Table of Content
- Vol.35, No.1, 2023
- Vol.35, No.2, 2023
- Vol.35, No.3, 2023
- Vol.36, No.1, 2023
- Vol.36, No.2, 2023
- Vol.36, No.3, 2023
- Vol.37, No.1, 2023
- Vol.37, No.2, 2023
- Vol.37, No.3, 2023
- Vol.38, No.1, 2023
- Vol.38, No.2, 2023
- Vol.38, No.3, 2023
- Vol.31, No.1, 2022
- Vol.31, No.2, 2022
- Vol.31, No.3, 2022
- Vol.32, No.1, 2022
- Vol.32, No.2, 2022
- Vol.32, No.3, 2022
- Vol.33, No.1, 2022
- Vol.33, No.2, 2022
- Vol.33, No.3, 2022
- Vol.34, No.1, 2022
- Vol.34, No.2, 2022
- Vol.34, No.3, 2022
- Vol.27, No.1, 2021
- Vol.27, No.2, 2021
- Vol.27, No.3, 2021
- Vol.28, No.1, 2021
- Vol.28, No.2, 2021
- Vol.28, No.3, 2021
- Vol.29, No.1, 2021
- Vol.29, No.2, 2021
- Vol.29, No.3, 2021
- Vol.30, No.1, 2021
- Vol.30, No.2, 2021
- Vol.30, No.3, 2021
- Vol.26, No.1, 2020
- Vol.26, No.2, 2020
- Vol.26, No.3, 2020
- Vol.26, No.4, 2020
- Vol.26, No.5, 2020
- Vol.26, No.6, 2020
About the Journal
Intelligent Automation & Soft Computing: An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of artificial intelligence, intelligent automation, control, computer science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, cyber security, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of computer engineering and soft computing.
Indexing and Abstracting
Scopus CiteScore (Impact per Publication 2023): 3.5; SNIP (Source Normalized Impact per Paper 2023): 0.613; Essential Science Indicators(ESI), etc.
Starting from Volume 39, Number 1, 2024, Intelligent Automation & Soft Computing will transition to a bi-monthly publication schedule.
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Open Access
ARTICLE
Automated Angle Detection for Industrial Production Lines Using Combined Image Processing Techniques
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 599-618, 2024, DOI:10.32604/iasc.2024.055385 - 06 September 2024
Abstract Angle detection is a crucial aspect of industrial automation, ensuring precise alignment and orientation of components in manufacturing processes. Despite the widespread application of computer vision in industrial settings, angle detection remains an underexplored domain, with limited integration into production lines. This paper addresses the need for automated angle detection in industrial environments by presenting a methodology that eliminates training time and higher computation cost on Graphics Processing Unit (GPU) from machine learning in computer vision (e.g., Convolutional Neural Networks (CNN)). Our approach leverages advanced image processing techniques and a strategic combination of algorithms, including More >
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Open Access
ARTICLE
Improving Low-Resource Machine Translation Using Reinforcement Learning from Human Feedback
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 619-631, 2024, DOI:10.32604/iasc.2024.052971 - 06 September 2024
Abstract Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation has always been unsatisfactory. When Reinforcement Learning from Human Feedback (RLHF) is applied to low-resource machine translation, commonly encountered issues of substandard preference data quality and the higher cost associated with manual feedback data. Therefore, a more cost-effective method for obtaining feedback data is proposed. At first, optimizing the quality of preference data through the prompt engineering of the Large Language Model (LLM), More >
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Open Access
ARTICLE
Data-Oriented Operating System for Big Data and Cloud
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 633-647, 2024, DOI:10.32604/iasc.2024.054154 - 06 September 2024
Abstract Operating System (OS) is a critical piece of software that manages a computer’s hardware and resources, acting as the intermediary between the computer and the user. The existing OS is not designed for Big Data and Cloud Computing, resulting in data processing and management inefficiency. This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes. The proposed algorithm utilizes the performance benefits from the improved Input/Output (I/O) performance. The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by… More >
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Open Access
ARTICLE
Mathematical Named Entity Recognition Based on Adversarial Training and Self-Attention
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 649-664, 2024, DOI:10.32604/iasc.2024.051724 - 06 September 2024
Abstract Mathematical named entity recognition (MNER) is one of the fundamental tasks in the analysis of mathematical texts. To solve the existing problems of the current neural network that has local instability, fuzzy entity boundary, and long-distance dependence between entities in Chinese mathematical entity recognition task, we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long short-term memory-Selfattention Conditional random field (AT-BSAC) model. In our model, the mathematical text was vectorized by the word embedding technique, and small perturbations were added to the word vector to generate adversarial samples, while More >
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Open Access
ARTICLE
A Hierarchical Two-Level Feature Fusion Approach for SMS Spam Filtering
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 665-682, 2024, DOI:10.32604/iasc.2024.050452 - 06 September 2024
Abstract SMS spam poses a significant challenge to maintaining user privacy and security. Recently, spammers have employed fraudulent writing styles to bypass spam detection systems. This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam. The system comprises five steps, beginning with the preprocessing of SMS data. RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis. Feature extraction is performed using a Convolutional Neural Network (CNN) for word-level analysis and a Bidirectional Long… More >
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Open Access
ARTICLE
Importance-Weighted Transfer Learning for Fault Classification under Covariate Shift
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 683-696, 2024, DOI:10.32604/iasc.2023.038543 - 06 September 2024
Abstract In the process of fault detection and classification, the operation mode usually drifts over time, which brings great challenges to the algorithms. Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset, the accuracy of these traditional methods usually drops significantly in the case of covariate shift. In this paper, an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process. It effectively alters the drift between the training and testing dataset. Firstly, the mutual information method is… More >
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Open Access
ARTICLE
Chase, Pounce, and Escape Optimization Algorithm
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 697-723, 2024, DOI:10.32604/iasc.2024.053192 - 06 September 2024
(This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)
