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检索条件"机构=B.E. Computer Engineering Vidyavardhini’s College of Engineering and Technology"

共 412 条记录，以下是1-10 订阅

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Ob.e.t Recognition based on Deep Learning Algorithms using embedded IoT with Interactive Interface 7

Object Recognition based on Deep Learning Algorithms using E...

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7th International Conference on Intelligent Computing and Control systems, ICICCs 2023

作者： borde, swapna Patil, Chandan sonawane, Chinmay singh, Mankrit Vidyavardhini's College of Engineering and Technology Vasai India B.E. Computer Engineering Vidyavardhini's College of Engineering and Technology Vasai India

ISBN: (纸本)9798350397253

Ob.e.t detection is one of the most popular applications of machine learning in the modern era. With the growth of IOT in recent times, dedicated devices offering real-time ob.e.t detection have seen overwhelming demand and applications in many sectors e.g. security, healthcare, workplace etc. Different algorithms and approaches have b.e. implemented and studied in terms of ob.e.t detection. To refer to a few the YOLO family, RCNN family, ssD etc. This research study compares the performance of YOLO and Faster RCNN based on a custom dataset containing different ob.e.ts and items. The IOU of each data point (image) is calculated and compared. YOLO performs better for a small margin. In this research study, a language learning interactive model is demonstrated based on ob.e.t detection(YOLO), NLP, python, flask and IoT (Raspberry Pi.) The web application is running on is divided into two parts, learning and practice. The learning part has a raspberry pi device which has a camera module that captures real-time footage, recognizes the ob.e.t and reads out its name in the language the user is learning. The practice has the same setup, with the application asking the user to show a particular ob.e.t. Points are awarded for correct guesses, making the learning process more interactive and involving. © 2023 Ieee.

关键词： Ob.e.t detection

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Ob.e.t Recognition based on Deep Learning Algorithms using embedded IoT with Interactive Interface

Object Recognition based on Deep Learning Algorithms using E...

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International Conference on ent Computing and Control systems (ICICCs)

作者： swapna borde Chandan Patil Chinmay sonawane Mankrit singh Vidyavardhini’s College of Engineering and Technology Vasai India B.E. Computer Engineering Vidyavardhini’s College of Engineering and Technology Vasai India

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battery Monitoring system with IoT Integration

Battery Monitoring System with IoT Integration

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Advancements in electrical, electronics, Communication, Computing and Automation (ICAeCA), International Conference on

作者： sanjith Krishna s Premchand V H shri Ram b sakthi satchidhanandan s Anitha N Hariharan s.V B.E. Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Mathematics Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

battery Monitoring systems (bMs) have played a pivotal role in our trajectory towards greener and sustainab.e.living, especially in our commutation methods. The rise of electric cars and two-wheelers has put renewed focus on high density energy storage and their safety. existing bMs’s often provide the bare minimum in terms of protection and regulation of energy, often falling short of certain safety standards or lack thereof. Within this research, we task ourselves to identify and provide an alternative, viab.e. cost-effective method to ensure safe monitoring and especially safe debugging of faulty battery packs. We employ off-the-shelf components and integrate them into existing bMs to provide an easier way for debugging via Internet of Things (IoT) to allow local technicians access to the same information as the Original equipment Manufacturer (OeM) themselves. Therefore, our aim is to provide a platform for people to troub.e.hoot their bMs’ on electric vehicles and perform predictive maintenance via remote monitoring, providing the same flexibility of a regular combustion car.

关键词： Temperature measurement buildings Debugging Programming electric vehicles batteries state of charge Internet of Things Remote monitoring Predictive maintenance

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smart Remote Physical Therapy: Deep Learning-based Patient Monitoring 1

Smart Remote Physical Therapy: Deep Learning-Based Patient M...

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Green Industrial electronics and sustainab.e.Technologies (GIesT), Ieee International Conference on

作者： Thirumangai s Thilagan P Anitha N B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Mechatronics Engineering Kumaraguru College of Technology Coimbatore India Mathematics School of Foundational Sciences Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

This study is centred on the development of a methodology for remote health surveillance of patients undergoing physical therapy, employing pose detection technology. The approach utilizes advanced deep learning algorithms to assess patients’ body positions during physical exercises, offering instantaneous feedback to both patients and therapists. This method of remote monitoring coupled with personalized feedback aims to enhance patient outcomes, decrease healthcare expenditures, and bolster patient engagement. by integrating pose detection technology, the system ensures precise tracking of patients’ advancements, facilitating tailored therapy interventions and optimizing treatment effectiveness.

