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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Gorde, Swapnil Gulabrao Singh, Archana Tamilselvi, M. Ravivarman, G. Rajendiran, M. Maranan, Ramya MIT College of Management MIT Art Design & Technology University Pune India Private University Maharashtra India Roever Engineering College Department of Computer Science and Engineering Tamil Nadu Perambalur India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This study suggests a powerful Three- Tier Architecture that combines machine learning models, Internet of Things (IoT) devices, and sophisticated sensors to provide continuous cardiac health monitoring. A range of physiological data is provided by the sensors in the Data Collection Tier, which measure blood pressure, temperature, acceleration, and electrocardiogram (ECG). In the Data Processing Tier, a local server applies complex algorithms to efficiently assess the gathered data and provide insightful outcomes. By providing easy access, authentication, and alarm production, the Application Tier acts as the interface and guarantees efficient contact with healthcare providers. The unique aspect of the system is the integration of machine learning models, which were trained on recorded data for predictive analysis. k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Decision Trees (DT), and Recurrent Neural Networks (RNN) are some examples of these models. Impressive accuracy levels are seen in the results, with ANN leading at 96.44% The measures of accuracy, precision, recall, and F1 score all demonstrate the algorithms' capacity to anticipate possible health risks, which supports the models' effectiveness. This proactive paradigm in healthcare is made possible by the comprehensive approach of continuous heart health monitoring, which enables prompt interventions and customized treatment strategies. This study increases our knowledge of the dynamics of cardiovascular health and the rapidly growing field of health care technology by demonstrating the synergy of sensor-based monitoring, IoT connectivity, and machine learning analytics in a unified architecture. In the end, patient care is improved. © 2024 IEEE.

关键词： Blood pressure

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Performance Analysis of Novel Saw-Tooth Shaped Fractal Boundary Square Micro Strip Patch Antenna 7

Performance Analysis of Novel Saw-Tooth Shaped Fractal Bound...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Sathesh, M. Govindaraju, P. Kalyanasundaram, P. Gurusamy, Ravikumar Sengottaiyan, N. Rajasekar, M. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Avadi Department of ECE Tamil Nadu Chennai600062 India KSR Institute for Engineering and Technology Department of ECE Tiruchengode637215 India KSR Institute for Engineering and Technology Department of IT Tiruchengode637215 India M.P.Nachimuthu M.Jaganathan Engineering College Department of ECE Erode India Alliance College of Engineering and Design Computer Science Faculty of Science and Technology Alliance University Karnataka Anekal562106 India Institute of CSE SIMATS School of Engineering Saveetha Institute for Medical and Technical Sciences Chennai602105 India

ISBN: (纸本)9798350340600

Aim: This study contributes in the modelling of a high gain Saw-Tooth shaped fractal boundary square antenna (SFBS Antenna) in comparison with the square shaped microstrip patch antenna (MPA) in the 1.0 GHz to 10.0 GHz band of frequency. Materials and Methods: Group 1 is assumed to be SFBS Antenna with sample size (n = 26). Group 2 is a Square shaped MPA with sample size (n = 26). The substrate is FR4 Epoxy type. The confidence interval is 95%, the threshold is 0.05%, and the G power value is 80%. Results: The Saw-Tooth shaped fractal boundary square antenna has higher gain between 4.34 dB to 43.56 dB than the Square shaped micro strip patch antenna gain of 3.56 dB to 38.78 dB. The Saw-Tooth shaped fractal boundary square antenna has maximum gain at the frequency range of 10.0 GHz with significance 0.005. The square shaped microstrip patch antennas have maximum gain at 10.0 GHz. Conclusion: This study concluded that the Saw-Tooth shaped fractal boundary square antenna gives significantly better gain at frequency range of 10.0 GHz in comparison with the square shaped MPA. © 2023 IEEE.

