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Enhancing Traffic Engineering Policy in SDN Addressing TCAM limitations using Bloom Filters 4

Enhancing Traffic Engineering Policy in SDN Addressing TCAM ...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Ghosh, Akash Barik, Lalbihari Rout, Suchismita Rawat, Bhupender Singh Jena, Prakash Chandra Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed To be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia College of Smart Computing COER University Roorkee India Department of Computer Sc and Engg. Hi-Tech College of Engineering Bhubaneswar India

ISBN: (纸本)9798350305258

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches. © 2023 IEEE.

关键词： Bloom Filter False positive IP addressing multi-hashing Prefix match SDN TCAM Traffic Engineering

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In silico analysis of detrimental mutation in EPHB2 gene causing Alzheimer’s disease

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BMC Genomics 2014年第2期15卷 1-2页

作者： Iftikhar Aslam Tayubi Omar M Barukab Sayane Shome Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia School of Biosciences and Technology Vellore Institute of Technology Vellore 632014 India

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Drug formulation studies on regulation of BCL-2 family for treatment of autism

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BMC Genomics 2014年第2期15卷 1-2页

作者： Iftikhar A Tayubi Sayane Shome Sarosh Hashmi Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia School of Biosciences and Technology Vellore Institute of Technology Vellore 632014 India

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A-LIOn - Alignment Learning through Inconsistency negatives of the aligned Ontologies 17

A-LIOn - Alignment Learning through Inconsistency negatives ...

引用

17th International Workshop on Ontology Matching, OM 2022

作者： Alghamdi, Sarah M. Zhapa-Camach, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia King Abdul-Aziz University Faculty of Computing and Information Technology Rabigh25732 Saudi Arabia

Ontologies play an important role in sharing and reusing knowledge. Several ontologies have been developed to describe a particular domain but from different perspectives from communities of developers and users. This has led to the existence of multiple ontologies covering the same or a different domain with varying degrees of variability. Ontology Alignment is typically used to identify correspondences between semantically related elements of two or more ontologies in order to address this problem. We propose A-LIOn a system that learns alignments by combining lexical and semantic approaches as well as machine learning. The system utilizes OWL EL reasoning for negative sampling which is iteratively used to inform the correction of the learning of the alignments. We demonstrate that A-LIOn produces alignments that are coherent with respect to OWL EL. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Efficiency measurement of various denoise techniques for moving object detection using aerial images

Efficiency measurement of various denoise techniques for mov...

引用

International Conference on Electrical Engineering and Informatics, ICEEI

作者： Zainal Rasyid Mahayuddin A. F. M. Saifuddin Saif Anton Satria Prabuwono Faculty of Information Science and Technology University Kebangsaan Malaysia Bangi Selangor MY Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia

Noise reduction from aerial images is considered an image restoration mechanism in which it attempts to recover image from a degraded image. Denoising image is considered as the key factor for minimizing the functionality for piecewise smooth image intensity. Efficiency of various denoising techniques depends on frame rate and finally computation time for overall object detection mechanism. Moving object detection is the first step of image denoising as well as object detection. This technique uses segmentation, motion detection and feature extraction technique. The main goal of this paper is to compare various noise reduction techniques incorporated with various moving object detection methods along with various features like edges and corners based detection. Experimentation was done based on two parameters frame rate and computation time which initiate to choose the best denoising method for moving object detection.

关键词： Image edge detection Motion segmentation Object detection Smoothing methods Motion detection Wiener filters

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Towards Context-aware Deployment and Reconfiguration

Towards Context-aware Deployment and Reconfiguration

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IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises

作者： Amir Hammami Thierry Villemur Tom Guerout Faculty of Computing and Information Technology Rabigh (FCITR) King Abdulaziz University CNRS LAAS Univ de Toulouse

ISBN: (纸本)9781479904051

This paper describes issues related to context-aware deployment and reconfiguration of services within networked nodes. A deployment platform has been designed and implemented on top of a peer-to-peer layer to carry on the whole deployment process. The platform is mainly composed of P2P communication facilities such as: announcements, search and retrieval of services. These facilities are analyzed and evaluated according to local and LAN experimentations. Results show the efficiency and the effectiveness of our approach.