关键词： Deep learning electric potential Accuracy Telemedicine surveillance Medical treatment Wearab.e.devices Wearab.e.sensors Research and development Remote monitoring

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Comparative study and Analysis of Cloud Container technology 18

Comparative Study and Analysis of Cloud Container Technology

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18th INDIAcom;11th International Conference on Computing for sustainab.e.Global Development, INDIACom 2024

作者： Kumar, e. saravana Ramamoorthy, Raghu Kesavan, selvaraj shobha, T. Patil, seema Vighneshwari, b. The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Dxc Technology Bengaluru India B.M.S.C.E. Department of Ise Bengaluru India B.M.S.C.E. Department of Cse Bengaluru India The Oxford College Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9789380544519

The advent of digital transformation has revolutionized the way businesses operate. Applications have become the focal point of this transformation, shifting the focus from being organization-centric to user-centric. To realize the full potential of businesses, high-quality, secure, and agile applications are essential. Containers are a cutting-edge invention in the world of virtualization, gaining immense popularity in recent years. They have replaced traditional business continuity solutions and are now used to address highly demanding business needs. Multiple containers and orchestration frameworks are availab.e. both as standalone and cloud-based services. However, developers and industry experts face challenges in identifying and evaluating the appropriate containers and orchestration frameworks for their application needs. The selected tools may not always be feasib.e.due to a lack of availab.e.features, inability to provide agility, and lack of platform support. When applications are deployed across multiple containers, coordination and management among container clusters become critical. since containers play a pivotal role in edge deployment, cloud-native, continuous integration, and continuous deployment, it is vital to have centralized orchestration for proper re-source management and scheduling. This paper discusses and compares emerging container platforms and cloud-centric orchestration frameworks, highlighting the challenges involved. © 2024 bharati Vidyapeeth, New Delhi.

关键词： Cloud Containers Digital transformation framework Orchestration

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effective segmentation and Classification of brain Tumour MRI Images Using YOLOv8 Technique

Effective Segmentation and Classification of Brain Tumour MR...

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2024 Ieee International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Taradale, Aniket Prabhu Kattimani, suvarna Laxmikant Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura India

ISBN: (纸本)9798331505134

brain tumour classification is crucial for prompt diagnosis and life-saving treatment. A novel approach employing You Only Look Once (YOLO) technology for brain tumour segmentation and classification We used pre-existing data for tumour diagnosis and categorised in four main types: meningioma, glioma, pituitary, and no tumours. In diagnosis, segmentation is necessary to identify regions containing tumours seen in medical images such as MRI scans. Our method offers use of YOLO's real-time product search capabilities and makes them relevant for clinical use. The method achieves 95% F1-score, 96.2% precision, 93.6% recall, and 97.2% mAP50 in identifying and segmenting tumour regions while using a large dataset of MRI scans as training. This method is superior to traditional segmentation methods as it provides a fast and accurate target detection method. © 2024 Ieee.

关键词： Magnetic resonance imaging

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barcode Recognition using sequential and Parallel Approach

Barcode Recognition using Sequential and Parallel Approach

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2024 Ieee International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Kusur, Chidanand s. shirdhonkar, M.s. Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G.H. College of Engineering and Technology Karnataka Vijayapur586103 India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9798331505134

In a computer vision and image processing, the barcode detection and recognition systems play very important role for the interpretation of the encoded information for the system automation and inventory management. Hence, we proposed an enhanced approach to barcode recognition from video frames by extending our previous work focused on barcode detection in static images i.e. for the detection and recognition of barcodes from the videos of racks of books. The proposed system showed that, the parallel approach is time efficient in comparison with sequential approach. © 2024 Ieee.

关键词： Inventory control

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Deep Learning for Precision Grade-Quality Classification of Pomegranates

Deep Learning for Precision Grade-Quality Classification of ...

