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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

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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...

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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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Impact of Beacon Order and superframe order on IEEE 802.15.4 for nodes association in WBAN

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2018年第17期5卷

作者： Sheraz, Adil Khan, Wajid Ullah Bangash, Javed Iqbal Ullah, Syed Irfan Salam, Abdus Khan, Abdul Waheed Ahmed, Sheeraz Department of Computing Abasyn University Peshawar Pakistan Faculty of Computing and Information Technology Rabigh Campus King Abdul Aziz University Saudi Arabia Iqra National University Peshawar Pakistan

IEEE 802.15.4, Low data Rate low power Wireless Personal Area Network (LR-WPAN) standard uses fixed duty cycle of the superframe operation. Fixed duty cycle operation of the stated standard suffers from several network issues like Personal Area Network (PAN) tedious initialization interval, nodes association duration, number of associated nodes, average network delay, throughput and nodes energy etc. Low duty cycle control operation with lower energy consumption of the network is one of the attractive feature of IEEE 802.15.4. Two superframe parameters Beacon Order (BO) and Superframe Order (SO) decide the beacon interval and duty cycle of the standard and has a strong impact on the performance of other network parameters. In this paper we analyze the IEEE 802.15.4 MAC with brief simulations by varying the two superframe parameters BO and SO to accomplish the finest performance of the WBAN network. © 2018 Adil Sheraz et al.

关键词： Personal communication systems

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EVMAS: An energy-aware virtual machine allocation scheme in fog centers 2

EVMAS: An energy-aware virtual machine allocation scheme in ...

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2nd International Conference for Emerging technology, INCET 2021

作者： Naik, Madhusudan Barik, Lalbihari Kandpal, Meenakshi Patra, Sudhansu Shekhar Jena, Sudarson Barik, Rabindra K. Department of Computer Science Hakimpada Angul 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 Engineering Bhubaneswar India Sambalpur University SUIIT Department of Computer Science and Engineering Sambalpur India

ISBN: (纸本)9781728170299

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange's undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm. © 2021 IEEE.

关键词： Fog computing

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Analysis and Detection of Skin Disorders using Artificial Intelligence-based learning 2

Analysis and Detection of Skin Disorders using Artificial In...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

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

ISBN: (纸本)9798350372892

Skin conditions present considerable health challenges globally, as their covert susceptibility to infections not only induces physical afflictions but also precipitates episodes of depression. Regular and adequate skin examinations are a big step towards the early detection of any damaging or precancerous skin changes that could lead to skin disease. It is necessary to first distinguish between skin and non-skin in order to detect skin ailments. In this paper five different algorithms of artificial intelligence have been selected and used to skin disease dataset. Techniques based on machine learning can help advance frameworks that are capable of classifying different types of skin diseases. We have worked with naive bayes, CNN, Random Forest, logistic regression, kernel SVM, and a few more machine learning techniques. Using graphs, a comparable analysis that was dependent on training accuracy and confusion matrix parameters was carried out. It is found that, out of all the available candidates, CNN provides the best training precision for correctly predicting skin disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products

Automatic image segmentation using sobel operator and k-mean...

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International Conference on Science in information technology (ICSITech)

作者： Joko Siswantoro Anton Satria Prabuwono Azizi Abdullah Bahari Idrus Faculty of Engineering Universitas Surabaya Surabaya Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Malaysia Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia

Image segmentation is one of important step in visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes k-means clustering combined with Sobel operator for automatic food product image segmentation. Sobel operator was used to determine region of interest (ROI) and k-means clustering was then employed to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The validation results show that the proposed segmentation method achieves good segmentation result.

关键词： Image segmentation Food products Gray-scale Image edge detection Inspection Image color analysis Visualization

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