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2024 Ieee International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Taradale, Aniket Prabhu Kattimani, Suvarna Laxmikant Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura India

ISBN: (纸本)9798331505134

brain tumour classification is crucial for prompt diagnosis and life-saving treatment. A novel approach employing You Only Look Once (YOLO) technology for brain tumour segmentation and classification We used pre-existing data for tumour diagnosis and categorised in four main types: meningioma, glioma, pituitary, and no tumours. In diagnosis, segmentation is necessary to identify regions containing tumours seen in medical images such as MRI scans. Our method offers use of YOLO's real-time product search capabilities and makes them relevant for clinical use. The method achieves 95% F1-Score, 96.2% precision, 93.6% recall, and 97.2% mAP50 in identifying and segmenting tumour regions while using a large dataset of MRI scans as training. This method is superior to traditional segmentation methods as it provides a fast and accurate target detection method. © 2024 Ieee.

关键词： Magnetic resonance imaging

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Comparative Study and Analysis of Cloud Container Technology 18

Comparative Study and Analysis of Cloud Container Technology

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18th INDIAcom;11th International Conference on Computing for Sustainab.e Global Development, INDIACom 2024

作者： Kumar, e. Saravana Ramamoorthy, Raghu Kesavan, Selvaraj Shobha, T. Patil, Seema Vighneshwari, b. The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Dxc Technology Bengaluru India B.M.S.C.E. Department of Ise Bengaluru India B.M.S.C.E. Department of Cse Bengaluru India The Oxford College Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9789380544519

The advent of digital transformation has revolutionized the way businesses operate. Applications have become the focal point of this transformation, shifting the focus from being organization-centric to user-centric. To realize the full potential of businesses, high-quality, secure, and agile applications are essential. Containers are a cutting-edge invention in the world of virtualization, gaining immense popularity in recent years. They have replaced traditional business continuity solutions and are now used to address highly demanding business needs. Multiple containers and orchestration frameworks are availab.e, both as standalone and cloud-based services. However, developers and industry experts face challenges in identifying and evaluating the appropriate containers and orchestration frameworks for their application needs. The selected tools may not always be feasib.e due to a lack of availab.e features, inability to provide agility, and lack of platform support. When applications are deployed across multiple containers, coordination and management among container clusters become critical. Since containers play a pivotal role in edge deployment, cloud-native, continuous integration, and continuous deployment, it is vital to have centralized orchestration for proper re-source management and scheduling. This paper discusses and compares emerging container platforms and cloud-centric orchestration frameworks, highlighting the challenges involved. © 2024 bharati Vidyapeeth, New Delhi.

关键词： Cloud Containers Digital transformation framework Orchestration

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Local search yields a PTAS for fixed-dimensional k-means prob.em with penalties

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Journal of the Operations Research Society of China 2024年第2期12卷 351-362页

作者： Fan Yuan Da-Chuan Xu Dong-Lei Du Dong-Mei Zhang Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China Faculty of Management University of New BrunswickFrederictonNB E3B 9Y2Canada School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101ShandongChina

We study a prob.em called the k-means prob.em with penalties(k-MPWP),which is a natural generalization of the typical k-means *** this prob.em,we have a set D of client points in R^(d),a set F of possib.e centers in R^(d),and a penalty cost Pj>O for each point j∈*** are also given an integer k which is the size of the center point *** want to find a center point set S■F with size k,choose a penalized sub.et of clients P■D,and assign every client in D\P to its open *** goal is to minimize the sum of the squared distances between every point in D\P to its assigned centre point and the sum of the penalty costs for all clients in *** using the multi-swap local search technique and under the fixed-dimensional euclidean space setting,we present a polynomial-time approximation scheme(PTAS)for the k-MPWP.

关键词： Approximation algorithm k-means Local search Penalty

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Data dimensionality reduction techniques for Industry 4.0: Research results, challenges, and future research directions

Data dimensionality reduction techniques for Industry 4.0: R...

