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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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Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics 7

Utilizing Random Forests for High-Accuracy Classification in...

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

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Srivastava, Abhinav 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 Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Master of Computer Application Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results. © 2024 IEEE.

关键词： Random forests

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Relevance Feature Discovery in Text Mining Using NLP 3

Relevance Feature Discovery in Text Mining Using NLP

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3rd International Conference for Emerging Technology, INCET 2022

作者： Naik, Darshana A Mythreyan, S Seema, S Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665494991

The volume of text that is created and formed each day is dramatically increased and increasing. This colossal sum or volume of profoundly unstructured text cannot be basically and effortlessly handled and seen by computer systems. Therefore, we can get an useful patterns from the text using an efficient and effective techniques and algorithms. Text mining is a strategy for dissecting text records to extricate valuable data and knowledge from the text document, analysis is done using Text mining. Mining contains a few exercises that assists you with deriving data from unstructured text information. Before you'll apply diverse text mining procedures, you might want to begin with text preprocessing, which is that the act of cleaning and rebuilding text information into a usable organization. This training could likewise be a center part of Natural Language handling (NLP) and it generally includes the utilization of strategies like language identification, tokenization, filtering, lemmatization and stemming to design information suitably for examination. At the point when text preprocessing is finished, after that you will apply text mining calculations to get experiences from the information. However, ensuring the excellent of chose highlights from message is a test because of the monstrous measure of insignificant (boisterous) data in message report is a major test, to guarantee the norm of found significance highlights in the message archives for portraying client inclinations because of huge scope terms and information designs. © 2022 IEEE.

关键词： Data mining

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Lake Ice Infrared Image De-noising using Deep Learning Convolutional Neural Network 4

Lake Ice Infrared Image De-noising using Deep Learning Convo...

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Bharathi, P.T. Kiran, K. Prashanth, G.K. Virupakshaiah, H.K. Karegowda, Asha Gowda Siddganga Institute of Technology Department of Computer Science & Engineering Tumakuru India Siddganga Institute of Technology Department of Master of Computer Applications Tumakuru India

ISBN: (纸本)9781665456357

Elimination of noise from the acquired images is one of the fundamental tasks so as to restore the high quality of images to increase the results of segmentation, classification, recognition, detection etc. In this research work, Infrared imaging is used to capture the images with various temperature ranges during day and night time. Infrared thermography is used as a nondestructive image acquisition sensor. So infrared imaging sensors are used to identify and classify different ice types formed on the rivers during winter season in western countries. However, you can observe blur and degradation in the acquired images through infrared imaging, as the climatic nature is involved in the process. These factors affect the quality of the image and also the quantitative analyses of the images. Especially, when there are deeper faults which are found in an under laying substance and high thermal conductivity materials can also be studied. Acquired infrared images are usually affected with noises. In this paper, de-noising of infrared images is carried out using convolution neural network model in deep learning. In this work Gaussian white noise is added to the infrared images with varying noise intensities from 1% to 10% and then the convolution neural network model is applied to de-noise the infrared images. In the qualitative analysis of the infrared images edge factor, uniform region, texture, smoothness, non-uniform region, structure of the region of interest is considered. For quantitative analysis, mean square error, peak-signal- noise-ratio and structure similarity index measure is used. The results of traditional methods of image de-noising are compared with convolution neural network based method. It is concluded from the experimental work that the convolution neural network based method proves to be better for removal of Gaussian white noise when compared with traditional methods. © 2022 IEEE.

关键词： Mean square error

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Design and Analysis of High-Speed Communication Systems for 5G and Beyond 9

Design and Analysis of High-Speed Communication Systems for ...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jegadeesan, R. Srinivas, Dava Deivakani, M. Joshi, Sunita Dinesh, P.M. Kumar, M. Jogendra Jyothishmathi Institute of Technology And Science Department of Computer Science And Engineering Telangana Karimnagar505481 India Psna College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Dindigul624622 India Manav Rachna International Institute of Research And Studies Department of Faculty of Computer Applications Haryana Faridabad121004 India Sona College of Technology Department of Electronics And Communication Engineering Tamil Nadu Salem636005 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350365092

Fifth Era Innovation (5G) is the late portable network in wireless communications. This article presents an assessment of the advancements in portable communications. The different issues that arise at each stage are reported in our guide to state-of-the-art wearable networks. The fifth era (5G) of portable networks offers everybody admittance to high-speed web at whatever point they need, any place they are. Since to its inventive qualities, such connecting individuals and controlling contraptions, machines, and items, 5G is somewhat unique. Various levels of execution and capacities presented by the 5G versatile innovation will make new client encounters and connection new organizations. It is pivotal to comprehend where the business can utilize5G's benefits. This work examines and models a microstrip fix antenna working at 29 GHz for impending 5G communication innovation. The result of this reproduction is better than past work that was finished. It can then be used as a significant source of strength for 5G wireless technologies. The ongoing logic diary demonstrates that the proposed antenna performs better than those currently in use. This antenna may therefore be adequate for his 5G wireless connection architecture. © 2024 IEEE.

关键词： 5G mobile communication systems

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Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning 3

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Jagendra Dabas, Partibha Bhati, Sunita Kumar, Santosh Upreti, Kamal Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India Dr Akhilesh das Gupta Institute of Technology and Management Dept. of Computer Science & Engineering Delhi India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India Department of Computer Science Ghaziabad India SRIT-Autonomous Anantapur Department of Computer Science Engineering India

ISBN: (纸本)9798350342338

This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. © 2023 IEEE.

关键词： Cloud Data Centers Deep Reinforcement Learning Edge Computing Energy Efficiency Smart Surveillance

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A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition 1

A CNN-Based Framework for Detection and Recognition of Objec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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Cloud Top Management Role in Reducing Mobile Broadband Transmission Hazards and Offering Safety 3

Cloud Top Management Role in Reducing Mobile Broadband Trans...

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

作者： Manjunath, C.R. Sharma, Sachin Sharma, Aditi Sunil Kumar, M. Kelkar, Sandeep Vishal Deshmukh, Sheetal Department of Computer Science and Engineering Bangalore India Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India Parul University PIT Department of Computer Science and Engineering Gujarat Vadodara India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Computer Science and Engineering AP Tirupathi India Prin L.N. Welingkar Institute of Management Development and Research India Yashwantrao Mohite Institute of Management Department of Computer Applications Karad India

ISBN: (纸本)9798350399264

In both the academic and industrial worlds, cloud technology is becoming a widely used research area. Both providers of cloud services and consumers profit from cloud computation. The literature has mostly focused on the security issues related to the cloud. This thorough literature study's objective is to assess the state of knowledge on dangers, difficulties, and privacy for public cloud. This studies from different countries at the areas of study that some well rising institutions are focusing between on 2010. and 2020. We chose 81 papers after carefully examining several attempts to solve the stated research issues. The study's results revealed seven serious security threats to internet data centres. The results revealed that a few of the extensively covered subjects with in chosen works included data modification and leaking. Network discontinuities, storing, and information inside the realm of public cloud were also recognised potential threats. The findings of this Study also shown that CSPs and internet users alike still have difficulties when reassessing client data. The cryptocurrency was identified in our research report as an innovation activitythat would help to allay security worries. The SLR findings reveal a few things that should be done in efforts to provide categorization, legitimacy, and information availability in the present. © 2023 IEEE.

关键词： Industrial research

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