough review ends with an assessment of the field's current state, a critical analysis of ongoing issues, and specific suggestions for further research and development. © 2023 IEEE.

关键词： Classification Convolutional Neural Networks (CNNs) Deep Learning Detection Healthcare Image Analysis Image Processing and Medical Image Analysis Localization Machine Learning Medical Imaging

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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

作者： Vivek, V. Savita Dore, Aruna 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

In wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average. © 2023 IEEE.

关键词： Cluster Head Election Clustering Algorithms Energy Efficiency Fuzzy Inference Systems Fuzzy Logic Model Gini Index Measurement Network Longevity Routing Protocols Separation Distance Wireless Sensor Networks

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Nanofibrous Filter Synthesis and Characterization of ZiF-8 Metal Organic Framework for Efficient PM Particle Filtration on Cotton Substrate 25

Nanofibrous Filter Synthesis and Characterization of ZiF-8 M...

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25th International electronics Symposium, IES 2023

作者： Kumar, Ashish Kanungo, Vinay Saini, Vikas Harjule, Priyanka Tiwari, Vinita Agarwal, Basant National Institute of Technology Raipur Department of Electronics and Communication Engineering Raipur India Swami Keshvanand Institute of Technology Management Gramothan Jaipur Department of Computer Science and Engineering Jaipur India Indian Institute of Information Technology Department of Electronics and Communication Engineering Kota India Malaviya National Institute of Technology Department of Mathematics Jaipur India Central University of Rajasthan Department of Computer Science and Engineering Ajmer India

ISBN: (纸本)9798350314731

In this work, we optimized the electrospinning method and prepared a ZiF-8@PAN (Zinc Imidazolate Framework-8/Polyacrylonitrile) solution to fabricate a ZiF-8 MOF (Metal Organic Framework) nano fibrous filter for efficient filtration of PM2.5 and PM10 particles. The solution was deposited on a cotton substrate, chosen for its high flexibility, low air flow resistance, abundant interactive sites for pollutants, and cost-effectiveness. Structural characterization was performed using X-Ray Diffraction (XRD) analysis with the Bruker D8 Advance instrument, confirming the successful preparation of the ZiF-8@PAN sample. The XRD spectra exhibited distinct diffraction peaks corresponding to the associated planes of the deposited thin film, indicating the presence of the Sodalite structure. The surface morphology of the synthesized sample was examined using Field Emission Scanning Electron Microscopy (FESEM), revealing a uniform distribution of nano fibrous structures with a mesh-like surface morphology. The fabricated sample was further characterized using a Nova Touch LX2 gas sorption analyzer, which revealed a BET surface area of 664.953 m2 g-1 and a pore volume of 0.3916 cm3 g-1 for the composite ZIF-8@PAN. © 2023 IEEE.

关键词： Electrospinning

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