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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Anshika Rawat Shubham Singh Pawan Singh Department of Computer Science and Engineering Amity School of EngineeringandTechnology Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India India Computer Engineering and Application Gla University India Department of Computer Science and Engineering Amity School of of Engineering and Technology Amity University Uttar Pradesh India

ISBN: (纸本)9798400709418

The management and use of computer resources within enterprises has changed as a result of the emergence of cloud computing as a paradigm shift. With its diverse and comprehensive portfolio of cloud services, including AWS CloudWatch for monitoring, Amazon Web Services (AWS) is at the vanguard of this revolution. In order to fully utilize the capabilities of these platforms and achieve cost- effectiveness, scalability, and optimal performance, proper cloud resource management is essential. This, study offers a thorough investigation of AWS's cloud resource management, with a focus on Particle Swarm Optimization (PSO) integration as a cutting-edge optimization technique. The dynamic and ever-changing workloads seen in cloud computing systems present a special set of issues for resource allocation and optimization. However, finding the ideal compromise between performance and cost-efficiency is still a challenging task. This study examines the changing environment of cloud resource management with an eye toward the future. In order to enable even more intelligent resource allocation decisions, we take into account the possible integration of machine learning and predictive analytics into our PSO framework. We also look at the broader implications of PSO-driven resource management.

关键词： Amazon Web Services (AWS)

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Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced Visual Understanding 3

Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Bhatt, Chandradeep Rai, Sumit Chauhan, Rahul Dua, Deepika Kumar, Mukesh Sharma, Sanjay Graphic Era Hill University CSE Department Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Uttranchal University Department of School of Computing Dehradun India Computer Application Department SGRRITS Dehradun India

ISBN: (纸本)9798350303360

Photo caption generators had gained huge recognition recently from the areas of NLP and computer vision. These generators produce captions that explain an image's content using deep learning algorithms. A sizable collection of image-caption pairs is used to train the algorithm. The photos are fed into a pre-trained CNN after the dataset has undergone preprocessing to extract its visual properties. Along with the picture features, the necessary captions are tokenized and supplied to the LSTM decoder. In order to accelerate the caption generation process, the LSTM decoder is trained to utilize beam search and maximum likelihood estimation methods. To assess the effectiveness of the suggested model, several metrics such as BLEU and METEOR are employed. The suggested CNN-LSTM image caption generator has a lot of promise for use in a variety of contexts, such as picture comprehension, information retrieval, and assistive technology for the blind. Combining CNN and LSTM models takes advantage of each architecture's capabilities, allowing the model to produce insightful and contextually appropriate captions for a variety of images. We also explore present difficulties and potential directions for additional investigation in this area. This research study concludes by presenting an innovative method for creating image captions that combines the strength of CNN and LSTM. The experimental outcomes demonstrate the efficacy and reliability of the suggested model, highlighting its potential to advance the field of image captioning and contribute to the creation of intelligent systems that can comprehend visual content and produce precise and insightful textual descriptions. © 2023 IEEE.

关键词： Intelligent systems

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Particle Swarm Optimization Technique for Current Equalization of PV Systems to Achieve Higher GMPP Under PSCs

Particle Swarm Optimization Technique for Current Equalizati...

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International Conference on Smart Energy and Advancement in Power Technologies, ICSEAPT 2021

作者： Pachauri, Rupendra Kumar Prasad, Hanuman Gupta, Pankaj Kumar Sharma, Manish Electrical and Electronics Engineering Department School of Engineering University of Petroleum and Energy Dehradun248007 India Electrical Engineering Department Model Institute of Technology Jammu181123 India School of Computer Science and Application IIMT University Meerut250001 India

ISBN: (纸本)9789811949708

The sun irradiations levels are always vary at different times, which affect the performance of solar photovoltaic (PV) systems in terms of power losses (PL). But many natural and un-natural causes exist and behave an obstruction to extract best performance from PV systems. The popular unacceptable causes are partial shading conditions (PSCs), which affects the PV performance highly. To diminish the effect of PSCs, relevant PV module rearrangement method is another good solution. In this paper, particle swarm optimization (PSO) technique demonstrates the current equalization of PV array systems under PSCs. In that method, a particle reaches the optimum value depending on its present velocity, past experience, and the experience of its nearby group members. Also, traditional arrangement techniques such as the series–parallel (SP) and total cross-tied (TCT) configurations have been used. As a result, performance parameters such as power and voltage at global maximum power point (GMPP), PL, and fill factor (FF) have been assessed. This paper displays the methods of implementation of the techniques to avert the effect of PSCs and to improve the energy output of the PV array under non-uniform irradiation circumstances. MATLAB/Simulink modelling is carried out to compare the results of various reconfiguration techniques and to find out which one is the best one under considered PSCs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Data control in public cloud computing: Issues and challenges

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第2期14卷 564-579页

作者： Sharma, Ashok Jha, Pranay Singh, Sartaj Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India School of Computer Application Lovely Professional University Jalandhar Punjab India

