Table of Content
- Vol.44, No.1, 2023
- Vol.44, No.2, 2023
- Vol.44, No.3, 2023
- Vol.45, No.1, 2023
- Vol.45, No.2, 2023
- Vol.45, No.3, 2023
- Vol.46, No.1, 2023
- Vol.46, No.2, 2023
- Vol.46, No.3, 2023
- Vol.47, No.1, 2023
- Vol.47, No.2, 2023
- Vol.47, No.3, 2023
- Vol.40, No.1, 2022
- Vol.40, No.2, 2022
- Vol.40, No.3, 2022
- Vol.41, No.1, 2022
- Vol.41, No.2, 2022
- Vol.41, No.3, 2022
- Vol.42, No.1, 2022
- Vol.42, No.2, 2022
- Vol.42, No.3, 2022
- Vol.43, No.1, 2022
- Vol.43, No.2, 2022
- Vol.43, No.3, 2022
- Vol.36, No.1, 2021
- Vol.36, No.2, 2021
- Vol.36, No.3, 2021
- Vol.37, No.1, 2021
- Vol.37, No.2, 2021
- Vol.37, No.3, 2021
- Vol.38, No.1, 2021
- Vol.38, No.2, 2021
- Vol.38, No.3, 2021
- Vol.39, No.1, 2021
- Vol.39, No.2, 2021
- Vol.39, No.3, 2021
- Vol.35, No.1, 2020
- Vol.35, No.2, 2020
- Vol.35, No.3, 2020
- Vol.35, No.4, 2020
- Vol.35, No.5, 2020
- Vol.35, No.6, 2020
- Vol.34, No.1, 2019
- Vol.34, No.2, 2019
- Vol.34, No.3, 2019
- Vol.34, No.4, 2019
- Vol.34, No.5, 2019
- Vol.34, No.6, 2019
- Vol.33, No.1, 2018
- Vol.33, No.2, 2018
- Vol.33, No.3, 2018
- Vol.33, No.4, 2018
- Vol.33, No.5, 2018
- Vol.33, No.6, 2018
About the Journal
The Computer Systems Science and Engineering journal is devoted to the publication of high quality papers on theoretical developments in computer systems science, and their applications in computer systems engineering. Original research papers, state-of-the-art reviews and technical notes are invited for publication.
Indexing and Abstracting
Scopus Cite Score (Impact per Publication 2023): 3.1; SNIP (Source Normalized Impact per Paper 2023): 0.739; ACM Digital Library.
Starting from Volume 48, Number 1, 2024, Computer Systems Science and Engineering will transition to a bi-monthly publication schedule.
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Open Access
REVIEW
IoMT-Based Healthcare Systems: A Review
Computer Systems Science and Engineering, Vol.48, No.4, pp. 871-895, 2024, DOI:10.32604/csse.2024.049026
（This article belongs to the Special Issue: Advanced Architecture of Fog Computing in Real-Time IoT Devices)
Abstract The integration of the Internet of Medical Things (IoMT) and the Internet of Things (IoT), which has revolutionized patient care through features like remote critical care and real-time therapy, is examined in this study in response to the changing healthcare landscape. Even with these improvements, security threats are associated with the increased connectivity of medical equipment, which calls for a thorough assessment. With a primary focus on addressing security and performance enhancement challenges, the research classifies current IoT communication devices, examines their applications in IoMT, and investigates important aspects of IoMT devices in healthcare. The More >
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Open Access
REVIEW
Biometric Authentication System on Mobile Environment: A Review
Computer Systems Science and Engineering, Vol.48, No.4, pp. 897-914, 2024, DOI:10.32604/csse.2024.050846
Abstract The paper discusses the importance of biometric verification systems in mobile environments and highlights the challenges and strategies used to overcome them in order to ensure the security of mobile devices. Emphasis is placed on evaluating the impact of illumination on the performance of biometric verification techniques and how to address this challenge using image processing techniques. The importance of accurate and reliable data collection to ensure the accuracy of verification processes is also discussed. The paper also highlights the importance of improving biometric verification techniques and directing research toward developing models aimed at reducing More >
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Open Access
ARTICLE
Intelligent Image Text Detection via Pixel Standard Deviation Representation
Computer Systems Science and Engineering, Vol.48, No.4, pp. 915-935, 2024, DOI:10.32604/csse.2024.046414
Abstract Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images’ pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an… More >
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Open Access
ARTICLE
Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm
Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123
（This article belongs to the Special Issue: Impact of Internet of Medical Things (IoMT) on Smart and Secure Healthcare Applications)
Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >
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Open Access
ARTICLE
A Novel Optimization Approach for Energy-Efficient Multiple Workflow Scheduling in Cloud Environment
Computer Systems Science and Engineering, Vol.48, No.4, pp. 953-967, 2024, DOI:10.32604/csse.2024.050406
Abstract Existing multiple workflow scheduling techniques focus on traditional Quality of Service (QoS) parameters such as cost, deadline, and makespan to find optimal solutions by consuming a large amount of electrical energy. Higher energy consumption decreases system efficiency, increases operational cost, and generates more carbon footprint. These major problems can lead to several problems, such as economic strain, environmental degradation, resource depletion, energy dependence, health impacts, etc. In a cloud computing environment, scheduling multiple workflows is critical in developing a strategy for energy optimization, which is an NP-hard problem. This paper proposes a novel, bi-phase Energy-Efficient… More >
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Open Access
ARTICLE
FFRA: A Fine-Grained Function-Level Framework to Reduce the Attack Surface
Computer Systems Science and Engineering, Vol.48, No.4, pp. 969-987, 2024, DOI:10.32604/csse.2024.046615
