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Hierarchical Representation Learning for Improved Diagnosis of Prostate Cancer on MRI Scans

Hierarchical Representation Learning for Improved Diagnosis ...

引用

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

作者： Prabhu, A. Pandeya, Megha Kaushik, Harshita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

It focuses on using hierarchical illustration mastering (HRL) for the progressed prognosis of most prostate cancers on MRI scans. HRL is a gadget getting-to-know technique using a hierarchy of function vectors to encode record sets, allowing extra complicated non-linear styles to be recognized and applied. In this examination, HRL is compared to a fashionable convolution neural network (CNN) classifier for the challenge of prostate cancer analysis on MRI scans. The effects show that HRL outperforms the CNN classifier, providing a higher accuracy fee and universal predictive performance. Furthermore, HRL results in fewer fake positives and extra correct type accuracy. Its improved overall performance suggests that utilizing HRL in medical decision-making can offer a more correct, practical analysis of most prostate cancers. © 2024 IEEE.

关键词： convolutional improved performance predictive representation

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An Enhanced Optimization of Automated Detection of Cardiac Abnormalities Using Deep Learning

An Enhanced Optimization of Automated Detection of Cardiac A...

引用

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

作者： Ojha, Ananta Yadav, Dhyan Chandra Goyal, Manish Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep gaining knowledge of is a place of artificial intelligence that is becoming increasingly famous inside the scientific area. This paper provides an improved optimization of computerized detection of cardiac abnormalities the use of deep learning. particularly, the authors recommend using a convolutional neural community (CNN) to stumble on abnormalities from ECG records. They use an ensemble of models to further improve accuracy and reduce false superb quotes. moreover, they apply transfer learning techniques to higher generalize the mastering from the EEG facts. The authors take a look at their optimized set of rules on two datasets of ECG recordings and file an normal accuracy of 88.9%. This demonstrates the potential for deep getting to know techniques to end up an increasing number of reliable and sturdy for detecting cardiac abnormalities. The authors also talk the feasible directions of future studies and the potentials of deep learning for clinical safety and diagnostics in phrases of fee and efficiency. © 2024 IEEE.

关键词： abnormalities accuracy diagnostics specifically techniques

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Privacy Preservation in Federated Learning, its Attacks and Defenses using SMC-GAN 3

Privacy Preservation in Federated Learning, its Attacks and ...

引用

3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

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

ISBN: (纸本)9798350300710

When using collaborative machine learning, maintaining privacy is a major concern because different parties want to train a model on their own data without sharing it with others. In collaborative machine learning, the approaches of Secure Multiparty Computation (SMC) and Generative Adversarial Networks (GAN) can be utilized to protect privacy. While GANs can create artificial data samples that are comparable to the real data, SMC allows participants to collaboratively compute a function without disclosing their inputs. In collaborative machine learning, this work provides a method for privacy preservation utilizing SMC and GANs. The proposed approach can produce high model accuracy while maintaining privacy, according to experimental data. The method is assessed using a number of benchmark datasets and contrasted with other privacy-preserving strategies. The findings demonstrate that, in terms of model correctness and privacy preservation, the suggested approach performs better than competing techniques. © 2023 IEEE.

关键词： Generative adversarial networks

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Exploring the Impact of LSTM Parameters on Network Performance for Automatic Text Summarization

Exploring the Impact of LSTM Parameters on Network Performan...

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

作者： Naaz, Rohaila Kavitha, R. Yadav, Surendra Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This technical abstract explores the impact of extended quick-term reminiscence (LSTM) parameters on net overall performance for automated textual content summarization within the Korean language. It observes and considers the parameters of phrase embedding size, sentence length, and encoding intensity. Embedding length substantially affects the community's overall performance, and a pair of dimensional representations of word embedding can improve summarization accuracy. Increasing sentence duration additionally showed enhancements, with the very best accuracy executed at triple sentence embedding lengths. Eventually, encoding intensity had a low effect on network performance, with only barely better results visible with double and triple encodings. Overall, this observation concluded that gold standard network performance for textual content summarization in the Korean language is pleasant and finished via a mixture of two-dimensional embedding with an elevated sentence length and unmarried encoding intensity. © 2024 IEEE.

关键词： Embedding Encoding Enhancements Explores Low Effect Pleasant Summarization

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Patients Medical Record Monitoring Using IoT Based Biometrics Blockchain Security System

Patients Medical Record Monitoring Using IoT Based Biometric...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Gautam, Kamlesh Kumar Prakash, Shiva Dwivedi, R.K. Madam Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India

ISBN: (纸本)9798350302820

The Patient medical record monitoring using IoT based Biometrics and blockchain security system suggests an innovative strategy for patient monitoring of their health and disease that combines the Internet of Things (IoT) devices and blockchain technology through biometrics. The paper presents a proposed model of combine system architecture that uses IoT devices and biometrics system to collect patient health data and store it on hospital database and that data synchronized biometrics database which is used in system that is government data of Adhaar identification of patient data with a blockchain-based platform for secure and transparent sharing and analysis in the medical record of hospital and medical science. The proposed system architecture includes IoT devices such as smart wearable things such as smart watch, smart digital globs, digital face mask, smart goggles and sensors that collect patient health data and transmit it to a blockchain-based platform that is Ethereum node. The platform uses smart contracts to manage data access and sharing, ensuring that the patient data and information will be only shared to authorized person and parties. The platform also enables data analysis and machine learning algorithms to provide insights into patient health trends and enable early intervention in case of health issues. © 2023 IEEE.

