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Detecting and recognizing faces from video images using multi-deep CNN based rank-level fusion 2

Detecting and recognizing faces from video images using mult...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ghosh, Manas Sing, Jamuna Kanta RCC Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350371314

The advent of deep neural networks has provided a competitive edge in facial analysis efforts, especially the detection and recognition of faces from video images. This paper outlines a methodology wherein a face database is created from an outdoor video clip, and a rank-level fusion mechanism has been proposed for face recognition using a database that makes use of the probabilities produced by the softmax functions of multiple deep convolutional neural network (CNN) models. These probabilities are referred to as certainty indices for the potential classes to which a test sample may belong. For each of the CNN models, fuzzy ranks are obtained for a subset of classes with the top certainty indices by applying a Gaussian distribution function followed by a complementation operation. For each class, appearing in one or more subsets corresponding to deep network models, average of the certainty indices is complemented and fused with the sum of complemented fuzzy ranks. While calculating the cumulative sum of the fuzzy ranks, a penalty, is foisted to the class for not being present in any subset. This can avoid the chance of becoming an unexpected winner. The class with the lowest final ranking was identified as the class pertaining to the test sample. In addition to testing with the custom-made outdoor video-based face dataset, the projected method was evaluated with a standard surveillance indoor dataset, ChokePoint. Considering all test sets on ChokePoint dataset, the average recognition accuracy of the proposed method is 99.54%. The same for custom-made dataset is 93.96%. For both datasets, precision and macro-average scores of the proposed method in all cases are higher than the individual deep CNN models. © 2024 IEEE.

关键词： Face recognition

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Heart Disease Prediction and Diagnosis Using IoT, ML, and Cloud Computing 6th

Heart Disease Prediction and Diagnosis Using IoT, ML, and Cl...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Maurya, Jyoti Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819940707

Heart disease is currently regarded as the main cause of illness. Regardless of age group, heart disease is a serious condition nowadays because most individuals are not aware of their kind and level of heart disease. In this fast-paced world, it is essential to be aware of the different types of cardiac problems and the routine disease monitoring process. As per the statistics from the World Health Organization, 17.5 million deaths are because of cardiovascular disease. Manual feature engineering, on the other hand, is difficult and generally requires the ability to choose the suitable technique. To resolve these issues, IoT, machine learning models and cloud techniques, are playing a significant role in the automatic disease prediction in medical field. SVM, Naive Bayes, Decision Tree, K-Nearest Neighbor, and Artificial Neural Network are some of the machine learning techniques used in the prediction of heart diseases. In this paper, we have described various research works, related heart disease dataset, and comparison and discussion of different machine learning models for prediction of heart disease and also described the research challenges, future scope and discussed the conclusion. The main goal of the paper is to review the latest and most relevant papers to identify the benefits, drawbacks, and research gaps in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Enhancement of Farming Using IoT Based Unmanned Aerial Vehicles 2

Enhancement of Farming Using IoT Based Unmanned Aerial Vehic...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Tiwari, Vanshika Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9798350371055

The agriculture sector faces the pressing challenge of increasing food production while optimizing resource use and minimizing environmental impact. One promising solution to address these challenges is the integration of Internet of Things (IoT) based Unmanned Aerial Vehicles (UAVs) into farming practices. This research paper explores the potential benefits, challenges and implications of using IoT-based UAVs in agriculture. It examines how this technology can enhance farming by providing real-time data, precision agriculture capabilities and improved resource management. There are detecting diseases in agriculture field to reduce background noise using the Median filters, Temporal filters. This helps to remove noise and vestiges caused by the movement of the drone or the target factory. © 2024 IEEE.

关键词： Image Processing Internet of Things (IoT) Median Filter Temporal Filter Unmanned Aerial Vehicles (UAVs)

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Pneumonia Identification and Classification Using CNN Model Based on Chest X-Ray Image in Healthcare 7th

Pneumonia Identification and Classification Using CNN Model ...

引用

7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Kumar, Suraj Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819950140

Pneumonia is a bacterial, viral, or fungal disease within one or even both lungs. It is a serious disease where the air sacs in both lungs fill up with pus as well as other substances. Many architectures and models were developed to evaluate such diseases, but still, the possibility to further enhancement. We built a CNN model to identify and categorise Pneumonia disease in the lungs using CXR images. The dataset was developed and pre-processed by the Kaggle CXR dataset. In the research, the convolutional neural network method has been used in conjunction with other data augmentation approaches to improve classification accuracy which will help to enhance the training and validation accuracies, as well as characterisation of the precision of the CNN model and achieve various results. Our model's training accuracy and validation accuracy are 0.9784 and 0.9511, respectively, and its training loss and validation loss are 0.0634 and 0.1476. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Blockchain Enabled Autonomous Vehicle Based Vehicular IoT System

Blockchain Enabled Autonomous Vehicle Based Vehicular IoT Sy...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Hemani Singh, Daya Shankar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

Blockchain technology is gaining its popularity in many areas with huge success which ultimately attracts all researchers to study this technology. And the result of that shows more and more research to be conducted. Which makes blockchain technology relevant in the field of transportation systems. It is expected that in near future their integration will achieve greater heights, as compared to other technology blockchain is still new and many more unknown things still need to be found out. The aim of this study is to find out how blockchain benefits in Autonomous vehicles. We have compared the blockchain integrated autonomous vehicle systems to get the picture of the present environment and the challenge of the future for this integration. AV's or we say intelligent transportation is the future. We are going to study the potential application of the blockchain which will help in improving this particular section of industry. Finally their limitations and challenges associated with this integration. © 2023 IEEE.

