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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Identification of Intracranial Hemorrhage using ResNeXt Model 2

Identification of Intracranial Hemorrhage using ResNeXt Mode...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Navaneet Bhat, K. Biradar, Vidyadevi G. Mallya, Adithya Krishna S. Sabat, Sohan Shekar Mohan, S.G. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Intracranial hemorrhage is a disease with a greater mortality rate. The only way to provide a definitive diagnosis of intra cranial hemorrhage is through neuroimaging. Deep learning tools can help identify and classify intracranial hemorrhages in a clinical setting and can also provide insights into previously unknown features of the disease. This paper summarizes state-of-the-art techniques for classification of intracranial hemorrhages using deep learning models and a novel model based on ResNext 101 is presented for interpretable diagnostic tool. The contributions of the paper include recommendations on pre-processing steps, model identification and training strategies and selection of datasets. © 2022 IEEE.

关键词： Diagnosis

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Enhanced Text-to-Image Synthesis Using Generative Adversarial Networks and Skip-Thought Vectors for Improved Visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought Vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

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PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images 2

PFCM Based Segmentation and TFA Based DCNN Model for Skin Ca...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Bandeppa, Lalitha Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Worldwide, skin lesions have emerged as a serious health concern;however, the survival rate can be improved by identifying skin lesions sooner with dermoscopy pictures. It will be a painstaking process to classify the skin lesion from those dermoscopy photographs. Using deep learning models improves the accuracy of skin lesion categorization. There has been a recent influx of convolutional neural networks (CNN) into this field;CNNs have well-established methods for feature extraction, which improves classification. This project aims to create AI-based solutions, namely deep learning (DL) procedures, to differentiate among benign and malignant skin lesions in dermoscopy pictures. Seven types of skin lesions were used in the Skin Cancer MNIST: HAM10000 dataset. Visual illness diagnosis using deep learning models is becoming increasingly common. For the purpose of skin cancer classification, this study suggests using a deep convolutional neural network (DCNN). In order to enhance the accuracy of the proposed DCNN classifier, the Tangent Flight Algorithm (TFA) is recommended as the optimum approach for optimising the weights, and possibilistic fuzzy clustering (PFCM) is employed for segmentation. Results from this study demonstrate that, compared to convolutional approaches, the suggested technology provides superior accuracy. The proposed TFA based DCNN model outperformed state-of-the-art prediction methods with training accuracy of 0.962, testing accuracy of 0.947, sensitivity of 0.824, and F1-score of 0.822. © 2024 IEEE.

关键词： Convolutional neural networks

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Lip Reading with 3D Convolutional and Bidirectional LSTM Networks on the GRID Corpus 2

Lip Reading with 3D Convolutional and Bidirectional LSTM Net...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Prashanth, B.S. Manoj Kumar, M.V. Puneetha, B.H. Lohith, R. Darshan Gowda, V. Chandan, V. Sneha, H.R. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (纸本)9798350372892

In recent years, the application of artificial intelligence has revolutionized the field of lip reading by enabling the development of sophisticated models capable of accurately interpreting lip movements from video data. This work presents a novel deep learning approach to lip reading, focused on decoding spoken text from video sequences of lip movements. Traditional lip reading methods involve separate stages for visual feature design and prediction. The proposed system utilizes an endto-end deep learning model to directly map video frames to text transcriptions, leveraging 3D convolutional neural networks and bidirectional Long Short-Term Memory. By analyzing visual cues from lipmotions, the system can interpret speech, improving accessibility for individuals with hearing impairments and enabling communication in noisy environments. Compared to existing lip reading techniques, the deep learning model achieves superior performance on benchmark datasets, demonstrating its effectiveness in this challenging task. The model has achieved a character error rate of 1.54% and a word error rate of 7.96% on the best model. © 2024 IEEE.

