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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Jawahry, Hassan M. Shrivastava, Anurag Bansal, Saloni Raju, Hemanth Begum, Rowsonara Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

One of the most groundbreaking approaches to the processing and analysis of large datasets has been created as a result of the combination of MapReduce techniques and machine learning algorithms within the framework of big data analytics. The problems of traditional data processing in terms of handling the volume, velocity, and variety of big data are addressed by the distributed computing architecture introduced by MapReduce. This research investigates the ways in which MapReduce may be used to simplify the process of machine learning, with a particular focus on the capabilities of the framework to manage enormous volumes of data and to parallelize operations. By contrasting the performance of MapReduce with that of more traditional methods, the study solves the problems of scalability and computing efficiency that are brought about by massive amounts of data. Through an in-depth analysis, the paper highlights the optimization potential of MapReduce as well as its implications for next-generation machine learning applications across a variety of industries. © 2024 IEEE.

关键词： MapReduce

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Deep Learning: The Future of Medical Image Processing

Deep Learning: The Future of Medical Image Processing

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Singh, Kamred Udham Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Teekam Kumar, Gaurav Kumar, Ankit School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Health care is a vital service that is constantly in high demand since everyone needs it. Individuals have higher hopes for advancement in this profession than for receiving the status quo since they would rather be treated better. Sometimes, or maybe more accurately, in many situations, the findings are not clear and the sickness cannot be understood at the first stage from a manual reading of the report. When it comes to viewing medical images, the fact that they must be interpreted by hand inevitably leads to delays and errors. Many deep learning and machine learning approaches can be used to address this issue. Therefore, although machine learning may be thought of as a subset of deep learning, the reverse is not true. Let's talk about how deep-learning models will be used to process medical images. Medical image processing is one area where deep learning models are having a profound effect. Convolutional neural networks (CNNs) and other deep learning techniques have made it feasible to automate the processing of medical pictures and improve diagnostic and treatment accuracy. It is widely used in radiology to examine medical pictures such as X-rays, CT scans, and MRIs. In order to aid radiologists in their diagnostic work, deep learning models may be taught to identify patterns and characteristics in medical pictures that are diagnostic of certain diseases or ailments. © 2023 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence-Enabled regression model for used car price prediction

Artificial Intelligence-Enabled regression model for used ca...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Kamred Udham Singh, Teekam Kumar, Ankit Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Deciding if the recorded cost of a pre-owned vehicle is a difficult errand, because of the numerous variables that drive a pre-owned car's cost. The focal point of this venture is creating AI models that can precisely anticipate the cost of a trade-in vehicle dependent on its highlights, to make educated buys. We'll be going to look at changed models and calculations which can anticipate the cost of pre-owned vehicles proficiently. More than 70 million passenger vehicles were shipped in 2016. This represents a significant increase in vehicle production over the past decade. As a result, the secondary market for used cars has grown rapidly in recent years. We'll be noticing various strategies and afterward propose the best-fit model to foresee the cost of pre-owned vehicles. Choosing whether a pre-owned vehicle merits the posted cost when you see postings online can be troublesome. A few components, including mileage, make, model, year, and so forth can impact the real worth of a vehicle. From the point of view of a dealer, it is likewise a quandary to value a pre-owned vehicle properly. In light of existing information, the point is to utilize AI calculations to foster models at anticipating utilized vehicle costs. © 2023 IEEE.

关键词： Decision trees

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Modelling a Risk-based Network Model for Epileptic Seizure Prediction using Learning Approaches 6

Modelling a Risk-based Network Model for Epileptic Seizure P...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Anandaraj, A. Alphonse, P.J.A. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India National Institute of Technology Department of Computer Applications Tiruchirappalli India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

