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arXiv 2020年

作者： ur Rehman, Ghani Ghani, Anwar Zubair, Muhammad Khan Ghayyure, Shahbaz Ahmad Muhammad, Shad Department of Computer Science & Software Engineering International Islamic University Islamabad44000 Pakistan Faculty of Computer Science & Bioinformatics Khushal Khan Khattak University Karak27000 Pakistan Department of Computer Science & Software Engineering International Islamic University Islamabad

Many Internet of things (IoT) applications have been developed and implemented on unreliable wireless networks like the Delay tolerant network (DTN), however, efficient data transfer in DTN is still an important issue for the IoT applications. One of the application areas of DTN is Vehicular Delay Tolerant Network (VDTN) where the network faces communication disruption due to lack of end-to-end relay route. It is challenging as some of the nodes show selfish behavior to preserve their resources like memory, and energy level and become non-cooperative. In this article, an Honesty based Democratic Scheme (HBDS) is introduced where vehicles with higher honesty level are elected as heads – during the process. Vehicles involved in the process would maximize their rewards (reputation) through active participation in the network activities whereas nodes with non-cooperative selfish behavior are punished. The honesty level of the heads is analyzed using Vickrey, Clarke, and Groves (VCG) model. The mathematical model and algorithms developed in the proposed HBDS technique are simulated using the VDTNSim framework to evaluate their efficiency. The performance results show that the proposed scheme dominates current schemes in terms of packet delivery probability, packet delivery delay, number of packets drop, and overhead ratio. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Pl-Densenet: Enhancing Disease Classification in Pear Leaves Using Deep Learning

SSRN

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SSRN 2023年

作者： Gulzar, Yonis Ünal, Zeynep Ayoub, Shahnawaz Wani, Sharyar Mir, Mohammad Shuaib Soomro, Arjumand Bano Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Biosystem Engineering Niğde Ömer Halisdemir University Central Campus Türkiye Niğde51240 Turkey Mirzapur Pole U.P. Saharanpur247121 India Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Software Engineering Faculty of Engineering and Technology University of Sindh Sindh Pakistan

Over the past two decades, climate changes have negatively impacted agriculture. It hasn’t left any stage of production unaffected. These noticeable changes have left formers no choice but to adopt new technological measures to tackle such changes. Deep learning has played a great role in improving decision-making in agriculture. This research introduces a novel deep learning model, PL-DenseNet, designed to accurately identify various disease types present in pear leaves. The PL-DenseNet model is an adaptation of the original DenseNet architecture, incorporating significant modifications to enhance its disease classification capabilities. The modifications involve replacing the classification layer of the DenseNet with three additional layers: Global Average Pooling 2D, Batch Normalization, and Dropout layers. Moreover, the final layer of the PL-DenseNet model comprises four nodes dedicated to classifying pear leaf diseases. The integration of these modifications aims to improve disease identification accuracy and enhance overall classification performance within the context of pear leaf diseases. The PL-DenseNet model also incorporated transfer learning and data augmentation to increase the variety and quantity of training data. The model's performance was evaluated on the DiaMOS Plant dataset, which comprises four different pear leaf diseases under real field conditions with varying brightness, disease similarity, complex background, and multiple leaves. Experimental evaluation reveals that the proposed PL-DenseNet model achieves notable advancements compared to the baseline model. It demonstrates higher accuracy (99.18%), precision (98.83), recall (99.06), and F1-score (93.58). Additionally, the proposed model surpasses other state-of-the-art models, including EfficientNetB0, InceptionV2, MobileNetV2, ResNet50, and VGG16, underscoring its superior performance in disease classification. Moreover, this research demonstrates how transfer learning and data augmentati

关键词： Image classification

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Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

SSRN

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SSRN 2022年

作者： Onile, Abiodun Emmanuel Belikov, Juri Levron, Yoash Petlenkov, Eduard Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Electricity consumers are often faced with challenges relating the choice of optimal energy saving plan, increasing integration of transient’s renewable sources of energy promises tantalizing solution which also constitutes emerging stability challenges for the electricity Grid. Demand side management using Battery storage as grid balancing measures are crucial towards extending physical limits of existing electricity grid. On the one hand, battery storage is assuming a critical role in decarbonization efforts partly due to its tendencies to store excess electricity obtained via renewable energy sources thus enabling unlimited potentials for seasonal storage. On the other hand, while solving the identified problem, other problems relating consumer behavior towards adoption of energy/battery efficiency technologies, consumers comfort-feedback and control loop integration exist. In this study, we presented electricity energy storage (EES) technologies which are embedded into the grid and further enhanced with the use of Reinforcement learning control and recommendation system technologies for improving the grid management, the grid reliability and sustainability, mitigated operational issues, improved BESS utilization and attain self-consumption goals. The result of the study shows that operating the BESS using the multi-agent RL control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2025年 1294卷 vii-viii页

