EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index

Editor(s)-in-Chief: Trung Q. Duong, Le Nguyen Bao and Nguyen-Son Vo

Aims & Scope
Indexing
Editorial Board

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing. This journal is co-organised, and managed by Duy Tan University, Vietnam.

INDEXING: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP

Applications of wireless sensor networks, body area networks in large-scale industrial applications, such as fault theories of wireless networks, including routing, network control and management, reliable transmission and architectures, etc.
Applications of social networking, big data, ubiquitous computing, mobile computing, and cloud computing in various industries and services (e.g., intelligent systems enhanced by social networking, cloud-based industrial networks, cloud-assisted intelligent systems, etc.)
Analysis of industrial control and communication networks, including network lifetime, security, network scalability, reliability, stability, etc.
Design and choice of industrial, intelligent, application-specific network protocols and algorithms (e.g., EtherNet/IP, Ethernet Powerlink, EtherCAT, Modbus-TCP, Profinet, SERCOS III, etc.) at any communication layer
Opportunistic networks in the industry, such as underwater sensor networks in sewage treatment systems, including establishing a temporary data transmission structure using available devices (e.g., underwater robot, surface data station, surface sink and under water sink), optimizing horizontal multi-hop data links (e.g., 3D data transmission), etc.
Applications of intelligent systems in various industries, including collaborative systems, quality control, optimization, decision support, planning, high-level control concepts (e.g., multi-agent and holonic systems, service-oriented architectures), low-level control concepts (e.g., IEC 61131-3 and IEC 61499-based control), advanced system engineering concepts (e.g., model-driven development, component-based design), supply chains, value chains, virtual organizations, and virtual societies, emergency preparedness, crisis management, business channels, electronic marketplaces, enterprise resources planning, etc.
Design and analysis of real-time embedded industrial systems, including real-time computing, real-time operating systems, real-time communications, networked embedded systems technology, etc.
Novel control techniques, with respect to process control, equipment control, supervisory control, adaptive control, motion control, etc.
Automated manufacturing systems, regarding formal modeling and analysis of manufacturing systems, scheduling of manufacturing systems, queuing systems and petri nets in manufacturing systems, etc.
Computational intelligence in automation, including neural, fuzzy, evolutionary approaches in automation, ant colonies optimization and swarm intelligence in automation, machine learning, expert systems, etc.
Hardware and software design and development for intelligent systems, such as intelligent and humanized production monitoring and control, etc.
Big data analysis and processing in various industries and services, including constructing data analysis models, providing data analysis and processing tools and designing various optimization algorithms based on data analysis.
Crowd-sourced behavior analysis in various industry and services, such as measuring and calculating the diffusion direction and speed of gas in the petrochemical industry based on crowd-sourced data from a large number of and various types of sensors, as well as product and service evaluation.
Simulation and testbed of current industrial networks and intelligent systems, including network performance analysis, automated manufacturing, intelligent monitoring, disaster prevention, etc.
Vision of future smart factories, service, marketing, and their integration, incorporating current existing technologies.
Multimedia applications, content management, process management and knowledge management for various industries, services, and engineering education: including multimedia processing, multimedia retrieval, multimedia indexing, image sensing, image processing, image coding, image recognition, etc.
Pattern recognition methods for various industries and services: including statistical theory, clustering, similarity measures, unsupervised learning, supervised learning, etc.
Survey, review and essay of current industrial networks researches and intelligent systems development.