Abstract System calls are essential interfaces that enable applications to access and utilize the operating system’s services and resources. Attackers frequently exploit application’s vulnerabilities and misuse system calls to execute malicious code, aiming to elevate privileges and so on. Consequently, restricting the misuse of system calls becomes a crucial measure in ensuring system security. It is an effective method known as reducing the attack surface. Existing attack surface reduction techniques construct a global whitelist of system calls for the entire lifetime of the application, which is coarse-grained. In this paper, we propose a Fine-grained Function-level framework… More >
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Open Access
ARTICLE
A Multivariate Relevance Frequency Analysis Based Feature Selection for Classification of Short Text Data
Computer Systems Science and Engineering, Vol.48, No.4, pp. 989-1008, 2024, DOI:10.32604/csse.2024.051770
Abstract Text mining presents unique challenges in extracting meaningful information from the vast volumes of digital documents. Traditional filter feature selection methods often fall short in handling the complexities of short text data. To address this issue, this paper presents a novel approach to feature selection in text classification, aiming to overcome challenges posed by high dimensionality and reduced accuracy in the face of increasing digital document volumes. Unlike traditional filter feature selection techniques, the proposed method, Multivariate Relevance Frequency Analysis, offers a tailored solution for diverse text data types. By integrating positive, negative, and dependency… More >
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Open Access
ARTICLE
MG-YOLOv5s: A Faster and Stronger Helmet Detection Algorithm
Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475
Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >
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Open Access
ARTICLE
Unleashing User Requirements from Social Media Networks by Harnessing the Deep Sentiment Analytics
Computer Systems Science and Engineering, Vol.48, No.4, pp. 1031-1054, 2024, DOI:10.32604/csse.2024.051847
Abstract The article describes a novel method for sentiment analysis and requirement elicitation from social media feedback, leveraging advanced machine learning techniques. This innovative approach automates the extraction and classification of user requirements by analyzing sentiment in data gathered from social media platforms such as Twitter and Facebook. Utilizing APIs (Application Programming Interface) for data collection and Graph-based Neural Networks (GNN) for feature extraction, the proposed model efficiently processes and analyzes large volumes of unstructured user-generated content. The preprocessing pipeline includes data cleaning, normalization, and tokenization, ensuring high-quality input for the sentiment analysis model. By classifying… More >
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Open Access
ARTICLE
Reducing the Encrypted Data Size: Healthcare with IoT-Cloud Computing Applications
Computer Systems Science and Engineering, Vol.48, No.4, pp. 1055-1072, 2024, DOI:10.32604/csse.2024.048738
（This article belongs to the Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
Abstract Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness… More >
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Open Access
CORRECTION
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Open Access
CORRECTION
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Open Access
RETRACTION
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Open Access
ARTICLE
Impact of Human Vulnerabilities on Cybersecurity
Computer Systems Science and Engineering, Vol.40, No.3, pp. 1153-1166, 2022, DOI:10.32604/csse.2022.019938
Abstract Today, security is a major challenge linked with computer network companies that cannot defend against cyber-attacks. Numerous vulnerable factors increase security risks and cyber-attacks, including viruses, the internet, communications, and hackers. Internets of Things (IoT) devices are more effective, and the number of devices connected to the internet is constantly increasing, and governments and businesses are also using these technologies to perform business activities effectively. However, the increasing uses of technologies also increase risks, such as password attacks, social engineering, and phishing attacks. Humans play a major role in the field of cybersecurity. It is… More >
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Open Access
ARTICLE
Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol
Computer Systems Science and Engineering, Vol.43, No.3, pp. 1297-1313, 2022, DOI:10.32604/csse.2022.024018
Abstract The Internet of Things (IoTs) is apace growing, billions of IoT devices are connected to the Internet which communicate and exchange data among each other. Applications of IoT can be found in many fields of engineering and sciences such as healthcare, traffic, agriculture, oil and gas industries, and logistics. In logistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially available logistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote… More >
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Open Access
ARTICLE
Stock-Price Forecasting Based on XGBoost and LSTM
Computer Systems Science and Engineering, Vol.40, No.1, pp. 237-246, 2022, DOI:10.32604/csse.2022.017685
（This article belongs to the Special Issue: Data Analytics in Industry 4.0)
Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and… More >
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Open Access
ARTICLE