关键词： Fractals

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Integrating Beetle Swarm Optimization into Cross-Layer Routing for Improved Qos In Cluster-Based Wsns 4

Integrating Beetle Swarm Optimization into Cross-Layer Routi...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Soni, Anita Sonare, Satendra Puneeth, S. Yadav, Anjali Sathiamoorthy, J. Tiwari, Aman Raj Ies University Department of Computer Science and Engineering Bhopal India Gyan Ganga Institute of Technology & Sciences Dept of CSE-IOT Jabalpur India Dayananda Sagar University School of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Sait Indore India Rmk Engineering College Department of Computer Science and Design Kavarapettai India Ggits MP India

ISBN: (纸本)9798331518981

An overview of cross-layer architecture and quality-of-service optimizing for router is provided, with a focus on improving the efficiency of cluster oriented wireless sensors via the integration of the Beetle Swarm Optimization (BSO) method with the enhanced Dynamic Source Routing (DSR) approach. Cross Layer design (CLD) is the name of the suggested approach;in this, processing restrictions related to energy are taken into primary consideration. While the IEEE 802.11 (Wi-Fi) standard was evaluated as a gateway in Chapter 3, it does have a few drawbacks, such as the fact that it can only handle small networks and that every router has only a certain amount of battery capacity. Thus, for the sake of cross-layer layout, this component takes into account the ZigBee/IEEE 802.15.4 (WPAN) wirelessly personalized area network design. A number of settings are adjusted to enhance the wireless sensor network's efficiency depending on the ZigBee structure of layers. Power from batteries is the main focus of the physical component. The media access control (MAC) layer handles packet sequencing and bandwidth that is available. Both the transmission and network layers are responsible for controlling overcrowding. The networking layer handles routable technologies. This chapter concludes, in its experimental part, that the suggested strategy improves the Quality of Services in wireless networks of sensors by outperforming in terms of throughput, consumption of energy, packet delivery ratio, and latency. While Dynamic Source Routing (DSR) and Adhoc On-Demand Distance Vector Routing (AODV) demonstrate substantially lower and more variable levels of accomplishment, the suggested approach Cross Layer design (CLD) maintains an accuracy in the vicinity of 95%. This is in contrast to the other two methods. © 2024 IEEE.

关键词： Zigbee

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A Machine Learning Approach in Predictive Maintenance in the IoT Enabled Industry 4.0 4

A Machine Learning Approach in Predictive Maintenance in the...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Shankar, B. Prabhu Yadav, Rakesh Kumar Jayalakshmi, K.P. Solution Architect Pilvi Systems Inc LewisvilleTX75057 United States Alliance University Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka562106 India Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India St Joseph Engineering College Department of Electronics and Communication Engineering Karnataka Mangaluru India

ISBN: (纸本)9798350300888

Predictive maintenance using machine learning is a powerful technique for industries seeking to enhance their operations with minimize downtime. In an IoT-enabled Industry 4.0 environment, this approach can be taken to a new level by leveraging the vast amounts of data generated by connected devices. To implement a machine learning methodology to projecting conservation in an Industry 4.0 environment, several key steps need to be taken. First, data from IoT devices across the industrial ecosystem should be collected and centralized in a data lake or similar storage system. This data should include information on equipment health, sensor readings, and other relevant metrics. Next, the data should be preprocessed and transformed to ensure its quality and consistency. This may involve cleaning, normalization, and feature engineering to create relevant variables for use in machine learning models. Once the data has been preprocessed, a range of machine learning models can be trained on it to predict equipment failures or other maintenance issues. This may involve ongoing tuning and optimization of model hyperparameters or retraining the models on new data as it becomes available. Finally, the predictions generated by the machine learning models should be integrated into a broader maintenance management system to enable timely action. This may include triggering maintenance requests, generating work orders, or even automating maintenance tasks through the use of robots or other industrial automation technologies. By implementing a machine learning method to projecting preservation in an IoT-enabled Industry 4.0 environment, industries can optimize their operations, minimize downtime, and improve overall equipment effectiveness. © 2023 IEEE.

关键词： Industry 4.0

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Fraud Detection in Monetary Transactions

Fraud Detection in Monetary Transactions

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International Conference on Advances in Information technology (ICAIT)

作者： Rekha R Nair V. Vjeya Kaveri Shrinidhi Sivakumar Srinithi Janani R Tina Babu Rajesh Sharma R Department of Computer Science and Engineering Alliance University Bangalore India Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Fraudulent transactions pose significant challenges to the banking industry, leading to significant losses. Conventional identification methods often struggle to keep up with changing fraud techniques. In this study, proposed a new approach using a hybrid algorithm combining Bagged Trees Classifier and Random Forest. Bagged Trees Classifier classifies instances by tree struc- ture, while Random Forest aggregates predictions from multiple trees to increase accuracy in detecting fraudulent behavior the proposed approach aims to overcome existing limitations role by reducing false positives and improving detection accuracy. out line the method for data set partitioning, model selection, training, and evaluation, and demonstrate the effectiveness of our approach. By combining a bagged trees classifier with a random forest, the proposed approach provides a robust framework for detecting fraudulent transactions in banking, providing proactive anti-fraud strategies while reducing financial losses.

