EAI Endorsed Transactions on Industrial Networks and Intelligent Systems最新文献

{"title":"Performance Analysis of Deep Neural Networks Using Computer Vision","authors":"Nidhi Sindhwani, Rohit Anand, M. S, Rati Shukla, Mahendra Pratap Yadav, Vikash Yadav","doi":"10.4108/eai.13-10-2021.171318","DOIUrl":"https://doi.org/10.4108/eai.13-10-2021.171318","url":null,"abstract":"INTRODUCTION: In recent years, deep learning techniques have been made to outperform the earlier state-of-the-art machine learning techniques in many areas, with one of the most notable cases being computer vision. Deep learning is also employed to train the neural networks with the images and to perform the various tasks such as classification and segmentation using several different models. The size and depth of current deep learning models have increased to solve certain tasks as these models provide better accuracy. As pre-trained weights may be used for further training and prevent costly computing, transfer learning is therefore of vital importance. A brief account is given of their history, structure, benefits, and drawbacks, followed by a description of their applications in the different tasks of computer vision, such as object detection, face recognition etc. OBJECTIVE:. The purpose of this paper is to train a deep neural network to properly classify the images that it has never seen before, define techniques to enhance the efficiency of deep learning and deploy deep neural networks in various applications. METHOD: The proposed approach represents that after the reading of images, 256x256 pixel image’s random parts are extracted and noise, distortion, flip, or rotation transforms are applied. Multiple convolution and pooling steps are applied by controlling the stride lengths. RESULT: Data analysis and research findings showed that DNN models have been implemented in three main configurations of deep learning: CNTK, MXNet and TensorFlow. The proposed work outperforms the previous techniques in predicting the dependent variables, learning rate, image count, image mean, performance analysis of loss rate and learning rate during training, performance Analysis of Loss with respect to Epoch for Training, Validation and Accuracy. CONCLUSION: This research encompasses a large variety of computer applications, from image recognition and machine translation to enhanced learning. DNN models have been implemented in three main configurations of deep learning: CNTK, MXNet and TensorFlow. Extensive research has been conducted using the various deep architectures such as AlexNet, InceptionNet, etc. To the best of authors’ knowledge, this is the first work that presents a quantitative analysis of the deep architectures mentioned above.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"17 1","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79677777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning","authors":"N. Sharma, N. Yadav, Saurabh Sharma","doi":"10.4108/eai.13-10-2021.171319","DOIUrl":"https://doi.org/10.4108/eai.13-10-2021.171319","url":null,"abstract":"Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for comparative analysis among all the classifiers used. This analysis gives knowledge, investigates difficulties, and future opportunities to propel machine learning in networking. This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"228 1","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86459674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability-Security Analysis for Harvest-to-Jam based Multi-hop Cluster MIMO Networks Using Cooperative Jamming Methods Under Impact of Hardware Impairments","authors":"Ngo The Anh, C. M. Nguyen, Tran Trung Duy, Tan Hanh, Hoang Dang Hai","doi":"10.4108/eai.17-9-2021.170963","DOIUrl":"https://doi.org/10.4108/eai.17-9-2021.170963","url":null,"abstract":"In this paper, we consider harvest-to-jam based secure multi-hop cluster multi-input multi-output networks, where a multi-antenna source sends its data to a multi-antenna destination via multi-antenna intermediate cluster heads. The data transmission at each hop is realized by using transmit antenna selection and selection combining techniques, and is overheard by a multi-antenna eavesdropper using selection combining. In addition, joint antenna and jammer selection methods are performed at each hop to reduce quality of the eavesdropping channels. The cluster members can harvest wireless energy from the previous cluster head, and use the harvested energy for emitting jamming noises on the eavesdropper. We propose three cooperative jamming algorithms, named best antenna and best jammer selection (BA-BJ), random antenna and all jammer selection (RA-AJ) and all antenna and all jammer selection (AA-AJ). Then, end-to-end outage probability and intercept probability of the proposed algorithms are evaluated via both simulation and analysis, under impact of hardware impairments, over Rayleigh fading channel.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"13 1","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88061779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of Optimization in Improving Extreme Learning Machine","authors":"Nilesh Rathod, S. Wankhade","doi":"10.4108/eai.17-9-2021.170960","DOIUrl":"https://doi.org/10.4108/eai.17-9-2021.170960","url":null,"abstract":"Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hiddenlayer feedforward neural networks and the kernel functions. Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to the underlying state of the hidden neurons, input weights and the choice of functions of activation. To overcome the limitations of traditional ELM, analysts have devised numerical methods to optimise specific parts of ELM in order to enhance ELM performance for a variety of complicated difficulties and applications. Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives, training time, accuracy, error rate and the hidden neurons. This study will help other researchers to find out the research issues that lowering the performance of the ELM.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"48 1","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89561862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A compact 1×3 two-mode selective silicon photonic router/switch using two tunable phase shifters","authors":"Duong Quang Duy, Truong Cao Dung, D. Bac, Nguyen Binh, N. Hung, Chien Tang-Tan, Le Thi Phuong Mai, Nguyen Vy Rin, Phan Vu Thi Van","doi":"10.4108/eai.17-9-2021.170962","DOIUrl":"https://doi.org/10.4108/eai.17-9-2021.170962","url":null,"abstract":"In this work, we propose a design in the proof-of-concept of a 1×3 two-mode selective silicon-photonics router/switch. The proposed device composes of a Y-junction coupler, two multimode interference (MMI) couplers, and two phase-shifters on the silicon-on-insulator (SOI) rib waveguides. The input modes of TE0 and TE1 can be arbitrarily and simultaneously routed to the yearning output ports by setting appropriate values (ON/OFF) for two tunable phase shifters (PSs). The structural optimization and efficient characterization processes are carried out by numerical simulation via three-dimensional beam propagation method. The proposed device exhibits the operation ability over the C-band with good optical performances in terms of insertion loss smaller than 1 dB, crosstalk under -19 dB, and relatively large geometry tolerances. Moreover, the proposed device can integrate into a footprint as compact as 5 μm × 475 μm. Such significant advantages are beneficial and promising potentials for very large-scale photonic integrated circuits, high-speed optical interconnects, and short-haul few-mode fiber communication systems. Received on 30 July 2021; accepted on 13 September 2021; published on 17 September 2021","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"104 1","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79197494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Neural Network-Based Approach for Extracting Textual Images from Deteriorate Images","authors":"B. Pandey, D. Pandey, Subodh Wariya, Gaurav Agarwal","doi":"10.4108/eai.17-9-2021.170961","DOIUrl":"https://doi.org/10.4108/eai.17-9-2021.170961","url":null,"abstract":"INTRODUCTION: The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth. The digital information in images and videos could be used for fully automated captions, indexing, and image structuring. The online image and video data system has seen a significant increase. In such a dataset, images and videos must be retrieved, explored, as well as inspected. OBJECTIVES: Text extraction is crucial for locating critical as well as important data. Disturbance is indeed a critical factor that affects image quality, and this is primarily generated during image acquisition and communication operations. An image can be contaminated by a variety of noise-type disturbances. A text in the complex image includes a variety of information which is used to recognise textual as well as non-textual particulars. The particulars in the complicated corrupted images have been considered important for individuals seeing the entire issue. However, text in complicated degraded images exhibits a rapidly changing form in an unconstrained circumstance, making textual data recognition complicated METHODS: The naïve bayes algorithm is a weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. Stroke width transform, Gabor transform, and weighted naïve bayes classifier methodologies have been used in complicated degraded images to segment, features extraction, and detect textual and non-textual elements. RESULTS: Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimization is being used. The dataset IIIT5K is used for the development portion, and also the performance of the suggested methodology is assessed by utilizing parameters like as accuracy, recall, precision, and F1 score. It performs well enough for record collections such as articles, even when significantly distorted, and is thus suitable for creating library information system databases CONCLUSION: A combination of deep neural network and particle swarm optimization is being used to recognise classified text. The dataset IIIT5K is used for the development portion, and while high performance is achieved with parameters such as accuracy, recall, precision, and F1 score, characters may occasionally deviate. Alternatively, the same character is frequently extracted [3] multiple times, which may result in incorrect textual data being extracted from natural images. As a result, an efficient technique for avoiding such flaws in the text retrieval process must be implemented in the near future.