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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,
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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-organized, 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.
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tion (ProQuest) Google Scholar
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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,
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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) Phuong Bui (Duy Tan University, Vietnam) 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) Weiwei Jiang, (Beijing University of Posts and Telecommunications (BUPT), China) 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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Recently Published
Most Popular
Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach
Appears in:
inis 24(2):
Authors:
Tran Cong-Hung, Thi-Tuyet-Hai Nguyen, Tran Trung Duy, Nguyen Hong-Son, Lam-Thanh Tu, Tan Hanh
Published:
5th Dec 2024
Abstract:
The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the con
...
sidered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory.
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Joint Adaptive Modulation and Power Control Scheme for Energy Efficient FSO-based Non-Terrestrial Networks
Appears in:
inis 24(1):
Authors:
Ngoc T. Dang, Thang V. Nguyen, Hien T. T. Pham
Published:
4th Dec 2024
Abstract:
Free-space optics (FSO)-based non-terrestrial networks (NTN) have garnered significant attention as a potential technology for forthcoming 6G wireless communications due to their exceptional data rate
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and extensive global coverage capability. Nevertheless, atmospheric attenuation, cloud attenuation, geometric loss, and atmospheric turbulence present numerous difficulties in developing these networks. To cope with these difficulties, we propose to apply a joint adaptive modulation and power control (JAMPC) scheme to FSO-based NTN. Our proposed JAMPC algorithm aims to enhance energy efficiency while guaranteeing the targeted outage probability, bit-error rate, and the required data rate. We develop mathematical models and derive closed-form expressions to implement the proposed algorithm and solve the optimization problem. The numerical results confirm that the JAMPC scheme helps NTN provide better energy efficiency and the ability to adapt to various channel conditions.
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Predicting the Severity of COVID-19 Pneumonia from Chest X-Ray Images: A Convolutional Neural Network Approach
Appears in:
inis 24(1):
Authors:
Thien B. Nguyen-Tat, Vuong M. Ngo, Viet-Trinh Tran-Thi
Published:
4th Dec 2024
Abstract:
This study addresses significant limitations of previous works based on the Brixia and COVIDGR datasets, which primarily provided qualitative lung injury scores and focused mainly on detecting mild an
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d moderate cases. To bridge these critical gaps, we developed a unified and comprehensive analytical framework that accurately assesses COVID-19-induced lung injuries across four levels: Normal, Mild, Moderate, and Severe. This approach’s core is a meticulously curated, balanced dataset comprising 9,294 high-quality chest X-ray images. Notably, this dataset has been made widely available to the research community, fostering collaborative efforts and enhancing the precision of lung injury classification at all severity levels. To validate the framework’s effectiveness, we conducted an in-depth evaluation using advanced deep learning models, including VGG16, RegNet, DenseNet, MobileNet, EfficientNet, and Vision Transformer (ViT), on this dataset. The top-performing model was further enhanced by optimizing additional fully connected layers and adjusting weights, achieving an outstanding sensitivity of 94.38%. These results affirm the accuracy and reliability of the proposed solution and demonstrate its potential for broad application in clinical practice. Our study represents a significant step forward in developing AI-powered diagnostic tools, contributing to the timely and precise diagnosis of COVID-19 cases. Furthermore, our dataset and methodological framework hold the potential to serve as a foundation for future research, paving the way for advancements in the detection and classification of respiratory diseases with higher accuracy and efficiency.
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Bi-objective model for community detection in weighted complex networks
Appears in:
inis 24(4):
Authors:
Miguel Ángel Gutiérrez-Andrade, Pedro Lara-Velazquez, Eric Alfredo Rincón-García, Edwin Montes-Orozco, Roman Anselmo Mora-Gutiérrez, Sergio Gerardo de-los-Cobos-Silva, Gilberto Sinuhe Torres-Cockrell
Published:
4th Dec 2024
Abstract:
In this study, we introduce an innovative approach that utilizes complex networks and the k_core method to address community detection in weighted networks. Our proposed bi-objective model aims to sim
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ultaneously discover non-overlapping communities while ensuring that the degree of similarity remains below a critical threshold to prevent network degradation. We leverage the k_core structure to detect tightly interconnected node groups, a concept particularly valuable in edge-weighted networks where different edge weights indicate the strength or importance of node relationships. Beyond maximizing the count of k_core communities, our model seeks a homogeneous weight distribution across edges within these communities, promoting stronger cohesion. To tackle this challenge, we implement two multi-target algorithms: Non-dominated Sorting Genetic Algorithm II (NSGAII) and a Multi-Objective Simulated Annealing (MOSA) algorithm. Both algorithms efficiently identify non-overlapping communities with a specified degree 'k'. The results of our experiments reveal a trade-off between maximizing the number of k_core communities and enhancing the homogeneity of these communities in terms of their minimum weighted interconnections. Notably, the MOSA algorithm outperforms NSGAII in both small and large instances, demonstrating its effectiveness in achieving this balance. This approach sheds light on effective strategies for resolving conflicting goals in community detection within weighted networks.