Ala Al-Fuqaha (Western Michigan University, USA)
Al-Sakib Khan Pathan (Southeast University, Bangladesh)
Ammar Rayes (Cisco Systems, USA)
Antonino Masaracchia (IIT-CNR, Italy)
Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)
Berk Canberk (Northeastern University, USA)
Ca V. Phan (Ho Chi Minh City University of Technology and Education, Vietnam)
Chau Yuen (Singapore University of Technology and Design, Singapore)
Chengfei Liu (Swinburne University of Technology, Australia)
Chinmoy Kundu (University of Texas at Dallas, USA)
Christer Carlsson (Åbo Akademi University, Finland)
Chunsheng Zhu (University of British Columbia)
Constandinos Mavromoustakis (University of Nicosia, Cyprus)
Der-Jiunn Deng (National Changhua University of Education, Taiwan)
Dickson Chiu (The University of Hong Kong)
Eleanna Kafeza (Athens University of Economics and Business, Greece)
Fu-ren Lin (National Tsing Hua University, Taiwan)
Gerhard Hancke (University of London, UK)
Guangjie Han (Hohai University, China)
Guojun Wang (Central South University, China)
Hacene Fouchal (University of Reims Champagne-Ardenne, France)
Haklae Kim (Chung-Ang University, South Korea)
Halil Yetgin (Bitlis Eren University, Turkey)
Hideyasu Sasaki (Ritsumeikan University, Kyoto, Japan)
Ho-fung Leung (Chinese University of Hong Kong, Hong Kong)
Honggang Wang (University of Massachusetts Dartmouth, USA)
Hua Hu (Hangzhou Dianzi University, China)
Ibrahim Kushchu (Mobile Government Consortium International, UK)
Irene Kafeza (Irene Law Office, Greece)
Isabelle Comyn-Wattiau (ESSEC Business School Paris, France)
Jaime Lloret- Mauri (Universitat Politècnica de València, Spain)
Javier M. Aguiar (Universidad de Valladolid, Valladolid, Spain)
Jesus Alonso-Zarate (Telecommunications Technology Center of Catalonia, Spain)
Jian Yang (Macquarie University, Australia)
Jiankun Hu (University of New South Wales, Australia)
Jianmin Jiang (Shenzhen University)
Jianwei Niu (Beihang University, China)
Jinlei Jiang (Tsinghua University, China)
Jinsong Wu (Bell Laboratory, China)
Joel Rodrigues (Inst. Telecomunicações, Univ. of Beira Interior, Portugal)
Juan Trujillo (University of Alicante, Spain)
Jucheng Yang (Tianjing University of Technology, China)
Junqing Zhang (Queen's University Belfast)
KUN WANG (Nanjing University of Posts and Telecommunications)
Kuo-Ming Chao (Leader – Distributed Systems and Modelling Research Group, UK)
Leandros A. Maglaras (De Montfort University, UK)
Lei Wang (Dalian University of Technology, China)
Liang Zhou (Nanjing University of Posts and Telecommunications, China)
Long D. Nguyen (Dong Nai University, Vietnam)
Maggie M. Wang (The University of Hong Kong, Hong Kong)
Nghia Duong-Trung (German Research Center for Artificial Intelligence, Germany)
Ngo Hoang Tu (Seoul National University of Science and Technology, South Korea)
Nguyen Van Nam (Viettel, Vietnam)
Nicholas C Romano (Oklahoma State University, USA)
Noel Crespi (Institut Mines-Telecom, Telecom SudParis, France)
Panlong Yang (PLA University of Science and Technology, China)
Pasi Tyrväinen (University of Jyväskylä, Finland)
Patrick C.K. Hung (University of Ontario Institute of Technology, Canada)
Periklis Chatzimisios (Alexander TEI of Thessaloniki, Greece)
Pierluigi Siano (Università degli Studi di Salerno, Italy)
Pirkko Walden (Abo Akademi University, Finland)
Raymond Y.K Lau (City University of Hong Kong, Hong Kong)
Richard Yu (Carleton University, Canada)
Rong Yu (Guangdong University of Technology, China)
Rose Hu (Utah State University, USA)
Sammy Chan (City University of HongKong, HK)
Shing-Chi Cheung (Hong Kong University of Science and Technology, Hong Kong)
Stephen J. H. Yang (National Central University, Taiwan)
Syed Hassan Ahmed (University of Central Florida, USA)
Thanh-Phuong Nguyen (University of Toulon, France)
Tran Trung Duy (PTIT, VietNam)
Trang Hoang (Ho Chi Minh City University of Technology - Vietnam National University Ho Chi Minh City, Vietnam)
Tuan-Minh Pham (Phenikaa University, Vietnam)
Umar Zakir Abdul Hamid (Sensible 4 Oy, Helsinki)
Victor Leung (The University of British Columbia)
Vo Nguyen Son Dr. (Duy Tan University, Vietnam)
Wai-Wa Fung (Information Security and Forensics Society, Hong Kong)
Walid Gaaloul (Institut National des Télécommunications, France)
Wendy W. Y. Hui (University of Nottingham at Ningbo, China)
William Cheung (Hong Kong Baptist University, Hong Kong)
Xianfu Chen (VTT Technical Research Centre of Finland, Finland)
Xiang Gui (Massey University, New Zealand)
Xiaoling Wu (Chinese Academy of Sciences, China)
Xu Wang (Heriot Watt University, UK)
Yan Bai (University of Washington Tacoma, USA)
Yan Zhang (Simula Research Laboratory and University of Oslo, Norway)
Yi Zhuang (Zhejian Gongshang University, China)
Yong Li (Tsinghua University, China)
Yong Tang (South China Normal University, China)
Yuanfang Chen (Institute Mines-Telecom, University Pierre and Marie Curie )
Yuexing Peng (Beijing University of Posts and Telecommunications, China)
Yuqing Sun (Shangdong University, China)
Zakaria Maamar (Zayed University, UAE)
Zhangbing Zhou (China University of Geosciences, China)
Zhichao Sheng (Shanghai University, China)
ZhiMing Cai (Macau University of Science and Technology, Macau)
Mithun Mukherjee (Nanjing University of Information Science and Technology, China)

