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High Quality Segmentation Using Deep Learning Centered Detection and Correction of Cardiac MR Motion Artefacts Throughout Reconstruction 6

High Quality Segmentation Using Deep Learning Centered Detec...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rawat, Urvashi Batra, Vandana Sharma, Raj Kumar Kulandhaivel, M. Mukuntharaj, C. Dongre, Deepika School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India K. R. Mangalam University Department of Computer Science Gurugram India School of Allied Medical Sciences IIMT University Meerut India Karpagam Academy of Higher Education Department of Microbiology Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Deep learning techniques have been effectively used for a variety of purposes to separate anatomical features in healthcare imaging. Yet, the caliber of the picture being split has a significant impact on this outcome. The large quantity of clinical photographs with significant picture artefacts caused by organ motion, patient mobility, and/or picture acquisition-related difficulties is a frequently ignored topic inside the medical imaging evaluation field. This study compares various methods for concurrently compensating for artefacts and segregating the heart cavity. We address the effects of picture image noise on cardiac MR segments. The technique is based on our most current joint artefact detecting and reconstructing approach, which creates high-quality MR pictures from k-space the use of a By requiring a data integrity term, the associated logistic regression effectively transforms the artefacts correction problem into an under-sampled image enhancement challenge. In this research, we suggest coupling this with a recognition networks in edge architecture. Our instruction enhances three distinct tasks: Segmentation process, artefact restoration, and identification of artifacts are the first two. Using ventricular MR k-space data that has been artificially damaged with untreated rebuilt pictures;we train the reconstructing networks to autonomously repair formation-related aberrations. We demonstrate a high level of classification performance and clearly good pixel density in the absence of manufactured motion artifacts to use a training dataset comprising 500 2D+time cine MR observations from of the UK Biobank set of data. In comparison to several image correcting structures, we demonstrate improved efficiency. © 2023 IEEE.

关键词： cardiac MRI deep learning Image artefacts Image quality image segmentation

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An In-Depth Prediction on IoT-Driven Precision Location Using Particle Swarm Optimization 15

An In-Depth Prediction on IoT-Driven Precision Location Usin...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Manimegalai, L. Neelima, P. Pandian, A. Sevuga Bandili, Sunil Kumar Jai Ganesh, B. Ramachandran, Lalitha R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Spmvv School of Engineering and Technology Department of Cse Andhra Pradesh Tirupati India Karnataka Bengaluru India Saint Louis University School for Professional Studies Department of Computer and Information Sciences St. LouisMO63103 United States R.M.D. Engineering College Department of Ece Tamilnadu Kavaraipettai India R.M.K.Engineering College Department of Science and Humanities Tamilnadu Kavaraipettai India

ISBN: (纸本)9798350370249

A whole new era of connectedness has begun with the introduction of the Internet of Things (IoT), which makes it possible for inanimate objects to speak with one another in a straightforward manner. Within Internet of Things frameworks, accurate position has become a key component as a result of applications such as asset tracking, navigation, and environmental monitoring. When it comes to accuracy, scalability, and the ability to react to changing environments, the solutions that are currently available for IoT-driven precise positioning present some limits. When the precise location of something is of the utmost significance, it is necessary to have a solution that is both effective and flexible. The purpose of this research is to investigate the IoT precise location and to provide a novel approach that makes use of Particle Swarm Optimization (PSO) to enhance both efficiency and accuracy. For the purpose of achieving pinpoint accuracy in locating, the proposed method integrates PSO algorithms into the existing framework of the IoT. Through a series of simulations and tests, we will evaluate the performance of the PSO-enhanced system in comparison to that of traditional methods of localization. © 2024 IEEE.

关键词： Dynamic Environments IoT Localization Particle Swarm Optimization Precision Location

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The Implementation of Blockchain Technology to Manage Data in a Secure and Transparent Manner for Biomedical Applications 9

The Implementation of Blockchain Technology to Manage Data i...

