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IAENG International Journal of computer science 2021年第3期48卷 1-9页

作者： Desiani, Anita Erwin, Member Suprihatin, Bambang Yahdin, Sugandi Putri, Ajeng I. Husein, Fathur R. Assistant Professor of Mathematics Department and Phd candidate of Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Computer Engineering Department Computer Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Head of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia An undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia

This paper is concern about segmentation on pap smear images to use for classification cervical cancer. A cervical cancer screening by recognizing the cell shape pattern on the Pap-smear image can provide information about the presence of cervical cancer Pap-smear. The manual screening process for classifying cells is a challenging endeavor prone to the risk of error. There are several studies on the classification of cervical cell images which are sometimes not segmented first. Segmentation is very important to get the features contained in the image of cervical cells including the cell nucleus and cytoplasm. However, the segmentation results are also determined by the quality of images. This study proposed 2 paths that had a combination of image segmentation and classification. Before segmentation, image enhancement was carried out to improve image quality using Normalization, CLAHE and Adaptive Gamma Correction. The first path, the segmentation based on the CNN architecture. The second path is a classification process to test the segmentation results used by applying the KNN and ANN methods. The accuracy (ACC) result at the segmentation path was used to measure the match between the location of the segmented pixels and the results on ground truth, while the accuracy at the classification path was the success of the machine in classifying or predicting the segmentation results based on the class label for each group according normal and abnormal Pap-smear's label. The ACC result obtained in segmentation was only 0.77. However, the results of the segmentation when used in classification to classify which Pap-smears were normal and which are abnormal could provide excellent accuracy results, which were above 0.9. The results of the performance of Sensitivity (SN), Specificity (SP) and F1-score on the segmentation path also only gave results below 0.72, but the performance on the classification path that used the results on the segmentation path gave well results, wh

关键词： Image enhancement

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Comparative Analysis of ARIMA Time Series Model and Other Techniques for Cloud Workloads Performance Prediction 5th

Comparative Analysis of ARIMA Time Series Model and Other Te...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Amulya, P. Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisharh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Pune412115 India Research Professor CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9789819997060

Accurate performance prediction of cloud workloads is essential for optimizing resource allocation, meeting service level agreements (SLAs), and ensuring efficient cloud service delivery. In this research paper, we conduct a comparative analysis of the ARIMA (Auto Regressive and Integrated Moving Average) time series model with other popular techniques for cloud workloads performance prediction. We evaluate the performance of ARIMA in comparison with other models, including machine learning algorithms and statistical methods, using real-world cloud workload performance data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Design of a Multiple Phased Convolution Model for Pre-emption of Breast Cancer via Extensive Learning Operations (MPC2EL)

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Procedia computer science 2024年 233卷 638-650页

作者： Pravin Malve Dr. Vijay Gulhane Research Scholar Department of Computer Science & Engineering Sipna College of Engineering & Technology Amravati 444607 India Professor Department of Information Technology Sipna College of Engineering & Technology Amravati 444607 India

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment.

关键词： Cancer Mammogram Multiple Phased CNN Gabor Cosine

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts

Evaluative Analysis of Secure approaches in Simulated Real-W...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A Kannagi Pavan Chaudhary Muthupandi G Associate Professor Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.

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An efficacy analysis of data encryption architecture for cloud platform

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Procedia computer science 2023年 218卷 989-1002页

作者： Sheenam Malhotra Williamjeet Singh Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.

关键词： Cloud Computing Symmetric Searchable Encryption Machine Learning

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Optimized Prognostic Models for Oral Cancer Survival using Feature Selection Methods

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Procedia computer science 2024年 235卷 1832-1840页

作者： Deepali Neelam Goel Padmavati University Institute of Engineering and Technology Panjab University Chandigarh 160014 India Assistant Professor Department of Computer Science Guru Nanak College Budhlada 151502 India Department of Computer Science Punjab Engineering College (deemed to be university) Chandigarh 160012 India

Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.

关键词： Oral Cancer Survival Time Feature Selection Accuracy

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Design and Implementation of an IoT-Based Control System for Precision Food Manufacturing 3rd

Design and Implementation of an IoT-Based Control System for...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Suneel, Sajja Veeramanikandan, P. Anil Kumar, N. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Institute of Aeronautical Engineering Telangana Dundigal Hyderabad India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9789819751457

The food processing sector is undergoing a rapid transformation, primarily driven by the infusion of groundbreaking technologies like the Internet of Things (IoT). This study aims to explore the creation and incorporation of an IoT-centric monitoring and control mechanism within food processing to amplify its quality, safety, and efficiency. By melding sensor technologies, instantaneous data interpretation, and cloud tech, we crafted a sophisticated monitoring framework specifically for the diverse demands of food processing. The results highlighted substantial enhancements in live tracking, product traceability, and foresight-based upkeep, resulting in reduced waste and streamlined production timelines. Moreover, the system's versatile nature allows its use across various food processing domains, ranging from milk products to baked goods. This technological integration not only denotes financial gains for the sector but also suggests a transformative change in the industry's perspective toward ensuring quality, safety, and efficiency in our increasingly interconnected era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Processed foods

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Automated Detection of Multiple Sclerosis Lesions in MRI with SegNet 1