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An Intelligent Fashion Object Classification Using CNN
Appears in:
inis 23(4): e2

Authors:
Debabrata Swain, Kaxit Pandya, Jay Sanghvi, Yugandhar Manchala

Published:
6th Nov 2023

Abstract:
Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled per…Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.
more »
Context-Aware Device Classification and Clustering for Smarter and Secure Connectivity in Internet of Things
Appears in:
inis 23(3): e5

Authors:
Priyanka More, Sachin Sakhare

Published:
2nd Oct 2023

Abstract:
With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variet…With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variety of heterogeneous devices, ever-changing network structures, and diverse contextual elements. Managing IoT devices effectively is a complex task in maintaining network security.
This study introduces a context-driven approach for IoT Device Classification and Clustering, aiming to address the unique characteristics of IoT systems and the limitations of existing access control methods. The proposed context-based model utilizes contextual information such as device attributes, location, time, and communication patterns to dynamically establish clusters and cluster leaders. By incorporating contextual factors, the model provides a more accurate and adaptable clustering mechanism that aligns with the dynamic nature of IoT systems. Consequently, network administrators can configure dynamic access policies for these clusters.
more »
SHELF: Combination of Shape Fitting and Heatmap Regression for Landmark Detection in Human Face
Appears in:
inis 23(3): e3

Authors:
Ngo Thi Ngoc Quyen, Tran Duy Linh, Vu Hong Phuc, Nguyen Van Nam

Published:
26th Sep 2023

Abstract:
Today, facial emotion recognition is widely adopted in many intelligent applications including the driver monitoring system, the smart customer care as well as the e-learning system. In fact, the human emotions can be well represented by facial landmarks which are hard to be detected from images, d…Today, facial emotion recognition is widely adopted in many intelligent applications including the driver monitoring system, the smart customer care as well as the e-learning system. In fact, the human emotions can be well represented by facial landmarks which are hard to be detected from images, due to the high number of discrete landmarks, the variation of shapes and poses of the human face in real world. Over decades, many methods have been proposed for facial landmark detection including the shape fitting, the coordinate regression such as ASMNet and AnchorFace. However, their performance is still limited for real-time applications in terms of both accuracy and efficiency. In this paper, we propose a novel method called SHELF which is the first to combine the shape fitting and heatmap regression approaches for landmark detection in human face. The heatmap model aims to generate the landmarks that fit to the common shapes. The method has been evaluated on three datasets 300W-Challenging, WFLW, 300VW-E with 31557 images and achieved a normalized mean error (NME) of 6.67% , 7.34%, 12.55% correspondingly, which overcomes most existing methods. For the first two datasets, the method is also comparable to the state of the art AnchorFace with a NME of 6.19%, 4.62%, respectively.
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Attention ConvMixer Model and Application for Fish Species Classification
Appears in:
inis 23(3): e2