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9th International Conference on communication and electronics Systems, ICCES 2024

作者： Velmurugan, M. Nirmala, D. Pandi, V. Samuthira Jeyanthi, P. Monisha, T. Bhagyalakshmi, T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi-Vel Tech Road Vel Nagar Avadi Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India Jeppiaar Engineering College Department of Cse Semmencherry Chennai India Jeppiaar Engineering College Department of It Semmencherry Chennai India

ISBN: (纸本)9798350377972

This research investigates the application of Blockchain technology to enhance the security, transparency, and efficiency of biomedical data management. By analyzing existing data management practices in the biomedical industry and identifying their limitations, the study explores the potential of blockchain to address these challenges. The research focuses on developing a blockchain-based framework for secure data sharing, leveraging the inherent features of blockchain technology, such as decentralization, immutability, and cryptographic security. Through simulations and case studies, the study evaluates the feasibility and effectiveness of the proposed framework in addressing critical issues such as data security, privacy, and interoperability. The findings of this research demonstrate the potential of blockchain technology to revolutionize biomedical data management, enabling secure and efficient data sharing while ensuring patient confidentiality and regulatory compliance. This research has broader implications for other data-intensive sectors, highlighting the potential of blockchain technology to enhance data security and integrity across various domains. © 2024 IEEE.

关键词： Data integrity

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Profiling and Non-Profiling Key Retrieval Attacks in Programmable Object Interfaces Using Deep Learning Cryptanalytic Techniques: A Survey

Profiling and Non-Profiling Key Retrieval Attacks in Program...

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Device Intelligence, Computing and communication Technologies, (DICCT), International Conference on

作者： R. Maheswari M. Krishnamurthy Department of Electronics and Communication Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering KCG College of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

Intensive Research in the area of Hardware & Networking involves Security. This key concept is bypassed in the initial Design & Development of Hardware devices (IoT), paving way for vulnerable attacks. Attacks in Hardware Devices (IoT) are broadly classified into (i) Active attacks (ii) Passive attacks. This survey focusses on Passive attacks that include side-channel analysis aimed at retrieving data or scanning open ports and network vulnerabilities. In this Technological era, Side Channel Attacks are threatening the whole world, because communication is almost digital everywhere and anywhere. Security experts and cryptologists are working on a method to prevent such attacks on cryptographic implementations or devices, in order to prevent personal information from being leaked or misused. Adapting ML/DL methodologies for providing security has been advocated in a number of researches. This paper gives an overall survey of all the Side channel attacks involving the execution of Cryptanalytic algorithms and its counter measures encountered in the last few decades.

关键词： Surveys Recurrent neural networks Circuits Side-channel attacks Hardware Real-time systems Task analysis

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Implementation of Collaborative Filtering-based Recommendation System for Grading Diabetic Foot Thermograms

Implementation of Collaborative Filtering-based Recommendati...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Josephine Selle Jeyanathan P. Nagaraj Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu Department of Computer Science Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

In recent years, foot thermography has shown an extensive potential for the diagnosis of diabetic foot ulcers. However, how effectively the thermograms are interpreted determines how accurate the detection is. The patient will require less severe treatment, such as amputation, the earlier the ulcer has been identified. Foot thermography still has difficulties in becoming a clinically useful diagnostic tool because there is still no exact imaging methodology or grading system in practice. In this paper, Diabetic Mellitus (DM) foot thermograms from the IEEE data port repository were considered for statistical analysis and development of a grading system. The Thermal Change Index (TCI) data were ranked using a collaborative filtering-based recommender system, and regression analysis was used to examine the relationship between the right and left foot. Angiosomes played an important role in assessing the temperature distribution of the two feet, as evidenced by the results, which show a difference in the correlation coefficient when angiosomes are considered for the analysis. The linear fit for the CG group is (r = 0.89) and for the DM group is (r = 0.83). The paper concludes with suggestions for future work based on the study's findings.

关键词： Temperature distribution Collaboration Infrared imaging Amputation Diabetes Regression analysis Thermal analysis Recommender systems Medical diagnostic imaging Foot

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fAMC: A FPGA Implemented Automatic Modulation Classification via Deep Learning for IoV Applications

fAMC: A FPGA Implemented Automatic Modulation Classification...