The process of developing across space and time in the networks of a person with epilepsy occurs through Epileptic seizures. The generalizable technique is developed in this research to predict a particular patient seizure using the evaluation of featurerepresentation to obtain the features from the signals of multichannel EEG. The features are revealed for the signals of EEG using the available parameters. The features are input to the Risk-based Elman learning model (r-ELM) to evaluate feature representation to collectively train the data. The suggested model of r-ELM obtains 0. 096/h as the rate of false prediction, 85% as sensitivity, and 10% as the time in warning to perform the tests from the EEG dataset of CHB-Mn scalp using 10 patients. The suggested method has superiority over the existing results. Various metrics are used in the experiment which shows the epileptic stage as the essential factor affecting seizures' performance. A subject-oriented method for seizure prediction is presented in the proposed system, which is powerful for the unbalanced data and created for any dataset of scalp EEG with no requirement of subject-oriented engineering. © 2022 IEEE.

关键词： Risk assessment

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Solve DNA sequence assembly problem using hybrid crow search optimization and multi classification techniques

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International Journal of Information Technology (Singapore) 2022年第5期14卷 2541-2547页

作者： Raja, G. Reddy, U. Srinivasulu Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Applications National Institute of Technology Trichy India

The reconstruction of a DNA chain from the randomly collected fragments set is known as the Deoxyribonucleic Acid Fragment Assembly Problem (DNA-FAP). In the genome project, this problem is usually considered an important step. Genome fragment assembly is the main objective in a much more important human genome sequence and helps to cure several genetic problems. Cost plays an important part in the process of genome fragment assembly. However, metaheuristics and machine learning model help to reduce the complexity level and error rates of genome assembly problem. The proposed work for hybrid Crow search optimization and multiple classifiers to calculate high similarity consensus sequence. The implementation result exhibits the proposed model produces better results and is compared with existing art techniques. This model performed types of benchmark datasets and provide different metric results in DNA sequence assembly. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Classification Crow search DNA sequence Fragment Genome

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Optimizing Random Forest Algorithms for LargeScale Data Analysis 7

Optimizing Random Forest Algorithms for LargeScale Data Anal...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bhadula, Shuchi Adnan, Myasar Mundher Kumar, Rakesh Rana, Ajay Kaliyaperumal, Gopal Rao, Bolleddu Devananda Boob, Nandini Shirish Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350067

The exponential growth of data in recent years has necessitated the development of more efficient and scalable machine learning algorithms to handle large-scale data analysis. Random Forest (RF) algorithms, known for their robustness and accuracy, find extensive use in the realms of classification and regression. However, their performance can be significantly hindered when dealing with massive datasets due to increased computational complexity and resource demands. This paper presents a comprehensive approach to optimizing Random Forest algorithms for large-scale data analysis. We explore various strategies, including data partitioning, algorithmic changes and parallel processing to make RF algorithms more efficient and scalable. Additionally, we introduce novel techniques for feature selection and hyperparameter tuning to improve model performance. Extensive experiments conducted on multiple large-scale datasets demonstrate the effectiveness of the proposed optimizations in reducing computation time and memory usage while maintaining or even enhancing predictive accuracy. From genomics to finance, the optimised Random Forest method shows promise as a robust tool for large-scale data processing in many applications, according to the findings. © 2024 IEEE.

关键词： Data accuracy

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Don’t Train, Just Prompt: Towards a Prompt engineering Approach for a More Generative Container Orchestration Management 14

Don’t Train, Just Prompt: Towards a Prompt Engineering Appr...

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Kratzke, Nane Drews, André Department of Electrical Engineering and Computer Science Lübeck University of Applied Sciences Germany Expert Group Artificial Intelligence in Applications Lübeck University of Applied Sciences Germany

ISBN: (纸本)9789897587016

Background: The intricate architecture of container orchestration systems like Kubernetes relies on the critical role of declarative manifest files that serve as the blueprints for orchestration. However, managing these manifest files often presents complex challenges requiring significant DevOps expertise. Methodology: This position paper explores using Large Language Models (LLMs) to automate the generation of Kubernetes manifest files through natural language specifications and prompt engineering, aiming to simplify Kubernetes management. The study evaluates these LLMs using Zero-Shot, Few-Shot, and Prompt-Chaining techniques against DevOps requirements and the ability to support fully automated deployment pipelines. Results show that LLMs can produce Kubernetes manifests with varying degrees of manual intervention, with GPT-4 and GPT-3.5 showing potential for fully automated deployments. Interestingly, smaller models sometimes outperform larger ones, questioning the assumption that bigger is always better. Conclusion: The study emphasizes that prompt engineering is critical to optimizing LLM outputs for Kubernetes. It suggests further research into prompt strategies and LLM comparisons and highlights a promising research direction for integrating LLMs into automatic deployment pipelines. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Automation

