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Innovations In Ai and Deep Learning for Scalable Network data Processing

Innovations In Ai and Deep Learning for Scalable Network Dat...

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2024 ieee international conference on intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Kanchibhotla, Chaitanya Kota, Krishna Teja Srinivas, Pattllola Channappa, Sharvani Kumar, Ch. Kishore Katta, Siva Koteswararao Carelon Global Solutions Telangana Hyderabad India Department of Cse Maisammaguda India Ballari Institute of Technology and Management Ballari India Vaagdevi Degree & Pg College Department of Computer Science Warangal India Kitew Department of Mechanical Kakinada India

ISBN: (纸本)9798331508432

The exponential rise of AI in the last several years has altered whole markets and predicted the future of our technological infrastructure. As the use of AI systems increases across many industries, the concept of scalability is becoming more important. In order to offer a thorough grasp of its importance, difficulties, and potential solutions, this research paper explores the complex domain of AI scalability. The article starts by providing background on how the AI landscape is changing so quickly, and how scalability is crucial for meeting the increasing demand for AI-driven solutions. A comprehensive literature analysis served as the basis for our concept of scalability. In order to shed light on the many facets of scalability, including scalable AI models, algorithms, infrastructure, and cloud computing services, we reviewed previous studies. The report acknowledges the potential benefits of scalability, but it mostly addresses the common challenges that corporations and academics have when attempting to develop AI systems. Within this framework, we go over some tried-and-true methods for dealing with scalability concerns, such as optimizing resources and taking system architecture into account. Case studies demonstrating firms that have accomplished scalability in their AI endeavors are presented in the article to demonstrate the real-world impact of scalable AI solutions. The tactics and technology used in these case studies shed light on the revolutionary consequences of scalability on AI initiatives, which are very helpful. © 2024 ieee.

关键词： AI-driven solutions data Processing Machine Learning

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iSample: intelligent Client Sampling in Federated Learning 6

iSample: Intelligent Client Sampling in Federated Learning

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6th ieee international conference on Fog and Edge computing (ICFEC)

作者： Imani, HamidReza Anderson, Jeff El-Ghazawi, Tarek George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

ISBN: (纸本)9781665495240

The pervasiveness of AI in society has made machine learning (ML) an invaluable tool for mobile and internet-of-things (IoT) devices. While the aggregate amount of data yielded by those devices is sufficient for training an accurate model, the data available to any one device is limited. Therefore, augmenting the learning at any of the devices with the experience from observations associated with the rest of the devices will be necessary. This, however, can dramatically increase the bandwidth requirements. Prior work has led to the development of Federated Learning (FL), where instead of exchanging data, client devices can only share weights to learn from one another. However, heterogeneity in device resource availability and network conditions still impose limitations on training performance. In order to improve performance while maintaining good levels of accuracy, we introduce iSample. iSample, an intelligent sampling technique, selects clients by jointly considering known network performance and model quality parameters, allowing the minimization of training time. We compare iSample with other federated learning approaches and show that iSample improves the performance of the global model, especially in the earlier stages of training, while decreasing the training time for both CNN and VGG by 27% and 39%, respectively.

关键词： federated learning heterogeneous systems resource constrained devices edge computing machine learning

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Disease Prediction with Ayurvedic Remedies Suggestion and Hospital Recommendation using ML and DL

Disease Prediction with Ayurvedic Remedies Suggestion and Ho...

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2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Shinde, Swapnil K. Buchade, Amar Mahalle, Parikshit Raut, Prashant Dirgule, Archana Mandale, Prashant Vishwakarma Institute of Technology Maharashtra Pune India KJ College of Engineering and Management Research Maharashtra Pune India Sinhgad College of Engineering Maharashtra Pune India International Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9798331539948

Leveraging machine learning (ML) and deep learning (DL) for disease prediction has demonstrated tremendous potential in improving diagnostic efficiency and accuracy in the modern healthcare landscape. Diagnosis of disease at early stage saves the patient from several critical health issues. Many diseases can be diagnosed based on the symptoms. This paper we implemented model which can predict disease based on provided symptoms and suggest the Ayurvedic remedies based on predicted disease. With this we have implemented hospital recommendation based on city and state. Random Forest and Decision tree algorithms from machine learning and CNN from deep learning are used for prediction of disease and KNN algorithm used for nearby hospital recommendation. Freely available and open access datasets are used for implementation. dataset consist of 4920 records, 41 unique disease and 132 unique symptoms. Accuracy measures are used to assess the models Performance;the Accuracy of decision tree and random forest is 95 and 98%, deep learning algorithm performed better on both train and test data with accuracy of 99%. This combined technique really helpful to improve patient outcomes with Ayurvedic remedies suggestion and nearby hospital recommendations. © 2024 ieee.

关键词： Prediction models

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data-Driven intelligent Prediction Model for Gearbox Temperature in Wind Turbines 21

Data-Driven Intelligent Prediction Model for Gearbox Tempera...

