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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Gnana Soundari, A. Al-Fatlawy, Mohammed Hussien Omkar, Jamvant Arun Balaji, N. Suseela, D. Asha, V. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics & Communication IES College Of Technology MP Bhopal462044 India New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Master of Computer Applications New Horizon College of Engineering Bellandur Post Bengaluru560103 India

ISBN: (纸本)9798350305418

The traditional majorization design method makes too many restrictions on the objective function to be optimized and its constraints, which brings a lot of inconvenience to solving majorization problems in practical projects. A new stochastic Optimization algorithm based on swarm intelligence - granule swarm algorithm (PSO) was put forward in, and it has been widely concerned by researchers. When the algorithm is initialized, the granules are randomly divided into several sub-granule groups, each sub-granule group evolves independently according to a given strategy, and random migration and adaptive mutation of granules are carried out in the specified period of evolution to maintain the diversity of the entire population., to avoid premature convergence. The inverse analysis of geotechnical engineering majorization is essentially a typical majorization problem of complex nonlinear functions. The use of global majorization algorithm is an ideal way to solve this problem. low productivity. The new algorithm is applied to the elastic-plastic parameter inversion of geotechnical materials. The results show that compared with the conventional granule swarm majorization algorithm, the improved algorithm significantly improves the search efficiency of parameters, and the results that meet the accuracy requirements can be obtained with less iterations, which reduces the amount of calculation of elastic-plastic back analysis of geotechnical engineering. It is a feasible parameter inversion method. © 2023 IEEE.

关键词： Efficiency Geotechnical engineering Granule Swarm Particle swarm majorization Structural majorization

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Enhanced Particle Swarm Optimization based Node Localization Scheme in Wireless Sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Singh, Shashank Poonkuzhali, R. Nithya, G. Kumar, R. Anil Kartigeyan, J. Ramya, S. Department of Computer Science and Engineering Integral University Kurshi Road Uttar Pradesh Lucknow India Department of Information Technology K.S.Rangasamy College of Technology Tamilnadu Tiruchengode India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Telangana Secunderabad India Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Telangana Hyderabad India Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu Thalavapalayam India

ISBN: (纸本)9781665489621

Wireless sensor networks (WSNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WSNs generally must be known. WSN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. Some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle Swarm Optimization based Node Localization Scheme (EPSONLS) in WSN. The presented EPSONLS technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPSONLS model applies the PSO algorithm to identify the node position. In addition, the EPSONLS model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPSONLS model over recent approaches. © 2022 IEEE.

关键词： Sensor nodes

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection

AI-Powered IoT Framework for Enhancing Building Safety throu...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S R Raja T. Rathi Devi J. Relin Francis Raj V. Krishna Sankar R. Santhana Krishnan R. Venkatalakshmi Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Christ University Bangalore India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Civil Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain.

关键词： Support vector machines Fuzzy logic Accuracy Buildings Boosting Stability analysis Real-time systems Safety Ensemble learning Monitoring

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Improved Piano Music Recognition Algorithm Based on Artificial Intelligence 2

Improved Piano Music Recognition Algorithm Based on Artifici...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

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

ISBN: (纸本)9798350305418

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. © 2023 IEEE.

关键词： Artificial intelligence improved recognition algorithm piano music

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management

Resilient Framework for Next-Generation Heterogeneous Wirele...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan S. Balamurugan G.Vinoth Rajkumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Associate professor Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Associate Professor Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments.

关键词： Wireless networks Heuristic algorithms Disasters Focusing Disaster management Network coding Routing Automobiles Next generation networking Resilience

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An Improved Network Segmentation Performance in Lesion Segmentation based on Mask R-CNN 6

An Improved Network Segmentation Performance in Lesion Segme...