Authors:
Thanh Viet Le, Hoang-Minh-Quang Le, Van Yem Vu, Thi-Thao Tran, Van-Truong Pham

Published:
6th Sep 2023

Abstract:
Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image proc…Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method.
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Availability of Free-Space Laser Communication Link with the Presence of Clouds in Tropical Regions
Appears in:
inis 23(3): e1

Authors:
Thang Nguyen, Hoa Le, Hien Pham, Ngoc Dang

Published:
23rd Aug 2023

Abstract:
Free-space laser communication (lasercom), a great application of using free-space optics (FSO) for satellite communication, has been gaining significant attraction. However, despite of great potential of lasercom, its performance is limited by the adverse effects of atmospheric turbulence and clou…Free-space laser communication (lasercom), a great application of using free-space optics (FSO) for satellite communication, has been gaining significant attraction. However, despite of great potential of lasercom, its performance is limited by the adverse effects of atmospheric turbulence and cloud attenuation, which directly affect the quality and availability of lasercom links. The paper, therefore, concentrates on evaluating the cloud attenuation in the FSO downlinks between satellite and ground stations in tropical regions. The meteorological ERA-Interim database provided by the European Center for Medium-Range Weather Forecast (ECMWF) from 2015 to 2020 is used to get the cloud database in several areas in tropical regions. This study proposed a novel probability density function of cloud attenuation, which is validated by using a well-known curve-fitting method. Moreover, we derive a closed-form of satellite-based FSO link availability by applying the site diversity technique to improve the system performance. Numerical results, which demonstrate the urgency of the paper, reveal that the impact of clouds on tropical regions is more severe than in temperate regions.
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Performance Analysis of NOMA Over n-m Fading Channels with imperfect SIC
Appears in:
inis 23(1): e5

Authors:
Trần Quý Hữu, Mau Dung Ong

Published:
11th May 2023

Abstract:
In this paper, a downlink non-orthogonal multiple access (NOMA) network with two users is considered. In particular, the performance of NOMA is evaluated by assuming perfect and imperfect channel state information (CSI). We derive the closed-form expressions for the outage probability over η − µ fa…In this paper, a downlink non-orthogonal multiple access (NOMA) network with two users is considered. In particular, the performance of NOMA is evaluated by assuming perfect and imperfect channel state information (CSI). We derive the closed-form expressions for the outage probability over η − µ fading channels in the special case of two users. Moreover, the proposed system model-based NOMA always achieves better performance than that with perfect CSI in the medium SNR region. Monte Carlo simulations are then performed to confirm a good match with the analytical results.
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A Fully Convolutional Network with Waterfall Atrous Spatial Pooling and Localized Active Contour Loss for Fish Segmentation
Appears in:
inis 23(1): e4

Authors:
Thanh Viet Le, Van Yem Vu, Van Truong Pham, Thi-Thao Tran

Published:
20th Apr 2023

Abstract:
Accurate measurements and statistics of fish data are important for sustainable development of aqua-enviroment and marine fisheries. For data measurements and statistics, automatic segmentation of fish is one of key tasks. The fish segmentation however is a challenging task due to arterfacts in und…Accurate measurements and statistics of fish data are important for sustainable development of aqua-enviroment and marine fisheries. For data measurements and statistics, automatic segmentation of fish is one of key tasks. The fish segmentation however is a challenging task due to arterfacts in underwater images. In this study, we introduce a deep-learning approach, namely FCN-WRN-WASP for automatic fish segmentation from the underwater images. In particular, we introduce a computational-efficient variation called Waterfall Atrous Spatial Pooling (WASP) module into a Fully convolutional network with Wide ResNet baseline. We also proposed a loss function inspired from active contour approach that can exploit the local intensity information from the input image. The approach has been validated on the DeepFish data and the SIUM data set. The results are promissing for fish segmentation, with higher Intersection over Union (IoU) scores compared to state of the arts. The evaluation results showed that the incorporation of the image based active contour loss helps increase the segmentation performance. In addition, the use of the WASP in the architecture is effective especially for forground fish segmentation.
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Sub-optimal Deep Pipelined Implementation of MIMO Sphere Detector on FPGA
Appears in:
inis 23(1): e3

