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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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Investigating the Integrity of Irrigation Systems Through Hyper Spectral Image Analysis

Investigating the Integrity of Irrigation Systems Through Hy...

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

作者： Ahmed, Zahid Preethi, D. Yadav, Dhananjay 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 research aims to use hyperspectral picture analysis to research the integrity of irrigation systems, hoping to reduce power and water utilization. Traditional methods for measuring irrigation integrity are timeingesting, luxurious, and unreliable. With the software of the advanced imaging era, this project seeks to create a detailed model of an irrigation system so one can discover and diagnose troubles quickly and appropriately. A huge-statistics technique utilizing spectral statistics mixed with professional gadget wisdom gives the potential irrigation structures control. analysis might be conducted using a combination of airborne imaging and computational strategies along with satellite tv for pc and in-area evaluation. Those techniques will assist in offering a value-effective monitoring machine for identifying and diagnosing irrigation troubles and detecting modifications that can suggest water misuse or inefficient layout. Using this era, we intend to enhance irrigation structures' overall performance and integrity. © 2024 IEEE.

关键词： combination diagnosing hyperspectral measuring research aims

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

Prediction of Reliability of Data in Underwater Communicatio...

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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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Correlating Time Series Analysis with Radiotherapy for the Early Detection of Hodkin Lymphoma

Correlating Time Series Analysis with Radiotherapy for the E...

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

作者： Varun Somanna, M.N. Yadav, Rakesh Kumar Inzimam 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 study aimed to quantify the temporal capabilities of radiotherapy facts from sufferers with Hodgkin lymphoma (HL) and correlate those functions with time collection evaluation (TSA) measures to perceive potential temporal relationships with early analysis of HL. Excessive-decision 3-dimensional computed tomography (CT) scans of HL sufferers have been analyzed using batch-clever TSA methods to derive the temporal collection that characterizes the radiotherapy facts. In the end, a set of temporal features from the measured temporal series have been extracted and correlated with HL's medical and laboratory analysis. The outcomes showed that the derived temporal capabilities had undoubtedly correlated with the analysis, indicating a capability for using radiotherapy information to diagnose HL early. The mean and general deviation of the temporal collection in the radiotherapy curves from HL sufferers has been notably one-of-a-kind from wholesome patients. Furthermore, the temporal series of radiotherapy statistics from HL sufferers showed notably better peaks in the temporal capabilities than healthful patients, indicating an elevated stage of radiotherapy for HL sufferers. The effects of this observation recommend that TSA may be a viable tool for early analysis of HL, presenting a more comprehensive view of the temporal traits of radiotherapy data for HL sufferers. © 2024 IEEE.

关键词： capabilities evaluation radiotherapy relationships tomography

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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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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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Presentation Analysis of Reasoning Radio-Based Routing Protocols in Wireless Mesh Networks

Presentation Analysis of Reasoning Radio-Based Routing Proto...

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

作者： Kumar, Ritesh Kharbas, Vikas Kumar Mehta, Deepak 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

Reasoning radio-based total routing protocols present a promising technique for permitting powerful conversation in wi-fi mesh networks. By using the spectrum sensing capabilities of Reasoning radio, routers can intelligently pick out and pick out spectrum assets to ahead records, thereby allowing for improved network throughput and reliability. In this paper, we carry out a comprehensive Presentation evaluation of routing protocols primarily based on Reasoning radio in wi-fi mesh networks. Mainly, we Presentation metrics including packet shipping fee, latency, jitter, and energy Presentation. We examine various routing protocols from an empirical and theoretical angle and speak about the strengths and weaknesses of every technique. Subsequently, our consequences provide insights into the capability advantages and tradeoffs of Reasoning radio-based routing protocols in wireless mesh networks, offering practical steerage to designers and engineers within the subject. © 2024 IEEE.

关键词： Consequences Engineers Networks Practical Reasoning Routing Spectrum

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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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