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Comparative Assessment of Brain Tumor Detection Techniques: A Comparative Study 2

Comparative Assessment of Brain Tumor Detection Techniques: ...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Dahiya, Pooja Sharma, Anshu Sharma, Manisha Bharti, Vijay Panipat India Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Department of Computer Science and Engineering Panipat India Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Department of Computer Application Panipat India Apiit SD India Panipat Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798331519056

Medical picture segmentation refers to the process of categorizing the different parts of an image into anatomical components or defining the changes in structure and intensity in relation to the underlying functional process. The primary objective of this research is to analyze atherosclerotic lesions in living organisms using Magnetic Resonance Imaging (MRI). This research primarily aims to utilize computer technology to aid in the diagnosis and assessment of illness, with a particular emphasis on finding vulnerable plaques. MRI is a commonly used technique that helps to detect and forecast brain cancers in different neurological conditions and circumstances. Standard MRI sequences are frequently employed to distinguish various forms of brain tumors through visual examination of their characteristics and analysis of the contrast texture of the soft tissue. The brain tumor detection and classification system are developed using Discrete Wavelet Transform (DWT) and Support Vector Machines (SVMs). An efficient automated method for identifying a potential Region of Interest (ROI) where the existence of a tumor is certain. This study assesses the efficacy of the brain tumor extraction technique using symmetry analysis by calculating the mean and median values of the picture, specifically focusing on precision and sensitivity. Hence, the suggested technique is appropriate for rapid initial tumor extraction rather than precise segmentation. © 2024 IEEE.

关键词： Tumors

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A Comprehensive Study on Intrusion Detection System for Distributed Blockchain Ledger Security with Using Advanced Smart Contracts 8th

A Comprehensive Study on Intrusion Detection System for Dist...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Vats, Shailender Kumar Peddi, Prasadu Vats, Prashant Department of Computer Application Institute of Computer Science and Engineering Shri Jagdishprasad Jhabarmal Tibrewala University Jhunjhunu Rajasthan Chudela India Uttar Pradesh Noida India Department of Computer Science Engineering and Information Technology Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan Jhunjhunu India Department of Computer Science and Engineering SCSE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819766772

The potential of blockchain technology to transform several industries, including supply chains, health care, and banking, has drawn much interest in recent years. Blockchain is becoming widely used, but this has raised questions about its security and ability to withstand attacks and threats. An overview of blockchain security issues and risk-reduction strategies is given in this abstract. The decentralized and unchangeable character of blockchain, which is accomplished through the application of cryptographic algorithms and consensus processes, is its primary security feature. However, it remains susceptible to security flaws such as 51% assaults, smart contract glitches, and compromised private keys. This abstract investigates these flaws and how they might affect the security and integrity of blockchain data. Several strategies have been put out and put into practice to increase blockchain security. The abstract looks at how important it is to set best practices and standards to protect blockchain networks from malicious activity and unauthorized access. Security issues will arise as the blockchain ecosystem develops, requiring constant study and cooperation between government, business, and academia. The appeal for a coordinated effort to address blockchain security issues in their entirety and guarantee the technology’s safe and dependable use across a range of applications and sectors is made in the abstract’s conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Dynamic Load Balancing with Task Migration: A Genetic Algorithm Approach for Optimizing Cloud Computing Infrastructure 2

Dynamic Load Balancing with Task Migration: A Genetic Algori...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Priyadarshini, Aliva Pradhan, Sateesh Kumar Laha, Suprava Ranjan Nayak, Soumen Pattanaik, Bikash Chandra Utkal University Department of Computer Science & Application Odisha Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Science & Engineering Iter Odisha Bhubaneswar India Gandhi Institute for Education and Technology Affiliated to Biju Patnaik University of Technology Department of Computer Science & Engineering Odisha Rourkela India

ISBN: (纸本)9798350370188

The advancement of cloud computing is contingent upon the effective establishment of infrastructure and the flexible use of accessible resources. An essential aspect of this evolutionary process is load balancing, a critical task involving fair allocation of dynamic workloads among different nodes. The primary aim is to mitigate the issues of resource overload or underutilization issues, which presents a complex challenge in optimization. A proficient load balancer must be able to adapt its method to the dynamic environment and work demands. This research presents a novel load-balancing technique incorporating task migration using a Genetic Algorithm (GA). The objective is to achieve optimal load distribution within the cloud infrastructure while minimizing the makespan for a given set of tasks. By utilizing the inherent optimization capabilities of genetic algorithms and the flexibility of task migration, the suggested approach demonstrates dynamic adaptation to the constantly evolving demands of cloud computing. In order to evaluate the effectiveness of the plan, it is implemented and assessed using simulations carried out on the Cloud Analyst simulator. The findings from the simulation outcomes, as demonstrated in a representative application scenario, provide evidence for the effectiveness of the task migration-integrated genetic algorithm-based approach compared to existing methods such as First Come, First Serve (FCFS), Round Robin (RR), and the local search algorithm Stochastic Hill Climbing (SHC). This comprehensive method not only improves resource utilization efficiency but also showcases the capacity to mitigate performance limitations through intelligent task reallocation. The proposed technique, which incorporates task migration capabilities, shows potential in facilitating the development of a more efficient and adaptable cloud infrastructure as the cloud landscape undergoes ongoing changes. © 2024 IEEE.