关键词： Training Machine learning algorithms Accuracy Banking Predictive models Prediction algorithms Credit cards

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Deep Learning-Based ECG Analysis for Anomaly Detection and Classification Using DCNN

Deep Learning-Based ECG Analysis for Anomaly Detection and C...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Moghal Yaseen Pasha Nitin Sharma Priyanka Vipul Kumar Verma Amar Choudhary Rahul Sharma Department of CSE-DS CVR College of Engineering Hyderabad India Department of Information Technology Graphic Era Hill University Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Applications Chandigarh University Gharuan India Department Computer Science and Engineering United College Of Engineering & Research Prayagraj India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

This investigation shows how effective deep learning-based Electrocardiogram (ECG) analysis for anomaly detection and classification can be realized in an IoT-enabled health monitoring system, employing Deep Convolutional Neural Networks (DCNN). In this research ECG data from IoT sensors, transmitting to the cloud storage and are preprocessed for the study. Dataset contains 3200 records, and of this data 70% is used for training, whereas the rest (30%) are test instances. After that preprocessing the raw ECG signals are denoised and artefact is corrected, normalized then segmented. Different models like DCNN, LSTM networks, SVM and RF were trained & tested. The highest accuracy with the DCNN model was 96.88%, along with showing 97.20, 96.50, and 96.85 for precision, recall as well as F1 score respectively in giving us a very high understanding that it can accurately predict steriod induced cardiac anomalies. The LSTM model also had a well performance with 94.5% accuracy, and SVM about 91.2%, RF was the less predictable one achieving on average only 88.98%. Confusion matrix also attested the generalizability of DCNN with 460 true positives and 515 true negatives. This study illustrates the promise of deep learning models to model even more nuanced features from ECG signals and produce trustworthy, truthful forecasts. This research could be integrated into IoT health monitoring solutions to provide continuous real-time cardiac health surveillance, early anomaly detection and timely medical intervention resulting in better patient care and outcomes for clinical as well as home healthcare.

关键词： Deep learning Support vector machines Analytical models Accuracy Electrocardiography Predictive models Data models Long short term memory Anomaly detection Classification tree analysis

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A Multi-Task Learning Framework for Joint Sub-Nyquist Wideband Spectrum Sensing and Modulation Recognition

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China Communications 2025年第1期22卷 128-138页

作者： Dong Xin Stefanos Bakirtzis Zhang Jiliang Zhang Jie Department of Electronic and Electrical Engineering University of SheffieldSheffield S102TNUnited Kingdom Department of Computer Science and Technology University of CambridgeCambridge CB21TNUnited Kingdom College of Information Science and Engineering Northeastern UniversityShenyang 110819China Ranplan Wireless Network Design Ltd. Cambridge CB233UYUnited Kingdom

The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication ***,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high *** paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this *** this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband *** demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband ***,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.

关键词： automated modulation classification cognitive radio convolutional neural networks deep learning spectrum sensing sub-Nyquist sampling

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Scalable and Reliable Cloud-Based UV Monitoring for Public Health Applications 4

Scalable and Reliable Cloud-Based UV Monitoring for Public H...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Dankan Gowda, V. Shekhar, R. Prasad, K.D.V. Kumar, Pullela S.V.V.S.R. Gangadharan, Sreeti Srividya, C.N. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Pune India Department of Computer Science & Engineering Aditya College of Engineering Andhra Pradesh Surampalem India Department of Science and Humanities Mahaguru Institute of Technology Kerala India Karnataka B.G Nagara India

