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Integrating Federated Learning and Blockchain for Enhanced Financial Portfolio Management

Integrating Federated Learning and Blockchain for Enhanced F...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： V. G. Murugan Sudhansu Sekhar Nanda Sudindra V R Gajendra Naidu Jatty N.Rajesh Kumar Vijaya Chamundeeswari Department of Management Studies Madanapalle Institute of Technology & Science (MITS) Madanapalle Andhra Pradesh India Sri Sri University Cuttack Odisha India Department of Finance ICFAI Business School Bangalore India Accounting & Finance University of Botswana Gaborone Botswana Master of Business Administration St.Michael College of Engineering and Technology Sivagangai India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Financial portfolio management receives secure and efficient processing through the combination of Federated Learning (FL) with blockchain technology. FL protects data privacy through its distributed training capabilities that prevent financial data exposure. Blockchain secures operations and maintains transparency through the creation of unalterable model change logs on its ledger systems. A proposed approach reaches 91.3% model accuracy level and a 1.27 Sharpe Ratio and delivers a 14.3% return on investment which surpasses conventional approaches. The training duration reaches 4.8 hours while security enhancements and increased efficiency become available in the system. The designed framework supports 200 transactions per second at 250 ms response time while maintaining complete security with 0.10 USD transaction fees. It demonstrates the ability of FL and blockchain technology to boost security measures and risk-adjusted performance metrics and computational capability. Future Work will concentrate on blockchain performance optimization while testing practical applications of algorithms and revising algorithms to optimize financial applications.

关键词： Training Federated learning Computational modeling Data models Blockchains Security Portfolios Optimization Investment Testing

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Comparison of SVM and Random Forest for Detection of Malicious Node in Wireless Sensor Networks

Comparison of SVM and Random Forest for Detection of Malicio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sankara Reddy Thamma E. Gurumoorthi C.Om Prakash S K Muthusundar M.S. Nidhya Balajee Maram Deloitte Consulting LLP USA Information Technology DC Technical Delivery Master II Concord North Carolina USA School of Engineering Malla Reddy University CMR University Bengaluru Dept of AI & DS Chennai Institute of Technology college Chennai School of Computer Applications Dayananda Sagar University Bangalore School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Warangal

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Wireless Sensor Networks (WSNs) are used in various contexts and offer a wide range of applications, despite being a decentralized, resource-scarce network type; this is why this study deals with the problem of malicious nodes. Identifying such nodes is important to preventing the compromise of the network and all the nodes in it. In this paper, a comparative study of two popular classifiers named Support Vector Machine (SVM) and Random Forest (RF) in order to detect hostile nodes in WSNs. By nature, a malicious node in a network will cause significant problems to the users. There are number of models also there to detect the malicious node. Based on the above-discussed parameters the paper compares these algorithms in terms of detection accuracy, computational complexity, scalability and performance measure of imbalanced data. The comparison draws on different targets such as accuracy, capacity/large scale, speed, and small sample imbalance, and noise. As stated in this paper, both SVM and Random Forest have its own strengths where the selection between them mainly depends on the network environment and the features of the malicious activity.

关键词： Support vector machines Radio frequency Wireless sensor networks Accuracy Noise Network security Feature extraction Real-time systems Random forests Tuning

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Smart Thermal Management and Control of Interleaved Boost Converters in High-Power Photovoltaic Systems

Smart Thermal Management and Control of Interleaved Boost Co...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Kavitha Dasari Asha V. Lavish Kansal Praveen Ashish Kumar Parashar Adil Abbas Alwan Institute of Aeronautical Engineering Dundigal Hyderabad Department of Master of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Lloyd Institute of Management and Technology Greater Noida Uttar Pradesh India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

In the realm of high-power photovoltaic (PV) systems, ensuring efficient and reliable performance demands precise thermal management. This study delves into the intricacies of thermal management and control strategies for interleaved boost converters (IBC), pivotal components in these systems. Interleaved topology, though recognized for its capability to reduce ripple currents and enhance efficiency, presents unique thermal challenges due to uneven current distribution among the parallel converters. This research introduces a smart thermal control methodology that amalgamates real-time temperature monitoring with adaptive control strategies, ensuring optimal thermal performance and preventing hotspot formation. Employing a combination of advanced sensors and sophisticated algorithms, the proposed system autonomously adapts to varying solar irradiances and ambient conditions, thereby ensuring consistent thermal stability. Experimental results demonstrate a significant reduction in temperature deviations among parallel converters and an overall enhancement in system reliability and efficiency. The findings from this research set the groundwork for future designs of PV systems, ensuring their sustainable and efficient operation under diverse conditions.