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作者： Chhikara, Prateek Jain, Nikhil Tekchandani, Rajkumar Kumar, Neeraj B.E. Computer Engineering Thapar Institute of Engineering and Technology Patiala India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

From the last few years, we have witnessed the fourth generation industrial revolution (Industry 4.0), impact of which will be seen in the years to come in various disciplines such as healthcare, transportation, IoT, smart grid, autonomous vehicles, and image processing. These applications in Industry 4.0 may have data in the form of images, speech signals, videos having high dimensions containing multiple dimensions to represent data along different axis. So, the complexity of data processing increases with an increase in the dimensions of the dataset. Complexity can be viewed in terms of detecting and exploiting the relationships among different features of the dataset. These complexities among different attributes can be reduced with the help of dimensionality reduction techniques. These techniques reduce the dimensions from the original input dataset to a lower dimensional dataset. Dimensionality reduction methods are broadly categorized into two types as feature extraction and feature selection. In feature selection method, out of the original set, a sub.et of features are identified to get a smaller sub.et which can be used to build the model whereas, the feature extraction method reduces the dataset of high dimensions to a lower dimension space, that is, a space with a less number of features having different values in comparison to the original dataset. Keeping focus on these points, in this article, we have compared and analyzed different data dimensionality reduction techniques which reduce the dimensions of a large and complex dataset during data processing. In addition, we have discussed various data dimensionality reduction techniques and compared these techniques with respect to various parameters. The comparison among various techniques provides insights to the readers about the applicability of a specific technique to the stand-alone or a group of applications. © 2020 John Wiley & Sons, Ltd.

关键词： Smart power grids

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Disease Detection In Pomegranate Plant Using Image Processing Techniques 9

Disease Detection In Pomegranate Plant Using Image Processin...

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9th Ieee International Conference on electronics, Computing and Communication Technologies, CONeCCT 2023

作者： Sahebgouda Sumana, M. B.L.D.E.AsV.P.Dr.P.G.Halakatti College of Engineering and Technology Dept. of Computer Science and Engineering Vijayapur India Msrit Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350334395

The majority of the people in India is dependent on agriculture. every crop the farmers cultivate has some sort of illness. The most successfully adopted subtropical fruit crop in India is the pomegranate. Pomegranates have the highest economic value of all the fruits. Pomegranate has very little immunity to infection during cultivation, especially in the early stages. As a result, it is particularly susceptib.e to the sickness. The detection of plant diseases is a beneficial use of image processing. Picture acquisition, picture pre-processing, Image segmentation, feature extraction, ob.ect recognition, and classification are all steps within the infection discovery handle. © 2023 Ieee.

关键词： Image segmentation

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A Move Forward: exploring National Identity Through Non-linear Principal Component Analysis in Germany

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Quality and Quantity 2023年第1期57卷 885-903页

作者： bruinsma, bastiaan Mußotter, Marlene Department of Computer Science and Engineering Chalmers University of Technology Göteborg 412 96 Sweden University of Passau Dr. Hans-Kapfinger-Straße 14b Passau 94032 Germany

In research on national identity, scholars have developed a wide variety of approaches to measure and better understand this ubiquitous yet complex concept. To date, most of these approaches have b.en theory-driven, while only a very few have b.en data-driven. In this article, we aim to contribute to the latter by introducing a new data-driven method that has not b.en applied yet—that of non-linear principal component analysis (NLPCA). In contrast to other commonly used methods such as factor analysis, NLPCA distinguishes itself by making relatively few assumptions about the data and by allowing for greater flexibility when discovering underlying dimensions of such a complex concept as national identity. Drawing on the 2013 ISSP National Identity module, our analysis focuses on the case of Germany, also taking into account Western and eastern Germany. Running an NLPCA, we find four dimensions that cover the multidimensionality of national identity: nationalistic attitudes, national pride and attachment, cosmopolitan beliefs, and membership criteria defining national belonging. This article contributes to the empirical debate on measuring national identity by suggesting a new and flexib.e methodological approach that better grasps the concept’s complexity and which we believe can move empirical research on national identity forward in and beyond Germany. © 2022, The Author(s).

