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检索条件"机构=Be in Computer Engineering L D College of Engineering"

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Smart device to Check Harmful Chemicals in Fruits and Vegetables 7

Smart Device to Check Harmful Chemicals in Fruits and Vegeta...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Saranya, R. Kumar, Antony K. Rajendran, P. Kumar, Niresh S. Keerthana, P. Department of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Computer Science & Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Tamilnadu Chennai India Electronics and Communication Engineering Selvam College of Technology Tamilnadu Namakkal India

ISBN: (纸本)9781665464086

Fruits and vegetables square measure extraordinarily nutritive and kind as key food merchandise among the human consumption. They are extraordinarily destructible due to their low amount. Fruits and vegetables square measure the necessary provide of carbohydrates, proteins and minerals. These food commodities square measure reported to be contaminated with toxic and health unsafe chemicals. To overcome this problem, proposed model uses IOT to detect the chemical levels. In this method gas detector has placed and estimate the chemical level in fruits and vegetables. If any chemicals detected immediate alert is distributed through GSM to the owner. In most of the hostel mess and government schools, everybody is getting tormented by the food they consume. Milk and fruits like banana and alternative foods utilized in existence, as all of them don't provide quality since the existence of Fulani gases vary from time to time. To confirm food safety, it's tough to be monitored at every stage of the provision chain. Variation in hydrogen ions concentration is going to be fetch, style, flavor, shelf-life of farm product. These gases increase with time. The aim of this method is to note early food spoilage before signs square measure visible. © 2023 IEEE.

关键词： Proteins

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Identification of Wheat leaf diseases Based on deep learning Algorithms 7

Identification of Wheat Leaf Diseases Based on Deep Learning...

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

作者： Therasa, P.R. Hemalatha, R. Sivajothi, E. Jayashankari, J. Anni Princy, B. Uma Maheswari, S. RMK Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India RMD Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of IT Tamilnadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Department of Biosciences Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350340600

Utilizing a convolutional neural network (CNN) architecture, the proposed method reliably extracts pertinent information from wheat leaf images for disease diagnosis. Preprocessing and data augmentation methods enhance the quality of the wheat-leaf image. CNNs are designed to detect and understand certain characteristics of the images they receive. The network is trained and optimized to classify illnesses more accurately. Standard performance evaluation indicators are utilized to demonstrate the accuracy of wheat leaf disease diagnosis using the suggested technique. Experiments demonstrate that solutions based on deep learning outperform more conventional methods. The proposed work demonstrates how a proposed CNN model outperformed existing techniques by a significant margin (98.31 percent) and how this enhancement would assist in the early diagnosis and treatment of wheat leaf diseases, thereby reducing the likelihood of catastrophic crop loss. © 2023 IEEE.

关键词： Convolutional neural networks

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Empirical Evaluation of the Automated Generation of Software Tests from the Perspective of Test Smells 9

Empirical Evaluation of the Automated Generation of Software...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Josphineleela, R. Selvi, R. Panneer Jayasudha, T. Garg, Neha Ganesh, C.S. Sundar Kumar, M. Jogendra Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamil Nadu Chennai600062 India Kpr Institute of Engineering And Technology Department of English Tamil Nadu Coimbatore641407 India Graphic Era Deemed To Be University Department of Computer Science And Engineering Uttarakhand Dehradun248002 India Karpagam College of Engineering Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641032 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh vaddeswaram522302 India