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6th International Conference on electronics, Communication and Aerospace Technology, ICECA 2022

作者： Thivya, T. Kalaivanan, C. Juliet, N. Vengadapathiraj, M. Mary Valantina, G. Mohanavel, V. Velammal Engineering College Department of Information Technology Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (纸本)9781665482714

Computational intelligence has grown increasingly common in the analysis of healthcare data in recent years. The deep learning algorithms, in instance, have such a number of fruitful implications in the analysis of medical images. This research study has presented a double awareness deep convolution network-based system for end-to-end healthcare lesion classification. Several fully supervised and poorly supervised extracted features are included in this framework. The issue of the high cost of pixel-level labels with lesions in the medical pictures is alleviated by the weakly supervised segmentation module's use of bounding-box labels of lesion regions, which allows for correct lesion segmentation to be accomplished. In addition to this, a dual attention mechanism has been included for the purpose of improving the network's capacity for visual feature learning. The dual attention mechanism, which consists of both channel and spatial attention, may assist in drawing the network's attention to the extraction of features from significant locations. It is possible for it to considerably minimize the gaps between ground-truth labels and pseudo labels, in comparison to the current standard approach of weakly supervised segmentation using pseudo labels. Final results show that our proposed approach beat the competitors on to an oral disease collection, while our approach was extended to incorporate dermatological disease categorization in the ultimate analysis. © 2022 IEEE.

关键词： Intelligent systems

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To understand the relationship between Machine learning and Artificial intelligence in large and diversified business organisations

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Materials Today: Proceedings 2022年 56卷 2082-2086页

作者： Sandeep, S.R. Ahamad, Shahanawaj Saxena, Divya Srivastava, Kingshuk Jaiswal, Sushma Bora, Ashim Department of Electrical and Electronics Engineering SJB Institute of Technology Karnataka Bengaluru India Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India College of Computer Science and Engineering University of Hail Saudi Arabia Master of Science in Computer Science University of Texas at Arlington ArlingtonTX76019 United States Department of Informatics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Koni CG Bilaspur India Department of Mathematics. Diphu Government College Assam Diphu India

In the modern era, Machine learning and Artificial intelligence remain the most remarkable IT application, a technology that has seen unprecedented progress in recent decades. AI and machine learning are all closely linked with using a computer to simulate intelligent behaviour with little human interaction. It has been noticed that many large organisations are implementing technologies such as ML and AI to improve their performance and productivity in the business. Companies can create better, more customised and engaging campaigns as AI becomes increasingly adept at collecting massive data faster than ever before. As AI and ML technologies become more widely used, being a technology-powered company will undoubtedly be a must for survival. AI and ML are guiding on everything from production to delivering products and services to customers. Machine translation, chatbots, and self-learning algorithms are examples of AI technology that may help people better comprehend their surroundings and respond appropriately. Organisations have been embracing artificial intelligence technology advancements to create and maximising their strategic and competitive advantages. Hence, businesses rely heavily on AI to enhance their performance and develop new services to boost productivity and generate new offerings. In addition, they are fundamentally reshaping companies' business and operation processes to better meet customers’ requirements and expectations, resulting in increased efficiency. Machine learning and artificial intelligence are broad concepts that will be examined further in this article. The report aims to understand the relationship between Machine Learning and Artificial Intelligence in large and diversified business organisations. The researcher used a study methodology oriented toward a more inclusive and comprehensive approach to better account for the intangible aids of ML and AI within organisations. © 2021

关键词： Efficiency

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A Novel Approach to Identify the Anomaly Detection in Electricity usage based on Machine Learning Algorithms and Big Data 3

A Novel Approach to Identify the Anomaly Detection in Electr...

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3rd International Conference on Smart electronics and Communication, ICOSEC 2022

作者： Panneerselvam, M. Neela, K. Rajeshwari, R. Vengadapathiraj, M. Sobitha, S. Mohanavel, V. Sona College of Technology Department of Electrical and Electronics Engineering Salem India Sri Sairam Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Comprehension mistakes and frauds may both be detected via the analysis of advanced metering information. By means of on-site examinations or even other techniques, the energy suppliers will be made aware of any suspect usage patterns by this study. The analysis of usage patterns using Machine Learning (ML) techniques may reveal errors, measuring interruptions from assaults, or fine motor control with utility companies. Another of the basic instances of an anomaly detection is fraud detection since it is difficult to categories transactional or utilization information. Additionally, scams vary in type, making it difficult to consistently learn from them. This research study presents an unsupervised ML method to identify out-of-the-ordinary qualities in time series, set a threshold for the proportion of out-of-the-ordinary readings towards the overall measurements, and then classify the moving average as questionable or not. Initially, two different methods are provided to find the differences in time-series based data streaming: Spectral Residual-Convolutional Neural Network and anomaly trained model based on martingales. The properly established information is then subjected to Fisher Linear Discriminant analysis and Two-Class Boosted Decision Tree applications. © 2022 IEEE.