Video Identification Based on Watermarking Schemes and Visual Cryptography
Computer Systems Science and Engineering, Vol.40, No.2, pp. 441-453, 2022, DOI:10.32604/csse.2022.018597
Abstract Related to the growth of data sharing on the Internet and the wide - spread use of digital media, multimedia security and copyright protection have become of broad interest. Visual cryptography () is a method of sharing a secret image between a group of participants, where certain groups of participants are defined as qualified and may combine their share of the image to obtain the original, and certain other groups are defined as prohibited, and even if they combine knowledge of their parts, they can’t obtain any information on the secret image. The visual cryptography… More >
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Open Access
ARTICLE
Learning-Based Metaheuristic Approach for Home Healthcare Optimization Problem
Computer Systems Science and Engineering, Vol.45, No.1, pp. 1-19, 2023, DOI:10.32604/csse.2023.029058
Abstract This research focuses on the home health care optimization problem that involves staff routing and scheduling problems. The considered problem is an extension of multiple travelling salesman problem. It consists of finding the shortest path for a set of caregivers visiting a set of patients at their homes in order to perform various tasks during a given horizon. Thus, a mixed-integer linear programming model is proposed to minimize the overall service time performed by all caregivers while respecting the workload balancing constraint. Nevertheless, when the time horizon become large, practical-sized instances become very difficult to More >
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Open Access
REVIEW
Intrusion Detection Systems Using Blockchain Technology: A Review, Issues and Challenges
Computer Systems Science and Engineering, Vol.40, No.1, pp. 87-112, 2022, DOI:10.32604/csse.2022.017941
（This article belongs to the Special Issue: Emerging Trends in Intelligent Communication and Wireless Technologies)
Abstract Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains… More >
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Open Access
ARTICLE
CNN Based Automated Weed Detection System Using UAV Imagery
Computer Systems Science and Engineering, Vol.42, No.2, pp. 837-849, 2022, DOI:10.32604/csse.2022.023016
（This article belongs to the Special Issue: Soft Computing and Big Data Mining)
Abstract The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient… More >
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Open Access
ARTICLE
Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks
Computer Systems Science and Engineering, Vol.46, No.3, pp. 2651-2666, 2023, DOI:10.32604/csse.2023.031720
Abstract With the advent of Reinforcement Learning (RL) and its continuous
progress, state-of-the-art RL systems have come up for many challenging and
real-world tasks. Given the scope of this area, various techniques are found in
the literature. One such notable technique, Multiple Deep Q-Network (DQN) based
RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in
such a scheme and the inter-entity communication protocol has to be carefully
designed. As more complex DQNs come to the fore, the overall complexity of these… More >
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Open Access
ARTICLE
TC-Net: A Modest & Lightweight Emotion Recognition System Using Temporal Convolution Network
Computer Systems Science and Engineering, Vol.46, No.3, pp. 3355-3369, 2023, DOI:10.32604/csse.2023.037373
Abstract Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines. Speech Emotion Recognition (SER) is one of the critical sources for human evaluation, which is applicable in many real-world applications such as healthcare, call centers, robotics, safety, and virtual reality. This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state. The authors designed a Temporal Convolutional Network (TCN) core block to recognize long-term dependencies in speech signals and then feed these temporal More >
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Open Access
ARTICLE
A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification
Computer Systems Science and Engineering, Vol.40, No.1, pp. 223-235, 2022, DOI:10.32604/csse.2022.018430
Abstract Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of… More >
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Open Access
ARTICLE
A Storage Optimization Scheme for Blockchain Transaction Databases
Computer Systems Science and Engineering, Vol.36, No.3, pp. 521-535, 2021, DOI:10.32604/csse.2021.014530
Abstract As the typical peer-to-peer distributed networks, blockchain systems require each node to copy a complete transaction database, so as to ensure new transactions can by verified independently. In a blockchain system (e.g., bitcoin system), the node does not rely on any central organization, and every node keeps an entire copy of the transaction database. However, this feature determines that the size of blockchain transaction database is growing rapidly. Therefore, with the continuous system operations, the node memory also needs to be expanded to support the system running. Especially in the big data era, the increasing More >
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Open Access
ARTICLE
Comparative Study of Valency-Based Topological Descriptor for Hexagon Star Network
Computer Systems Science and Engineering, Vol.36, No.2, pp. 293-306, 2021, DOI:10.32604/csse.2021.014896