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IoT Based Fish Pond Monitoring System to Enhance Its Productivity 3

IoT Based Fish Pond Monitoring System to Enhance Its Product...

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Kumar, S M Ravi Mohammed, Thayyaba Khatoon Rao, Seeram Srinivasa Dinesh Anton Raja, P. Bakhare, Ruhi Kumar, Ashok Sarkar, Swagata G. Pullaiah College of Engineering and Technology Department of Mechanical Andhra Pradesh India School of Engineering Malla Reddy University Telangana India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh India R.M.K. Engineering College Department of Electronics & Communication Chennai India Dr. Ambedkar Institute of Management Studies & Research Department of Master of Business Administration Maharashtra India Banasthali Vidyapith Department of Computer Science Rajasthan India Sri Sai Ram Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350336238

Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location. © 2023 IEEE.

关键词： Fish

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Integration of the Use of Conventional technology Towards Smart Home technology for People in the City of Jakarta

Integration of the Use of Conventional Technology Towards Sm...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Febian Wijaya Lintang Gerald Christian Ongko Griselda Valerie Michelle Benedicta Vieri Aska Juneio Sembiring Zaldy Raditya Ananta Takaaki Hosoda Ford Lumban Gaol Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology.

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NextGen Robot: An Efficient and Effortless Wall Painting Robot Design Based on Artificial Intelligence (AI) Principles

NextGen Robot: An Efficient and Effortless Wall Painting Rob...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S Jeya Mary Sajithra S G Divya Lakshmi. N Pallavi Giri K. Al-Qawasmi Department of ECE Einstein College of Engineering Tirunelveli Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TamilNadu India Department of Master of Computer Applications S.A. Engineering College Chennai Department of Civil engineering Laxminarayan Innovation Technological University Nagpur India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Painters’ eyes and respiratory systems are particularly vulnerable to the toxic chemicals used in the industry. Additionally, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. The entire construction process may be enhanced and time and labor saved by using robots and human workers appropriately into building jobs. The Dynamic Smart Painting Robo Model (DSPRM) is a novel robot that this study introduces for intense wall painting. To test its efficacy, it is crossvalidated with the usual technique, the usual Painting Bot (CPB). Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. An autonomous robotic painting system is being developed as a result of the wall painting robot.

关键词： Service robots Buildings Toxic chemicals Resists Chatbots Artificial intelligence Respiratory system Protection System analysis and design Painting

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Improved Piano Music Recognition Algorithm Based on Artificial Intelligence

Improved Piano Music Recognition Algorithm Based on Artifici...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： M. Sahaya Sheela S. Surendran P. Muthu Krishnammal Vinothkumar K Surendra Shetty B. Venkataramanaiah Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh Department Of ECE PSNA College Of Engineering And Technology Dindigul Tamilnadu India Department of Master of Computer Application NMAM Institute of Technology Nitte(Deemed University) Karnataka India

Not only do people enjoy listening to piano music, but it may also be differentiated from vocal music in the realm of digital music. With its ongoing development, the piano music recommendation algorithm has found solutions to a great number of practical issues, and its technical path has also progressed from its initial recommendation based on the behavioral preferences of users to the correlation recommendation among users to the mining of users' potential preferences in recent years. The birth of The ONE smart piano means that a new teaching mode will be produced, that is, piano learning and user experience will be integrated, and the latest achievements of modern science and technology will be applied and rooted in the field of music education and teaching, so that the piano, a large musical instrument, can be easily combined with a variety of intelligent devices. This is significant in terms of the combination of offline artificial intelligence and music education because it means that a new teaching mode will be produced. Additionally, this new teaching mode will The improved piano music recognition system is investigated in this study, which uses artificial intelligence as its foundation. In the beginning, both the overall architecture of the system and the functional module design plan are shown. It is well known that the feature matrix of a piano is distinct from that of other musical instruments. This distinction was discovered when a digital recognition approach was used to extract the music timbre feature matrix of a piano. The findings of the experiments indicate that the suggested system has a high recognition accuracy for the purpose of timbre recognition of piano arrangements, which demonstrates that it is deserving of further promotion and application.