关键词： Large dataset

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Patient health monitoring system using smart IoT devices for medical emergency services

Patient health monitoring system using smart IoT devices for...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sreelakshmy, R. Sruthy, R. Rajeshwari, R. Thyla, B. Suresh Kumar, M. Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi600062 India NSS Polytechnic College Department of Electronics & Communication Engineering PandalamPandalamPandalam689501 India S.A. Engineering College Department of Master of Computer Applications Thiruverkadu Chennai600077 India KCG College of Technology Department of Electronics and Communication Engineering KCG College Rd Tamil Nadu Karapakkam Chennai600097 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai Thandalam602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

When devices, applications, monitors, and network services are connected together, the Internet of Things (IoT) is created, which allows these organizations to collect and share data more efficiently. The aware of the status of a person via the testing of numerous metrics, as well as the inference of a positive outcome from the past of this kind of continuous supervision, distinguishes the Internet of Things inside the medical system. A difficult endeavor, the prognosis of cardiovascular disease survival is important in assisting medical practitioners in making the best judgments possible for their individuals. Patients with heart failure need the knowledge and skill of health doctors to be properly cared for. The use of machine learning techniques may aid in the comprehension of the signs of cardiovascular disease. Hand - crafted feature development, on the other hand, is difficult and necessitates the use of specialized knowledge to determine the most suited approach. This research presents a smart health monitoring structure that makes use of the Internet of Things and cloud mechanism to enhance the mortality forecast of patients with chronic heart failure with no need for human classifier, as previously done. In addition, the smart IoT-based infrastructure analyzes individuals just on based entirely information and offers cardiac rehabilitation with fast, efficient, and high-quality medical care. Additionally, the suggested model analyses whether deep learning models are effective in distinguishing between heart failure patients who are alive and those who are died. The framework makes use of Internet of Things-based sensors to collect signals and transmit them to a cloud web application for analysis. Learning algorithms are used to further analyse these data in order to identify the condition of the patients. Health information and process monitoring are communicated with a medical expert who will respond to the patient in the event that emergency assistance is

关键词： Internet of things

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Deep Learning for Energy Demand Forecasting in Electric Vehicle Charging Stations

Deep Learning for Energy Demand Forecasting in Electric Vehi...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sudheer Hanumanthakari Anita S M. Beulah Viji Christiana Vellayan Srinivasan Sathish Kannan S.K. Nandha Kumar Department of Electronics and Communication Engineering Faculty of Science and Technology ICFAI Foundation for Higher Education Hyderabad Department of Electrical and Electronics Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Master of Business Administration S. A. Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electrical and Electronics Engineering PSNA College of Engineering & Technology Dindigul Tamil Nadu India

Electric vehicle (EV) growth in the Indian market has resulted in an enormous rise in energy demand. Because EVs are becoming more popular, effective charging infrastructure and energy management technologies are essential. Intelligent energy management systems are necessary to reduce the risks of power outages and disruptions resulting from the increasing number of EVs on the road. This paper proposes a novel Deep Learning (DL) model called Fractional Gradient-Based Recurrent Neural Network (FG-RNN) for predicting energy demand at electric vehicle charging stations (EVCS) to solve these challenges. This study examines and preprocesses data from an adaptable charging network (ACN). The proposed FG-RNN model was tested against two DL models: Deep Neural Network (DNN) and Long Short-Term Memory (LSTM). The FG-RNN model is one of the better solutions because of its high R-squared (R2) value of 97.682%. Furthermore, the model has a remarkably low Mean Absolute Percentage Error (MAPE) of 2.51% and a Mean Squared Error (MSE) of 0.072. Based on these results, the FG-RNN model appears to be a promising option for intelligent energy management systems for predicting energy consumption for EVCS.

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