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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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Image Super-Resolution Using Auto encoders

Image Super-Resolution Using Auto Encoders

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International Conference on Trends in electronics and Informatics (ICeI)

作者： K. Sandhya Rani Pancheti bhavya R. Vignesh e. Murali D. Deepa J. Cruz Antony B.E Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Image super-resolution (SR) remains a crucial prob.e. in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoders, a specific kind of neural network whose task is to learn compressed representations of the high resolution images. In the context of the proposed model, there is an encoder, which, alternately, compresses the input and a decoder which reconstruct the output is proposed as capturing as well as reproducing all the details of an image. experimental results show that the proposed method is one of the most efficient and accurate ones on benchmark datasets in terms of better details preservation and artefact reduction besides reducing processing time compared to the conventional methods. This makes this approach to be flexib.e.and efficient to be used in medical imaging, satellite and digital photography making it an effective solution to improving image quality and resolution.

关键词： Image quality computer vision Visualization Image coding Superresolution Neural networks Signal processing algorithms Signal processing Optical flow Image reconstruction

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A Framework for Video based Sign Language Interpretation Using Machine Learning and Statistical Methods

A Framework for Video Based Sign Language Interpretation Usi...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Swathi D Susmetha K Sreeji. S S. Mangairkarasi B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The Sign Language Recognition System has b.e. designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e.text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the sub.e.uent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time.

关键词： Hands Deep learning Sign language Translation Tracking Text recognition Speech recognition Streaming media Real-time systems Graphical user interfaces

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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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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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Identification of Therapeutic Plants Using Machine Learning

Identification of Therapeutic Plants Using Machine Learning

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Rohini S Keerthana Shree M.U Jayalakshmi R Malathi M L. Jabasheela M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier.

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enhancing the clinical relevance of haemorrhage prediction models in trauma

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Military Medical Research 2024年第3期11卷 467-468页

作者： Sankalp Tandle Jared M.Wohlgemut Max e.R.Marsden erhan Pisirir evangelia Kyrimi Rebecca S.Stoner William Marsh Zane b.Perkins Nigel R.M.Tai Centre for Trauma Sciences Blizard InstituteQueen Mary University of LondonLondon E12ATUK The Royal London Hospital Barts Health NHS TrustLondon E11FRUK Academic Department of Military Surgery and Trauma Research and Clinical InnovationThe Royal Centre for Defence MedicineBirmingham B152WBUK Department of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK

We read with interest the recent systematic reviewaArtificial intelligence and machine learning for hemorrhagic trauma careoby Peng et al.[1],which evaluated literature on machine learning(ML)in the management of trau... 详细信息

关键词： Trauma Injury blood transfusion Massive transfusion Prediction Artificial intelligence Machine learning

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Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics 4

Proactive Network Optimization Using Deep Learning in Predic...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Priya, S. Anu bhat, Niyati Kanna, b. Rajesh Rajalakshmi, S. Jeyavathana, R. beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Prob.e.s have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this prob.e.. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. effective modeling is enab.e. by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliab.e.forecasting of future IoT traffic trends. Mean Squared error (MSe) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 Ieee.

关键词： Time series analysis

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Detection and Identification of Lung Cancer Using an Improvised CNN Model: A Novel Approach to Assist Doctors in Diagnosing Lung Cancer

Detection and Identification of Lung Cancer Using an Improvi...

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International Conference on business Intelligence and Information Technology, bIIT 2022

作者： Mohammed, Sabah Govathoti, Sudeepthi Satyanarayana, K.V. Joshua, eali Stephen Neal Department of Computer Science Lakehead University 955 Oliver Road Thunder BayONP7B 5E1 Canada Department of CSE GITAM School of Technology GITAM Deemed-to-be-University Telangana Hyderabad India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science Engineering GITAM School of Technology GITAM Deemed-to-be-University Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9789819934157

Conditions that affect the lungs and make b.e.thing difficult are respiratory ailments. Lung cancer is a disease that affects people all over the globe. The greater the number of individuals aware of them, the better. The likelihood of surviving lung cancer jumps from 14 to 49% if the disease is detected early. even though CT scans are far more accurate than X-rays, a comprehensive diagnosis requires several scans. The development and testing of a deep neural network for identifying lung cancer in CT images are carried out here. The CNN, DenseNet software was used to determine if the lung image was normal or malignant. This was accomplished via the use of adaptive boosting. There are 201 photographs of lungs in the research collection. Only 15% of the pictures are chosen for testing and classification, while the rest are discarded. According to the testing results, the proposed approach is 95.85% accurate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computerized tomography

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Data analysis and interpretab.e.machine learning for HVAC predictive control: A case-study based implementation

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science and Technology for the built environment 2023年第7期29卷 698-718页

作者： Mao, Jianqiao Grammenos, Ryan Karagiannis, Konstantinos Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom General Technology Ltd Athens15233 Greece

energy efficiency and thermal comfort levels are key attributes to be considered in the design and implementation of a Heating, Ventilation and Air Conditioning (HVAC) system. With the increased availability of Internet of Things (IoT) devices, it is now possib.e.to continuously monitor multiple variab.e. that influence a user’s thermal comfort and the system’s energy efficiency, thus acting preemptively to optimize these factors. To this end, this paper reports on a case study with a two-fold aim;first, to analyze the performance of a conventional HVAC system through data analytics;secondly, to explore the use of interpretab.e.machine learning techniques for HVAC predictive control. A new Interpretab.e.Machine Learning (IML) algorithm called Permutation Feature-based Frequency Response Analysis (PF-FRA) is also proposed. Results demonstrate that the proposed model can generate accurate forecasts of Room Temperature (RT) levels by taking into account historical RT information, as well as additional environmental and time-series features. Our proposed model achieves 0.4017 °C and 0.9417 °C of Mean Absolute error (MAe) for 1-h and 8-h ahead RT prediction, respectively. Tools such as surrogate models and Shapley graphs are employed to interpret the model’s global and local behaviors with the aim of increasing trust in the model. © Copyright © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： energy efficiency

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