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)

Recently Published
Most Popular

Real-time Single-Channel EOG removal based on Empirical Mode Decomposition
Appears in:
inis 24(2): e5

Authors:
Kien Nguyen Trong, Nhat Nguyen Luong, Hanh Tan, Duy Tran Trung, Huong Ha Thi Thanh, Duy Pham The, Binh Nguyen Thanh

Published:
8th Apr 2024

Abstract:
In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of …In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.
more »
Improving Performance of the Typical User in the Indoor Cooperative NOMA Millimeter Wave Networks with Presence of Walls
Appears in:
inis 24(2): e4

Authors:
Sinh Cong Lam, Xuan Nam Tran

Published:
8th Apr 2024

Abstract:
INTRODUCTION: The beyond 5G millimeter wave cellular network system is expecting to provide the high quality of service in indoor areas. OBJECTIVES: Due to the high density of obstacles, the cooperative communication technique is employed to improve the user's desired signal power by finding more…INTRODUCTION: The beyond 5G millimeter wave cellular network system is expecting to provide the high quality of service in indoor areas.
OBJECTIVES: Due to the high density of obstacles, the cooperative communication technique is employed to improve the user's desired signal power by finding more than one appropriate station to serve that user.
METHODS: While the conventional system utilizes additional equipment such as Reconfigurable Intelligent Surfaces (RIS) and relays to enable the cooperative features, the paper introduces a new network paradigm that utilizes the second nearest Base Station (BS) of the typical user as the Decode and Forward (DF) relay. Thus, depends on the success of decoding the message from the user' serving BS of the second nearest BS, the typical user can work with and without assistance from the relay whose operation follows the discipline of the power-domain NOMA technique. In the case of with relay assistance, the Maximum Ratio Combining technique is utilized by the typical user to combine the desired signals.
RESULTS: To examine the performance of the proposed system, the Nakagami-m and the newly developed path loss model, which considers the density of walls and their properties, are adopted to derive the coverage probability of the user with and without relay assistance. The closed-form expressions of this performance metric are derived by Gauss quadrature and Welch-Satterthwaite approximation.
more »
Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks
Appears in:
inis 24(2): e3

Authors:
Mohamed Oulad-Kaddour, Hamid Haddadou, Daniel Palacios-Alonso, Cristina Conde, Enrique Cabello

Published:
13th Mar 2024

Abstract:
The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and cau…The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.
more »
Vehicle Type Classification with Small Dataset and Transfer Learning Techniques
Appears in:
inis 24(2): e2

Authors:
Quang-Tu Pham, Dinh-Dat Pham, Khanh-Ly Can, Hieu Dao To, Hoang-Dieu Vu

Published:
7th Mar 2024

Abstract:
This study delves into the application of deep learning training techniques using a restricted dataset, encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited data, the impracticality of training models from scratch becomes apparent, advocating instead for …This study delves into the application of deep learning training techniques using a restricted dataset, encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited data, the impracticality of training models from scratch becomes apparent, advocating instead for the utilization of pre-trained models with pre-trained weights. The investigation considers three prominent models—EfficientNetB0, ResNetB0, and MobileNetV2—with EfficientNetB0 emerging as the most proficient choice. Employing the gradually unfreeze layer technique over a specified number of epochs, EfficientNetB0 exhibits remarkable accuracy, reaching 99.5% on the training dataset and 97% on the validation dataset. In contrast, training models from scratch results in notably lower accuracy. In this context, knowledge distillation proves pivotal, overcoming this limitation and significantly improving accuracy from 29.5% in training and 20.5% in validation to 54% and 45%, respectively. This study uniquely contributes by exploring transfer learning with gradually unfreeze layers and elucidates the potential of knowledge distillation. It highlights their effectiveness in robustly enhancing model performance under data scarcity, thus addressing challenges associated with training deep learning models on limited datasets. The findings underscore the practical significance of these techniques in achieving superior results when confronted with data constraints in real-world scenarios
more »
Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network
Appears in:
inis 24(2): e1

Authors:
Le Anh-Hoang Ho, Viet-Dung Do, Xuan-Kien Dang, Thi Duyen-Anh Pham

Published:
5th Mar 2024

Abstract:
Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based …Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.
more »
On the Consistency of 360 Video Quality Assessment in Repeated Subjective Tests: A Pilot Study
Appears in:
inis 24(1):