关键词： Germany ISSP Measures National identity Non-linear principal component analysis

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barcode Recognition using Sequential and Parallel Approach

Barcode Recognition using Sequential and Parallel Approach

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2024 Ieee International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Kusur, Chidanand S. Shirdhonkar, M.S. Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G.H. College of Engineering and Technology Karnataka Vijayapur586103 India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9798331505134

In a computer vision and image processing, the barcode detection and recognition systems play very important role for the interpretation of the encoded information for the system automation and inventory management. Hence, we proposed an enhanced approach to barcode recognition from video frames by extending our previous work focused on barcode detection in static images i.e. for the detection and recognition of barcodes from the videos of racks of books. The proposed system showed that, the parallel approach is time efficient in comparison with sequential approach. © 2024 Ieee.

关键词： Inventory control

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Detection and Categorization of Conflict Flows Within SDN environments using Machine Learning Approach

Detection and Categorization of Conflict Flows Within SDN En...

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Kousalya S Kanthimathi Sundararajan I. Jaichitra Vasudevan L. Jaba Sheela Sarunya Thyagarajan M.E. Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E. Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering college Chennai Tamil Nadu India

Software-Defined Networking (SDN) is known for its remarkab.e versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. “As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.”

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Deep Learning for Precision Grade-Quality Classification of Pomegranates

Deep Learning for Precision Grade-Quality Classification of ...

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2024 Ieee International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Math, RajinderKumar M. Dharwadkar, Nagaraj V. Sajjanar, Vijaykumar Deshmukh, M.N. Department of Electronics and Communication Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapur India Department of Computer Science Central University of Karnataka Kadaganchi Kalaburgi India

ISBN: (纸本)9798331505134

The conventional methods of manual grading of fruits after agricultural harvest have always b.en laborious, time-consuming, and prone to human errors. In order to speed up the grading process without human intervention, an automatic system is the need of the hour. This research has led to the development a Convolutional Neural Network (CNN) based deep learning technique to provide real-time grading of pomegranate based on its visual features. This automatic grading technique would help farmers in minimizing their efforts and time spent on manual sorting, at the same time ensuring error-free and uniform sorting and packaging of quality fruits that would, in turn, enhance the storage time post packaging. The methodology uses nine pre-trained models to train on a custom pomegranate dataset with twelve grade-quality classes of pomegranates. The grade-quality grading is obtained using the fine-tuning approach in transfer learning, reducing the computational resource requirements and drastically improving inference speed, which is necessary for real-time model deployment scenarios. The results reveal that the fine-tuning of DenseNet-169 model outperformed the other models by achieving highest values of 99.20% and 1 for (Accuracy, Precision, Recall, and F1-score) and AUC, respectively. © 2024 Ieee.

关键词： Convolutional neural networks

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YOLOv8 based An Automated Segmentation And Classification Of Thyroid Nodules Using USG Images

YOLOv8 Based An Automated Segmentation And Classification Of...

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2024 Ieee International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Gondi, Savita Nagaral, M.U. Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijaypur586103 India Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India

ISBN: (纸本)9798331505134

Thyroid nodules are a common clinical ob.ervation that needs to be accurately classified and segmented in order to be managed and treated. The use of the sophisticated ob.ect detection model You Only Look Once (YOLO) version 8 to the issue of nodule of the thyroid identification and categorization is investigated in this work. We gathered and interpreted 701 thyroid ultrasound images for this study, identifying two different nodule types as benign (315 images) and malignant (344 images). To assess the model's performance thoroughly, the dataset was divided into training (70%), validation (20%), and test (10%) sets. After training YOLOv8 on these images, metrics including Precision (78.1%), Recall (81.9%), F1 score (80%), and mean average precision (mAPall) (88.1% mAP@50) are used, with benign (91.8% mAP) and Malign (84.5% mAP. The findings show that YOLOv8, outperforms earlier model YOLOv5, achieves good accuracy in both detecting and categorizing thyroid nodules. The ability of the model to accurately distinguish between benign and malignant nodules is critical for clinical decision-making. © 2024 Ieee.

关键词： Image annotation

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