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Application of Developmental Services based on IoT Efficiency 6

Application of Developmental Services based on IoT Efficienc...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Rawat, Vandana Singh, Neelam Bijalwan, Rahul Verma, Poonam Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Applications Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350398496

Internet of things (IoT) is a fast and conforming technology, having a standard emergence primarily connected to the wireless communication between actuators, gadgets, and electrical services all in general referred to as instructions. Developmental services refer to the activities and services promoting community development and are considered as an integral part of developmental services, which include seamless and coherent experience. It is a phenomenon rich in technical skills and broader experiences consisting of mechanical frameworks and platforms, which aim towards selecting the most efficient and cost-effective tools. The collection of valuable information and relaying on them accurately are the two most important features provided by the efficient IoT services. © 2023 IEEE.

关键词： Efficiency

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A Grading of PDN Based CC Networks 4

A Grading of PDN Based CC Networks

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Singh, Suresh Al-Farouni, Mohammed Singh, Devendra Varshney, Neeraj Arvind, Sudha Krishnaraj, N.S. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute of Technology Department of Computer Science & Engineering Dehradun India Gla University Department of Computer Engineering and Applications Mathura India Cmr Technical Campus Department of Ece Telangana Hyderabad India Department of Information Science and Engineering Nitte India

ISBN: (纸本)9798350360165

Network systems function based on rules that are intrinsically dynamic, subject to temporal circumstances set by outside events like host utilisation, bandwidth measurements, intrusion detection, or time-specific events. Software-defined networking (SDN) presents an opportunity to streamline network configuration through the provision of more sophisticated configuration tools. In an attempt to lower network monitoring costs and traffic overheads, we offer a software-defined cloud resource management solution that customises network resource distribution using a Flexible Analytical Hierarchy Procedure (Fuzzy-AHP). Through an Application Programme Interface, this framework may be easily integrated into cloud infrastructures that support SDN (API). We demonstrate how our system improves network resource management and efficiently responds to growing traffic requests using real-time data. Moreover, we confirm the performance of our framework using simulations. © 2024 IEEE.

关键词： and fuzzy analytical hierarchy process (fuzzy-AHP) Cloud computing network management resource management scheduling software-defined networking

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Smart Auto Image Captioning Using LSTM and Densenet Network 6

Smart Auto Image Captioning Using LSTM and Densenet Network

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arumugam, M. Vijayakumar, P. Siddharrth, G.M. Rohini, J. Karthikeyan, A. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350304480

The integration of semantic attention increase photo captioning ability. The premise underlying semantic attention-based techniques is that the model should prioritize words or qualities with high semantic value. Prior studies focused on the attribute detector and the captioning network as distinct entities. This hampers the best use of semantic data. Image captioning is the computational process of producing accurate written descriptions of an image's or video's visual content. Captioning images makes it easier to construct contextualized interpretations, allowing people to situate photographs within a larger context. Using photo captions can be useful in a variety of contexts, such as conducting research with an unlabeled image collection or attempting to identify. previously unknown patterns. Subtitles added to the photos using a Deep Learning Model. Recent advances in the fields of deep learning and natural language processing have made it much easier to generate captions for input photographs. This study will use neural networks to generate image captions on their own. The Long Short-Term Memory (LSTM) model, a type of recurrent neural network, used as an encoder, together with a pre-trained lexicon and visual attributes. This enables the extraction of image data and the generation of captions. © 2023 IEEE.

关键词： Deep Learning Model Densenet Network Long Short-Term Memory Remote Sensing

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