Authors:
Minh Thuong Nguyen, Xuan Nam Tran, Vu Duc Ngo, Quang-Kien Trinh, Duc Thang Nguyen, Tien Anh Vu

Published:
29th Mar 2023

Abstract:
Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input multiple-output (MIMO) system since it can achieve near-optimal performance while reducing significant computational complexity. In this work, we proposed a novel SD architecture that is suitable for impl…Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input multiple-output (MIMO) system since it can achieve near-optimal performance while reducing significant computational complexity. In this work, we proposed a novel SD architecture that is suitable for implementation on the hardware accelerator. We first perform a statistical analysis to examine the distribution of valid paths in the SD search tree. Using the analysis result, we then proposed an enhanced hybrid SD (EHSD) architecture that achieves quasi-ML performance and high throughput with a reasonable cost in hardware. The fine-grained pipeline designs of 4 × 4 and 8 × 8 MIMO system with 16-QAM modulation delivers throughput of 7.04 Gbps and 14.08 Gbps on the Xilinx Virtex Ultrascale+ FPGA, respectively.
more »
AgFAB - A Farmer-centered Agricultural Bower
Appears in:
inis 23(1): e2

Authors:
Muhammad Azhar Iqbal, Brianna B. Posadas, Fudi Qin, Bohan Liu, Ali Siddique

Published:
8th Feb 2023

Abstract:
Digital Agriculture aims to raise agricultural productivity while empowering the farming stakeholders (especially the farmers) with the availability of ICT-based applications on smart devices. However, despite putting in much effort, smallholder farmers’ willingness for adopting digital technologie…Digital Agriculture aims to raise agricultural productivity while empowering the farming stakeholders (especially the farmers) with the availability of ICT-based applications on smart devices. However, despite putting in much effort, smallholder farmers’ willingness for adopting digital technologies is low in developing countries. In this study, following the principles of the human-design process, we investigated the smallholder farmers’ core demands from mobile/computing application(s). Considering these core demands of the farming community, the developed prototypical interfaces were evaluated by farmers using the System Usability Scale (SUS) to check the acceptability of a proposed farmer-centered solution named AgFAB. The AgFAB prototypical interface design received an average SUS score of 72.37, which is an indication of an acceptable design. Moreover, the results of Paired T-test seem promising for the strong adoptability of AgFAB by farmers with reference to their aspect of usability in the agricultural context.
more »
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications
Appears in:
inis 23(1): e1

Authors:
Khoi Khac Nguyen, Antonino Masaracchia, Cheng Yin

Published:
3rd Jan 2023

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 signa…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.
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Smart Grid Attacks and Countermeasures
Appears in:
inis 15(2): e4

Authors:
Eric McCary, Yang Xiao

Downloads:
2521

Abstract:
The term “Smart Grid” has been coined and used for several years to describe the efforts of the current power grid modernization effort. This effort plans to introduce self-healing, energy efficiency, reliability, and security using two-way digital communications and control technology, along with …The term “Smart Grid” has been coined and used for several years to describe the efforts of the current power grid modernization effort. This effort plans to introduce self-healing, energy efficiency, reliability, and security using two-way digital communications and control technology, along with a host of other valuable attributes. As a bi-product of this modernization and newly gained systems interoperability, new communications and management interfaces are produced in both the cyber realm and physical domains. The increase of the public physical presence and cyber footprint opens up avenues for compromise to hackers and individuals with malicious intent. This survey paper will categorize and summarize vulnerabilities in the framework of the current power grid and the software and hardware which is currently being used to upgrade the grid. The paper will also detail known countermeasures which can be used to mitigate or eliminate attacks which exploit such vulnerabilities.
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Innovative Application of 5G and Blockchain Technology in Industry 4.0
Appears in:
inis 19(18): e4

