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Investigation of Routing Techniques to Develop a Model for Software-Defined Networks using Border Gateway Protocol

Investigation of Routing Techniques to Develop a Model for S...

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Advancements in computing (ICAC), international conference on

作者： Banuka Dayapala Vigneshwaran Palanisamy Satkunarajah Suthaharan Department of ICT University of Vavuniya Sri Lanka Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka. Sri Lanka Department of Physical Science University of Vavuniya Sri Lanka

ISBN: (纸本)9798350398106

Software-Defined Network (SDN) is a networking opportunity that brings together all control functions in one place and changes traditional network architecture by making centralized decisions. In SDN architecture, controllers perform control decision-making functions when directing packets. Unlike traditional routing protocols, efficient r outing protocols are required for efficient data center infrastructure services. the separation of the network data plane from the network control plane is advocated by SDN in a networking design concept. Traditional networks mix data and control planes on the same device and forcing each device to make its own routing decisions based on distributed routing protocols. In this paper, we develop Microsoft Azure Software Defined-Wide Area Networks (SD-WAN) with the concept of HUB-SPOKE to show how the controller’s centralized decision-making capability changes network architecture with network flexibility and programming capability by taking the SPOKE network. this research also focuses on the key status of controllers used in the network industry. Further, we analyze the performance of SD-WAN routing against conventional routing with respect to routing convergence time and address how much efficient SDN routing rather than the conventional routing in various network topology using real-time SD-WAN on corporate environment of Microsoft Azure Cloud Tenant.

关键词： data centers Decision making Network architecture Routing Routing protocols Software Real-time systems

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Hybrid model for detection of frauds in credit cards*

Hybrid model for detection of frauds in credit cards*

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Divya Sharma Sandeep Singh Kang Department of Computer Science & Engg Chandigarh University Mohali India

the goal of this study is to decide on the correct model that can predict a credit card holder’s likelihood of missing their next payment based on their demographic characteristics and payment history. Our goal is to reduce the number of defaulters that are labelled as non-defaulters. that’s because our company can afford to have non-defaulters who do default but not non-defaulters who do not default, as it is implied that the bank has a stringent no-NPA policy (more or less). When putting machine learning algorithms into practice, supervised learning must be used since we have known upstream and downstream values and anticipate a prediction model at the conclusion. the correlation amid attributes and target sets is established in the first stage. Classification stage classifies input data into certain categories. In order to examine the performance of recommended approach, the comparison of recommended approach and other available approaches has been conducted in this work along with this achieving the 85% accuracy *** accurately categorizing credit card delinquencies and non-defaulters, the mortgage lenders will be able to utilize the accuracy of the model to lower the high delinquent rate.

关键词： Loans and mortgages Machine learning algorithms Computational modeling Supervised learning Predictive models Credit cards Fraud

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Handwritten Digit Recognition using Ensemble learning techniques: A Comparative performance Analysis

Handwritten Digit Recognition using Ensemble learning techni...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Saima Afreen Pradeep Kumar Department of Computer Science and Information Technology Maulana Azad National Urdu University Hyderabad India

the detection of some digits written by humans may seem very easy; however, in reality, it is a very challenging and tedious task to extract text from real images because of the wide variety of font sizes, shapes, textures and backgrounds, etc. this study aimed to recognize handwritten digit images by extracting the features from datasets. then theses learned characteristics are subsequently submitted to the classifier to improve the overall performance. the Bagging and boosting techniques of ensemble learning were performed by integrating different base models. Each single base model corrected the errors of the previous model. Six bagging and boosting ensemble learning algorithms were used to recognize the handwritten digit images. therefore, with 98% of accuracy, the extreme gradient boosting machine outperformed the other methods in terms of overall performance. A detailed analysis of the allocation of data among the base learners and their overall performance, including the model correctness, computation time, and error rate, was evaluated and projected.

关键词： Handwriting recognition Image recognition Error analysis Text recognition Shape Boosting Feature extraction

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A Novel Double-threshold Neural Classifier for Non-Linearly Separable Applications

A Novel Double-Threshold Neural Classifier for Non-Linearly ...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Mohd. Kashif Syed Atiqur Rahman Mohammad Samar Ansari Department of Electronics Engineering Aligarh Muslim University India Faculty of Science and Engineering University of Chester United Kingdom

Classification of data finds applications in various engineering and scientific problems. When real-time operation is desired, hardware solutions tend to be more amenable as compared to algorithmic/heuristic solutions. this paper presents a novel current-mode dual-threshold neuron designed and implemented at 32nm CMOS technology node. Subsequently, a current-mode double-threshold classifier is presented which is capable of classifying input patterns of non-linearly separable problems. thereafter, application of the current-mode dual-threshold neuron in the realization of the XOR function using only a single neural unit is discussed. the proposed neuron as well as both the applications discussed are capable of operating from sub-1V power supplies. Computer simulations using HSPICE yield promising results with the values of delay and power consumption estimated to be lower than existing circuits.