关键词： Context-aware Deployment Reconfiguration

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Automated Skin Lesion Analysis and Classification Using AI 4

Automated Skin Lesion Analysis and Classification Using AI

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Patra, Sudhansu Shekhar Gourisaria, Mahendra Kumar Singh, Satyendr Barik, Lalbihari School of Computer Applications Kiit Deemed to be University Bhubaneswar India School of Law Kiit Deemed to be University Bhubaneswar India School of Computer Engineering Kiit Deemed to be University Bhubaneswar India Bml Munjal University Computer Science & Engg Department Gurugram India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Rabigh21911 Saudi Arabia

ISBN: (纸本)9798331540364

Skin lesions are becoming a major global health concern, and survival rates can be greatly increased by employing dermoscopic pictures for early identification. It is a difficult undertaking to classify these skin lesions from dermoscopic images. The application of deep learning models has significantly improved this classification's accuracy. Because convolutional neural networks (CNNs) are better at extracting features, which improves classification results, they have been shown to be quite useful in this field. In light of this, the article attempts to create deep learning (DL) algorithms using artificial intelligence (AI) to differentiate between skin lesions as benign or malignant in dermoscopic pictures. The study presents OSSAE-BPNN, an automated method for the detection and classification of skin lesions. This method uses a backpropagation neural network (BPNN) for classification and an optimized stacked sparse autoencoder (OSSAE) for feature extraction. To determine the damaged lesion region, the multi-level thresholding segmentation methodology is part of the proposed OSSAE-BPNN method. The OSSAE performs the feature extraction, and the BPNN handles the categorization. Furthermore, the SGO (seagull optimization) technique is used to optimize the SSAE model's parameters. Extensive testing were conducted on a standard dataset to show the OSSAE-BPNN method's efficacy. The OSSAE-BPNN methodology fared better than other current methods across a range of evaluation parameters, according to the results. © 2024 IEEE.

关键词： Convolutional neural networks

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SCSF: Supply Chain Sustainability Framework by Bayesian Theory and Markov Model for Risk Analysis 9th

SCSF: Supply Chain Sustainability Framework by Bayesian Theo...

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9th International Conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Mukherjee, Pratyusa Patra, Sudhansu Shekhar Samantaray, Swati Barik, Lalbihari Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Applications KIIT Deemed To Be University Bhubaneswar India School of Humanities KIIT Deemed To Be University Bhubaneswar India Department of Information Systems Faculty of Computing & Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789811666155

Industry 4.0 is a predominantly used terminology to denote the current fourth Industrial Revolution in progress. The use of new technology such as IoT, cloud computing, robotic additive manufacturing and artificial intelligence, and how industry and other working areas are performed facilitates this high-tech metamorphosis. Simultaneously accelerating the changes exponentially, the current technological changeover egresses sustainability as one of the major issues in the domestic and international markets. Sustainability strategies should consider the ambivalence level and the future risks that organizational decisions may impose on the natural and social environments and the obligatory investment costs to make the supply chains more sustainable. The system's performance may be affected by the ripple effect caused by supplier base disruption propagating en route for the supply chain. Although the vulnerability assessments in single-echelon-single-event disruptions are critical for many firms, nevertheless the ripple effect modelling for multi-echelon-correlated events is becoming important. In this paper, two models have been discussed Markov Chain in conjunction with Bayesian Network to quantify the ripple effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supply chains

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Performability analysis of FOGGIS model for geospatial web services 11

Performability analysis of FOGGIS model for geospatial web s...

引用

11th International Conference on Cloud computing, Data Science and Engineering, Confluence 2021

作者： Panigrahi, Símil K. Barik, Rabindra Kumar Behera, Satyaban Barik, Lalbihari Patra, Sudhansu Shekhar School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Information Science Electronics and Telecommunications G.M. University Sambalpur India Department of Information Systems Faculty of Computing and Information Technology Rabigh King Abdulaziz University Saudi Arabia School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9780738131603

The Internet of Spatial Things (IoST), continuously generates a large volume of geospatial data from a large number of connected smart devices. Cloud computing is inefficient to respond to IoST because of latency concern. Fog computing allows geospatial data processing as well as geospatial storage close to IoST devices and addresses this concern. This fog computing-based GIS model i.e. FogGIS is assisting to the cloud for invoking various geospatial web services. In many scenarios the service requests arrive to the fog layer and waits in a queue due to the current workload of fog servers. When all the Virtual Machines (VMs) are congested (busy), the arrived tasks are queued in the fog buffer until the fog servers becomes available to process the service. This creates the possibilities of task reneging or dropping. Reneged or dropped tasks could be resubmitted if the tasks were not dropped due to security reasons. In this paper, it experiments the performance of the fog layer based on the task arrival and the buffer size of the system using M/M/c/N Queuing theory. It also offers numerical illustrations to demonstrate the application of task reneging and feedback on the parameters such as queuing delay, probability of immediate service as well as probability of task rejection. © 2021 IEEE.

关键词： Fog computing

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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