关键词： Genetic algorithms

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Federated Learning CNN for Smart Agriculture: A Modeling for Soybean Disease Detection 2

Federated Learning CNN for Smart Agriculture: A Modeling for...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kumar, Mukesh Aeri, Manisha Chandel, Richa Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Cgc Landran Department of Computer Application Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350360523

With the help of federated learning and CNNs, this research paper aims to identify and categorize soybean leaf diseases. This study uses a federated learning approach to classify disease severity into four classes using four clients. The benefit of this method is that it preserves data privacy during analytics, which is highly important in our digital age. The information we obtain from each local customer is used to conduct our analysis. Using federated averaging, we produce an aggregate global model. It is possible to assess the model's performance on a local and international level by calculating macro, micro, and weighted averages. As a result of the macro standard stressing class equality, clients yu-1 through yu-6 performed well with grades of 91.42%, 91.08%, 90.92%, 93.15%, and 93.40. Based on the weighted average of those clients with class imbalance, 91.43%, 91.07%, 90.92%, 93.13%, 93.41%, and 92.86%, respectively. Finally, the micro average averaging all inputs to the model indicates its overall accuracy with the following percentages for each client: 91.43%, 91.06%, 90.91%, and 93.11%.Using federated averaging to transform local data into a global data model proved this approach feasible in a real-world scenario. Precision, recall, and F1-scores for the studied clients indicate high sensitivity and specificity of the model. A precise agriculture technique can be adapted to control soybean foliar disorders resulting from this study. As a part of our move towards more resilient and safe farm management approaches, this study helps us solve the urgent task of detecting and classifying diseases. © 2024 IEEE.

关键词： Data privacy

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Design and Modeling of Smart Intellegent Converters in Super Smart Grids

Design and Modeling of Smart Intellegent Converters in Super...

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

作者： Raghavendra, R. Yadav, Surendra Savita Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

The foundation of smart microgrid DC topologies are EPCs, or electrical converters of power. Most of the components are intended to be linked to the various busses via EPCs in order to improve the system's autonomy. Thus, the behavior of all EPCs and their control loops determines the microgrid's electrical supply, peace of mind, the dynamic responsiveness. Furthermore, because of the significant degree of unpredictability in their operating circumstances brought about by the intermittent nature of renewable sources of energy and user utilization of energy, DC micro grids represent an innovative model in energy distribution networks. Moreover, the power converters might alter their mode of operation in order to cope with this unpredictability, which complicates the system's fluidity and evaluation of stability. A brief description of the many quantitative experimental blackbox is modeling techniques used with smart DC micro/nanogrids is provided in this work. A study of several nonlinear as well as linear modeling techniques is conducted, outlining both their advantages and disadvantages. Lastly, using illustrated instances, distinctions between blackbox architectures will be shown. © 2023 IEEE.

关键词： Blackbox Modeling COTS Inverters Droop Control Devices Electrical Converters Epcs Hybrid Modeling And Nonlinear Behavior Power Distribution Networks Smart DC Micro/Nanogrids State-Space Averaging

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A Concise Overview of Cloud-based Platform for Intelligent Converters

A Concise Overview of Cloud-based Platform for Intelligent C...

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

作者： Narmadha, T. Shree, Ritu Alam, Intekhab Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

This research presents the mechanical implementation and management of freely available modules for the construction of smart conversions systems. Converts that are scattered or unsophisticated are not clever converts. To transmit the maximum power, they have stabilizers that have comparable impedance online. In current energy environments, intelligent adaptations provide fascinating solutions;in fact, their uses have become more and more varied. The suggested method was developed and tested in a laboratory environment using both real photovoltaic units and a solar cell simulator. Innovative conversions are shown in research, and enhanced intelligent capabilities allow for on-the-fly configuration adjustments, real-time diagnostics, and the management of the system's programs. Furthermore, the project's capabilities allows for a wide range of combinations and groups of various energy sources, generators, and modulation techniques. To the best of our knowledge, this project remains the sole platform for smart conversions that is available for free and is currently being utilized by research, industry, and government. © 2023 IEEE.