关键词： Power demand Computer simulation Neurons CMOS technology Real-time systems Hardware Delays

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Effectiveness of SVM kernel for YOUTUBE Comment Filtering

Effectiveness of SVM kernel for YOUTUBE Comment Filtering

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Shailender Kumar Shweta Gupta Department of Computer Science & Engineering Delhi Technological University Delhi India

the impact of Social networking platform is evident in each generation and almost all fields. It made many tasks more convenient that was not possible earlier. YouTube is one such platform. It can be utilized as a source of entertainment, discussion portal, information sharing, knowledge gathering. Various newborn and popularly known organizations display their advertisements over the platform as short video or links in comment section that help content creators of the video through some financial gains. these advertisements can be greatly helpful to visitors and firm. Also the comment section may contain some cogitative discussions. With all such activities found on the platform some spiteful ones are also lurking over it. Spammers used to suffuse malicious links and data over the platform. In this study, we directed towards the comment section of the platform and executed a comparative analysis of different SVM kernels for the classification of Spam or ham comments over the YouTube application. We also studied the impact of feature extraction incorporating two feature extraction techniques Tf-Idf and BOW.

关键词： Support vector machines Video on demand Filtering Social networking (online) Computational modeling Unsolicited e-mail Standards organizations

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Comparative Analysis of Compound TCP with Various End-to-End Congestion Control Mechanisms

Comparative Analysis of Compound TCP with Various End-to-End...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Subhra Priyadarshini Biswal Sheshank Gupta Arun Kumar Sanjeev Patel Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha India

Congestion control is a fundamental mechanism offered by the Transmission Control Protocol (TCP) to make sure stable data flow and efficient bandwidth allocations in the network. It mainly focuses on the adjustment of the incoming sending rate with respect to the receiving rate. the reliable TCP helps to manage the transmission rate by avoiding the state of network congestion. In recent years, different variants of TCP congestion control protocols have been proposed to enhance TCP reliability and performance under different network scenarios. Moreover, it is associated with several types of network states such as latency, throughput, and packet loss. In this paper, we have performed a comparative study on the performance analysis of various TCP congestion control protocols on NS2 simulator. the results show that CUBIC gives the highest average throughput, Vegas gives the lowest packet loss ratio, and Veno gives the lowest average end-to-end delay among other TCP congestion control techniques that are considered in this paper.

关键词： Wireless communication Protocols Packet loss Channel allocation throughput Delays Performance analysis

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Prediction of Durability, Resilient Modulus and Resistance Value of Cement Kiln Dust-Stabilized Expansive Clay for Flexible Pavement Application Using Artificial Neural Networks 4th

Prediction of Durability, Resilient Modulus and Resistance V...

引用

4th international conference on Transportation Geotechnics, ICTG 2021

作者： Salahudeen, Anigilaje B. Jalili, Mehdi Eidgahee, Danial R. Onyelowe, Kennedy C. Kabiri, Mohsen K. Department of Civil Engineering University of Jos Jos Nigeria Department of Civil Engineering Islamic Azad University Semnan Branch Semnan Iran Department of Civil Engineering Faculty of Civil Engineering Semnan University Semnan Iran Department of Civil Engineering Michael Okpara University of Agriculture Umudike Nigeria Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran Iran

ISBN: (纸本)9783030772291

Artificial neural networks (ANNs) can be used in soil stabilization aspect of geotechnical engineering. As such, this study aimed at applying the ANNs as a soft computing approach to predict the durability, resistance value and resilient modulus of Nigerian expansive clay also called black cotton soil. A soft computing approach using multilayer perceptrons (MLPs) artificial neural networks (ANNs) that are trained with the feed forward backpropagation algorithm was used in this study for the simulation of some strength properties of cement kiln dust-stabilized expansive clay. For each of the three ANN model development, eight input for one output data set were used. the mean squared error (MSE) and R-value were used as yardstick and criterions for acceptability of performance. In the neural network development, NN 8-17-1, NN 8-24-1 and NN 8-18-1, respectively, for durability, resilient modulus and resistance value that gave the lowest MSE value and the highest combined R-value were used in the hidden layer of the networks architecture which performed satisfactorily except for resistance value. For the normalized data set used in training, testing and validating the neural network, the performance of the simulated network was satisfactory having R-values of 0.8388, 0.8433 and 0.7572 for the durability, resilient modulus and resistance value, respectively. the values by durability and resilient modulus met the minimum criteria of 0.8 conventionally recommended for strong correlation condition. A strong correlation was observed between the experimental values as obtained by laboratory test procedures and the predicted values using ANN. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Durability

