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A survey on social networks and organization development

A survey on social networks and organization development

引用

2012 13th International Conference on Collaboration Technologies and Systems, CTS 2012

作者： Albalooshi, Noora Mavridis, Nikolaos Al-Qirim, Nabeel Faculty of Information Technology UAE University P.O. Box 17551 Al Ain United Arab Emirates Faculty of Computer Engineering NYU P.O. Box 129188 Abu Dhabi United Arab Emirates

ISBN: (纸本)9781467313803

Social networking sites emerged with the development of Web 2.0 tools and technologies. This phenomenon has grown in momentum with the appearance of popular websites such as Facebook, YouTube, LinkedIn and Twitter. However, apart from personal use, social networks have become an important constituent in the business field. This research introduces the evolution of social networking in relation to organizational development. Furthermore, it investigates multiple aspects and linkages between social networking and organizational development. This is in terms of creating a collaborative and a knowledge sharing social networks to produce more productive organization. © 2012 IEEE.

关键词： Social networking (online)

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High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types 1

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ali, Rabei Raad Dawd, Lahib Nidhal Mostafa, Salama A. Rachmawanto, Eko Hari Jubair, Mohammed Ahmed National University of Science and Technology Thi-Qar Nasiriyah Iraq Computer Techniques Engineering Department Dijlah University College Baghdad Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Informatics Engineering Dian Nuswantoro University 207 Imam Bonjol Street Semarang50131 Indonesia Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Support vector machines

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A Novel Online Bayes Classifier

A Novel Online Bayes Classifier

引用

2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016

作者： Nguyen, Thi Thu Thuy Nguyen, Tien Thanh Pham, Xuan Cuong Liew, Alan Wee-Chung Hu, Yongjian Liang, Tiancai Li, Chang-Tsun School of Information and Communication Technology Griffith University Australia Faculty of Computer Science and Engineering Water Resource University Hanoi Viet Nam School of Electronic and Information Engineering South China University of Technology Guangzhou China Institute of Research and Development GRG Banking Equipment Co. Ltd. Guangzhou China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9781509028962

We present VIGO, a novel online Bayesian classifier for both binary or multiclass problems. In our model, variational inference for multivariate Gaussian distribution technique is exploited to approximate the class conditional probability density functions of data in an online manner. Besides being a conservative learner with a low number of updates compared with many other popular algorithms, VIGO algorithm can be updated in a minibatch of an arbitrary size which makes it robust with noise data. Experiments over a large number of UCI datasets demonstrate the advantage of VIGO with many state-of-the-art methods presented in LIBOL - a prevalent library for online learning algorithms. © 2016 IEEE.

关键词： Probability density function

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An Intelligent Energy Management System with an Efficient IoT based Deep Learning Framework

An Intelligent Energy Management System with an Efficient Io...

引用

2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Bazil Wilfred, C. George, Santhi M Sivaranjani, S. Selvan, Saravana Feros Khan, J. Mohammed Beulah David, D Karunya Institute of Technology and Sciences Department of Mathematics Coimbatore India Rmk Engineering College Department of Electronics and Communication Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Aimst University Faculty of Engineering Computer Technology Kedah Malaysia Vickram College of Engineering Department of Eee Madurai India Institute of Information Technology Saveetha School of Engineering Chennai India

ISBN: (纸本)9781665478847

Efficient and economical energy utilization is ensured using green energy management systems that currently exist. However, integration of this technology with the Internet of Things (IoT) and edge intelligence is not completely explored. A smart energy management system with a deep learning framework is presented in this paper to address the requirements of energy management in smart industries, homes and grids. An efficient communication is established between the consumers and energy distributors while predicting the future energy consumptions over short time intervals. With least error rate and reduced time complexity, a smart energy management system with optimal normalization model selection and cloud-based data supervising server for energy forecasting in IoT and edge devices is introduced. Communication between the smart grids and the edge devices in the IoT networks connected to a common cloud server regarding efficient energy demand and response features occur in a secure and continuous manner. Short-term energy requirement forecasting is performed with an efficient decision making algorithm while using various preprocessing techniques to manage the electricity data which is of diverse nature. This model is implemented in resource constrained devices and shows promising outcomes. For commercial and residential datasets, the proposed system offers reduced mean-square error (MSE) and root MSE (RMSE) values. © 2022 IEEE.

关键词： Edge computing

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Identification of personalized driver genes for individuals using graph convolution network

Identification of personalized driver genes for individuals ...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Wei Yu, Piaofang Dai, Wei Fu, Xiaodong Liu, Li Pan, Yi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665468190

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels. © 2022 IEEE.

关键词： Genes

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Energy efficient least edge computation leach in wireless sensor network 2

Energy efficient least edge computation leach in wireless se...