关键词： Cloud-Connected Smart Converters DC/AC Transformer Electrical Architectural Experimental Results FPGA Technology Maximum Power Point Tracking MPPT Techniques Power Shield Design and Efficiency Analysis Renewable Energy Techniques Smart Conversion Devices Wi-Fi Networking Module

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Novel Based Controller Design to Control Grid Systems and to Maintain Stability

Novel Based Controller Design to Control Grid Systems and to...

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

作者： Haripriya Ahmed, Zahid Sing, Navjot Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

This study presents a novel cooperative control method for an interconnected hybrid DC/AC microgrid that uses an infinite impulses (IIR) filter as its core element. The creation of a digital proportional-resonant (PR) electrical controller forms the basis of the control system. The controller's integrated adaptive neural fuzzy induction system (ANFIS) effectively monitors the system's maximum battery life point (MPP). A particle swarm optimizing (PSO)-programmed ANFIS controls the inverter current that is output, ensuring that solar PV systems using boost converters and inverters may extract maximum power quickly and without oscillation. The suggested PR controller has the ability to cancel harmonics and shows remarkable gain at the resonance frequency. Fast reference signal tracking, flexibility in response to grid rate drift, a simple system design, and the lack of steady-state error are important characteristics. A frequency-domain study of the PR limiter is included in the report. Without changing the figure description, the proposed strategy improves the transient as steady-state responses and helps to stabilize the voltage. © 2023 IEEE.

关键词： Cooperative control Distributed Generation (DG) hybrid AC/DC microgrid converters Microgrid (MG) Power Electronics and Renewable Energy Proportional-Resonant controller Smart Grids

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Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

Utilizing Mixture Methods for Classifier in NLP: An Essentia...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kavita Singh, Harveer Chandigarh School of Business Department of Master of Computer Application India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue. © 2023 IEEE.

关键词： Natural language processing systems

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Performance Evaluation of Blockchain Platforms

Performance Evaluation of Blockchain Platforms

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2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Gupta, Manish Kumar Dwivedi, Rajendra Kumar Sharma, Annu Farooq, Mansoor Britto Raj, S. Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application Gida Gorakhpur India RajaRajeshwari College of Engineering Department of Computer Science & Application Bengaluru India University of Kashmir Department of Management Studies Jammu & Kashmir India Rrase College of Engineering Department of Computer Science & Engineering Chennai601301 India

ISBN: (纸本)9798350302820

Blockchain (BC) is a cryptographic technology that enables the decentralized storage of an immutable record of transactions. As a result, many industries are interested in integrating BC into their IT systems. Although BC-based platforms are used in enterprise solutions, concerns regarding accessibility, privacy, performance, and scalability are still legitimate. Permissioned Blockchain (PBC) frameworks provide a solution to securely store private records. The main objective of this paper is to focuses on the performance and scalability of popular private BC platforms. To evaluate each platform, we varied the workloads and assessed performance metrics. Our comparative analysis provides valuable insights into the strengths and weaknesses of each platform, enabling enterprises to make informed decisions when choosing a private BC solution. © 2023 IEEE.

关键词： Blockchain

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Genetic Algorithm-Based Data Allocation in Multi Media Using Cloud Computing 3

Genetic Algorithm-Based Data Allocation in Multi Media Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Yadav, Surendra Kaur, Manpreet Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The recent growth of Internet-of-Things (IoT) applications using cloud computing has been amazing. One of the advancements is heterogeneous cloud computing, which has made it possible to use the cloud for a range of infrastructure solutions, including multimedia big data. The optimizations of on-premise heterogeneous memory have been the subject of several past studies. However, the performance and financial limits brought on by hardware distributions and manipulative techniques are placing restrictions on the heterogeneous cloud memory. It is an NP-hard combinatorial issue to distribute data jobs across dispersed memory with different capacities. In order to provide high performance cloud-based heterogeneous memory service offerings, this study focuses on this problem and suggests a unique solution called Cost-Aware Heterogeneous Cloud Memory Model. It allocates data to the cloud-based memory via genetic programming. In our suggested method, we take into account a number of important elements that have a significant influence on how well Communication expenses, data transfer operating costs, energy performance, and time constraints all play a role in how cloud memories operate. Finally, we put our suggested paradigm to the test via experimental assessments. The trial findings have demonstrated the viability and scalability of our technique as a cost-conscious cloud-based solution. © 2023 IEEE.

关键词： Genetic algorithms

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