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检索条件"机构=M.Tech Computer Science and Engineering"

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Automated semantic segmentation and Diagnosis of Coconut Tree Infection Using Drone Imagery and machine Learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Vasumathy, m. Devakanth, J. Jude moses Anto Thirugnanam, mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of economy to the national GDP. About 19,247 million coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at 10-15%. major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity. © 2024 IEEE.

关键词： Drones

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Prognostic Pattern: A Hybrid methodology For Predictivity in medical Imaging Using VGG-16

Prognostic Pattern: A Hybrid Methodology For Predictivity in...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Uttkarsh Parmar Anil Kumar Dubey Computer Science and Enginnering Department M.Tech Abes Enginnering College Ghaziabad India Faculty of Computer Science and Enginnering Department Abes Enginnering College Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

At present, deep learning is being used to compute data related to medical images with the purpose of improving disease-related research. Deep learning based medical image analysis includes various important tasks such as medical image reformation, quality enhancement in different types of images, segmentation and classification of images (computer-Aided Detection) CAD, registration to distinguish between normal cells and tumor cells. This article focuses on the hybrid approach for analyzing medical images that makes use of both clustering and classification via k-mean, VGG, RNN) techniques on different diseases datasets like cardio-vascular diseases, breast cancer and brain disease dataset. This paper utilizes predictive analytics in medical image data which is spread across a combination of clustering and classification. The objective of this study is to demonstrate that integrating these two methods will help improve accuracy as well as save time in diagnosing illnesses from image data by avoiding the process where labeling the data for feature detection takes place. Furthermore, this paper aims at presenting an overview of the methodology employed during investigation along with results emanating from this analysis highlighting how predictive analytics can be applied in healthcare.

关键词： Deep learning Solid modeling Image analysis Accuracy Decision making Feature extraction Data models Predictive analytics medical diagnostic imaging Diseases

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Sitting posture Analysis using CNN and RCNN 9

Sitting posture Analysis using CNN and RCNN

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9th International Conference on Biosignals, Images, and Instrumentation, ICBSII 2023

作者： Nishitha, R. Sudhakar, T. Bethanney Janney, J. Krishnamoorthy, N.R. Dhanalakshmi, K. Vigneshwaran, S. Sathyabama Institute of Science and Technology Biomedical Engg. Department Chennai India Sathyabama Institute of Science & Technology Dept. of ECE Chennai India Sathyabama Institute of Science and Technology Dept. of Computer Science Engg Chennai India Sathyabama Institute of Science and Technology M.Tech-Medical Instrumentation Chennai India

ISBN: (纸本)9798350338171

Based to statistics, about 80 percent of the population follows incorrect sitting posture. From the year 2020, due to the Covid-19 curfew, the traditional work office of many professionals turned into Work from home and remote office lifestyle. many of the working professionals has no proper ergonomic workplace setup in their house leading to improper sitting position. monitoring the sitting posture is needed to avoid chronic complication of mSD, neck, and spine related injuries. many techniques were emerged for correcting and monitoring the posture. Neural Network based posture detection is being into the field of research as the come with more range of accuracy. This paper discusses about the various Neural Networks used and their accuracies. © 2023 IEEE.

关键词： Convolution

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Automated Object Detection and Recognition in Satellite Imagery Using CNNs 1

Automated Object Detection and Recognition in Satellite Imag...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Shanthi, H.J. Shobana, G. Kamalanaban, E. murugan, J. Senthil Nandhinieswari, S. Stalinbabu, R. Deemed to be University Hindustan Institute of Technology and Science Department of Computer Applications Padur India Takshashila University School of Computer Science Ongurdt Villupuram India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College India Kongunadu Arts and Science College Gounder Mills Coimbatore India M. Kumarasamy College of Engineering Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798350376517