Authors:
Ivan Jovović, Siniša Husnjak, Ivan Forenbacher, Sven Maček

Downloads:
2393

Abstract:
The Industry 4.0 is experiencing significant challenges, including the need for an increased amount of data transmission with improved security, transparency and credibility. The 5th Generation Mobile Network (5G) and Blockchain are innovative emerging technologies that can respond to these needs. …The Industry 4.0 is experiencing significant challenges, including the need for an increased amount of data transmission with improved security, transparency and credibility. The 5th Generation Mobile Network (5G) and Blockchain are innovative emerging technologies that can respond to these needs. 5G will enable extremely large channel capacities and reduce data latency, while Blockchain's innovative data-sharing mode of operation delivers an improved high level of security, transparency, and credibility of stored data. Therefore, this paper presents a general survey of the potential application of 5G network and Blockchain technology in Industry 4.0. The results may be used by first-movers firms for gaining technological leadership on the market.
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Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
Appears in:
inis 21(29): e4

Authors:
Neha Sharma, Narendra Singh Yadav, Saurabh Sharma

Downloads:
2303

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 d…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.
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Wireless Power Transfer Near-field Technologies for Unmanned Aerial Vehicles (UAVs): A Review
Appears in:
inis 20(22): e5

Authors:
Anh M. Le, Linh H. Truong, Toan V. Quyen, Cuong V. Nguyen, Minh T. Nguyen

Downloads:
2203

Abstract:
Wireless power transfer (WPT) techniques are being popular currently with the development of midrange wireless powering and charging technology to gradually substitute the need for wired devices during charging. Unmanned Aerial Vehicles (UAVs) are also being used with many practical purposes for a…Wireless power transfer (WPT) techniques are being popular currently with the development of midrange wireless powering and charging technology to gradually substitute the need for wired devices during
charging. Unmanned Aerial Vehicles (UAVs) are also being used with many practical purposes for agriculture,
surveillance, and healthcare, etc. There is a trade-off between the weight of the UAVs or their batteries and
their flying time. In order to support those UAVs perform better in their tasks, WPT is applied in UAVs to
recharge batteries which help to increase their working time. This paper highlights up-to-date studies that are
specific to near-field WPT deploying into UAVs. The charging distances, the transfer efficiency, and transfer
power, etc. are considered to provide an overview of all common problems in using and charging UAVs,
especially for autonomous landing and charging. By classification and suggestions in specific problems will
be provided opportunities and challenges with respect to apply near-field WPT techniques for charging the
battery of UAVs and other applications in the real world.
more »
Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation
Appears in:
inis 21(28): e1

Authors:
Krishna Kumar, R. P. Saini

Downloads:
2083

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 a…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.
more »
A Survey of System Level Power Management Schemes in the Dark-Silicon Era for Many-Core Architectures
Appears in:
inis 18(15): e5

Authors:
Emmannuel Ofori-Attah, Xiaohang Wang, Michael Opoku Agyeman

Downloads:
2028

Abstract:
Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, w…Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, with many-core integration set to be heading towards its thousands, power consumption and temperature increases per time, meaning the number of active nodes must be reduced drastically. Therefore, optimized techniques are demanded for continuous advancement in technology. Existing eﬀorts try to overcome this challenge by activating nodes from diﬀerent parts of the chip at the expense of communication latency. Other eﬀorts on the other hand employ run-time power management techniques to manage the power performance of the cores trading-oﬀ performance for power. We found out that, for a signiﬁcant amount of power to saved and high temperature to be avoided, focus should be on reducing the power consumption of all the on-chip components. Especially, the memory hierarchy and the interconnect. Power consumption can be minimized by, reducing the size of high leakage power dissipating elements, turning-oﬀ idle resources and integrating power saving materials.
more »
Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems
Appears in:
inis 18(17): e4