关键词： Blockchain

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Prediction of Reliability of Data in Underwater Communication Network Using Fuzzy Logic

Prediction of Reliability of Data in Underwater Communicatio...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Surendra Gobi, N. Yadav, Rakesh Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper uses fuzzy logic to propose a reliability prediction set of rules for the underwater communication community. The set of rules uses fixed fuzzy rules to expect communication reliability among nodes in an underwater environment. Those fuzzy rules are derived from a reliability model based on the characteristics of an ordinary underwater conversation system, such as physical and environmental elements. The algorithm is then examined using accurate statistics from an underwater communication test. The effects imply that this approach is promising in predicting the reliability of the community. It has also been discovered that reliability prediction accuracy can be advanced if the bushy guidelines include extra factors, the sign channel's records rate, and the machine's performance parameters. This study offers a practical approach to improving the performance and reliability of underwater communique networks. © 2024 IEEE.

关键词： algorithm communication environmental fuzzy logic

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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Prabha, S. Singh, Vaishali Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels. © 2024 IEEE.

关键词： category Conditional evaluating hyper spectral schooling

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Improving Efficiency and Accuracy in Clinical Diagnostic Decision Making with Deep Learning

Improving Efficiency and Accuracy in Clinical Diagnostic Dec...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kharbas, Vikas Kumar Gobi, N. Kumar, Dinesh Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Deep learning is a modern form of synthetic intelligence (AI) based on large quantities of statistics and algorithms that allow computer systems to approximate features and solutions, ensuing in more excellent, efficient, and accurate selections. In the scientific field, deep gaining knowledge is increasingly used to help clinicians with diagnostic decision-making, decreasing the value and attempt associated with diagnostic approaches. This paper discusses the potential of deep learning to enhance the performance and accuracy of medical diagnostic selection-making. The paper provides an overview of the cutting-edge nation of the field, with a particular awareness of picture processing and its application to improve accuracy and decrease the price associated with scientific diagnoses. It also provides a few capacity challenges and pitfalls and outlines the issues essential to ensure a hit implementation and successful results. Eventually, it offers a few recommendations for similar studies to increase deep getting to know algorithms further. Deep gaining knowledge has been broadly explored in recent years as a powerful tool for enhancing the accuracy and efficiency of clinical diagnostic decision-making. Deep studying models can investigate patterns in medical datasets, deliberating the traits of affected person information and ailment signs to generate extra specific and reliable diagnoses. By incorporating extra facts such as transcripts from affected person-clinician interactions and medical imaging, those fashions may be trained to assist clinicians in identifying illnesses early and provide well-timed and correct analysis. Moreover, deep learning allows for incorporating case-primarily based reasoning and presents the capacity to combine scientific knowledge in real-time. By having extra sturdy and accurate models, clinicians can reduce the time required to diagnose a condition and make treatment choices. Deep gaining knowledge also lets in for the integration o

关键词： disorder enhance healthcare value

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A Robust Ensemble Learning Approach for Medical Image Segmentation with Transfer Learning

A Robust Ensemble Learning Approach for Medical Image Segmen...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Kavitha, R. Singh, Shweta Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes a robust ensemble mastering approach for clinical picture segmentation. The proposed technique combines a convolution neural community (CNN) with a switch studying-based totally ensemble model. The CNN is pre-educated with a dataset containing clinical photo modalities, particularly Magnetic Resonance Imaging (MRI) and computed tomography (CT)., a weight-averaged ensemble model is acquired between the two scientific photograph modalities. This version is an initial way to the clinical image segmentation problem. Eventually, the ensemble model is great-tuned with additional imaging statistics from an unmarried modality to improve the segmentation accuracy. The proposed technique is evaluated on a range of datasets, and the effects display that it achieves competitive performance compared to the country of the artwork. Moreover, the experiments reveal that combining the transfer gaining knowledge of-based totally ensemble mastering approach with extra imaging information enhances the accuracy of medical picture segmentation. © 2024 IEEE.

关键词： additional convolution learning state subsequently

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Utilizing Deep Learning in Pre-Clinical and Clinical Diagnosis of Peripheral Neuropathies

Utilizing Deep Learning in Pre-Clinical and Clinical Diagnos...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Kharbas, Vikas Kumar Mahajan, Prerna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Peripheral neuropathies are a group of problems that affect the peripheral frightened machine, often leading to a spread of symptoms and impairments. Diagnosis of these situations is frequently imprecise and time-consuming, leading to delays in remedy. Deep studying techniques provide the capacity to automate and enhance the analysis of peripheral neuropathies. Through deep gaining knowledge, clinicians can obtain more excellent correct diagnoses based on enter from MRI pics, photographs of nerve biopsies, or different imaging facts. Moreover, deep studying may be used to create characteristic vectors from different medical features and EEGs that may facilitate the popularity of signs and symptoms and diagnoses extra speedy and more appropriate than traditional methods. This paper explores the capacity of deep learning to accurately diagnose and stratify peripheral neuropathies in pre-scientific and medical settings. It gives a top-level view of contemporary studies on deep getting-to-know strategies for spotting signs and symptoms and diagnosing peripheral neuropathies. Similarly, the paper outlines capacity applications and blessings of deep studying for diagnosing peripheral neuropathies and discusses areas of destiny studies. The software of deep mastering in the pre-medical and clinical prognosis of peripheral neuropathies has been increasingly studied in latest years. Deep mastering fashions, consisting of convolutional neural networks, have shown promising effects in analyzing electromyography (EMG) alerts for the early detection, quantification, and class of peripheral neuropathies, complementing and, in a few cases surpassing conventional diagnostic methods. Moreover, those methods have been advanced as they should be detected and differentiated between myopathic and neuropathic abnormalities and muscle activity. Those algorithms can also distinguish between the diverse kinds of peripheral neuropathies and are potentially useful for early detection and reme

关键词： illnesses peripheral photographs prognosis styarchitectures ling

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