As increasingly high-resolution satellite photography becomes available, it has led to a growing need for efficient and accurate methods to analyse large databases and extract valuable information. This research presents a comprehensive approach for automated item identification and detection in satellite data using Convolutional Neural Networks (CNNs). This research abstract outlines the critical steps involved in a suggested technique. These steps encompass data cleaning and organisation, model architecture development, training and evaluation, post- processing, deployment, and subsequent management. This study investigates the ability of CNNs to learn intricate spatial characteristics. The research focuses on carefully selecting and annotating training and validation datasets incorporating items of interest. This study uses data augmentation methods to enhance the generalisation capabilities of the model. This research abstract discusses the importance of the preprocessing stage in ensuring the consistent performance of a model by normalising the input data. This research abstract discusses the components of the CNN architecture, namely convolutional, pooling, and fully connected layers. The CNN architecture is specifically designed to capture hierarchical characteristics. This study investigates the utilisation of a cutting-edge optimiser in the training process to optimise a meticulously constructed loss function. This study examines the integration of the model into real-world applications, considering the trade-off between processing speed and resource constraints as crucial considerations for successful deployment. The findings suggest that this methodology holds promise as a valuable tool in various fields. © 2024 IEEE.

关键词： Satellite imagery

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Automated Email Notifications and Security monitoring for Cloud Storage Access using Cloud Function 3

Automated Email Notifications and Security Monitoring for Cl...

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3rd International Conference on Innovative mechanisms for Industry Applications, ICImIA 2023

作者： Renuka, N. Panneer Selvam, m. Ponmurugan, P. Kiruthika, S. Selvam, N. Kannan, N. KSR Institute for Engineering and Technology Department of Information Technology Tiruchengode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Deemed to Be Univeristy Dean - IPR Meenakshi Academy of Higher Education & Research Chennai600078 India Erode Sengunthar Engineering College Department of M.Tech - Computer Science and Engineering Erode Thudupathi India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350343632

To monitor a specified Google Cloud Storage bucket for any changes or accesses made to its contents. When an access or change is detected, the cloud function will automatically send an email notification to a specified email address with information about the access or change, including the time of access, the user who accessed the data, and the specific data that was accessed. The paper represents the Google Cloud Platform services, which includes Cloud Storage and Cloud Functions. The Cloud Function will be triggered by a Cloud Storage whenever an object is accessed. The Cloud Function will then parse the notification message and extract the relevant information, such as the object name and access time, and send an email notification. The automated email notification system will help cloud storage administrators monitor access to their storage buckets, identify potential security breaches or unauthorized access, and take appropriate action in a timely manner. It also provides a record of all access activity, which can be useful for audit purposes and compliance requirements. © 2023 IEEE.

关键词： Cloud Function Cloud Storage Access Email Notification Google Cloud Platform Security monitoring

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Transforming Homes into Smart Havens: Advanced Control with NodemCU and Android technology 4

Transforming Homes into Smart Havens: Advanced Control with ...

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

作者： Selvarathi, C. Santhi, P. Deepa, K. Shri, G. Ramya Visali, C. Abirami, T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamilnadu Chennai600 062 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirapalli India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Thalavapalyam639113 India Department of Information Technology Tiruchengode Namakkal637 205 India 638060 India

ISBN: (纸本)9798331508845

Creating groundbreaking solutions that benefit the populace is imperative in the modern era. The Internet of Things (IoT) serves as a trailblazing sector, fostering the development of contrivances automating an array of tasks. The IoT encompasses an interlinked network of cyber-connected contrivances executing functions with minimal human intervention. Capitalizing on IoT principles, this exploration endeavors to contrive a device aiding individuals grappling with mobility impediments. The envisioned gizmo consists of two primary sectors: a hardware module featuring a channel relay, Arduino, and an astute luminescent bulb, and a software element encompassing cloud and IoT platforms alongside a purpose-built app. The chief function of this contraption is to discern specific vocalizations in an individual's utterances, effectuating or deactivating the astute contraption to institute an intelligent habitation. In the initial phase, a luminous bulb will serve as a demonstration, employing for app origination. The distinctive aspect of this program lies in its vocalization-receptive microphone, with Bluetooth and Wi-Fi chips augmenting transmission velocity between the luminescent bulb and the mobile device. meticulous attention will be accorded to auditory sensitivity and expeditious data transmission, with potential integration of diverse contrivances like ventilators and temperature-regulating apparatus into the envisioned gizmo. Furthermore, contemplation extends to a fortified safety attribute ensuring singular response to a particular vocal identity. © 2024 IEEE.