Authors:
Majed Elwardy, Hans-Juergen Zepernick, Thi My Chinh Chu, Yan Hu

Published:
8th Jan 2024

Abstract:
Immersive media such as virtual reality, augmented reality, and 360◦ video have seen tremendous technological developments in recent years. Furthermore, the advances in head-mounted displays (HMDs) offer the users increased immersive experiences compared to conventional displays. To develop novel i…Immersive media such as virtual reality, augmented reality, and 360◦ video have seen tremendous technological developments in recent years. Furthermore, the advances in head-mounted displays (HMDs) offer the users increased immersive experiences compared to conventional displays. To develop novel immersive media systems and services that satisfy the expectations of the users, it is essential to conduct subjective tests revealing users’ perceived quality of immersive media. However, due to the new viewing dimensions provided by HMDs and the potential of interacting with the content, a wide range of subjective tests are required to understand the many aspects of user behavior in and quality perception of immersive media. The ground truth obtained by such subjective tests enable the development of optimized immersive media systems that fulfill the expectations of the users. This article focuses on the consistency of 360◦ video quality assessment to reveal whether users’ subjective quality assessment of such immersive visual stimuli changes fundamentally over time or is kept consistent with each user having their own behavior signature. A pilot study was conducted under pandemic conditions with participants given the task of rating the quality of 360◦ video stimuli on an HMD in standing and seated viewing. The choice of conducting a pilot study is motivated by the fact that immersive media impose high cognitive load on the participants and the need to keep the number of participants under pandemic conditions as low as possible. To gain insight into the consistency of the participants’ 360◦ video assessment over time, three sessions were held for each participant and each viewing condition with long and short breaks between sessions. In particular, the opinion scores and head movements were recorded for each participant and each session in standing and seated viewing. The statistical analysis of this data leads to the conjecture that the quality rating stays consistent throughout these sessions with each participant having their own quality assessment signature. The head movements, indicating the participants’ scene exploration during the quality assessment task, also remain consistent for each participant according their individual narrower or wider scene exploration signature. These findings are more pronounced for standing viewing than for seated viewing. This work supports the role of pilot studies being a useful approach of conducting pre-tests on immersive media quality under opportunity-limited conditions and for the planning of subsequent full subjective tests with a large panel of participants. The annotated RQA360 dataset containing the data recorded in the repeated subjective tests is made publicly available to the research community.
more »
Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs
Appears in:
inis 23(4): e5

Authors:
Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Carnelli, Woon Hau Chin, Ahmed Khalil, Jennifer Chakravarty, Lucia Cipolina Kun, Robert J. Piechocki, Colin Robbins, Daniel Cunnington, Leigh Chase, Lamogha Chiazor, Chris Preston, Rahul , Aftab Khan

Published:
31st Dec 2023

Abstract:
The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this pr…The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs.
more »
Jointly power allocation and phase shift optimization for RIS empowered downlink cellular networks
Appears in:
inis 23(4):

Authors:
Phuc Quang Truong, Tan Do-Duy, Van-Ca Phan, Antonino Masaracchia

Published:
11th Dec 2023

Abstract:
Reconfigurable Intelligent Surfaces (RIS) have been highlighted by the research community as a key enabling technology for the enhancement of next-generation wireless network performance, including energy efficiency, spectral efficiency, and network throughput. This paper investigates how RIS-assis…Reconfigurable Intelligent Surfaces (RIS) have been highlighted by the research community as a key enabling technology for the enhancement of next-generation wireless network performance, including energy efficiency, spectral efficiency, and network throughput. This paper investigates how RIS-assisted communication can effectively maximize the downlink throughput of a cellular network. Specifically, the paper considers a communication scenario where a single base station serves multiple ground users with the aid of an RIS placed on a building facade. For such a communication scenario, we considered an optimization problem aimed at maximizing the overall downlink throughput by jointly optimizing power allocation at the base station and phase shift of RIS reflecting elements, subject to power consumption and quality-of-service constraints. To address its non-convex nature, the original optimization problem has been divided into two subproblems. The first one, for power control with fixed phase shift values, is a convex problem that can be easily solved. Subsequently, a phase shift searching procedure to solve the non-convex problem of RIS phase shift optimization has been adopted. The results from numerical simulations show that the proposed method outperforms other conventional methods proposed in the literature. In addition, computational complexity analysis has been conducted to prove the low complexity of the proposed method.
more »
On the Security and Reliability Trade-off of the Satellite Terrestrial Networks with Fountain Codes and Friendly Jamming
Appears in:
inis 23(4): e3

Authors:
Quang Sang Nguyen, Van Hien Nguyen, Trung Duy Tran, Luong Nhat Nguyen, Lam-Thanh Tu

Published:
7th Dec 2023

Abstract:
The performance of the satellite-terrestrial network with Fountain codes (FCs) is conducted in the present work. More precisely, the air-to-ground link is modeled according to the shadow-Rician distribution to capture the strong light-of-sight (LOS) path as well as the impact of the shadowing. As a…The performance of the satellite-terrestrial network with Fountain codes (FCs) is conducted in the present work. More precisely, the air-to-ground link is modeled according to the shadow-Rician distribution to capture the strong light-of-sight (LOS) path as well as the impact of the shadowing. As a result, we employ the directional beamforming at both the satellite and relay to mitigate such an ultra-long transmission distance. We investigate the trade-off between the reliability and security aspects. Particularly, we derive the outage probability (OP) and intercept probability (IP) in the closed-form expressions. To further facilitate the security of the considered networks, the friendly jamming scheme is deployed as well. Finally, simulation results based on the Monte-Carlo method are given to corroborate the exactness of the developed mathematical framework and to identify key parameters such as antenna gain, and transmit power that have a big impact on the considered networks.
more »
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 »

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation
Appears in:
inis 21(28): e1

Authors:
Krishna Kumar, R. P. Saini

Downloads:
2753

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 »
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:
2717

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.
more »
Smart Grid Attacks and Countermeasures
Appears in:
inis 15(2): e4

Authors:
Eric McCary, Yang Xiao

Downloads:
2619

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:
2509

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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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:
2384

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.
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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:
2252

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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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:
2143

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.
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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:
2130

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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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:
2101

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:
1990

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.
more »

Publisher: EAI
ISSN: 2410-0218
Number of Volumes: 11

Last Published: 2024-04-08