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"25 2 1","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87326375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications","authors":"K. Nguyen, Antonino Masaracchia, Cheng Yin, L. Nguyen, O. Dobre, T. Duong","doi":"10.4108/eetinis.v10i1.2864","DOIUrl":"https://doi.org/10.4108/eetinis.v10i1.2864","url":null,"abstract":"In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network’s sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D transmitter and the associated D2D receiver. Our objective is to jointly optimise the transmit power at the D2D transmitter and the phase shift matrix at the IRS to maximise the network sum-rate. We formulate a Markov decision process and then propose the proximal policy optimisation for solving the maximisation game. Simulation results show impressive performance in terms of the achievable rate and processing time.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"PP 1","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84160393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation","authors":"Krishna Kumar, R. Saini","doi":"10.4108/EAI.6-8-2021.170560","DOIUrl":"https://doi.org/10.4108/EAI.6-8-2021.170560","url":null,"abstract":"Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and timely execution of work are the important activities of a hydropower plant. Energy generation in hydropower plants are mainly depends on discharge and head. Therefore, an accurate estimation of discharge and head is important to decide the plant capacity. Erosion, cavitation, and operation & maintenance are the key challenges in hydropower energy generation. Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation & maintenance optimization. In this paper, a literature review on applications of AI in hydropower has been presented, and an attempt has also been made to identify the future potential areas of hydropower plants.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"146 1","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76264309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formation Control Algorithms for Multiple-UAVs: A Comprehensive Survey","authors":"Hai T. Do, Hoang T. Hua, M. Nguyen, Cuong V. Nguyen, H. T. Nguyen, Hoa T. Nguyen, N. T. Nguyen","doi":"10.4108/eai.10-6-2021.170230","DOIUrl":"https://doi.org/10.4108/eai.10-6-2021.170230","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been widely deployed in many applications such as transportation, data collection, monitoring, or tracking objects. Nowadays, numerous missions require UAVs to operate in a large area or to complete missions in a stringent period of time. Using a single UAV may not meet the performance requirements because of its small size and limited battery. In this situation, multiple Unmanned Aerial Vehicles (UAVs) have emerged as an e ff ective measure that can address these limitations. A group of UAVs cooperatively working together could o ff er a solution that is more e ffi cient and economical than using a powerful UAV alone. To better utilizing the multiple-UAVs system, control of formation UAVs is a critical challenge that needs to overcome. Therefore, formation control has become an active research topic that gains great attention from researchers. Extensive research e ff orts have been dedicated to studying the formation control problem with numerous control protocols which have been proposed. This paper reviews the profound studies on formation control in literature. Each approach is investigated based on di ff erent criteria, which highlights its distinct merits and demerits. The comparison is provided to facilitate the readers in their future researches in the field of formation control. Finally, some open challenges and research directions are also discussed.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"94 1","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75653557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis for RF Energy Harvesting Mobile Edge Computing Networks with SIMO/MISO-NOMA Schemes","authors":"Dac-Binh Ha, Van-Truong Truong, Yoonill Lee","doi":"10.4108/EAI.28-4-2021.169425","DOIUrl":"https://doi.org/10.4108/EAI.28-4-2021.169425","url":null,"abstract":"In this paper, we study an RF energy harvesting mobile edge computing network based on a SIMO/MISO system and NOMA schemes over Nakagami-m fading. Specifically, a multi-antenna user harvests RF energy from a power station by using a selection combining/maximal ratio combining scheme and offload its tasks to two MEC servers through downlink NOMA by employing transmit antenna selection/maximal ratio transmission scheme. Accordingly, we investigate the performance of six schemes, namely SC-TAS1, SC-TAS1, MRC-TAS1, MRC-TAS2, SC-MRT, and MRC-MRT, for this considered system. To evaluate the performance, exact closedform expressions of successful computation probability are derived. We further propose the optimal algorithm to find the best parameter sets to achieve the best performance. Moreover, the impacts of the network parameters on the system performance for these schemes are investigated. Finally, the simulation results are also provided to verify the accuracy of our analysis. Received on 29 March 2021; accepted on 19 April 2021; published on 28 April 2021","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"107 1","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89972073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}