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Scalable Cloud-Integrated Analytics and Visualization for Real-Time Smart Grid Management

Scalable Cloud-Integrated Analytics and Visualization for Re...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： B Santosh Kumar Yagateela Pandu Rangaiah Navdeep Singh K. Jyothsna Reddy Taqi Mohammed Khattab Al-Rubaye K. Butchi Raju Department of Computer Science Engineering New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Department of CSE GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The installation of new smart technologies has been growing exponentially, resulting in the creation of a large quantity of diverse data which require sophisticated tools for analysis. This paper presents CloudVIZ-GRID as a new cloud-based visual analytical framework that was developed to accommodate the severity of the big data issues in smart grids. CloudVIZ-GRID combines simple web-based grid accounts with up-to-date visualization tools and high-processing power to give a real time snap shot and ‘finger on the pulse’ of grid performance and operations, or deviations in energy usage. Using distributed data processing engines, CloudVIZ-GRID can process high velocity data feeds, and with limited latency deliver analytics output. Moreover, the application of the machine learning algorithms within the framework strengthens the predict ability and thus pre-empts the management of the grid. This study lays emphasis on the importance of addressing the issues of computation, which is even more challenging in case of complex systems such as smart grids; use of cloud based solutions enable the conversion of huge amount of data available in smart grid into valuable insights that can be helpful for grid operators and players in the energy sector. Due to having a modular structure, the CloudVIZ-GRID has the ability to change to fit future possible grids and additions, making it an essential part of the growing smart grid applications.

关键词： Resistance Cloud computing Visualization Machine learning algorithms Data visualization Big Data Predictive models Real-time systems Smart grids Monitoring

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Intelligent Gates Exploring the Massiveness of Random Access Points in NGMA

Intelligent Gates Exploring the Massiveness of Random Access...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sunil Prashanth Kumar P. Aswini H Pal Thethi K Smitha R. Akhilesh Reddy Ali Albawi Department of Mechanical Engineeering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With the rapid progress of Next-Generation Mobile Architecture come several obstacles in improving user experience and network performance. Active methods frequently face issues in allocating network resources correctly. Consequently this can lead to mismanagement or poor resource utilization affecting how users experience the network. As access requirements increase with the growth of architecture complexity protecting personal data is becoming more challenging. To tackle these issues the Dynamic Access Point Management (DAPM) framework shows great potential as it delivers a creative strategy for resource allocation and network optimization. This model modifies standard resource management practices to become sharper and more adaptable. Thorough examination is needed to confirm DAPM’s superiority over standard methods regarding the resolution of these problems comprehensively. This research will examine the gains in network efficiency and security outcomes from the sophisticated models of DAPM. The detailed methodology seeks to construct a network framework that is both dependable and secure and will boost NGMA’s operational effectiveness and durability.

关键词： Data privacy Adaptation models Aerodynamics Dynamic scheduling Throughput User experience Stability analysis Security Resource management Standards

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Hybrid Modeling Approaches in Energy Internet: Bridging Cyber, Physical, and Social Realms

Hybrid Modeling Approaches in Energy Internet: Bridging Cybe...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Prateek Chaturvedi H Pal Thethi N V Uma Reddy M Kalaiarasi Taqi Mohammed Khattab Al-Rubaye K. Anusha Department of Mechanical Engineering Amity University Greater Noida India Lovely Professional University Phagwara India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Effective modeling approaches are required for the management and operation of the Energy Internet due to the system's complexity and mobility. New hybrid models that link the Energy Internet's digital, real-world, and social components are the primary focus of this article. We propose a novel approach that integrates adaptive reinforcement learning, social behavior analysis, and multi-agent coordination optimization to enhance the system's performance, robustness, and long-term sustainability. We intend to address issues that arise when several factors, such as evolving technology, user behavior, and social interactions, converge by employing these mixed modeling approaches. Because of this, the Energy Internet setting will run more efficiently and effectively. Through extensive simulations and case studies, we demonstrate that our proposed method outperforms conventional ones. Our main focus is on how technology can enhance energy management, better allocate resources, and back user-centered and socially inclusive policies. To ensure the seamless operation of the Energy Internet environment's cyber, physical, and social components, the results highlight the significance of mixed modeling methodologies.