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An Enhanced Prediction Logic to Forecast Bitcoin Price Variations Using Elevated Machine Learning Principles

An Enhanced Prediction Logic to Forecast Bitcoin Price Varia...

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International Conference on science technology Engineering and Management (ICONSTEM)

作者： J. Bhuvaneswari M. Saranya Nair S. Durga Devi J. Santhiya Juliet. N Department of AI&DS RMK College of Engineering and Technology Puduvoyal Chennai School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai Vel Tech High Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai Department of Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Pollachi Master of Computer Applications S.A.Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The volatility of Bitcoin prices presents both challenges and opportunities for investors and researchers seeking to understand and forecast cryptocurrency market dynamics. In this study, we propose a Hybrid Recurrent Neural Network and Autoregressive Integrated Moving Average (RNN-ARIMA) model to predict Bitcoin price variations. Leveraging minute-to-minute Bitcoin price data sourced from Bitstamp exchange, our methodology integrates the capabilities of RNNs and ARIMA models to capture both temporal dependencies and linear trends in the data. Through rigorous experimentation, we evaluate the performance of the hybrid model against baseline models and alternative configurations. Our results demonstrate the superiority of the Hybrid RNN-ARIMA model, with a Mean Absolute Error (MAE) of 120.54, Mean Squared Error (MSE) of 25098.23, Root Mean Squared Error (RMSE) of 158.45, R-squared (R2) of 0.87, and Mean Absolute Percentage Error (MAPE) of 3.8%. These findings underscore the effectiveness of the hybrid modeling approach in accurately predicting Bitcoin price variations and provide valuable insights for stakeholders navigating the volatile cryptocurrency market.

关键词： Recurrent neural networks Navigation Bitcoin Machine learning Predictive models Market research Mathematical models

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Analyzing Robotics and computer Integrated Manufacturing of Key Areas Using Cloud Computing 5

Analyzing Robotics and Computer Integrated Manufacturing of ...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Gupta, Awaneesh Dixit, Anil Kumar Kumar, K. Suresh Lavanya, C. Chakravarthi, M. Kalyan Gangodkar, Durgaprasad United Institute of Management Prayagraj Dr. AKTU Department: Master of Computer Applications Uttar Pradesh Lucknow India Uttaranchal University Department of LCD Dehradun India Panimalar Engineering College Department of MBA Poonamallee Chennai Varadarajapuram India Bannari Amman Institute of Technology Department of CSE Sathyamangalam India VIT - AP University School of Electronics Engineering Andhra Pradesh Amaravati India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The rising customer demands are fulfilled by the industrial sector through increased output and expedited shipping. The objectives or satisfied through the manufacturers by using cloud technologies. Tiers of manufacturing sectors adopt with the proposed technology in the future for maintaining the connection shows with the supply chain as well as customers. Cloud computing is referred as abstraction of storage, computation and infrastructure of network altogether as the platform which allows application deployment in speedy manner and dynamic scaling as well. The crucial area in cloud computing is self-service that can allow users to fill the web-form easily and quickly. A novel technical approach to job execution and resource sharing compared to conventional industrial robots is provided by Smart Manufacturing Environments, which uses cloud robotics. SME can be built up on the combination of cloud computing and industrial robots. The main focus of the proposed study is to successfully enabling the manufacturing services on demand which is provided over the internet. Manufacturing services with Cloud-based equipment can also be used by companies attempting to convert Collaborative Robots, Pa-as-you go business models, and production of increasing the productivity of production facilities, raising the caliber of products, enhancing producers' capacity to adapt to shifting consumer needs, and entering new markets. © 2022 IEEE.

关键词： Cloud computing

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A Novel Development of Artificial Intelligence Enabled Learning Methodology for Human Depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： G. Ramkumar R Priyadharshini K. Selvi K. Kalaiselvi S. Rathika N. Kanagavalli Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology R.M.K Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Human Depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human Depression Prediction Scheme (HDPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HDPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment.

关键词： Deep learning Accuracy Mental health computer architecture Depression Prediction algorithms Internet

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