关键词： Blockchain

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Enhancing Automated Industrial Guiding Through computer Vision-Based Detection of Centerlines and Patterns

Enhancing Automated Industrial Guiding Through Computer Visi...

引用

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

作者： Kumari, Neeraj Ojha, Ananta Goyal, Manish Kumar Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This paper gives a method for enhancing automatic industrial guiding via pc vision-based detection of centerlines and patterns. The principal intention of this painting is to utilize vision algorithms to offer a reliable steering system for commercial robots. The technique combines numerous vision-primarily based detection techniques that allow you to come across the favored center lines and patterns. The consequent steerage device is then examined with actual robot experiments for validation. Moreover, a novel metric for measuring the accuracy of the detection device is also proposed. The effects show that the proposed technique gives a dependable answer for automatic commercial guiding. This paper discusses an automated business steerage technique, which leverages laptop imagination and is prescient to become aware of centerlines and patterns in business additives. This method uses a Convolutional Neural community (CNN) to discover those strains and patterns. To carry out detection, a CNN is educated on a fixed of photographs containing centerlines or styles and corresponding ground reality that is manually annotated. The effects of the CNN are used to guide robots to their favored function and become aware of element edges. © 2024 IEEE.

关键词： algorithms commercial experiments misalignments robots

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Leveraging Self-Supervised Transfer Learning for Robust Medical Image Classification

Leveraging Self-Supervised Transfer Learning for Robust Medi...

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

作者： Yadav, Surendra Dwivedi, Rakesh Kumar Gobi, N. Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this study appears to use self-supervised transfer mastering for sturdy scientific photo classes. Switch getting to know is a powerful approach for enhancing the accuracy of deep mastering fashions in scientific imaging. This paper investigates using self-supervised getting-to-know techniques for scientific picture classes within characteristic-based procedures. By leveraging self-supervised schooling strategies, consisting of contrastive mastering, distributed representations, clustering, pseudo-venture gaining knowledge of, and self-supervised multi-undertaking gaining knowledge of, the proposed technique can learn representations that are extra sturdy to the area shift of various clinical imaging datasets. Experiments performed on an extensive x-ray and ultrasound snapshots dataset reveal that the proposed approach affords extra improvement in type accuracy compared to traditional feature-primarily based techniques. © 2024 IEEE.

关键词： classification clustering greater representations training

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Understanding the Limitations of Authentication Protocols Employed by Existing information Security Models for Networked applications

Understanding the Limitations of Authentication Protocols Em...

引用

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

作者： Shree, Ritu Kumar, Vivek Prabhu, A. Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

The potential to comfortable systems and networks from unauthorized get right of entry is essential for any virtual community. Authentication protocols are a cornerstone to protecting such structures from malicious actors and imposing facts security. Despite the myriad of authentication protocols, however, there are boundaries to their efficacy in nicely securing digital networks. Authentication protocols, even those strongly encouraged through protection professionals, have recognized flaws and weaknesses which can be exploited by means attackers-resulting in violations of confidentiality, integrity, and availability of machine and application facts. Further, authentication protocols can be too hard to apply and cumbersome to maintain, main customers to bypass them-setting structures at risk and exposing them to capability breaches. This paper intends to research the strengths and weaknesses of authentication protocols and endorse techniques to maximize their effectiveness while deliberating applicability, usability, practicality, and cost. We can inspect adequate controls that can be used to bolster authentication protocols hired in existing facts protection models, as well as talk about the need for advanced authentication protocols and verification techniques. Authentication protocols are used in records protection fashions to offer cozy get right of entry to networked applications. Those protocols have to offer sturdy identification assurance and securely authenticate each gets the right of entry to try. This summary discusses the restrictions of the authentication protocols presently employed by current statistics protection fashions. Authentication protocols rely on cryptography and get admission to manipulate mechanisms to ensure that the most straightforward authenticated users gain access to sources. A few of the authentication protocols currently in use, including fundamental username and password authentication, are often a problem to assault due to thei

关键词： authentication community exploited implementation protocols

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Privacy Preservation in Federated Learning: its Attacks and Defenses 3

Privacy Preservation in Federated Learning: its Attacks and ...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

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

ISBN: (纸本)9798350322842

In the context of artificial intelligence, the traditional centralized server-based machine learning model training approaches have significant security and privacy issues. The federated learning (FL) approach was created to address these problems. FL uses a privacy-by-design architecture, in which several devices work together to develop any machine learning system that does not reveal users' personal information under the control of a single server. In federated learning, privacy preservation is achieved through several techniques that involve adding noise, secure aggregation, local training, and model compression. These techniques aid in making sure that raw data is not transmitted over the network and that only the required information is shared to train a global model. This research study has discussed about the federated learning-related privacy threats and defenses. The existing research works are divided into two types as attack methods and corresponding defense mechanism and further analyzed the relevant research papers from the last few years, and then further discussed the research challenges, and finally concluded the research study. © 2023 IEEE.

关键词： Machine learning

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Accurate Diagnosis by Magnetic Resonance Imaging Using Deep Learning

Accurate Diagnosis by Magnetic Resonance Imaging Using Deep ...

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

作者： Rengarajan, A. Ahmed, Zahid Pandey, Rajendra P. Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance Imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis. © 2024 IEEE.

关键词： classification demonstration performance prognosis visualization

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