关键词： Convolutional neural networks

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SINN Based Federated Learning Model for Intrusion Detection with Blockchain technology in Digital Forensic 2

SINN Based Federated Learning Model for Intrusion Detection ...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Pramod Pawar, Priyanka Kumar, Deepak Krupa, R. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Today, smart devices permeate every part of our lives, ushering in a new technological era known as Internet of Things (IoT). There ration of cybersecurity risks due to the proliferation of smart devices in IoT contexts and the massive volumes of sensitive data they store. In order to determine the time and place of these attacks and to collect evidence that can lead to the identification of the perpetrators, digital forensics is essential. Research into digital forensics in an IoT setting is fraught with difficulties owing to factors such as the dispersed nature of data storage, the need to be able to track and verify the provenance of evidence, the difficulty in obtaining data from many sources, and the lack of visibility into the evidence collection procedure. This is why the research suggested merging two potential technologies to offer a complete answer. The research employed federated learning to train representations locally using data kept by IoT devices, with the help of a dataset that was created to mimic attacks on the IoT ecosystem. The study utilises the BoT-IoT dataset and employs a classification method called Shepard Interpolation Neural Network (SINN) with the Shuffled Frog-Leaping algorithm (SFLA). After that, in order to make the blockchain lightweight, the study aggregated data using blockchain by gathering parameters from the IoT gateway © 2024 IEEE.

关键词： Cybersecurity

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An Ameliorate Analysis of Cryptocurrencies to Determine the Trading Business with Deep Learning Techniques 7th

An Ameliorate Analysis of Cryptocurrencies to Determine the ...

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7th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

作者： Sakure, Neeshad Kumar Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Thomas, Likewin Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering PESIT—M Shivamogga India

ISBN: (纸本)9789811954818

As the range of COVID-19 sufferers increased, many nations imposed a complete lockdown. As a result, it caused a devastating international financial disaster everywhere in the world. Technical and essential evaluation are two methods for determining future worth. Different strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events, to forecast future charge. We use technical evaluation forecasts potential charge using buying and selling statistics from the market, which includes charge and buying and selling volume, whereas other strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events. The objective of this project is to give technical and fundamental analysis using machine learning approaches. In business, AI is broadly used to remedy and optimize various problems, including marketing, credit score card fraud detection, algorithmic trading, patron service, portfolio management, and product advice primarily based totally on patron needs. Furthermore, the technology due used in this finished greater to optimize the proposed set of rules to attain the maximum correct result primarily based totally on the current valuation of cryptocurrency. This project is to use machine learning techniques to provide technical analysis. The incorporation of new technology into financial institutions has the potential to propel cryptocurrency values to time highs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Brain tumor Segmentation and Classification using SMA based Modified HBTNet Model from MRI images 3

Brain tumor Segmentation and Classification using SMA based ...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Vyas, Garvit Kalshetty, Jagadevi N Pareek, Piyush Kumar Birla Institute of Technology and Science Pilani - Hyderabad Campus Department of Electrical and Electronics Engineering India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350340358

When it comes to diagnosing and treating brain tumors, the ability to segregate them using multimodal MRI is crucial. Segmenting brain tumors relies heavily on multimodal information. The majority of current approaches, however, disregard some aspects that are particularly relevant to the segmentation problem in favor of extracting and selecting deep semantic features. This study suggests employing structural multimodal magnetic resonance imaging (mMRI) for brain tumor segmentation and subtype classification using context aware deep learning. The paper begins by suggesting a deep learning process for tumor segmentation, one that takes into account the ambiguity of tumor localization in the subregions of radiology mMRI images. The research then uses the tumor segments to classify the tumor subtype using the Human Brain Tumor Network (HBTNet). By using the Slime Module Optimization Algorithm (SMOA), the HBTNet's learning rate is optimized. This work applies the suggested approaches to the Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS 2018) dataset for tumor segmentation and classification in order to assess their performance. Utilizing several measures for segmentation and classification, the study is confirmed. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Automated Identity Document Recognition and Classification (AIDRAC)-A Review