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ERKT-Net: Implementing Efficient and Robust Knowledge Distillation for Remote Sensing Image Classification
Appears in:
inis 24(3):
Authors:
Yangyan Zhu, Xiaowen Li, Yafang Li, Yuxuan Zhang, Huaxiang Song, Yong Zhou
Published:
4th Dec 2024
Abstract:
The classification of Remote Sensing Images (RSIs) poses a significant challenge due to the presence of clustered ground objects and noisy backgrounds. While many approaches rely on scaling models to
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enhance accuracy, the deployment of RSI classifiers often requires substantial computational and storage resources, thus necessitating the use of lightweight algorithms. In this paper, we present an efficient and robust knowledge transfer network named ERKT-Net, which is designed to provide a lightweight yet accurate Convolutional Neural Network (CNN) classifier. This method utilizes innovative yet simple concepts to better accommodate the inherent nature of RSIs, thereby significantly improving the efficiency and robustness of traditional Knowledge Distillation (KD) techniques developed on ImageNet-1K. We evaluated ERKT-Net on three benchmark RSI datasets and found that it demonstrated superior accuracy and a very compact volume compared to 40 other advanced methods published between 2020 and 2023. On the most challenging NWPU45 dataset, ERKT-Net outperformed other KD-based methods with a maximum Overall Accuracy (OA) value of 22.4%. Using the same criterion, it also surpassed the first-ranked multi-model method with a minimum OA value of 0.7 but presented at least an 82% reduction in parameters. Furthermore, ablation experiments indicated that our training approach has significantly improved the efficiency and robustness of classic DA techniques. Notably, it can reduce the time expenditure in the distillation phase by at least 80%, with a slight sacrifice in accuracy. This study confirmed that a logit-based KD technique can be more efficient and effective in developing lightweight yet accurate classifiers, especially when the method is tailored to the inherent characteristics of RSIs.
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Drug classification system based on drug composition and usage instructions
Appears in:
inis 24(1):
Authors:
Quang-Dung Le, Hoang-Dieu Vu, Vu-Hien Pham
Published:
4th Dec 2024
Abstract:
This study presents a natural language processing (NLP) approach to classify drugs based on compositional and usage descriptions. NLP techniques including text preprocessing, word embedding, and deep
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learning models were applied to a Vietnamese drug dataset. Traditional machine learning models like Support Vector Machines (SVM) and deep models including Bidirectional Long Short-Term Memory (BiLSTM) and PhoBERT were evaluated. Besides, since there is a limitation in the information of our own collected data, some data augmentation techniques were applied to increase the variation of the dataset. Results show PhoBERT achieving 95% accuracy, highlighting the benefits of transferring knowledge from large language models. Errors primarily occurred between similar drug categories, suggesting taxonomy refinement could improve performance. In summary, an automated drug classification framework was developed leveraging state-of- the-art NLP, validating the feasibility of analyzing drug data at scale and aiding therapeutic understanding. This supports NLP’s potential in pharmacovigilance applications.
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An Efficient Method for BLE Indoor Localization Using Signal Fingerprint
Appears in:
inis 24(1):
Authors:
Phuc Nguyen Dinh, Vu Nguyen Long, Hung Dinh Tan, Trong-Thanh Han, Toan Nguyen Duc
Published:
25th Nov 2024
Abstract:
The rise of Bluetooth Low Energy (BLE) technology has opened new possibilities for indoor localization systems. However, extracting fingerprint features from the Received Signal Strength Indicator (RS
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SI) of BLE signals often encounters challenges due to significant errors and fluctuations. This research proposes an approach that integrates signal filtering and deep learning techniques to improve accuracy and stability. A Kalman filter is employed to smooth the RSSI values, while Autoencoder and Convolutional Autoencoder models are utilized to extract distinctive fingerprint features. The system compares random test points with a reference database using normalized cross-correlation. Performance is assessed based on metrics such as the number of reference points with the highest cross-correlation (), average localization error, and other statistical indicators. Experimental results show that the combination of the Kalman filter with the Convolutional Autoencoder model achieves an average error of 0.98 meters with . These findings indicate that this approach effectively reduces signal noise and enhances localization accuracy in indoor environments.