引用

2nd International Conference on computer and information Sciences, ICCIS 2020

作者： Us Sama, Najm Bt Zen, Kartinah Ur Rahman, Atiq Bibi, Baseerat Ur Rahman, Abd Chesti, Ikra Afzal Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia Ras Al Khaimah Women Camps Higher Collages of Technology United Arab Emirates School of Physics Dalian University of Technology Dalian China University of Engineering and Technology Peshawar Pakistan

ISBN: (纸本)9781728154671

Due to the energy resource restriction, the sensor nodes drain out their power. Therefore the routing mechanism among nodes and sink need to consider the balanced energy utilization. Low-Energy Adaptive Clustering Hierarchy (LEACH) consider direct data communication from source cluster head to sink, which results unbalanced energy consumption of cluster heads CHs and leads to routing holes in the network. For balanced energy consumption and maximum network lifetime an Energy efficient Least Edge Computation routing protocol (ELEC) is proposed in literature. The simulation results show better performance of ELEC as compared to LEACH. Therefore, an energy-efficient least edge computation multi-hop clustering LEACH (ELEC-LEACH) is proposed, where the LEACH routing protocol is modified by merging the ELEC multi-hop routing protocol with it. Simulation results prove that ELEC-LEACH enhance lifetime, residual energy, reduce the percentage of node failure and packet drop as compared to MR-LEACH. The result shows that the ELEC-LEACH routing protocol almost doubles the network lifetime, in addition just 9% of total energy left squander. © 2020 IEEE.

关键词： Routing protocols

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

引用

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Drawbacks of Step Response Method for Detection of Foreign Metal Objects in Wireless Power Transfer System

Drawbacks of Step Response Method for Detection of Foreign M...

引用

Proceedings of the International Convention MIPRO

作者： D. Vinko D. Bilandž ija L. Filipović Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

Step response method is a method for detection of foreign metal objects in the proximity of the transmitter coil in WPT system. It is based on a measurement of the resonant frequency of the transmitter’s LC tank. As such, it can be used on the fly, while the wireless power transfer takes place, and it should have low impact on WPT system performance. This paper discusses drawbacks of the step response method with respect to required modifications of the transmitter and receiver circuits, and also the impact it has on the DC power supply through increased inrush currents. The discussion is verified through measurements on the built prototype with implemented step response method.

关键词： Time-frequency analysis Smoothing methods Auxiliary transmitters System performance Capacitors Metals Receivers

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NEHCM: A novel and efficient hash-chain based certificate management scheme for vehicular communications

NEHCM: A novel and efficient hash-chain based certificate ma...

引用

作者： Sun, Yipin Lu, Rongxing Lin, Xiaodong Su, Jinshu Shen, Xuemin School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada Faculty of Business and Information Technology University of Ontario Institute of Technology Oshawa ON Canada

ISBN: (纸本)9780984589333

In this paper, we propose a Novel and Efficient Hash-chain based Certificate Management (NEHCM) scheme for vehicular communications. In NEHCM, to protect driver privacy, each vehicle is equipped with a large set of short-time certificates, and most importantly, serial numbers of these certificates satisfy hash-chain relationship. As a result, the certificate revocation becomes an easy task by simply releasing two hash chain seeds. However, without knowing the seeds, it is infeasible to reveal the linkability among these certificates. Thus, not only vehicles can obtain enough certificates for privacy preservation, but also the size of Certificate Revocation List (CRL) remains linear to the number of revoked vehicles, irrelative to the large number of revoked certificates in NEHCM. Furthermore, NEHCM adopts Roadside Units (RSUs) aided certificate service architecture, but the service overhead for an RSU is very low and irrelated to the number of the updated certificates. Extensive simulations demonstrate that the proposed scheme outperforms previously reported works in terms of the revocation cost.

关键词： Vehicles

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Metasearch engine result optimization using reformed genetic algorithm

Metasearch engine result optimization using reformed genetic...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Somayeh Adeli Mohammad Pourmahmood Aghababa Faculty of Information Technology and Computer Engineering Urmia University of Technology Urmia Iran Faculty of Electrical Engineering Urmia University of Technology Urmia Iran

Metasearch engine is a system that applies several different search engines, merges the returned results from the search engines and presents the best results. Principal component of the metasearch engine is the method applied for merging the given results. The most of existing merging algorithms are relied on the information achieved by ranking scores which is integrated with the results of different search engines. In this paper, a reformed genetic algorithm (RGA) is proposed for aggregating results of different search engines. In the RGA, a chaotic sequence is applied to select the parents to mate, preventing the RGA to get stuck in local optima. The combination of pitch adjustment rule and uniform crossover (CPARU) is also proposed to further mutate of chromosomes. In the problem of optimizing search engine results, the proposed method tries to find weights of documents' place to allocate each document to the best place. Therefore, the only required information is to know the number of the search engines that finds each document in the corresponding place. Accordingly, this technique works independently of the different search engines' ranking scores. The experimental results have depicted that the RGA outperforms the genetic algorithm (GA), Borda method, Kendall-tau genetic algorithm (GKTu) and Spearmen's footrule genetic algorithm (GSFD) methods.

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