Abstract A class of graph invariants referred to today as topological indices are inefficient progressively acknowledged by scientific experts and others to be integral assets in the depiction of structural phenomena. The structure of an interconnection network can be represented by a graph. In the network, vertices represent the processor nodes and edges represent the links between the processor nodes. Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks. A topological descriptor is a numerical total related to a structure that portray the topology of structure and… More >
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Open Access
ARTICLE
Comparative Design and Study of A 60 GHz Antenna for Body-Centric Wireless Communications
Computer Systems Science and Engineering, Vol.37, No.1, pp. 19-32, 2021, DOI:10.32604/csse.2021.015528
（This article belongs to the Special Issue: Sensors and Nano-sensors Technologies for Health-Care Applications)
Abstract In this paper performance of three different designs of a 60 GHz high gain antenna for body-centric communication has been evaluated. The basic structure of the antenna is a slotted patch consisting of a rectangular ring radiator with passive radiators inside. The variation of the design was done by changing the shape of these passive radiators. For free space performance, two types of excitations were used—waveguide port and a coaxial probe. The coaxial probe significantly improved both the bandwidth and radiation efficiency. The center frequency of all the designs was close to 60 GHz with… More >
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Open Access
ARTICLE
Atrocious Impinging of COVID-19 Pandemic on Software Development Industries
Computer Systems Science and Engineering, Vol.36, No.2, pp. 323-338, 2021, DOI:10.32604/csse.2021.014929
Abstract COVID-19 is the contagious disease transmitted by Coronavirus. The majority of people diagnosed with COVID-19 may suffer from moderate-to- severe respiratory illnesses and stabilize without preferential treatment. Those who are most likely to experience significant infections include the elderly as well as people with a history of significant medical issues including heart disease, diabetes, or chronic breathing problems. The novel Coronavirus has affected not only the physical and mental health of the people but also had adverse impact on their emotional well-being. For months on end now, due to constant monitoring and containment measures to… More >
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Open Access
ARTICLE
Fuzzy Adaptive Filtering-Based Energy Management for Hybrid Energy Storage System
Computer Systems Science and Engineering, Vol.36, No.1, pp. 117-130, 2021, DOI:10.32604/csse.2021.014081
Abstract Regarding the problem of the short driving distance of pure electric vehicles, a battery, super-capacitor, and DC/DC converter are combined to form a hybrid energy storage system (HESS). A fuzzy adaptive filtering-based energy management strategy (FAFBEMS) is proposed to allocate the required power of the vehicle. Firstly, the state of charge (SOC) of the super-capacitor is limited according to the driving/braking mode of the vehicle to ensure that it is in a suitable working state, and fuzzy rules are designed to adaptively adjust the filtering time constant, to realize reasonable power allocation. Then, the positive… More >
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Open Access
ARTICLE
Human-Animal Affective Robot Touch Classification Using Deep Neural Network
Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992
Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is… More >
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Open Access
ARTICLE
Generalized Class of Mean Estimators with Known Measures for Outliers Treatment
Computer Systems Science and Engineering, Vol.38, No.1, pp. 1-15, 2021, DOI:10.32604/csse.2021.015933
Abstract In estimation theory, the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares (OLS) method or robust regression techniques for estimating regression coefficients. But when the correlation is negative and the outliers are presented, the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates. Hence, this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method. Precisely, we have proposed generalized estimators by using the… More >
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Open Access
ARTICLE
COVID-19 Pandemic Data Predict the Stock Market
Computer Systems Science and Engineering, Vol.36, No.3, pp. 451-460, 2021, DOI:10.32604/csse.2021.015309
Abstract Unlike the 2007–2008 market crash, which was caused by a banking failure and led to an economic recession, the 1918 influenza pandemic triggered a worldwide financial depression. Pandemics usually affect the global economy, and the COVID-19 pandemic is no exception. Many stock markets have fallen over 40%, and companies are shutting down, ending contracts, and issuing voluntary and involuntary leaves for thousands of employees. These economic effects have led to an increase in unemployment rates, crime, and instability. Studying pandemics’ economic effects, especially on the stock market, has not been urgent or feasible until recently.… More >
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Open Access
ARTICLE
A Generative Adversarial Networks for Log Anomaly Detection
Computer Systems Science and Engineering, Vol.37, No.1, pp. 135-148, 2021, DOI:10.32604/csse.2021.014030
Abstract Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The More >
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Open Access
ARTICLE
Stock Price Forecasting: An Echo State Network Approach
Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189
Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage… More >
Copyright © 2024 The Author(s). Published by Tech Science Press.
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