Automated Identity Document Recognition and Classification (...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Vijaya Shetty, S. Madhumitha, R. Reddy, Mekala Meghana Shettar, Shreya Murthy, Tejashree Krishna Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781665489621

In this technological era, official documents like Government approved IDs, Certificates, and other documents are paper-based or image based. The problem arises when an organization has to go through each of these documents manually and extract, enter or search for information. Automated Identity Document Recognition and Classification (AIDRAC) is one of the techniques to tackle this problem by decreasing manual effort. This kind of model automatically classifies government-issued identity documents into predefined categories such as AADHAR card, PAN card, and Driving License. It also recognizes and extracts details from the documents uploaded, providing the users the option to autofill their details upon scanning. This can be achieved through the application of Image Processing algorithms using Machine Learning. The extracted information can be stored succeeding relevant access and permissions. The data can be viewed by authenticated consumers when required. Due to the automation of manual work, the implementation of this model saves time, thus saving resources. This paper presents an overview of the exhaustive survey of the techniques employed to build such models and, a comparative analysis of these techniques. It has been found from the survey that there are no models existing that completely accomplish the said automation. Our future research is to build an automation model that can be used in various organizations like Banks, Education Institutions, Law firms, Travel Agencies etc. to autofill the details extracted from the scanned images of Identity proofs. The Sequence diagram of the proposed system is also presented in this paper. The proposed system is expected to outperform the existing models. © 2022 IEEE.

关键词： Optical character recognition

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Designing a Smart Healthcare framework based on disease prediction using Optimizer based AI 3

Designing a Smart Healthcare framework based on disease pred...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Vyas, Garvit Kalshetty, Jagadevi N Pareek, Piyush Kumar Birla Institute of Technology and Science Department of Electrical and Electronics Engineering Pilani - Hyderabad Campus India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350340358

In light of the rapid shift towards an ageing population and the corresponding rise in healthcare spending, disease prediction and control through various healthcare devices and services have lately garnered considerable interest. The development of smart healthcare services is a direct result of the advancements in the Internet of Medical Things (IoMT), wearable sensors, and communication technologies that have occurred in this technological era. The Internet of Things (IoT) in healthcare also helps bring down the cost of many different diseases by analysing data quickly and thoroughly. One exciting new area of medical research is the use of machine learning to make illness predictions from patient symptoms. In order to collect signals, the framework makes use of (IoT) sensors, which then transmit them to a web server in the cloud. In order to ascertain the patients' conditions, these signals are further analysed by a deep learning network. By taking both spatial and temporal considerations into account, the given method shortens the time it takes to discover anomalies by minimising the vanishing gradient problem of Recurrent Neural Networks (RNN). Someone in the medical field has access to patients' medical records and the results of any processing that may be necessary in the event of an emergency. Heart Failure Clinical Records, a repository at UCI, provided the dataset utilised in this investigation. It has thirteen characteristics. The suggested model outperformed competing deep learning and ML models in the experiments, which yielded an accuracy value of 0.9417. © 2023 IEEE.

关键词： Recurrent neural networks

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MultiFlow: Leveraging Combined Time Series Algorithms for Accurate Traffic Forecasting 3

MultiFlow: Leveraging Combined Time Series Algorithms for Ac...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Naik, Archana Ushashree, P. Disha, N. Reddy, Hrushika Dheemanth, M. Kirthan, Shaun Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India BMS College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350340358

Anticipating website traffic is a significant issue today because it might interfere with the functioning of important websites. Research on time-series forecasting has been very active. Predicting future time series values is one of the most difficult problems in the field. This study of time series data covers a wide range of topics, from classification and forecasting to inference and analysis. The forecast is typically accurate when the time series databases are utilized with the quickest growth rates, despite the fact that it can be predicted using a variety of techniques. The work proposed is a unique deep learning-based time series hybrid model dependent on regression, which is a combination of different algorithms for predicting web traffic. © 2023 IEEE.

关键词： Time series

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