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Transformer Based Ship Detector: An Improvement on Feature Map and Tiny Training Set
Appears in:
inis 24(1):
Authors:
Van-Linh Vo, Hoc-Phan , My-Ha Le , Duc-Dat Ngo, Manh Hung Nguyen
Published:
7th Nov 2024
Abstract:
The exponential increment of commodity exchange has raised the need for maritime border security in recent years. One of the most critical tasks for naval border security is ship detection inside and
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outside the territorial sea. Conventionally, the task requires a substantial human workload. Fortunately, with the rapid growth of the digital camera and deep-learning technique, computer programs can handle object detection tasks well enough to replace human labor. Therefore, this paper studies how to apply recent state-of-the-art deep-learning networks to the ship detection task. We found that with a suitable number of object queries, the Deformable-DETR method will improve the performance compared to the state-of-the-art ship detector. Moreover, comprehensive experiments on different scale datasets prove that the technique can significantly improve the results when the training sample is limited. Last but not least, feature maps given by the method will focus well on key objects in the image.
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A Secure Cooperative Image Super-Resolution Transmission with Decode-and-Forward Relaying over Rayleigh Fading Channels
Appears in:
inis 24(4):
Authors:
Hien-Thuan Duong, Ca V. Phan, Quoc-Tuan Vien
Published:
3rd Sep 2024
Abstract:
In addition to susceptibility to performance degradation due to hardware malfunctions and environmental influences, wireless image transmission poses risks of information exposure to eavesdroppers. Th
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is paper delves into the image communications within wireless relay networks (WRNs) and proposes a secure cooperative relaying (SCR) protocol over Rayleigh fading channels. In this protocol, a source node (referred to as Alice) transmits superior-resolution (SR) images to a destination node (referred to as Bob) with the assistance of a mediating node (referred to as Relay) operating in decode-and-forward mode, all while contending with the presence of an eavesdropper (referred to as Eve). In order to conserve transmission bandwidth, Alice firstly reduces the size of the original SR images before transmitting them to Relay and Bob. Subsequently, random linear network coding (RLNC) is employed by both Alice and Relay on the downscaled poor-resolution (PR) images to obscure the original images from Eve, thereby bolstering the security of the image communications. Simulation results demonstrate that the proposed SCR protocol surpasses both secure relaying transmission without a direct link and secure direct transmission without relaying links. Additionally, a slight reduction in image quality can be achieved by increasing the scaling factor for saving transmission bandwidth. Furthermore, the results highlight the SCR protocol’s superior effectiveness at Bob’s end when compared to Eve’s, which is due to Eve’s lack of access to the RLNC coefficient matrices and reference images utilised by Alice and Relay in the RLNC process. Finally, the evaluation of reference images, relay allocations and diversity reception over Rayleigh fading channels confirms the effectiveness of the SCR protocol for secure image communications in the WRNs.
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Emotional Inference from Speech Signals Informed by Multiple Stream DNNs Based Non-Local Attention Mechanism
Appears in:
inis 24(4):
Authors:
Oscal T.C. Chen, Duc-Chinh Nguyen, Long Quang Chan, Manh-Hung Ha
Published:
5th Aug 2024
Abstract:
It is difficult to determine whether a person is depressed due to the symptoms of depression not being apparent. However, the voice can be one of the ways in which we can acknowledge signs of depressi
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on. Understanding human emotions in natural language plays a crucial role for intelligent and sophisticated applications. This study proposes deep learning architecture to recognize the emotions of the speaker via audio signals, which can help diagnose patients who are depressed or prone to depression, so that treatment and prevention can be started as soon as possible. Specifically, Mel-frequency cepstral coefficients (MFCC) and Short Time Fourier Transform (STFT) are adopted to extract features from the audio signal. The multiple streams of the proposed DNNs model, including CNN-LSTM based on an attention mechanism, are discussed within this research. Leveraging a pretrained model, the proposed experimental results yield an accuracy rate of 93.2% on the EmoDB dataset. Further optimization remains a potential avenue for future development. It is hoped that this research will contribute to potential application in the fields of medical treatment and personal well-being.