关键词： Adaptation models Analytical models Adaptive systems Computational modeling System performance Energy Internet Reinforcement learning

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Enhanced Vgg-19 Model for Accurate Brain Tumor Prediction

Enhanced Vgg-19 Model for Accurate Brain Tumor Prediction

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： D. Vetrithangam PM. Ebin P. Nareshkumar B. Arunadevi Azra Fathima A. Ramesh Kumar Department of Computer Science & Engineering Chandigarh University Punjab India Department of Computer Science and Engineering Alliance University Bangalore India Department of CSE (AI&ML) KG Reddy College of Engineering and Technology Hyderabad India Department of ECE Dr. N.G.P Institute of Technology Coimbatore India Department of CSE KG Reddy College of Engineering and Technology Hyderabad India Department of Mechatronics Engineering K.S Rangasamy College of Technology Tamil Nadu India

One of the most serious diseases for both adults and children is a brain tumor. Through the scans, a substantial amount of image data is produced. The radiologist reviews these images to detect the abnormality. Due to the complexity of brain tumors and their features, a manual examination may be mistake-prone. To visualize the brain and spot any abnormal growths, many imaging techniques are used. Magnetic resonance imaging (MRI) is the method that works best to identify brain tumors. Predicting brain tumors from MRI images also contributes to medical research and the development of more sophisticated predictive models. This continuous improvement in technology can lead to better diagnostic tools and more effective treatment options for brain tumor patients. The existing models exhibit significant shortcomings concerning explanation, interpretability, computational complexity, and accuracy. In order to overcome the drawbacks available in the existing techniques, this research work proposes the Enhanced VGG-19 model by improving the convolutional neural network layers, fully connected layers, and weights of the layers and applying effective preprocessing techniques to the datasets to predict brain tumors from MRI images with high accuracy and less computation time. The proposed model produced an accuracy of 99.72% and performed better than the existing techniques.

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EAPD Diagnosis Diagnosis Using ai

EAPD Diagnosis Diagnosis Using AI

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： P. Sruthi Jajimoggala Sravanthi Renukadevi. M Zayd Balasse Shravanthi Mantra Prithvi Raj B. R. Supreeth Department of CSE(AI&ML) CMR College of Engineering & Technology Hyderabad Telangana India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science Engineering Sethu Institute of Technology Kariapatti Tamil Nadu India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq ENT Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The motivation behind this undertaking is to take a gander at the capability of computerized reasoning (A1) in giving dependable and fast diagnostics to pediatric problems. Pediatric disease determination accurately and speedily are basic for effective therapy and further developed wellbeing outcomes Nevertheless, the intricacies of these signs, as well as the shortage of pediatric specialists, make ID difficult Al has shown fantastic outcomes in different clinical fields, including assessment, treatment, and medicine find This study examines flow progresses in involving Al strategies for pediatric sickness location, evaluating their exactness and efficacy This examination accentuates the limitations and cutoff points of Al techniques, stressing the prerequisite for more innovative work to work on their accuracy and constancy, The exploration contains cases demonstrating the viability of Al in the analysis of pediatric afflictions like leukemia, mental imbalance, and flu. The discoveries propose that Al strategies have a likelihood to further develop finding exactness and immediately, limit botches, and empower brief treatment for pediatric sicknesses. In any case, further review is expected to evaluate the appropriateness of Al methods in certifiable medical services conditions while handling issues like as information quality, interpretability, and moral worries. The paper goes on by proposing possible turn of events and examination ways for Al techniques for the specific finding of pediatric infections, underlining the significance of interdisciplinary coordinated effort and moral issues.

关键词： Ethics Reviews Medical treatment Leukemia Cognition Artificial intelligence Medical diagnostic imaging Intelligent control Information integrity Diseases

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Enhancing Supply Chain Regulatory Compliance with Blockchain and Lasso Regression Integration