作者： Kalam, Akhtar Mekhilef, Saad Williamson, Sheldon S. Institute for Sustainable Industries and Liveable Cities Victoria University FootscrayVIC Australia Department of Engineering Technologies Swinburne University of Technology MelbourneVIC Australia Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University OshawaON Canada

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LocalDrop: A hybrid regularization for deep neural networks

arXiv

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arXiv 2021年

作者： Lu, Ziqing Xu, Chang Du, Bo Ishida, Takashi Zhang, Lefei Sugiyama, Masashi The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430079 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia RIKEN Center for Advanced Intelligence Project Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Japan

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutional neural networks (CNNs), including drop rates and weight matrices, has been developed based on the proposed upper bound of the local Rademacher complexity by the strict mathematical deduction. The analyses of dropout in FCNs and DropBlock in CNNs with keep rate matrices in different layers are also included in the complexity analyses. With the new regularization function, we establish a two-stage procedure to obtain the optimal keep rate matrix and weight matrix to realize the whole training model. Extensive experiments have been conducted to demonstrate the effectiveness of LocalDrop in different models by comparing it with several algorithms and the effects of different hyperparameters on the final performances. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Image-to-Image Translation Based Face Photo De-Meshing Using Gans

SSRN

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SSRN 2023年

作者： Jabbar, Abdul Assam, Muhammad Shloul, Tamara Al Hameed, Hala Abdel Al-Kahtani, Noor Alkahtani, Hend Khalid Ghamry, Nivin A. Tag El Din Bro, Wahii Elsayed M. Amin, Muhammad Shoib College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Software Engineering East China Normal University Shanghai200050 China Liwa College of Technology College of Business administration United Arab Emirates Khaybar Applied College Taibah University Saudi Arabia Department of Health Information Management and Technology College of Public Health Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Faculty of Computers and Artificial intelligence Cairo University Giza Egypt Faculty of Engineering and Technology Future University New Cairo11835 Egypt

Most of the existing face photo de-meshing methods have accomplished promising results;there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder-decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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EC-GAN: Inferring brain effective connectivity via generative adversarial networks 34

EC-GAN: Inferring brain effective connectivity via generativ...

引用

34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Liu, Jinduo Ji, Junzhong Xun, Guangxu Yao, Liuyi Huai, Mengdi Zhang, Aidong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Computer Science and Engineering SUNY at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9781577358350

Inferring effective connectivity between different brain regions from functional magnetic resonance imaging (fMRI) data is an important advanced study in neuroinformatics in recent years. However, current methods have limited usage in effective connectivity studies due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. The proposed framework EC-GAN infers effective connectivity via an adversarial process, in which we simultaneously train two models: a generator and a discriminator. The generator consists of a set of effective connectivity generators based on structural equation models which can generate the fMRI time series of each brain region via effective connectivity. Meanwhile, the discriminator is employed to distinguish between the joint distributions of the real and generated fMRI time series. Experimental results on simulated data show that EC-GAN can better infer effective connectivity compared to other state-of-the-art methods. The real-world experiments indicate that EC-GAN can provide a new and reliable perspective analyzing the effective connectivity of fMRI data. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

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Collaboration Energy Efficiency with Mobile Edge Computing for Target Tracking in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Yu, Gang Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786205

In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing target tracking effects. We deign the sensor selection scheme for carrying out the tracking tasks according to energy distribution of the sensor node. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency without reducing the accuracy of target tracking. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the target state. Simulations demonstrate the efficiency for tracking performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Target tracking

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Collaboration Energy Efficiency with Mobile Edge Computing for Data Collection in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786144

In this paper, we investigated collaboration energy efficiency with mobile edge computing (MEC) mechanism in internet of things (IoT), which is a challenge issue. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing energy balance. We deign the sensor selection scheme for collecting data according to Pareto optimality. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the data state. Simulations demonstrate the efficiency for the performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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