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Internet Traffic Prediction Using Recurrent Neural Networks
Appears in:
inis 22(4): e1
Authors:
Mircea Eugen Dodan, Quoc-Tuan Vien, Tuan Thanh Nguyen
Downloads:
898
Abstract:
Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-si
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ze networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed.
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An Intelligent Fashion Object Classification Using CNN
Appears in:
inis 23(4): e2
Authors:
Jay Sanghvi, Debabrata Swain, Yugandhar Manchala, Kaxit Pandya
Downloads:
709
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 ar
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eas 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.
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Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm
Appears in:
inis 22(4): e4
Authors:
To-Hieu Dao, Duc-Nghia Tran, Hai-Yen Hoang, Van-Nhat Hoang, Duc-Tan Tran
Downloads:
666
Abstract:
There has been increasing interest in the application of artificial intelligence technologies to improve the quality of support services in healthcare. Some constraints, such as space, infrastructure,
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and environmental conditions, present challenges with assistive devices for humans. This paper proposed a wearable-based real-time human activity recognition system to monitor daily activities. The classification was done directly on the device, and the results could be checked over the internet. The accelerometer data collection application was developed on the device with a sampling frequency of 20Hz, and the random forest algorithm was embedded in the hardware. To improve the accuracy of the recognition system, a feature vector of 31 dimensions was calculated and used as an input per time window. Besides, the dynamic window method applied by the proposed model allowed us to change the data sampling time (1-3 seconds) and increase the performance of activity classification. The experiment results showed that the proposed system could classify 13 activities with a high accuracy of 99.4%. The rate of correctly classified activities was 96.1%. This work is promising for healthcare because of the convenience and simplicity of wearables.
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An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning
Appears in:
inis 22(4): e2
Authors:
Thu Huong Truong, Tran Long Dang, Ngoc Son Pham, Thu Ngan Dao, Trung Dung Nguyen, Hung Nguyen
Downloads:
564
Abstract:
Nowadays, Virtual Reality is becoming more and more popular, and 360 video is a very important part of the system. 360 video transmission over the Internet faces many difficulties due to its large siz
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e. Therefore, to reduce the network bandwidth requirement of 360-degree video, Viewport Adaptive Streaming (VAS) was proposed. An important issue in VAS is how to estimate future user viewing direction. In this paper, we propose an algorithm called GLVP (GRU-LSTM-based-Viewport-Prediction) to estimate the typical view for the VAS system. The results show that our method can improve viewport estimation from 9.5% to near 20%compared with other methods.
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Functional Security and Trust in Ultra-Connected 6G Ecosystem
Appears in:
inis 22(4): e5
Authors:
Vishal Sharma
Downloads:
556
Abstract:
Security and trust are the entangled role players in the future generation of wireless networks. Security in 5G networks is currently supported using several functions. Given the advantages of such a
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system, this article explores the functional security and trust for the 6G ecosystem with ultra-connectivity. Several associated challenges, application-specific domains, and consumer issues related to 6G security are discussed. The article highlights the network security-by-design and trust-by-design principles and performance expectations from the security protocols in supporting handover in an ultra-connected scenario. Finally, potential research directions are presented for a road towards the 6G ecosystem.
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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:
Van Truong Pham, Van Yem Vu, Thanh Viet Le, Thi-Thao Tran
Downloads:
525
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 fis
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h 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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Availability of Free-Space Laser Communication Link with the Presence of Clouds in Tropical Regions
Appears in:
inis 23(3): e1
Authors:
Thang Nguyen, Ngoc Dang, Hien Pham, Hoa Le
Downloads:
516
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 potentia
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l 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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Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications
Appears in:
inis 23(1): e1
Authors:
Antonino Masaracchia, Khoi Khac Nguyen, Cheng Yin
Downloads:
512
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 intellige
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nt 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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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
Downloads:
473
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 stat
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e 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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Attention ConvMixer Model and Application for Fish Species Classification
Appears in:
inis 23(3): e2
Authors:
Van-Truong Pham, Van Yem Vu, Hoang-Minh-Quang Le, Thanh Viet Le, Thi-Thao Tran
Downloads:
473
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 acc
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urate, 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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Publisher
EAI
ISSN
2410-0218
Number of Volumes
12
Last Published
2024-12-05
