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Maximizing the Accuracy of Time Series Analysis for the Early Detection of Rhabdomyosarcomas

Maximizing the Accuracy of Time Series Analysis for the Earl...

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

作者： Shree, Ritu Prabhu, A. Gautam, Anupam 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

Rhabdomyosarcomas are a rare malignant tender tissue tumor that generally gives in younger children and teenagers. Early prognosis and remedy are essential for successful outcomes. Time collection evaluation is a valuable tool for recognizing styles and trends in medical facts, mainly for rare situations, which include Rhabdomyosarcomas. It has consequently been increasingly employed to detect early signs and symptoms of the ailment. On this look, we are conscious of investigating and optimizing techniques for time collection analysis. It is an excellent way to enhance its application and accuracy in identifying early symptoms and signs of rhabdomyosarcoma. We examine present strategies and suggest improvement techniques, along with function extraction and system mastering techniques. We further inspect the effectiveness of our strategies by conducting experiments on a dataset installed from scientific facts and literature of rhabdomyosarcoma instances. Those experiments show promising effects, indicating that our proposed strategies can considerably increase the accuracy and sensitivity of time series evaluation for the early detection of rhabdomyosarcoma and cause higher prognoses for affected sufferers. The focal point of this study is to maximize the accuracy of time series analysis for the early detection of rhabdomyosarcoma. Time collection analysis includes: • The gathering of temporal information from multiple sources. • The assessment of these records. • The interpretation of correlations between the facts points. This study aims to utilize these techniques to discover diffused adjustments in affected person information so that you can perceive the onset of the disorder in advance than would be possible with traditional techniques. The examination will expand algorithms to systematically and accurately procedure the temporal statistics and discover adjustments indicative of Rhabdomyosarcomas. In addition, the look will rent gadget learning to boost the dete

关键词： malignant recognizing Rhabdomyosarcomas strategies Time

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Developing Multi-Scale Patch Representations for Low Energy Data Aggregation Using Unit Block Building Model

Developing Multi-Scale Patch Representations for Low Energy ...

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

作者： Bhuvana, J. Kumar, Ritesh Kharbas, Vikas 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

Developing Multi-Scale Patch Representations using Low energy information Aggregation is a paper examining the development of multi-scale representations from massive aggregated datasets for pc vision duties. The paper presents the low-strength records aggregation (LEDA) framework, an aggregate of electricity-green processes to extract multi-scale representations from big datasets. This paper provides a unique method for developing multi-scale patch representations for image analysis and using low-energy data aggregation. The technique utilizes records aggregation to create texture capabilities from which neighbourhood patch representations are constructed. These representations are then used to assemble more than one scale of features for analysis in pc vision structures. The proposed approach is evaluated for texture segmentation, and the consequences endorse that the patch representations received from the proposed approach outperform those acquired from conventional strategies. The paper additionally discusses the consequences of using different levels of information aggregation for generating patch representations. Subsequently, the paper explores the want for further studies to assess the overall performance gains acquired from the proposed method. © 2024 IEEE.

关键词： aggregation datasets minimizing performance processes

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The Enhanced Analysis of FACT-Based Framework for Traffic Prediction in V2V Networks

The Enhanced Analysis of FACT-Based Framework for Traffic Pr...

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

作者： Inzimam Aadil, Mir Agarwal, Ankita 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

The vehicular-to-car (V2V) communique networks have enabled a new generation of urban transportation phase. Vehicular conversation technology can offer scalability, dependability, and accuracy in various vehicle offerings. Even though a few modern studies exist on predicting traffic behavior in vehicular networks, only some are primarily based on an analytical method., this paper presents a better analytical method primarily based on the Fuzzy Adaptive Cluster-based total model (fact) for visitor prediction in V2V networks. The truth-based total version includes an extensive fuzzy-common sense-based device to degree the continued conduct and site visitors for a selected target node and then extrapolate the conduct and site visitors for the upcoming frames. Moreover, the model also contains a k-method algorithm to prepare the visitor's statistics into clusters for analysis. The effects of the simulation indicate that the proposed truth-based version is capable of addressing greater precisely and accurately the site visitors' prediction in dynamic site visitor's conditions, attaining higher performance metrics in terms of prediction accuracy, latency, and jitter as compared to techniques, like k-Nearest Neighbor (KNN), assist Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The model has primary importance for its capability as it should be degree and expects V2V site visitors in V2V communications. © 2024 IEEE.