ISBN: (纸本)9798350365092

The expense of composing test code may be equivalent to or higher than the expense of composing creation code. Robotized unit test generators are every now and again utilized by designers to speed up programming testing. These instruments are intended to accelerate creation without compromising code quality. In any case, as unit tests normally miss the mark on quality control layer over the testing code, it could be trying to guarantee the honesty of the tests that are made. One way to ensure the quality of the tests is to break down the presence of test smells in the test code. Test code highlights known as 'test smells' may highlight imperfections in test origination and execution. Their reality could act as a measure of value. In this review, we utilized a multi-stage, cross-approved manual examination to order and name six different test fragrance types. We look at the viability of two test fragrance discovery frameworks, one that has been around for some time and one that was simply delivered with the express motivation behind copying engineer view of test smells. More specifically, the more established methods for identifying the devices incorrectly classified more than 70% of test smells, not only leaving out actual cases (false negatives) but also labeling many tests without smell as foul (misleading positives). According to our findings, these test smell recognition techniques performed a subpar job of describing the issues in naturally generated test suites). © 2024 IEEE.

关键词： Quality control

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Brain Tumor detection using MRI Image Processing Techniques

Brain Tumor Detection using MRI Image Processing Techniques

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Jadhav, Ambaji S. Pawar, Rashmi Hattaraki, Sunilkumar M. Bagali, Shwetha Patil, Rajeshwari S. Patil, Bhagyashree Department of Elecrtical and Electronics Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Electronics and Communication Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (纸本)9798331505134

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. Brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image preprocessing is done using ClAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSd-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection. © 2024 IEEE.

关键词： Median filters

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An Efficient OdR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object detection and Recognition (OdR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented OdR model on the Exdark dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： deep learning

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IoT-Connected Telehealth Environments with long Short-Term Memory Networks for Precise Time-Series Patient Behavior Analysis

IoT-Connected Telehealth Environments with Long Short-Term M...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Maruthukannan, B. Karpagalakshmi, R.C. lakshmi, d. Rajapriya, M. Wise, d.C. Joy Winnie Srinivasan, C. Npr College of Engineering and Technology Department of Computer Science and Engineering Tamil nadu Dindugal India Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Advanced analytics are becoming more important in telehealth environments as the number of connected devices continues to rise, necessitating the collection of massive amounts of time-series patient data. This system uses long Short-Term Memory (lSTM) networks in telemedicine that are linked to the Internet of Things (IoT), with an emphasis on accurate longitudinal monitoring of patient behavior. An extensive dataset gathered from IoT devices that track several health metrics in real-time is used in the research. lSTM networks are used to detect patterns and relationships in time-series data using a deep learning technique. In turn, this allows the system to analyze patient behavior with more precision and speed. IoT provides seamless connections for real-time data transfer it offers continuous monitoring within the telehealth framework. The lSTM-based model improves our comprehension of patient behavior, which in turn enables us to spot outliers, forecast health trends, and provide individualized healthcare recommendations. Through its demonstration of lSTM networks' usefulness in the context of IoT-connected telehealth settings, the study makes a valuable contribution to the rapidly growing area of digital health. Accurate time-series analysis has the ability to improve patient monitoring, early intervention, and healthcare outcomes, as shown by the results. © 2024 IEEE.

关键词： long short-term memory

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Innovative Practice on Tuberculosis detection for Pneumonia diagnosis using Machine learning

Innovative Practice on Tuberculosis Detection for Pneumonia ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Manju, K. dinesh, P.M. Thiyagarajan, M. Alagarsamy, Manjunathan Subramanian, Balambigai lakshmi, G. Prasanna Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy India Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641032 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India

ISBN: (纸本)9798350390049

One of the top 10 major causes of death is the bacterial disease tuberculosis (TB), which is brought on by Mycobacterium tuberculosis. Acute pulmonary TB manifests as acute tuberculosis pneumonia (TP). Acute TP and non-tuberculous community-acquired pneumonia, however can be mistaken for one another, which can cause TP to worsen if treatment is put off. In order to stem the spread of TB, an early and precise diagnosis of acute TP is essential. Additionally, the development of new diagnostic tools (technology and techniques) as well as the flexible implementation of various therapy plans will aid in lowering the prevalence of TP and advancing the objective of putting an end to the TB epidemic. © 2024 IEEE.

