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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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Research on Cloud Computing Security technology under computer Big Data Network 3

Research on Cloud Computing Security Technology under Comput...

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3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Zhou, Qiang Ou, Wenjisi Zheng, Wei Jiangxi Vocational and Technical College of Information Application Department of Computer Technology Nanchang330043 China

ISBN: (纸本)9798350398410

In this paper, a parameter estimation-based security access integration method for computer network smart data is designed. The scale of data clustering is determined according to the distribution and evolution characteristics in the clustering process. Finally, this paper proposes a new security access control scheme. The system uses local abnormal factors to judge non-safe data and eliminates abnormal data according to the judgment results. The system sorts and outputs the factors that may have abnormal data points in the current time window. The Java object-oriented programming language is used. The network attack will be divided into fuzzy measures of DoS attack and non-DoS attack, and finally, the results of various attacks will be displayed. Simulation results show that this paper's entire access control scheme is highly feasible. The proposed algorithm has a high recognition rate. © 2023 IEEE.

关键词： Access control

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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...

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

Enhancing Automated Industrial Guiding Through Computer Visi...

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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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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with Engineering applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Architecting lymphoma fusion: PROMETHEE-II guided optimization of combination therapeutic synergy

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Ansar, Syed Anas Arya, Swati Soni, Nupur Khan, Mohd Waris Khan, Raees Ahmad Department of Computer Applications Babu Banarasi Das University Lucknow 226028 India Department of Computer Application Integral University Lucknow 226026 India Department of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow 226025 India

Lymphoma, a malignancy originating from the aberrant proliferation of lymphocytes in the lymphatic system, becomes one of the prime concerns in health care across the world. The traditional, mono-therapeutic interventions using chemotherapeutic agents and radiation showed limitations in relation to disease control, suboptimal response, and development of intractable drug resistance. This study thus opens the reasons for combination therapies in detail by explaining the intrinsic rationale and revealing the intrinsic advantages of lymphoma treatment with an initiative underpinned by scrupulous analysis. Furthermore, the broad analysis had comparisons between combination therapies, were derived from studies, clinical trials, and actual instances. Key performance metrics like as overall response rates, progression-free survival, and overall survival, together with a thorough review of safety profiles, are used to determine the efficacy and long-term tolerability of various combination regimens. Using the PROMETHEE-II technique, this study has attempted to offer a comprehensive knowledge of the complex therapeutic landscape by addressing various issues and factors such as drug-disease interactions, toxicities, cost-effectiveness, and accessibility. This study avails practitioners and researchers with indelible understanding that seeks to enhance the optimum therapeutic trajectory and evidence-based decision-making toward an improved outcome of treatment in lymphoma. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Hodgkin lymphoma (HL) Lymphoma Non-Hodgkin lymphoma (NHL) PROMETHEE-II

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Investigating the applications of Deep Learning in Drug Discovery and Pharmaceutical Research

Investigating the Applications of Deep Learning in Drug Disc...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Desai, Karishma Goyal, Manish Kumar Nachappa, M.N. Atlas SkillTech University Department of UGDX Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

The widespread use of deep learning has ushered in a revolutionary age in the fields of drug development and pharmaceutical research. The many uses and consequences of deep learning in this area are briefly discussed in this abstract. Combining computing resources, large datasets, and cuttingedge algorithms has produced revolutionary progress. In this article, we look at how deep learning approaches have revolutionized the pharmaceutical and drug development industries. The development of new medicines has been hastened thanks to the use of deep learning methods. Potential drug candidates' potential of individualizing pharmacological therapies. There is promising potential for enhanced therapeutic outcomes with less undesirable effects using a tailored approach. Also, deep learning has rethought our understanding of how drugs and receptors work together. Computational modeling may be predicted with surprising accuracy by deep neural networks, which analyze large databases of chemical compounds and their interactions with biological targets. A constant goal is to find a middle ground where deep learning's computational power may coexist with more traditional types of biological understanding. Finally, deep learning may revolutionize the pharmaceutical sector and the drug-discovery process. Possible applications include accelerating the discovery of novel medications, enhancing the precision of individualized therapy, and shedding information on the nature of drug-receptor interactions. © 2024 IEEE.

关键词： Drug discovery

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Transforming Modern Medicine with the Role of the Healthcare Internet of Things in Enhancing Patient Care and Monitoring 13

Transforming Modern Medicine with the Role of the Healthcare...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Inzimam Bhuvana, J. Desai, Karishma Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Maharashtra Mumbai India

ISBN: (纸本)9798350305463

The present healthcare sector is experiencing a fundamental upheaval with the emergence of the Healthcare Internet of Things (HIoT). This article delves into the transformational potential of HIoT by examining its use in bettering patient care and monitoring. The paper dives into the introduction of HIoT, the methodologies adopted for research and development, the compelling findings gained, and the farreaching consequences in conclusion The opening part sets the scene by describing the fundamental transformation in healthcare facilitated by HIoT. It emphasizes the catalyst for rethinking patient care and monitoring as the combination of internet-connected devices, smart sensors, and data analytics. HIoT equips doctors with up-to-the-minute information, allowing for more precise diagnoses and more individualized plans of care. Furthermore, deep learning and artificial intelligence were incorporated to exploit the richness of HIoTgenerated data, allowing sophisticated analytics and informed decision-making. The compelling findings of this study are clear, with the suggested technique regularly beating existing methods across numerous criteria. A remarkable accuracy score of 0.94 was reached, demonstrating the reliability of the suggested strategy in supporting informed healthcare decisions. Furthermore, in terms of cost efficiency, the suggested approach scored 0.95, suggesting its costeffectiveness and potential for economic viability. The development of HIoT promises a healthcare system that is more patient-centered, data-driven, and efficient in the future. To ensure healthcare continues to grow into an age of unmatched patient care and monitoring, researchers, healthcare providers, and governments must work together to harness this transformational potential while tackling the issues that come with it. © 2024 IEEE.

关键词： Predictive analytics

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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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