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21st ieee international conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Hassan, Abdulbasit Shafiullah, Md Islam, Asif Gulzar, Muhammad Majid Hamanah, Waleed M. King Fahd University of Petroleum and Minerals Control and Instrumentation Engineering Department Dhahran31261 Saudi Arabia Interdisciplinary Research Centre for Sustainable Energy Systems KFUPM Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals Electrical Engineering Department Dhahran31261 Saudi Arabia KFUPM Applied Research Centre for Metrology Standards and Testing Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350378078

Wind power has become increasingly popular due to the demand for sustainable energy sources. A fundamental problem these renewable sources face is the energy cost, thus challenging their efficient operation and maintenance (O&M). Predictive maintenance (PM) has been developed to be an economical solution for the active overcoming of problems, such as wind turbine (WT) gearbox bearing failures that seriously affect the performance and stability of renewable energy systems (RES). This study develops a predictive model of gearbox-bearing temperature using advanced machine learning algorithms such as multiple linear regression (MLR), Extreme Gradient boosting (XGBoost), and Random forest (RF) from the WT supervisory control and data acquisition (SCADA) system. The RF model performed better than the MLR and XGBoost model, with an R-squared value of 0.9840, and the Root mean square error (RMSE) and the Mean absolute percentage error (MAPE) were discovered to be 0.7352 and 0.88%, respectively. © 2024 ieee.

关键词： Windmill

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Models and Methodologies for Collecting, Processing, and Storing Heterogeneous IoT data in the Field of Renewable Energy - а Review 5

Models and Methodologies for Collecting, Processing, and Sto...

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5th ieee international conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Valova, Irena Kaneva, Tsvetelina Halacheva, Tsvetana Valov, Nikolay Georgiev, Tsvetozar University of Ruse Dept. of Computer Systems and Technologies Ruse Bulgaria University of Ruse Dept. of Automatics and Electronics Ruse Bulgaria

ISBN: (纸本)9798350352863

This paper examines the models and methodologies for collecting, processing, and storing heterogeneous Internet of Things (IoT) data in the field of renewable energy. IoT technology plays a crucial role in the energy management of renewable sources such as solar, wind, and hydroelectric systems by enabling real-time monitoring and control. The study explores various data models, including those for data acquisition, prediction, and energy flow management, all of which are essential for optimizing energy production, distribution, and consumption. By integrating IoT with advanced data analytics and machine learning, energy systems can achieve greater efficiency, reduce operational costs, and support the development of smart grids. Key challenges discussed include the need for standardization of communication protocols and ensuring data security, as IoT systems handle sensitive energy information. This research highlights IoT's potential to drive innovation in renewable energy management and contribute to global sustainability goals by enhancing resource efficiency and reducing carbon emissions. © 2024 ieee.

关键词： Clean energy

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Efficient Feature Selection Techniques for Accurate Cancer Analysis Using Krill Heard Optimization 4

Efficient Feature Selection Techniques for Accurate Cancer A...

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4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Singh, Shubham Narendra Mishra, Shruti Satapathy, Sandeep Kumar Cho, Sung-Bae Mallick, Pradeep Kumar Vellore Institute of Technology Computer Science and Engg Tamil Nadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Yonsei University Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798350349856

In this research, we introduce a novel biologically-inspired optimization algorithm known as Krill Herd (KH) to address feature selection challenges in cancer analysis. Inspired by the collective behaviour of krill individuals, the KH algorithm optimizes feature subsets by minimizing individual krill distances from food sources and herd density. We model the dynamic positions of krill individuals considering factors such as interactions with peers, foraging behaviour, and random diffusion. To enhance the algorithm's performance, we incorporate adaptive genetic operators. Our method's efficiency is evaluated across various benchmark optimization problems, demonstrating its capability to outperform eight established algorithms. In parallel, we delve into the critical domain of Cancer Feature Selection and classification in computational molecular biology. Identifying cancer-causing genes is pivotal for precise treatments and drug discovery. To address these challenges, we propose an enhanced Krill Herd Optimization (KHO) technique for gene selection, combined with Random Forest (RF) for classification. RF is chosen for its high classification accuracy. This research advances the field of cancer analysis by offering efficient and accurate feature selection techniques, providing insights into cancer-related genes, and facilitating more precise treatments and drug discoveries. © 2024 ieee.

关键词： Feature Selection

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Predicting Drug-Drug Interactions with Machine-Learning and ATC-SMILES Combined Representation 6

Predicting Drug-Drug Interactions with Machine-Learning and ...