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Comparative Analysis of Accuracy of Supervised Learning Classifier for breast cancer classification

Comparative Analysis of Accuracy of Supervised Learning Clas...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Vrinda Sachdeva Vasudha Arora Department of Computer Science Engineering ITS Engineering College Greater Noida Uttar Pradesh India

Breast Cancer is widely spreading disease among women all over the world. this cancer is considered as one of the deadly disease among women. data mining algorithms play a vital role to predict the early stage cancer .the research problem is that there are lots of classifier with different level of accuracy. An approach for improving the performance and accuracy of three different classifiers, Decision Tree, Logistic Regression, and SVM, is proposed in this study. We also compare the classifier on Wisconsin breast cancer dataset. Accuracy of classifier depends on the shape of data. Imbalanced data is a big problem for the classification phase. Each classifier's efficiency is accessed in terms of confusion matrix accuracy, AUC, and *** are five sections to this study. First, we'll load the dataset and applied correlation to remove some features. For scaling and to filter data, preprocessing algorithm is applied then dataset is splited into training and testing data set and then applied CART algorithms for classification purpose. then generate some graph to visualize the data. Based on the WBC dataset using Jupyter notebook software, the goal of this paper is to discover the most accurate classifier. the results have been compared with the results of previous papers and reveal that they are extremely precise. the SVM classifier model was discovered to be the best classifier of all the types. It also removes the over fitting of data.

关键词： Support vector machines Training Shape Software algorithms Supervised learning Prediction algorithms Breast cancer

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A Machine learning approach to reduce the diabetes patient's readmission risk using a novel preprocessing technique

A Machine learning approach to reduce the diabetes patient's...

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international conference on Circuits, Communication, Control and computing

作者： G G Rajput Ashvini Alashetty Department of Computer Science Karnataka State Akkamahadevi Women's University Vijayapura Karnataka

High levels of glucose in the blood are the primary indicator of diabetes. Due to the fact that diabetes can cause several complications, many diabetics are hospitalized multiple times. the quality of treatment that is delivered and the overall cost of medical services are impacted when patients spend unnecessary time in the hospital. the healthcare systems inevitably capture data from clinical patient contacts; a machine learning based approach seems suited for addressing this issue. this paper includes one hundred thousand medical records for seventy thousand diabetic patients from 130 institutions in the United States (United States of America). Using a machine learning technique to process this massive dataset is time-consuming. Here, a unique preprocessing approach is demonstrated that reduces the 55 characteristics of the United States diabetes dataset to 21, with additional machine learning techniques used to predict early readmission. We employed machine learning (ML)-algorithms such as K-Nearest Neighbor (KNN), support vector machines (SVM), decision trees, random forests (RF), logistic regression (LR), gradient boosting (GB), and Gaussian naive bayes (GaussianNB) to evaluate the accuracy of a successful data engineering technique. Gradient Boosting, which beat the other machine learning methods, achieved 77.4 % accuracy in the end.

关键词： Support vector machines Machine learning algorithms Computational modeling Machine learning Predictive models Boosting Diabetes

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Determining Statistically Significant Road Accident Spatial Hotspots using Machine Learning Approaches

Determining Statistically Significant Road Accident Spatial ...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rishabh Jain Aruna Bhat Department of Computer Science Delhi Technological University New Delhi India

Spatial Clustering is a subclass of Unsupervised Machine Learning algorithms that utilizes a specified criteria to group a set of geographically dispersed data points together. Effective clustering algorithms are required in societal applications such as road safety to uncover useful patterns linked with the data. Many applications employ Spatial Scan Statistics to identify spatial clusters, however it needs users to first determine the shape of the cluster, which is ambiguous in the context of road safety and could result in unfavourable outcomes. the paper outlines a method for discovering statistically significant shape-invariant spatial clusters in order to identify high-risk road accident zones. the proposed approach incorporates the OPTICS and HDBSCAN clustering algorithms, as well as Cluster density and the Log Likelihood Ratio methods for evaluating the significance of spatial clusters. OPTICS and HDBSCAN are optimal for finding clusters of arbitrary shape and only require a single input parameter, making hyper-parameter tuning relatively easier. the paper incorporates the statistical significance of clusters to eliminate spurious patterns. To illustrate the obtained results, the proposed approach is applied to the UK Road-Accidents data. the paper also presented a comprehensive analysis of the seasonal and temporal variations of the significant spatial hotspots discovered.

关键词： Machine learning algorithms Road accidents Shape Clustering algorithms Machine learning Optics Road safety

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