Background: The Advancement in the Hardware and Progress in IoT based devices has led to significant transformation in digitalization and globalization of business models in the IT World. In fact Cloud Computing has attracted many companies to expand their business by providing IT infrastructure with very less budget in pay per use model. The Expansion and Migration of Companies to Cloud Computing facilities has really brought many pros and cons and opened new area of Research. The Management of IT infrastructure as per business requirement is a great challenge for the IT Infrastructure managers because of complex business models which needs to be updated with market trends and it requires huge and updated infrastructure to accelerate their business requirements. No doubt there are many benefits of moving to Cloud but several vulnerabilities and potential threats related to security is a major concern for any business sensitive data. These security challenges place restrictions on moving on-premises workloads to the Cloud. This paper has discussed key differences in cloud models and existing various Cloud Security Architectures and challenges in cloud computing related to Data Security at Rest and in Transit. Also data controlling mechanism need to be adopted by IT Industry along with end to end security mechanism has been explained. Objective: The main objective of this paper is to discuss about the prevailing issues in cloud in terms of data security which is discouraging the Industry and organizations to move their data into public cloud and also to discuss how to enhance security mechanism in cloud during data migration and multitenant environment. Methods: Based on different reports and analysis, it has been pointed that data breach and data security are most challenging and concerning factor for any customer when someone think to migrate the workloads from On-Premises datacenter to Cloud Computing. It needs more attention in every consideration. All criteria a

关键词： Cloud security

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Automated Anomaly Detection in Medical Imaging Using Support Vector Machines 5th

Automated Anomaly Detection in Medical Imaging Using Support...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Goyal, Manish Kumar Shahid, Mohammad Karthikeyan, M.P. Kumar, Ritesh Department of Computer Science & Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Clinical imaging techniques such as X-ray, Magnetic Resonance Imaging (MRI), and laptop Tomography (CT) scanners are used to diagnose, compare, and deal with sufferers. Automatic Anomaly Detection is an automatic technique for identifying and diagnosing abnormalities in clinical imaging. It has been diagnosed that Support Vector Machines (SVM) can be effectively used to generate correct automatic anomaly detection for scientific imaging. SVM is an approach that uses supervised getting-to-know to create a choice boundary between classes, with the decision primarily based on the information to be had. The gain of SVM is that it plays an automatic function extracting from the medical imaging facts and uses those extracted capabilities for anomaly detection. It combines photograph segmentation and classification to classify the abnormal regions in an image. So one can enhance the accuracy of SVM;it's miles viable to use an expansion of kernel capabilities and unique parameter tuning. Moreover, introducing an automatic pipeline from training to deploying SVM reduces the efforts of manual feature extraction from photos. Consequently, aid Vector Machines is a feasible approach for automatic anomaly detection in scientific imaging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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Improving Medical Image Classification Through Transfer Learning and Meta Learning 5th

Improving Medical Image Classification Through Transfer Lear...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Arora, Sonia Jebakumari, S. Adlin Singh, Vaishali Kharbas, Vikas Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This paper presents an observation of improving clinical picture classification via transfer getting to know and meta-studying. Switching getting-to-know and meta-learning methods are usually employed in medical imaging class tasks. Transfer studying objectives to switch information and illustration from a properly-trained version and mildew that expertise to higher healthy the problem at hand, while meta-gaining knowledge is a version of switch gaining knowledge of which targets to seize and adapt the generalizable expertise of a project domain, and to allow a progressed impact for the goal tasks. Inside the clinical imaging scenario, the two procedures can dig deeper into the several layers which might be involved in gaining deep knowledge of algorithms. The authors compare the effectiveness of the proposed strategies in five publicly available medical imaging datasets. Effects have shown that the proposed techniques notably advanced the usual type accuracy for a few datasets. Furthermore, it was found that the 2-step technique showed higher performance than the one-step technique. This study presents a higher understanding of the efficacy of transfer learning and meta-mastering techniques for clinical photo-type tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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Understanding the Role of Data-Driven Communications Software Design in Networks 5th

Understanding the Role of Data-Driven Communications Softwar...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Inzimam Dadhich, Shruti R, Murugan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Facts-pushed verbal exchange software program layout is a critical element in the design of statistics networks. It encompasses the identification, control, and manipulate of network traffics, which includes the prioritization of communications and associated statistics, in addition to the regulation of connection distributions and assets over the complete community. Furthermore, information-pushed communique software program design encompasses algorithms designed to mitigate potential verbal exchange bottlenecks, via controlling the drift of information via the network, thereby improving its reliability and serviceability. Thru this technique of design, it's far possible to create a dynamically optimized community that is capable of speedy and successfully routing facts to its supposed vacation spot. Through making use of these standards, designers and engineers are able to create high-appearing networks that fulfill the demands located upon them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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Applying Transfer Learning to Medical Image Classification Tasks 5th

Applying Transfer Learning to Medical Image Classification T...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Gupta, Manali Haripriya, V. Singh, Akhilendra Pratap Yadav, Surendra Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Transfer studying is a machine learning approach that permits a model to leverage know-how from a previous challenge to boost studying in the following tasks. It's been used increasingly in medical photo classification obligations, allowing area-particular knowledge to be leveraged from current fashions. This method can enhance the overall performance of clinical photograph-type structures and reduce the amount of labeled information required to analyze a brand-new venture. In this paper, we assess the present-day kingdom of switches, getting to know the scientific photo category and the results for clinical picture evaluation. We discuss present switch studying techniques and their influences on the accuracy of medical photo category responsibilities and the challenges related to constructing generalizable fashions. Eventually, we take a look at existing datasets and implementations available for the clinical photo category and factor into promising destiny studies guidelines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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