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

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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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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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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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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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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Fractal dimension analysis for improved decision-making in non-stationary multi-arm bandit problems: a machine learning approach

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The European Physical Journal Special Topics 2025年 1-16页

作者： Nandhini, S. Kala Pandian, J. Arun Department of Computer Applications School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

In this research, we propose a novel approach to addressing the exploration–exploitation dilemma in multi-armed bandit (MAB) algorithms using fractal dimensions. The fractal dimension is used in the algorithms to represent the reward distributions of arms which represents the uncertainty of the arm in receiving the reward. The fractal dimension of the reward distribution is implemented in the most popular MAB optimization algorithms, such as Epsilon-Greedy, Upper Confidence Bound (UCB), Exponential-weight algorithm for Exploration and Exploitation (EXP3), and Thompson Sampling in this study. The algorithm prefers to choose arm with the least fractal dimension, as a lower fractal dimension represents less uncertainty of the arm. The performance of the fractal-enhanced MAB optimization algorithms is compared with traditional algorithms in non-stationary environments with various numbers of arms. The proposed approach provides a novel way to quantify and utilize the uncertainty of each arm in MAB problems.

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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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Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnosis 4

Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnos...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Pattnayak, Parthasarathi Mishra, Debaswapna Patnaik, Sanghamitra Mohanty, Arpeeta School of Computer Applications KIIT Deemed to Be University Bhubaneswar India OUAT Department of Computer Science and Application Bhubaneswar India School of Law KIIT Deemed to Be University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

High blood glucose levels are a defining characteristic of diabetes, with type 2 diabetes being the most prevalent form. Early detection and treatment can prevent or delay complications. To precisely anticipate type 2 diabetes risk, we applied machine learning techniques to identify unidentified correlations with a person's health condition. In order to find potential nonlinear correlations between the beginning of type 2 diabetes with an ensemble of health factors, this research used a binary classifier that was trained from scratch. Three datasets - MIMIC-III, MIMIC-IV, and NHANES - were used. The equal number of people who had or did not have type 2 diabetes have been included in one data set that was created by combining the two sets of data. Reliability was the key metric, given the balanced dataset. The model achieved ROC AUC score of 0.934 with accuracy ratings up to 86%, which were positive results of this investigation. To increase the model's dependability, more research is required to take into account multiple readings taken throughout period for an identical person. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Fake Review Detection via Aspect Extraction and TPA-BiLSTM-CSSA 2

Advanced Fake Review Detection via Aspect Extraction and TPA...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, Deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. Dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BiLSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BiLSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

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