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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Maharaja Vannia Moorthi, V. Krishna, Megha R. Kumar, Gopika Anil Thanikaiselvan, V. Subashanthini, S. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Privacy violations are common in our technology-driven world, where almost everyone interacts with internet-connected electronic devices. To address this, cryptography has emerged, concealing data during transmission to deter hackers. Cryptography involves basic mathematical operations, exchanging data based on a shared key known only to the sender and receiver. However, this method suffers from key dependency at the receiver's end, requiring constant key knowledge. To overcome this issue, a novel image encryption method has been introduced in this paper that is independent of the key on the receiver side. This method leverages techniques such as DNA Encoding, Chaotic Dynamics, and Key Hiding Mechanisms to increase the security of the sent image. The proposed method has been tested experimentally, and the results demonstrate its effectiveness with entropy values exceeding 7.995 and correlation coefficient values below 1% for the vertical, horizontal, and diagonal directions. Overall, the proposed image encryption scheme offers a promising solution for protecting sensitive image data in various applications, combining advanced encryption techniques with key management enhancements to ensure confidentiality and integrity. © 2024 IEEE.

关键词： Cryptography

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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Network Traffic Prediction Using Radial Kernelized-Tversky Indexes-Based Multilayer Classifier

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computer Systems Science & engineering 2022年第3期40卷 851-863页

作者： M.Govindarajan V.Chandrasekaran S.Anitha Department of Computer Applications Velalar College of Engineering and TechnologyErode638012India Department of Electronics and Communication Engineering Velalar College of Engineering and TechnologyErode638012India Department of Information Technology Kongu Engineering CollegePerunduraiIndia

Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any *** the use of mobile devices,communication services generate numerous data for every *** the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time *** RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic *** this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input ***,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function *** obtaining the relevant attributes,the selected attributes are given to the next *** index function is used in this layer to compute similarities among the training and testing traffic *** similarity index outcomes are given to the output *** value is used as basis to classify data as heavy network or normal ***,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL *** evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.

关键词： Cellular network traffic prediction connectionist Tversky multilayer deep structure learning attribute selection classification radial kernelized long short-term memory

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Automated Aeroponics System: IoT-Based Monitoring and Control for Optimized Plant Growth 4

Automated Aeroponics System: IoT-Based Monitoring and Contro...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Tanna, Rajesh Santhosh, Talada Gollavilli, Sanjay Kumar Demudu, Lagudu Suri Naik, Ratala Koteswara Raghuram, Cheepurupalli Department of Electrical and Electronics Engineering Vignan's Institute of Information Technology Visakhapatnam530049 India Department of Electrical and Electronics Engineering Visakha Institute of Engineering & Technology Visakhapatnam530027 India Department of Electrical and Electronics Engineering Vignan's Institute of Engineering for Women Visakhapatnam530046 India Department of Electrical and Electronics Engineering A. M. Reddy Memorial College of Engineering and Technology Narasaraopet522601 India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram534204 India

ISBN: (纸本)9798331530402

Challenges of farming are getting worse due to pests, other environmental issues, and changes in the climate. Several novel technologies have been developed to improve plant growth and productivity, yet there are still numerous issues in this field. This suggests that daily improvements are needed. This paper provides an innovative automated system that uses a variety of environmental sensors, including as pH, TDS, and humidity, for hydroponics and aeroponics. These sensors continuously monitor and modify crucial components to provide the perfect growth environment. In addition, the system has autonomous nutrient dosing, which reduces the need for human intervention and boosts overall effectiveness. Better control over the farming process is made possible by the system's ability to scale and monitor in real-time thanks to the integration of IoT technologies. The study also looks at how machine learning (ML) and artificial intelligence (AI) might improve crop health monitoring and growth forecasts. Additionally, an LED-lit indoor farming setup is included in the system. © 2025 IEEE.

关键词： Agricultural robots

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Revolutionizing Home Connectivity with IoT-Enabled Smart Mirrors for Internet Browsing and Smart Home Integration 5

Revolutionizing Home Connectivity with IoT-Enabled Smart Mir...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Prabha, M. Manicka Jegadeesan, S. Jayavathi, S.D. Rajkumar, G. Vinoth Jothi, J. Nirmal Krishnan, R. Santhana SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India JP College of Engineering Department of Electronics and Communication Engineering Ayikudi Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350395235

This research study explores the transformative potential of IoT-Enabled Smart Mirrors to redefine home connectivity by enabling seamless internet browsing and smart home integration. It introduces a novel model algorithm, IoT-ViSense (IVS), which leverages IoT sensor data fusion with vision-based Convolutional Neural Networks (CNN) for real-time environmental monitoring and security. A comprehensive simulation analysis of IoT-ViSense is conducted and compared against existing algorithms using pertinent simulation metrics. IoT-Enabled Smart Mirrors serve as multifunctional interfaces, integrating personalized weather updates, news, calendar schedules, and real-time compliments. They also incorporate features such as a built-in camera for capturing high-quality selfies, on-demand workout sessions facilitated by fitness experts, and the ability to browse the internet, make calls, and interact with other connected devices. The proposed IoT-ViSense (IVS) algorithm combines IoT sensor data with Vision-based CNN analysis to enhance environmental monitoring and security. It continuously processes data from IoT sensors and camera feeds, detecting anomalies and patterns in real-time. By fusing these data streams, IVS enhances security by identifying unauthorized access and suspicious activities while concurrently optimizing resource usage and ensuring comfortable living and working environments. The simulation analysis of IoT-ViSense (IVS) is compared with suitable existing algorithms, assessing its efficiency in terms of accuracy, response time, and resource optimization. Results reveal that IoT-ViSense (IVS) outperforms other algorithms, demonstrating its prowess in achieving a harmonious blend of smart home integration and real-time environmental monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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Underwater Image Enhancement using CLAHE Fusion in YIQ and HSI Color Model 4