Enhancing Supply Chain Regulatory Compliance with Blockchain...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Vivekanand Aelgani E. Jothi Ramy Riad Hussein Banoth Ramesh Himanshu Sharma Uganya G Department of CSE CMR College of Engineering & Technology Hyderabad Telangana India Department of IT New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML GRIET Bachupally Hyderabad Telangana India Department of Computer Engineering & Applications GLA University Mathura Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Adopting lasso regression besides blockchain gives the best solution in improving the regulatory compliance in supply chains. For this integration, the feature of blockchain technology which applies for secure, transparency and unchangeable record keeping is used, For lasso regression the variable selection and predictive model are used. Thus, blockchain offers supply chain participants an opportunity to perform all transactions in a decentralized manner and maintain an unalterable, open record of all the transactions that occur in the supply chain. At the same time, the lasso regression models are used to predict compliance risks based on the data obtained and choosing the features that might be useful for this analysis from a vast set of possibilities. Smart contracts are the other strategies used in performing compliance checks in real-time and reporting instances that indicate non-compliance so that the necessary intervention is done. This system does not only administers and optimizes the press compliance procedure but also improves prediction rate and reduces the risk of encountering new compliance issues. Further, the changes are made continuously in the model and the feedbacks from the stakeholders add to the fact that the system is fit to adapt to the changes in the regulatory environment and supply chain industry. In doing so, it also enhances the visibility, relevancy, reliability, and effectiveness of the regulation compliance enhancing better trust and operation in the overall supply chain.

关键词： Supply chain management Supply chains Smart contracts Predictive models Real-time systems Regulation Blockchains Stakeholders Security Predictive analytics

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A Machine Learning Method for Detecting Behavior-Based Malware

A Machine Learning Method for Detecting Behavior-Based Malwa...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Javvaji Venkatarao V. Deeban Chakravarthy Singaraju Ramya V. Siva Prasad Srikanth Cherukuvada Annaram Soujanya Dept. of Computer Science and Engineering Sree Vidyanikethan Engineering College Tirupati India Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Dept. of AI&ML Mohan Babu University Tirupati India Department of Networking and Communications School of Computing SRM Institute of Scienceand Technology Chennai India Department of Computer Science and Engineering CVR College of Engineering Telangana India

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis.

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Exploring the Impact of ai-Driven Natural Language Processing in Computational Analysis

Exploring the Impact of AI-Driven Natural Language Processin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. Ranjith Singh S Rajarajeswari Rutuja S. Kadam Ravindra Kumar Trapty Agrawal R. Raghavendra Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Civil Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper explores the blessings of applying artificial intelligence and herbal language processing (NLP) strategies to massive-scale computation analysis. Natural language processing is fundamental to considering complex analytics tasks with relative ease using currently available technology. This paper mainly specializes in using NLP in the vicinity of sentiment evaluation. Sentiment evaluation uses numerous algorithms to determine sentiment from text, and it may be used to benefit insights from vast volumes of statistics. An exploration is performed to demonstrate the benefits of utilizing ai-driven NLP while appearing complicated sentiment analysis. The studies offer an overview of the techniques and equipment involved, as well as the demanding situations that should be confronted. Additionally, modern-day and potential programs are mentioned, together with their implications for company and purchaser decision-making. The paper concludes by providing several potential applications for ai-pushed NLP in the analytic space. Through the use of ai-pushed NLP, groups, and agencies can now gather and analyze records to benefit precious insights more effectively than ever before.

关键词： Sentiment analysis Decision making Collaboration Companies Natural language processing Artificial intelligence

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Sriramkumar R Computer Science & Engineering AI & ML Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bagalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The latest evolution in power systems, is ‘smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids.

关键词： Measurement Renewable energy sources Uncertainty Neural networks Power system stability Predictive models Stability analysis Real-time systems Smart grids Bayes methods

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Digital Imaging Analyzing and Reproducing Color, Texture, and Material Appearance

Digital Imaging Analyzing and Reproducing Color, Texture, an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujin Jose Arul Sajja Suneel Manish Gupta K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Automobile Engineering Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Digital photography has made great strides in showing colour and texture along with specific material details. Standard approaches usually do not attain realistic representations. The Multifaceted Realism Synthesis presented in this document resolves these problems and outshines classic strategies in accuracy and realism. The MRS framework incorporates three core algorithms: With this approach we can obtain more consistent and accurate colour reproduction using the Colour Harmonisation Algorithm. The Physically Informed Material Rendering and Typical Texture Synthesis (ATS) generate more natural textures that adapt to local details rather than using median filtering or morphological methods. The MRS approach adjusts materials appearance texture and colour to deliver superior realism adaptability and precision beating current methods. This new approach can change digital photography across different areas such as entertainment and healthcare. The technique of MRS has made a substantial improvement in the quest for hyper-real visuals in digital photography.

关键词： Measurement Visualization Accuracy Image color analysis MIMICs Transforms Medical services Rendering (computer graphics) Digital photography Standards

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