ISBN: (纸本)9798350305258

The creation and assessment of a scalable and trustworthy cloud-based UV monitoring system designed for public health applications are the main topics of this research article. Monitoring UV radiation is important because it plays a crucial part in understanding and reducing a variety of health hazards, such as vitamin D insufficiency and skin cancer. Traditional monitoring techniques frequently have limitations due to their confined character and lack of access to real-time data. We suggest a cloud-based UV monitoring system that integrates cutting-edge UV sensors with cloud computing infrastructure to overcome these constraints. The process entails placing UV sensors in various geographical places in order to collect UV radiation data in real time. Due to the seamless integration of these sensors with a cloud platform, continuous data transmission, archival, and analysis are possible. The raw sensor readings used in our study are transformed into meaningful UV index values using data processing techniques, giving us valuable information for public health initiatives. This study introduces a system for tracking the ultraviolet (UV) index that is based on the Internet of Things (IoT). The goal is to help avoid ailments that can be brought on by prolonged exposure to sun UV light. In reaction to variations in the UV index level over time, the system accomplishes this by sending warning messages and suggesting preventive steps. Monitoring the UV index is crucial for avoiding a number of illnesses, including calcium shortage, cardiovascular disease, and skin cancer. This study introduces a system for tracking the ultraviolet (UV) index that is based on the Internet of Things (IoT). The goal is to help avoid ailments that can be brought on by prolonged exposure to sun UV light. In reaction to variations in the UV index level over time, the system accomplishes this by sending warning messages and suggesting preventive steps. Monitoring the UV index is crucial for avoidin

关键词： Cloud-based system Data analysis Data transmission Public health applications Reliability Scalability UV radiation monitoring UV sensors

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Artificial intelligence technique based performance estimation of solid oxide fuel cells

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Materials Today: Proceedings 2023年 80卷 2573-2576页

作者： Subramanian, Yathavan Veena, R. Muhammed Ali, S.A. Kumar, Avinash Gubediran, Ramesh Kumar Dhanasekaran, Anitha Gurusamy, Dhanasekaran Muniandi, Kalpana Department of Mechanical Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India Faculty of Integrated Technologies Universiti Brunei Darussalam GadongBE1410 Brunei Fuel Cell Institute Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Arni India Department of Mechanical Engineering Vellore Polytechnic College Vellore India Department of Computer Science and Engineering Government College of Engineering Salem India

In General, a larger number of experimental data would be required for optimizing the performance efficiency of the electrodes in solid oxide fuel cells (SOFC), as it was likely to involve many complex chemical and physical reactions. Therefore, in this study, we have attempted the artificial intelligence (AI) technique for analyzing the performance of the anode and cathode electrodes of SOFC under various key parameters that normally influences the efficiency of the electrodes. Since the cost of SOFC elements is expensive, computational trials with the aid of AI would not only be curtailing the preparation cost but also ensures less time consumption. Among the variety of AI methods available in the literature, the Support Vector Machine (SVM) appears to be one of the excellent and effective machine learning techniques. Hence, we have developed a SVM model and predicted the maximum current density and power density of Nickel oxide-samarium-doped ceria (NiO-SDC) composite anode and La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) cathode electrodes of SOFC. The input parameters such as stack temperature, supply voltage were supplied to the SVM model and obtained maximum current density and power density as output and it decides the working performance of the electrodes. We have also predicted that the maximum current density and power density of 1160 mA cm2 and 225 mW cm2 respectively at 800 °C. To validate the results as predicted from SVM, we have experimented using Yttrium stabilized zirconia electrolyte-supported single cell with our prepared NiO-SDC anode and LSCF cathode which exhibited a maximum current and power density of 1170 mA cm2 and 227 mW cm2 at 800 °C using H2 as fuel. It has been found that that the theoretical predicted data current and power densities of the electrodes from the SVM approach reasonably agrees well with the experimental results. © 2021

关键词： Nickel oxide

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Automated Glaucoma Screening in Retinal Fundus Imagery: Leveraging a Convolutional Neural Network Framework

Automated Glaucoma Screening in Retinal Fundus Imagery: Leve...

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Advancement in technology (ICONAT), International Conference for

作者： Tina Babu M. Vengateshwaran K. Sasi Kala Rani Ramya Raghunath Joshi Rekha R Nair R Rajesh Sharma Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Glaucoma, a prevalent eye ailment, necessitates early detection and treatment to prevent irreversible vision loss. Conventional screening methods are often time-intensive and require specialized expertise, limiting accessibility, especially in remote areas lacking ophthalmologists. This study introduces a CNN model, harnessing deep learning to analyze retinal images and extract glaucomatous indicators. Evaluation of the CNN’s performance on a diverse dataset includes accuracy, sensitivity, specificity, and F-score assessment. Results underscore the CNN’s promise as an efficient and dependable tool for automated glaucoma screening. This advancement offers optimism for enhancing early diagnosis and intervention, critical for managing this sight-threatening condition.

关键词： Glaucoma Deep learning Training Analytical models Accuracy Transfer learning Collaboration Retina Convolutional neural networks Medical diagnostic imaging

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