关键词： Machine learning

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A Secure data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography 2

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sudha, I. donald, Cecil Navya, S. Nithya, G. Balamurugan, Manivannan Saravanan, S. Saveetha Institute of Medical and Technical Sciences - Simats Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science Bengaluru India Raghu Engineering College Department of Computer Science Engineering Visakhapatnam India Department of Cse V. S. B College of Engineering Technical Campus Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Tamil Nadu Avadi India

ISBN: (纸本)9798350306415

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. © 2024 IEEE.

关键词： Cloud computing cryptography data security ECC encryption

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digital Integrity: Enhancing Image Forensics with Error level Analysis and deep learning 2

Digital Integrity: Enhancing Image Forensics with Error Leve...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Arumugam, M. Arun, G. Jayanthi, C. Nelson Samuvel, A. Pg Kongu Engineering College Department of Computer Technology Perundurai Tamilnadu Erode638060 India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Pg& Research Department of Computer Science Tamilnadu Karur639005 India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368413

The widespread use of digitally altered images highlight the need for trustworthy methods to differentiate between real and altered content. This study introduces ElA as a more effective and economical method for detecting changes in digital images, diverging from the original method described in the base work, which employed Fourier transform of residual images. This study offers a reliable method for differentiating between authentic and altered photos using error level analysis (ElA) in combination with a lightweight convolutional neural network (CNN), which is MobileNet. The proposed methodology uses ElA to detect faint aberrations suggestive of picture alteration, from which MobileNet extracts significant features in an effective manner. An SVM is then used to classify the collected features, thereby drawing a line between genuine and artificial intelligence (AI)-generated images. The results of the experiments show that the suggested design performs robustly in picture classification tasks, achieving a high accuracy of 94.18%. This research adds to the field of image forensics by improving the dependability of fake image recognition and providing a scalable solution that can be used in a variety of real-world circumstances, such as cybersecurity and content verification. This integration of ElA with SVM and MobileNet is a major step forward in the ongoing actions taken to maintain the integrity of digital media and fight false information. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Hidden Potential of Cyber Security Oriented Intelligent data Hiding in Encrypted Images 1

Exploring the Hidden Potential of Cyber Security Oriented In...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Nirmaladevi, K. Nagarajan, Anandh Tamizharasi, A. Ramya, R. Allirani, P. Sreenivasa Reddy, M. Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Andhrapradesh Madanapalle India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India MLR institute of technology Department of EEE Telangana Hyderabad India

ISBN: (纸本)9798350364590

In recent years, there has been a surge of interest in the possibility of reversible data hiding inside encrypted images due to the promising practical uses of this technology, which include content owner identification, privacy protection, and encrypted picture authentication. data hiding refers to the process of concealing data within a cover material. It is possible to conceal data in visual, auditory, textual, and graphical formats. Pictures, particularly digital pictures, can be used to conceal data. This data may be authentication details or author information, or it might be any text that is relevant to the image. The receiving end has to be able to decipher the encrypted data and restore the original picture. Advanced Cipher logic based data concealing (ACldH) is a data concealing approach that meets this condition;it allows for accurate recovery of the host image. The losslessness of this method makes it well-suited for use in the medical field, the military, and cloud storage. After extracting the concealed data from an encrypted image, the suggested technique, known as ACldH, may restore the original image to its original state without introducing any deformation. These days, reversible data hiding in encrypted graphics is all the rage because of its great quality: you can have your original cover back after removing the hidden data. It safeguards the initial data as well. Reversible data Hiding (RdH) techniques that are now available for encrypted photos may be classified into two groups: those that do not preprocess the images and those that does. In this research, we show how to use adaptive thresholding to increase data concealing capacity and how to retrieve the original picture from a tagged image without distortion. © 2024 IEEE.

关键词： ACldH Advanced Cipher logic Cyber Security data Hiding Encrypted Images Intelligent data Hiding RdH Reversible data Hiding

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