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6th international conference on intelligent computing in data Sciences, ICDS 2024

作者： Radwan, Yasmin Atef Gouda, Karam Abdelghany Abdelbaky, Ibrahim Zaghloul Arafa, Mona Mohamed Higher Institute of Computer Science & Information Technology EI-Shorouk Academy Information Systems Department Egypt Benha University Faculty of Computers and Artificial Intelligence Information Systems Department Egypt

ISBN: (纸本)9798350351200

Polypharmacy is a potential strategy for managing such intricate disorders, encompassing conditions like cancer, diabetes, and age-related issues in older individuals. Nonetheless, when a medication is combined with one or more drugs that either enhance, diminish, or counteract its intended effects, it can lead to undesired adverse reactions. In severe cases, these interactions can cause serious morbidity and increased mortality rates globally. In this study, we collected a Drug-Drug interaction dataset from the DrugBank database. Various chemical features were then extracted from the Simplified Molecular-Input Line-Entry System of interacting drug pairs. Our emphasis was on representing the Molecular Access System fingerprints of these drug pairs. Molecular Access System fingerprints signify the presence or absence of specific substructures in the molecule and were generated using the RDKit Open-Source Cheminformatics Software. Furthermore, we incorporated Anatomical Therapeutic Chemical classifications into our analysis. Finally, we employed various machine learning algorithms, using K-Nearest Neighbor, Random Forest, Logistic Regression, Support Vector Machine, and XGBoost for learning the extracted features and predict large-scale Drug-Drug interactions among various drug pairs. Among these models, the XGBoost model exhibited superior performance across most measurement metrics. © 2024 ieee.

关键词： Nearest neighbor search

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Enhancing User Experiences: Evaluating Movie Recommendation Algorithms in Dynamic Environments

Enhancing User Experiences: Evaluating Movie Recommendation ...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Fahad Iqbal, T. Gnanajeyaraman, R. Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha School of Engineering Department of CSE Chennai India

ISBN: (纸本)9798350319200

The abundance of content and services in the modern digital environment makes it difficult to give people the best recommendations. To address this, recommendation systems use machine learning and data analysis to generate personalised suggestions that improve user experiences and decision-making. This study looks into recommendation systems, focusing in particular on algorithms for movie recommendations. In order to identify the best user-centric recommendations, it closely examines six noteworthy methods, illuminating their usefulness in sifting through the profusion of options. This investigation helps the study improve recommendation system tactics for providing relevant and interesting content suggestions in a changing digital environment. © 2023 ieee.

关键词： data analysis Movie recommendation algorithms Personalization Recommendation systems User experiences

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Fitness Certificate Validation Using Automatic Number Plate Recognition 10

Fitness Certificate Validation Using Automatic Number Plate ...

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10th international conference on advanced computing and Communication systems, ICACCS 2024

作者： Ramya, D. Sakthi Maheswari, S. Balaji, S. Shri Kishore, J.C. Deepanchakaravarthy, K. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As traffic congestion escalates, the need for precise and prompt vehicle regulation becomes increasingly critical. Ensuring vehicles comply with fitness certificate requirements is pivotal for road safety and legal conformity. However, traditional manual methods of verifying fitness certificates are prone to errors and time-consuming. This paper introduces an innovative solution: an Automated Number Plate Recognition (ANPR) system seamlessly integrated with a backend database for efficient and reliable verification of vehicle fitness certificates. Our proposed ANPR system utilizes computer vision to capture and analyze number plates, matching them with fitness certificate status stored in the database. This technology not only expedites the verification process but also significantly enhances accuracy compared to manual approaches. By merging ANPR technology with fitness certificate databases, this project has the potential to modernize vehicle regulation practices. This paper offers a detailed exploration of the system's design and functionality, showcasing its ability to revolutionize regulatory procedures and promote road safety. The project places a strong emphasis on data security, ensuring the safe storage and retrieval of sensitive vehicle information. Through this project, we aim to contribute to discussions surrounding intelligent transportation systems and their role in optimizing regulatory processes. By reducing errors, minimizing manual intervention, and enhancing overall efficiency, the ANPR-based approach offers a path towards safer roads and more streamlined regulation. This abstract provides an overview of the methodology, system architecture, implementation details, results, and conclusions, offering a comprehensive perspective on the proposed solution's effectiveness in automating fitness certificate verification. © 2024 ieee.

关键词： Traffic congestion

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Design of Wireless data acquisition System 15

Design of Wireless Data Acquisition System

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15th international conference on intelligent Human-Machine systems and Cybernetics, IHMSC 2023

作者： Zhang, Wendian Changchun Automobile Industry Institute Electrical Engineering Academ Changchun China

ISBN: (纸本)9798350326178

The measurement of oil pressure is an important reference factor in the process of oil extraction. The article conducts in-depth research on the oil pressure collection system and proposes a high-precision and high-performance wireless data collection system with MSP430 [1] series microcontroller as the information processing core and low-power RF chip CC1100, which solves the shortcomings of slow transmission speed, high power consumption, and poor accuracy in previous oil field pressure monitoring and management. © 2023 ieee.

关键词： Electric power utilization

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