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IEEE India Conference (INDICON)

作者： Dipanjan Sar Debanjan Das Rajarshi Mahapatra Venkanna Udutalapally Department of Electronics and Communication Engineering IIIT Naya Raipur India Centre of Excellence in Affordable Healthcare IIT Kharagpur India Department of Computer Science and Engineering NIT Warangal India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by enabling real-time data exchange and decision-making to enhance traffic safety and reduce congestion. However, effective spectrum management in dynamic vehicular environments presents a significant challenge, particularly for Automatic Modulation Classification (AMC) in the presence of noise and high mobility. Traditional AMC methods, reliant on manual feature extraction and classical algorithms, often struggle under these conditions due to limited accuracy and high computational demands. Existing deep learning solutions, while promising, face difficulties in real-time implementation on resource-constrained devices like FP-GAs. Our work introduces a novel deep learning-based AMC framework optimized for FPGA deployment named “fAMC”, addressing these limitations. The proposed “fAMC” achieves a classification accuracy of 96.00% on the FPGA platform, with real-time processing capabilities of 15.7 frames per second. This demonstrates significant advancements in accuracy and efficiency, providing a robust solution for IoV systems.

关键词： Deep learning Accuracy Modulation Real-time systems Safety Convolutional neural networks Vehicle dynamics Field programmable gate arrays Internet of Vehicles Radio spectrum management

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Improvement of a Conflict Prediction Model on Highways using Deep Learning 2

Improvement of a Conflict Prediction Model on Highways using...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Dalai, Chitaranjan Manoj, P. Christiana, M. Beulah Viji Karthik, K. Sathishkannan, R. Rathi, Vikas Prasad, A. Rajendra Odisha University Technology and Research Department of Civil Engineering Odisha Bhubaneswar751029 India Vidyavardhaka College of Engineering Department of Civil Engineering Karnataka Mysore570002 India Panimalar Engineering College Department of Management Studies Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Bannari Amman Institute of Technology Department of Computer Science and Engineerning Tamil Nadu Sathyamangalam638401 India Graphic Era Deemed to Be University Department of Computer Science and Engineerning Uttarakhand Dehradun248002 India Sri Sai Ram Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

Artificial intelligence has 3 high-priority fields of study: language processing, speech, and machine vision. The growing need for evolution associated with process automation requires increasingly effective and innovative solutions with replication capabilities. Artificial vision particularly has the ability to be applied within a wide range of processes, finding its maximum utility in tasks of detection, identification and tracking of objects. The application of solutions based on this technology to traffic analysis proposes highly flexible processing tools with a virtually zero degree of intrusion with the nature of the process. Reverse engineering procedures and the application of specialized algorithms allow the development of systems for the study of a problem closely related to the development of cities. Obtaining values such as the flow of vehicles that circulate on a highway is an essential requirement for the planning or readjustment of road works. Thus, the importance of systems based on artificial vision that allow this type of analysis is denoted. In this paper, a vehicle tracking algorithm is developed to determine the trajectory described, tracking time and average speed of the same. In addition, a vehicle counting algorithm is developed to determine the flow of different types of vehicles captured in the study frames. Also, for validation of operability and repeatability of the developed algorithms are established. Implement a program based on OpenCV - C++ for the analysis of vehicular traffic in offline mode. © 2023 IEEE.

关键词： Vehicles

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Design of Internet of Vehicles (IoV) based Vehicle to Vehicle communication System