关键词： Luminescent devices

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An Analysis of Face based eCrime Identification using First Information Report 3

An Analysis of Face based eCrime Identification using First ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Vanarasan, S. Dharani, S. Dharshini, R. Harani, S. Selvarathi, C. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Sri Sairam College of Engineering Department of Artificial Intelligence and Machine Learning Banglore562 106 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639 113 India

ISBN: (纸本)9798331509675

Face based e-crime identification initiative combines cutting-edge technologies to detect criminals in video footage, allowing for quick and effective online FIR (First Information Report) reporting. Furthermore, it uses blockchain technology to securely store and manage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crimes (eCrimes) in today's digital age poses a huge challenge to law enforcement organizations worldwide. In the face of continuously shifting criminal strategies, traditional means of identifying and reporting offenders frequently fall short. The fundamental functionality of the technology is around identifying offenders using video footage. It makes use of the potent Inception V3 algorithm, a cutting-edge deep learning model noted for its efficient image recognition skills. This algorithm analyses video frames, detects and matches facial traits, and identifies suspects. This not only improves the accuracy of criminal identification but also speeds up the investigation. In addition to identifying criminals, the streamlines the FIR reporting process. Victims and witnesses can now conveniently file FIRs online, eliminating the need for physical visits to police stations. The integration of digital reporting platforms enhances the efficiency of law enforcement agencies, making the process more accessible to the public. Blockchain technology is used to provide data security and immutability. A blockchain-based database stores all data relevant to criminal identification and FIR reporting. The combination of improved facial recognition, online reporting, and secure blockchain data management promises to make identifying and reporting criminals more efficient and secure. © 2025 IEEE.

关键词： Deep learning

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An Innovative method to Detect Cyberbullying on Social media 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. muthumanickam, K. Geetha, m. mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

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OpenCV Based Real-Time Traffic Analyzer 1

OpenCV Based Real-Time Traffic Analyzer

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Pandi, C. Ponnuviji, N.P. Srinivasan, R. Parthasarathy, C. Karthick, P. Rohith Kumar, V. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India R.M.K College of Engineering and Technology Puduvoyal India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (纸本)9798350365337

The report focuses on the development and implementation of a real-time traffic analysis system using OpenCV (Open Source computer Vision Library). The project aims to leverage computer vision techniques to analyze and monitor traffic patterns, providing valuable insights for traffic management and urban planning. The system utilizes video streams from surveillance camerasplaced at strategic locations such as traffic intersections or highways. These video streams are processed in real-time usingOpenCV algorithms to extract relevant information about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide meaningful insights for traffic management authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step employs specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various components and methodologies used in developing the system. We will explore the algorithms and techniques employed for vehicle detection and tracking, as well as the methods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancements for the system. The findings of this paper demonstrate the feasibility and effectiveness of using OpenCV-based systems for real-time traffic analysis. The insights gained from this research have the potential to contribute to more efficient traffic management strategies and informed decision-making in urban planning. © 2024 IEEE.

关键词： Traffic congestion

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Advancements in Autonomous Vehicle Object Detection and Tracking Systems 5

Advancements in Autonomous Vehicle Object Detection and Trac...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Prathap, Joe P.m. Kumar, mallu Dhanush Kiran, Kannedari Uday Praneeth, mallisetty Venkata Sai Beevi, L. Sherin Dani, W. Vinil Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai India Department of EEE St. Peter's College of Engg. and Tech. Chennai India

ISBN: (纸本)9798350361155

Autonomous vehicles represent a transformative technology with the potential to revolutionize transportation systems worldwide. One of the critical components enabling autonomous driving is robust object detection and tracking systems. This paper presents an in-depth exploration of recent advancements, challenges, and future directions in vision-based object detection and tracking for autonomous vehicles. We propose novel algorithms and methodologies aimed at enhancing detection accuracy, real-time performance, and adaptability to diverse environmental conditions. A comprehensive literature review discusses state-of-the-art techniques and identifies areas for improvement. Research challenges and objectives are outlined, followed by a detailed description of the proposed system architecture and implementation. The paper concludes with insights into the potential impact of these advancements on the future of autonomous driving technology. © 2024 IEEE.

关键词： Autonomous vehicles

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