Authors:
T. T. S. Nguyen, P. M. T. Do, T. T. Nguyen

Downloads:
1980

Abstract:
Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the It…Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the Item2Item model to improve the prediction accuracy. As a case study, this paper focuses on user behaviour prediction in restaurant recommender systems and uses a public dataset including restaurant information and user sessions. Within this dataset, user behaviour can be discovered for the collaborative filtering, and restaurant information is extracted for the content-based filtering. The idea of the pre-trained word embedding in Natural Language Processing is utilized in the item-based collaborative filtering to find the similarity between restaurants based on user sessions. Experimental results have shown that the combination of these techniques makes valuable recommendations.
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Prediction of dogecoin price using deep learning and social media trends
Appears in:
inis 21(29): e2

Authors:
Basant Agarwal, Priyanka Harjule, Lakshit Chouhan, Upkar Saraswat, Himanshu Airan, Parth Agarwal

Downloads:
1869

Abstract:
INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitc…INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance. In 2021, over 4,000 cryptocurrencies are already listed. There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin. Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency.

OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency. The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency.

METHODS: The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags “Dogecoin” and “Doge”. Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market.

RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset.

RESULTS: Experimental results show that the proposed approach performs efficiently.
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Control Algorithms for UAVs: A Comprehensive Survey
Appears in:
inis 20(23): e5

Authors:
Hoa T. Nguyen, Toan V. Quyen, Cuong V. Nguyen, Anh M. Le, Hoa T. Tran, Minh T. Nguyen

Downloads:
1814

Abstract:
The development of unmanned aerial vehicles (UAVs) has become a revolution in the ﬁelds of data collection, surveying, monitoring, and tracking objects in the ﬁeld. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are g…The development of unmanned aerial vehicles (UAVs) has become a revolution in the ﬁelds of data collection, surveying, monitoring, and tracking objects in the ﬁeld. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are geared towards reducing the inﬂuence of external disturbances to enhance the performance of UAVs. Nevertheless, designing cutting-edge controllers following the requirements of the applications is still a huge challenge. Based on the operating characteristics and movement principle of a quadrotor, this work reviews potential control algorithms of the current researches in the ﬁeld of the quadrotor ﬂight controller. Besides, a comparison has been made to provide an overview of the advantages and disadvantages of the mentioned methods. At last, the challenges and future directions of the quadrotor ﬂight controller are suggested.
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Correlation Analysis of Vital Signs to Monitor Disease Risks in Ubiquitous Healthcare System
Appears in:
inis 20(24): e1

Authors:
Hassan Murtaza, Muhammad Azhar Iqbal, Qammer H. Abbasi, Sajjad Hussain, Huanlai Xing, Muhammad A. Imran

Downloads:
1813

Abstract:
Healthcare systems for chronic diseases demand continuous monitoring of physiological parameters or vital signs of the patients’ body. Through these vital signs’ information, healthcare experts attempt to diagnose the behavior of a disease. Identifying the relationship between these vital signs i…Healthcare systems for chronic diseases demand continuous monitoring of physiological parameters or vital
signs of the patients’ body. Through these vital signs’ information, healthcare experts attempt to diagnose
the behavior of a disease. Identifying the relationship between these vital signs is still a big question for the
research community. We have proposed a sophisticated way to identify the affiliations between vital signs
of three specific diseases i.e., Sepsis, Sleep Apnea, and Intradialytic Hypotension (IDH) through Pearson
statistical correlation analysis. Vital signs data of about 32 patients were taken for analysis. Experimental
results show significant affiliations of vital signs of Sepsis and IDH with average correlation coefficient of 0.9
and 0.58, respectively. The stability of the mentioned correlation is about 75% and 90%, respectively.
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Publisher: EAI
ISSN: 2410-0218
Number of Volumes: 10

Last Published: 2023-11-06