Design of Internet of Vehicles (IoV) based Vehicle to Vehicl...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Nagaraj P Muneeswaran V G Haneesh M Sripadh S Chaitanya C Ganesh Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Vehicle-to-vehicle (V2V) communication involves remote communication in which vehicles communicate with each other using interactive messages. Information will include speed, fire warning, problem management, loss of area, direction, response, and safety. The most important objective of progressing vehicle-to-vehicle communication through LI-FI is to dispose of cheap and life-threatening issues. Li-Fi requires Drove lights, such as those currently utilized in numerous energy-efficient homes and workplaces. These bulbs are prepared with a chip that subtly controls the light for optical information transmission. Li-Fi information is at that point transmitted by the Drove seed and gotten by the photoreceptor. Standard microcontrollers enable V2V communication via LI-FI. Node MCU is a microcontroller that controls the entire module. Data such as distance, fire, and emergency preparedness are processed using ultrasonic sensors, fuel sensors, and marking and disabling emergency switches that alert multiple vehicles when activated. The framework has tall information rates and complies with the 802.11bb determination. There’s moreover information security. Car-to-car communication through Li-Fi is done on the Arduino Uno microcontroller. This comes about given the network of Bluetooth frameworks, the removal of the vehicle that can be sent, fire readiness, and the capacity to reply to crises. The farthest distance is about 10cm, and the compartment is controlled or controlled by ultrasonic sensors and an LCD screen, for example, if a fire situation occurs, the fire signal is recommended and if there is an emergency, the LCD screen is ready for fire, the LCD screen is emergency warning and ready to provide information to other vehicles.

关键词： Microcontrollers Urban areas Vehicular ad hoc networks Light fidelity Liquid crystal displays Acoustics Safety

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Smart Shoes for Fitness and Performance Analysis of Sportsmen

Smart Shoes for Fitness and Performance Analysis of Sportsme...

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Computing, Power and communication Technologies (IC2PCT), IEEE International Conference on

作者： Shirley Selvan Srimathi Jayaraman Srinivedhini Varadharajan Madhumitha Rajendran Elaveni Palanivel Jaspin Kumaresan Electronics and Communication Engg St.Joseph’s College of Engineering Chennai India Computer Science and Engineering St.Joseph’s Institute of Technology Chennai India

This work proposes smart wearable technology used in today’s world to benefit and improve our country’s sports. In any sport, an athlete must be both physically and mentally strong. We have come up with a smart shoe-based application that monitors athlete’s foot temperature, heart rate, SpO2 levels, and posture levels. The voltage created by the force applied by the legs generates electrical energy, which is detected by piezoelectric sensors. Foot temperature is measured with the LM35 temperature sensor, posture level with the ADXL345 accelerometer, heart rate, and SpO2 with the MAX30100 sensor. Heart rate and SpO2 values are displayed on the OLED display. The sensor is connected to an ADC module, from which digital data is sent to the node MCU. Digital data transmitted by the MCU node is received via Bluetooth module on the player’s and coach’s smartphones (Android application) for self-management. This hardware-based application is designed to monitor fitness and to provide a summary to the sportsman and his coach.

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Innovative Solar Ironing System: Combining Renewable Energy with Advanced Deep Learning Techniques

Innovative Solar Ironing System: Combining Renewable Energy ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： P. Priyadharshini X. Arogya Presskila R. Santhana Krishnan G. Vinoth Rajkumar V. Vinoth Kumar G. Rekha Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Computer Science and 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 Electronics and Communication Engineering JP College of Engineering Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research study introduces an innovative solar-based ironing system that integrates renewable energy with advanced deep-learning techniques to optimize energy consumption in household ironing operations. The system harnesses solar panels to collect solar energy, ensuring efficient ironing during daylight hours. In cases of insufficient solar generation, the system automatically switches to a conventional power supply, guaranteeing uninterrupted functionality. A key feature of this system is incorporating a Long Short-Term Memory (LSTM) model, which predicts solar power output based on historic data and real-time climate information sourced from the OpenWeatherMap API. This predictive capability enhances energy management by providing timely insights into expected solar energy availability, allowing users to plan ironing tasks effectively. Data preprocessing techniques, including imputation of missing values using the median and normalization, ensure the quality and reliability of the input data for training the LSTM model. The model is assessed using various performance metrics and compared to traditional forecasting methods, including ARIMA, Gated Recurrent Units (GRU), and Feedforward Neural Networks (FNN). The results demonstrate that the LSTM model significantly outperforms these alternatives in predicting solar energy generation. This research contributes to sustainable living by promoting renewable energy usage in everyday tasks and illustrates the potential of combining deep learning with solar technologies for improved energy efficiency in household applications.

关键词： Deep learning Renewable energy sources Solar energy Predictive models Data models Real-time systems Solar panels Forecasting Long short term memory Meteorology

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