Automated Detection of Multiple Sclerosis Lesions in MRI wit...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Agrawal, Pankaj Rose, Anthony Dewangan, Deepak Kumar Singh, Kamred Udham Somasekar, J. Gla University Department of Computer Engineering & Apllications UP Mathura India Department of Ele. & Tele. Raipur Institute of Technology Raipur India Pune India Department of Computer Science and Engineering Gwalior India School of Computing Graphic Era Hill University Dehradun India Professor of Cse Department Bangalore India

ISBN: (纸本)9798350330915

Using deep neural networks, the proposed approach segments MRI images into classes and achieves good visual quality as well as similarity to the ground truth image. For the proposed process the dice similarity index is near 0.8. Dice similarity index is calculated for each class i.e. for WM, GM, CSF. Also the ground truth images are compared with the reference images and segmented MRI images that are used in the paper. © 2023 IEEE.

关键词： CNN Deep learning MRI Multiple Sclerosis Lesions SegNet

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Utilizing CNN-Based architecture for Automated Differentiation between Gram-Positive and Gram-Negative Bacteria

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Procedia computer science 2025年 258卷 3900-3909页

作者： Debashree Borthakur Smriti Priya Medhi Ranbir Chowdhury Subrata Sinha Bipin Kumar Sharma Jayanta Aich Research Scholar Dept of Microbiology Tripura University Tripura India Assistant Professor (Senior) Dept of Bio Sciences Assam Don Bosco University Assam India Assistant Professor (Senior) Dept of Computer Science and Engineering Assam Don Bosco University Assam India Undergraduate student Dept of Computer Science and Engineering Assam Don Bosco University Assam India Associate Professor Dept of Computational Sciences Brainware University Kolkata India Centre for Multidisciplinary Research and Innovations Brainware University Barasat Kolkata Assistant Professor (Stage III) Dept of Microbiology Tripura University Tripura India Associate Professor Department of Computational Sciences Brainware University Kolkata India

Objectives: This study explores the integration of advanced machine learning methods, specifically convolutional neural networks (CNN), with bacterial image classification to enhance the reliability and efficiency of bacterial classification systems. We present a deep learning approach for the automatic classification of Gram-stained bacterial images into Gram-positive and Gram-negative categories. Methods: Gram staining is a widely used microbiological technique that distinguishes bacteria based on cell wall composition, classifying them as either Gram-positive or Gram-negative. We propose a CNN-based architecture designed to automate this classification process. The model is trained on a large, annotated dataset of Gram-stained bacterial images, leveraging the power of deep learning to achieve state-of-the-art performance in terms of both accuracy and processing speed. Results: Our results show that deep learning can significantly improve the accuracy and efficiency of Gram-stained bacterial image classification, paving the way for automated microbiological analysis in clinical and research settings. Our proposed architecture has classified the gram-positive and gram-negative images of bacterial cells, achieving an accuracy of 95.74% and precision of 96.97%. Novelty: Historically, Gram-stained bacterial image classification has been performed manually by microbiologists. This research pioneers the use of deep learning techniques for the automated classification of these images, achieving substantial improvements in both accuracy and speed. This innovation paves the way for the integration of automated image analysis into microbiological diagnostics and research.

关键词： Gram staining Bacterial Image Classification Deep Learning Convolutional neural network (CNN) Microbiology Gram-positive Gram-negative Automated analysis

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Design of K-Type Propeller Optimized with Lightweight Aluminum Material and Its CFD Analysis for Marine-Based Applications

Design of K-Type Propeller Optimized with Lightweight Alumin...

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International Conference on Mechanical engineering, INCOME 2023

作者： Sunil Kumar, K. Kamalakar, V. Palani, S. Muniamuthu, Sumathy Belinda, Carmel Mary Department of Marine Engineering Faculty of Engineering Sri Venkateswara College of Engineering Pennalur Sriperumbudur602117 India Department of Physics Faculty of Physics Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Department of Mechanical Engineering Professor and Faculty of Engineering Sri Sai Ranganathan Engineering College REC kalvi Nagar Thondamuthur Coimbatore641109 India Department of Mechanical Engineering Faculty of Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Department of Computer Science and Engineering Faculty of Engineering Saveetha School of Engineering SIMATS Chennai600025 India

ISBN: (纸本)9789819753727

The aim of this research is to do a CFD investigation on the K-type propeller for enumerating the various performance factors. This paper focuses on the design of rotor blades with the 3D pro-E software and analyzes a K-type rotor in computational fluid dynamics (CFD) analysis to predict the performance parameters such as pressure coefficients, magnitude vector capability, axial velocities, radial velocities, static pressures, and dynamic pressures of the propeller subjected to different loads, and its effect of pitch, rake, and skew formations of the analysis with aluminum material coated with bronze optimized by the K-type inferior design is implemented in this analysis. However, this study proves that aluminum material possesses better exposure to the replacement of conventional rotors to enumerate the dynamic performance. The performance results obtained from the CFD analysis show that the implementation of aluminum bronze with a K-type design gives maximum propulsive efficiency of up to 85% and is nearly 12% better than conventional rotors in terms of noise and cavitation formations. The study also revealed that there is an improvement in thrust coefficient by 5%, torque coefficient by 7%, and improvement in efficiency by 10% as the implementation of K-cap propeller in the field of the marine industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cavitation

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