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1st International Conference on Artificial Intelligence and Digital Twin technology, IconAIDTT 2023

作者： Salman, Rahama Gupta, Subodhini Department of Computer Science Sam Global University MP Bhopal India Department of Computer Application School of Information Technology Sam Global University MP Bhopal India

ISBN: (纸本)9783031777981

Fog is a data management and analytics service. This paper derives the most effective new approach for providing IoT-enabled services in healthcare applications using Fog Computing. In this study, data are collected from Google Scholar, Science Director, and MEDLINE databases. IoT-based Fog Computing techniques are proposed to provide quality services to users. An optimal resource provisioning method for boundary discovery, service level agreements, and administration services for an IoT client is proposed. The DeepQ residual information processing technique is applied to cloud data center connectivity, and the computing paradigm technique is to find the reference depth of fog levels. The proposed optimal resource provisioning algorithm studies the dataset and the TensorFlow tool is used to simulate the environment. The Deep Belief network is generated based on the above input using a 512 × 512 × 3-layer system and 3000 trained data, 1000 test data are taken for simulation. Each dataset simulation is recorded using supervised and unsupervised learning techniques. Based on the above results, IoT enables Fog Computing's data management and analytics systems to provide 95% accuracy, and compared to existing computing methods, our proposed systems show better performance in terms of security and convenience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data accuracy

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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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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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Review on Enhancing Predictive Accuracy in Alzheimer Detection: A Hybrid Approach 3

Review on Enhancing Predictive Accuracy in Alzheimer Detecti...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ambooj Khan, Ihtiram Raza Agarwal, Parul Kaur, Harleen School of Engineering Sciences & Technology Jamia Hamdard New Delhi India Bvicam Department of Computer Science and Application New Delhi India

ISBN: (纸本)9798331518592

Effective early detection techniques are essential because to the tripled global prevalence of dementia, which is mostly caused by Alzheimer's disease (AD) and is predicted to reach 152 million by 2050. AD pathology, which has a preclinical phase lasting 10-20 years before severe cognitive impairment, is associated to about 70% of dementia cases. Early detection of AD is essential since medication trial success rates may be increased and risk factor management may avoid 40% of cases. Currently, lengthy cognitive tests that are insensitive to the pre-clinical stage are used to detect dementia. Initiatives to make open data available have gathered so much data on AD that it is now too much for humans to analyze. In order to diagnose AD accurately, machine learning (ML) has become essential, especially when it comes to early-stage AD prediction. AI and ML-driven healthcare research places a strong emphasis on automated disease prediction systems, including dementia detection. Finding those who is susceptible of 'Alzheimer's disease' (AD) progression is still difficult, especially in light of the disease's severity and fast advancement. Preventing the progression of the disease and planning the course of treatment depend on early detection. Reviewing AD detection research conducted since 2016 has highlighted the contribution of machine learning (ML) to improving prediction accuracy. The reviews provide information on participant demographics, data modalities, feature extraction strategies, and ML models used. By combining earlier studies, promoting critical analysis, and directing future investigations for early AD diagnosis, this thorough review helps researchers. © 2024 IEEE.

关键词： Adversarial machine learning

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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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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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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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computer Modeling in Engineering & Sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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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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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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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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