关键词： communique networks dynamic predicting transportation

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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 ...

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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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Understanding the Dynamics of Sparse Coding in Auto-Encoders for Cancer Detection

Understanding the Dynamics of Sparse Coding in Auto-Encoders...

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

作者： Thiruvenkadam, T. Acharjya, Kalyan 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

This summary outlines how sparse coding in vehicle encoders may be used to come across most cancers. It explains that by using learning sparse representations of raw records, you can benefit from a higher understanding of the underlying dynamics of cancer improvement. Furthermore, this summary outlines two of the most commonplace packages of vehicle encoders for cancer detection: function extraction and fitness tracking. This look gives insights into the capability of car encoders for efficient cancer analysis. For this reason, it should prove to be a vital element of most cancer detection and prevention. Sparse coding has been gaining interest inside the region of auto-encoder-based total methods for most cancer detection. With the increasing complexity of deep getting-to-know fashions being used for detection, the information and mastery of this research approach moderately encoded representations have turned out to be vital. Auto-encoders are used to encapsulate and encode inputs from high-dimensional facts into a lower-dimensional representation to keep the most crucial or crucial data from the facts. A selected type of vehicle encoder, a sparse automobile encoder, is designed to analyze sparsely encoded representations of records by introducing sparsity into the hidden layer of the automobile encoder via sure specialized regularizes. © 2024 IEEE.

关键词： automobile commonplace detection element interest

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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...

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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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An Intelligent Video Surveillance System using Edge Computing based Deep Learning Model

An Intelligent Video Surveillance System using Edge Computin...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Singh, Rudra Pratap Srivastava, Harshit Gautam, Hitesh Shukla, Rohan Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665474511

With the rapid increase in global data volume, various factors like low latency, high efficiency video surveillance is impossible to achieve in a centralized cloud computing model. Therefore, this paper proposes a distributed computing model for intelligent video surveillance system. This paper presents a smart video surveillance system which can execute Deep Learning algorithms in low power consumption embedded de vices. The proposed intelligent video surveillance system based on the edge computing consists of multi-camera for smart cities and homes. In general, the sending of original video surveillance data to the centralized computing model is too much time consuming and this will keep us far away to achieve our objective of real time data transmission so through this paper the edge computing technique is proposed, the idea is perform computation locally at the edge devices and then the computed data will be sent to the centralized computing model which is capable of performing the real time video surveillance by using the deep learning algorithm. © 2023 IEEE.

关键词： Object detection

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

Hierarchical Representation Learning for Improved Diagnosis ...

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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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Blockchain and Internet of Things (IoT) Enabled Smart E-Voting System

Blockchain and Internet of Things (IoT) Enabled Smart E-Voti...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Kumar, Durgesh Kumar Dwivedi, Rajendra Madan Mohan Malaviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665474511

In the present situation, most people are not satisfied with the final result of the voting system. This is because the current system for voting is centralized and fully controlled by the election commission. So, there is a chance that the central body can be compromised or hacked and the final result can be tempered. In this direction, a decentralized, Blockchain and IoT based methodology for voting system is devised and presented in this paper. Blockchain is totally transparent, secured and immutable technique because it uses concept like encryption, decryption, hash function, consensus and Merkle tree etc. which make Blockchain technology an appropriate platform for storing and sharing the data in a secured and anonymous manner. IoT makes use of biometric sensors using which people can cast their votes in not only physical mode but also in digital mode. As a response, a message is received to the owner for casting his vote to ensure the authentication. In this way, the present voting system is more secure and trust-worthy by using the properties of both Blockchain and IoT, and therefore, election process in the democratic countries is valued more. The proposed method ensures security as well as reduces the computational time as compared to the existing approaches. © 2023 IEEE.

关键词： Smart contract

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Exploiting Convolutional Neural Networks for Automated Pathology Image Classification

Exploiting Convolutional Neural Networks for Automated Patho...

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

作者： Sharma, Sunil Menaka, C. Singh, Akhilendra Pratap 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

Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. Automatic picture evaluation has become increasingly crucial to exploit those photographs for clinical selections. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized, and their capability applicability in pathology picture classification is discussed. This overview also introduces the maximum hit CNN methods that have been proposed and have proven promising consequences in pathology photograph category duties. The paper concludes with a dialogue of future research directions in this area. Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. To exploit those photographs for clinical selections, automatic picture evaluation has grown to be an increasingly number of crucial. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized,

关键词： classification Convolutional development discussion pathology

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