Underwater Image Enhancement using CLAHE Fusion in YIQ and H...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumar, Ravi Kumar, Ashish Kumar, Manikant Rai, Mithilesh Kumar Kumari, Shweta Mourya, Vishal Electronics and Communication Engineering Muzaffarpur Institute of Technology Muzaffarpur India Computer Science Engineering Muzaffarpur Institute of Technology Muzaffarpur India Leather Technology Muzaffarpur Institute of Technology Muzaffarpur India Information Technology Muzaffarpur Institute of Technology Muzaffarpur India National Institute of Electronics and Information Technology Patna India

ISBN: (纸本)9798331528614

- Underwater image enhancement has been employed in a variety of areas, such as terrain scanning, underwater microscopic detection, telecommunication cables, mine detection, and autonomous underwater vehicles. However, underwater imagery suffers from strong scattering, noise, color distortion, and absorption from artificial light sources, causing haziness, image blur, and a greenish or bluish texture. To address these issues, the proposed work introduces a new enhancement approach based on Contrast Limited Adaptive Histogram Equalization (CLAHE) used for images taken underwater. The proposed method employs CLAHE to adaptively enhance local contrast while constraining the amplification of noise and artifacts. The fusion method is proposed to enhance the complementary advantages of different color spaces in YIQ and HSI color models. The YIQ color space separates intensity from chrominance, allowing for independent processing of luminance information, which is crucial for preserving image details and contrast. The proposed algorithm improves low-contrast underwater images with intact color and a natural appearance with reliability according to the results of the qualitative analysis. The proposed method outperforms the state-of-the-art methods in terms of image performance measures like GMSD, VSI, MCSD, and PSNR. ©2024 IEEE.

关键词： Underwater imaging

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Design of Intelligent Solar Cooling System with IoT Monitoring 3rd

Design of Intelligent Solar Cooling System with IoT Monitori...

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3rd International Conference on Emerging electronics and Automation, E2A 2023

作者： Kushagra, C. Kirtimaan, S. Rushit, P. Jane Joshita, K. Divya Navamani, J. Lavanya, A. Department of Computer Science Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819730896

Currently, IoT rules many unmanned applications to improve supervision and productivity. The proposed work concentrates on the need for a cooling system for solar Photovoltaic (PV) panels to enhance its efficiency. An increase in temperature will reduce the efficiency of the solar panel. The cooling system should be monitored, automated, and controlled without human intervention. This is achieved by developing a web App by which the cooling system can be controlled automatically. DHT11–Temperature and Humidity Sensor is used to monitor the temperature of the panel. If the module temperature increases more than 35 °C to 60 °C, the sensor sends the signal to the actuator to cool the PV panel, which will increase the output power. This technique is proposed for solar rooftop home applications where the grey water can be treated and used for cooling the solar panel rather than using fresh, clean water. Finally, a web-interface is done to cool the solar panel with water pumping through the tubes below the panel. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cooling IoT Solar Temperature Web interface

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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CardioNet: An Integrative AI-Machine Learning Framework for Enhanced Prediction and Management of Cardiovascular Diseases Using Deep Data Analytics and Clinical Insights 4

CardioNet: An Integrative AI-Machine Learning Framework for ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Senthil, G.A. Prabha, R. Thamarai, I. Roopa, D. Rajesh Kanna, R. Srimathi, C. Agni College Of Technology Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798331508845

Cardio Vascular Disease (CVD) causes dysfunction in our heart and blood arteries, which frequently results in death or paralysis. Therefore, many lives can be prevented by the early and automatic recognition of CVD. Although this goal has been the subject of numerous studies, performance and dependability can yet be enhanced. This work represents another advancement in that direction. Given that cardiovascular diseases (CVDs) remain the primary cause of death worldwide, effective strategies for early detection and management are necessary. To help with CVD prediction and management, we introduce CardioNet, an integrated machine learning framework with strong data analytics and clinical insights. To accurately predict the risk and progression of CVD, CardioNet integrates state-of-the-art machine learning algorithms with a variety of medical data sources, including genetic profiles, imaging data, and electronic health records. CardioNet integrates clinical information and feedback mechanisms to offer customized risk assessment and therapy suggestions. This, in turn, improves patient outcomes and lowers the cost of healthcare. The potential of CardioNet as a comprehensive strategy to counter the widespread threat of CVDs is highlighted. © 2024 IEEE.

关键词： Electronic health record

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