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2024 Ieee International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Math, RajinderKumar M. Dharwadkar, Nagaraj V. sajjanar, Vijaykumar Deshmukh, M.N. Department of Electronics and Communication Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapur India Department of Computer Science Central University of Karnataka Kadaganchi Kalaburgi India

ISBN: (纸本)9798331505134

The conventional methods of manual grading of fruits after agricultural harvest have always b.e. laborious, time-consuming, and prone to human errors. In order to speed up the grading process without human intervention, an automatic system is the need of the hour. This research has led to the development a Convolutional Neural Network (CNN) based deep learning technique to provide real-time grading of pomegranate based on its visual features. This automatic grading technique would help farmers in minimizing their efforts and time spent on manual sorting, at the same time ensuring error-free and uniform sorting and packaging of quality fruits that would, in turn, enhance the storage time post packaging. The methodology uses nine pre-trained models to train on a custom pomegranate dataset with twelve grade-quality classes of pomegranates. The grade-quality grading is obtained using the fine-tuning approach in transfer learning, reducing the computational resource requirements and drastically improving inference speed, which is necessary for real-time model deployment scenarios. The results reveal that the fine-tuning of DenseNet-169 model outperformed the other models by achieving highest values of 99.20% and 1 for (Accuracy, Precision, Recall, and F1-score) and AUC, respectively. © 2024 Ieee.

关键词： Convolutional neural networks

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Ant Colony Optimization in MANeT for Decreasing energy Consumption with ML Approaches

Ant Colony Optimization in MANET for Decreasing Energy Consu...

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2023 Ieee International Conference on Integrated Circuits and Communication systems, ICICACs 2023

作者： suresha, P. Math, Gayatri Arvind, siddapuram bhaskar, M. Naveen, I.G. S.E.A College of Engineering and Technology Department of Mechanical Engineering Bengaluru India HKE Society's B.V. Bhoomaraddi College of Arts Science and Commerce Degree College Department of Electronics & Communication Engg Bidar India Hyderabad Institute of Technology and Management Department of Computer Science & Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

To coordinate access from the active nodes, MAC protocols are used in ad hoc networks. Many media access control (MAC) protocols have b.e. suggested for use in ad hoc networks, each with its own unique set of goals. The goal of many ad hoc networks is to increase the amount of time that its individual nodes, and by extension, the network, can function. Ad-hoc nodes are considered dead when their batteries run out, hence it is crucial to reduce power drain. because to the nature of MANeTs, in which nodes must depend on limited, portab.e.power sources, energy efficiency is a significant barrier to their widespread implementation. In multi-hop systems, where nodes must additionally expend energy to route packets for other nodes and assure the connection of the network, energy saving is difficult. since energy-efficient routing is a relatively new area of study, there are still many open questions that need to be investigated and no widely accepted standards in place. several routing protocols used the shortest route as a criterion for path selection;however, this is inappropriate due to the topology's significant and unexpected variability and does not assume the most efficient use of energy. simulation is the primary tool for testing the efficacy of several routing protocols in MANeTs, including RIP, RIPng, Ps-RIP, PsLLC-RIP, Ps-RIPng, PsLLC-RIPng, FLbUDTCRIP, and ANFIsbUDTCRIP. exata 5.4 GUI network simulator and OPNeT tools are used to analyse the efficiency of the suggested protocols. All the simulation results show \that the suggested protocols perform better than the current RIP protocols. © 2023 Ieee.

关键词： multi-protocol label switching

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YOLOv8 based An Automated segmentation And Classification Of Thyroid Nodules Using UsG Images

YOLOv8 Based An Automated Segmentation And Classification Of...

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2024 Ieee International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Gondi, savita Nagaral, M.U. Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijaypur586103 India Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India

ISBN: (纸本)9798331505134

Thyroid nodules are a common clinical ob.e.vation that needs to be accurately classified and segmented in order to be managed and treated. The use of the sophisticated ob.e.t detection model You Only Look Once (YOLO) version 8 to the issue of nodule of the thyroid identification and categorization is investigated in this work. We gathered and interpreted 701 thyroid ultrasound images for this study, identifying two different nodule types as benign (315 images) and malignant (344 images). To assess the model's performance thoroughly, the dataset was divided into training (70%), validation (20%), and test (10%) sets. After training YOLOv8 on these images, metrics including Precision (78.1%), Recall (81.9%), F1 score (80%), and mean average precision (mAPall) (88.1% mAP@50) are used, with benign (91.8% mAP) and Malign (84.5% mAP. The findings show that YOLOv8, outperforms earlier model YOLOv5, achieves good accuracy in both detecting and categorizing thyroid nodules. The ability of the model to accurately distinguish between benign and malignant nodules is critical for clinical decision-making. © 2024 Ieee.

关键词： Image annotation

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