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5th International Conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Verma, Shanti Modi, Hetal Desai, Jaynesh H Rami, Khyati Lokmanya Institute of Management and Computer Application Gujarat Technological University Gujarat Ahmedabad India Bhagwan Mahavir College of Computer Application Bhagwan Mahavir University Gujarat Surat India Vidyadeep University Gujarat Surat India SAL Institute of Technology & Engineering Research Department of Computer Science Gujarat Ahmedabad India

ISBN: (纸本)9798331530013

Thyroid disease has recently adopted the related classification which has been worthy in the contemporary system of healthcare delivery because of the advances in diagnostics. This work proposes an automated diagnostic system for thyroid disease using machine learning approach to improve the diagnostic accuracy of the disease. The presented model also uses the features selection and classifiers like SVM and Random Forest for the classification of different thyroid disorders including hypothyroidism, hyperthyroidism, and euthyroidism. Missing values in the dataset are treated to provide actual worth in the results while imbalance is also dealt with. The process involves feeding its input a well-constructed data set after which it is checked for its performance through a validation process. The performance of the model is evaluated based on thyroid disease related parameters and the findings show a better performance compared to conventional diagnosis techniques. In this study, the authors also present their future work on how to possibly incorporate this machine learning framework into clinical practice although the study shows how this framework will help the healthcare workers to make better decisions in relation to thyroid diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms 15

A novel Approach for optimizing the crop yield by considerin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suganthavalli, K. Meenakshi, C. Parvathy, K. Department of Computer Science Chennai Tamil Nadu Pallavaram India Department of Computer Application Chennai Tamil Nadu Pallavaram India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350370249

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India's economy, which covers a significant 60.45% of the country's land and stands as the world's second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques. © 2024 IEEE.

关键词： Agriculture crop Prediction DL LSTM and RNN ML SVM

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Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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

Improving Efficiency and Accuracy in Clinical Diagnostic Dec...

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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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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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Machine Learning for Privacy Preservation with Minimal Information Leakage 3

Machine Learning for Privacy Preservation with Minimal Infor...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Verma, Mr. Vikas Kaur, Simerjeet Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

A wide range of application sectors are progressively using machine learning (ML). A successful machine learning (ML) model often requires a lot of training data and powerful processing resources, especially the newly developing deep neural network model. Due to the possibility of information leakage and the changing legislative landscapes that progressively limit Privacy-sensitive data access and use, the need for large amounts of available data, raises significant privacy concerns. Additionally, adversarial techniques. Therefore, carefully thought-out privacy-preserving machine learning (PPML) solutions are essential and are generating more and more research attention from academia and business. More and more PPML initiatives are being put forth through the incorporation of privacy-preserving methods into machine learning algorithms. As existing PPML techniques intersect with ML, systems, security, and privacy, among others, there is an important requirement to understand current research, associated barriers, and roadmaps for future research. This study offers a PGU model to help evaluate various PPML solutions by intricately breaking down each solution's privacy-preserving functions. It also evaluates and summarizes existing privacy-preserving methodologies in a methodical manner. Phase, Guarantee, and Technical Utility make up the PGU model's trinity. We also address the special features and difficulties of PPML and suggest future study avenues that will be beneficial to a wide variety of research communities in the fields of Machine learning, security systems, distributed systems and privacy system. © 2023 IEEE.

关键词： Deep neural networks

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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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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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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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