Abstract While many metaheuristic optimization algorithms strive to address optimization challenges, they often grapple with the delicate balance between exploration and exploitation, leading to issues such as premature convergence, sensitivity to parameter settings, and difficulty in maintaining population diversity. In response to these challenges, this study introduces the Chase, Pounce, and Escape (CPE) algorithm, drawing inspiration from predator-prey dynamics. Unlike traditional optimization approaches, the CPE algorithm divides the population into two groups, each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima. By incorporating a unique search mechanism that integrates More >
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Open Access
ARTICLE
Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024
Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >
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Open Access
ARTICLE
A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 745-764, 2024, DOI:10.32604/iasc.2024.052983 - 06 September 2024
Abstract Identifying faces in non-frontal poses presents a significant challenge for face recognition (FR) systems. In this study, we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0° to ±90°. We initially selected the most suitable feature vector size by integrating the Dlib, FaceNet (Inception-v2), and “Support Vector Machines (SVM)” + “K-nearest neighbors (KNN)” algorithms. To train and evaluate this feature vector, we used two datasets: the “Labeled Faces in the Wild (LFW)” benchmark data and the “Robust… More >
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Open Access
ARTICLE
Ensemble Modeling for the Classification of Birth Data
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 765-781, 2024, DOI:10.32604/iasc.2023.034029 - 06 September 2024
Abstract Machine learning (ML) and data mining are used in various fields such as data analysis, prediction, image processing and especially in healthcare. Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results. Using ML algorithms, researchers have developed applications for decision support, analyzed clinical aspects, extracted informative information from historical data, predicted the outcomes and categorized diseases which help physicians make better decisions. It is observed that there is a huge difference between women… More >
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Open Access
ARTICLE
Optimal and Energy Effective Power Allocation Using Multi-Scale Resource GOA-DC-EM in DAS
Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1049-1063, 2022, DOI:10.32604/iasc.2022.025127
Abstract Recently many algorithms for allocation of power approaches have been suggested to increase the Energy Efficiency (EE) and Spectral Efficiency (EE) in the Distributed Antenna System (DAS). In addition, the method of conservation developed for the allocation of power is challenging for the enhancement because of their high complication during estimation. With the intention of increasing the EE and SE, the optimization of allocation of power is done on the basis of capacity of the antenna. The main goal is for the optimization of the power allocation to improve the spectral and energy efficiency with… More >
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Open Access
ARTICLE
Hybrid Optimized PI Controller Design for Grid Tied PV Based Electric Vehicle
Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1523-1545, 2023, DOI:10.32604/iasc.2023.033545
Abstract Nowadays, researchers are becoming increasingly concerned about
developing a highly efficient emission free transportation and energy generation
system for addressing the pressing issue of environmental crisis in the form of
pollution and climate change. The introduction of Electric Vehicles (EVs) solves
the challenge of emission-free transportation while the necessity for decarbonized
energy production is fulfilled by the installation and expansion of solar-powered
Photovoltaic (PV) systems. Hence, this paper focuses on designing an effective
PV based EV charging system that aids in stepping towards the achievement of
a pollution free future. For overcoming the inherent intermittency… More >
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Open Access
ARTICLE
Differential Evolution Algorithm with Hierarchical Fair Competition Model
Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1045-1062, 2022, DOI:10.32604/iasc.2022.023270
Abstract This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, More >
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Open Access
ARTICLE
Development of IoT Based Fish Monitoring System for Aquaculture
Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 55-71, 2022, DOI:10.32604/iasc.2022.021559
Abstract Aquaculture mainly refers to cultivating aquatic organisms providing suitable environments for various purposes, including commercial, recreational, public purposes. This paper aims to enhance the production of fish and maintain the aquatic environment of aquaculture in Bangladesh. This paper presents the way of using Internet of Things (IoT) based devices to monitor aquaculture’s basic needs and help provide things needed for the fisheries. Using these devices, various parameters of water will be monitored for a better living environment for fish. These devices consist of some sensors that will detect the Potential of Hydrogen (pH) level, the… More >
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Open Access
ARTICLE
Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques
Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1671-1687, 2022, DOI:10.32604/iasc.2022.019892
Abstract Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations’ success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect… More >
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Open Access
ARTICLE
Security and Privacy Aspects of Cloud Computing: A Smart Campus Case Study
Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 117-128, 2022, DOI:10.32604/iasc.2022.016597
Abstract The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost-effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365.… More >
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Open Access
ARTICLE
CNN Based Driver Drowsiness Detection System Using Emotion Analysis
Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 717-728, 2022, DOI:10.32604/iasc.2022.020008
Abstract
The drowsiness of the driver and rash driving are the major causes of road accidents, which result in loss of valuable life, and deteriorate the safety in the road traffic. Reliable and precise driver drowsiness systems are required to prevent road accidents and to improve road traffic safety. Various driver drowsiness detection systems have been designed with different technologies which have an affinity towards the unique parameter of detecting the drowsiness of the driver. This paper proposes a novel model of multi-level distribution of detecting the driver drowsiness using the Convolution Neural Networks (CNN) followed
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More >
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Open Access
ARTICLE
Cyber-Attack Detection and Mitigation Using SVM for 5G Network
Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 13-28, 2022, DOI:10.32604/iasc.2022.019121
Abstract 5G technology is widely seen as a game-changer for the IT and telecommunications sectors. Benefits expected from 5G include lower latency, higher capacity, and greater levels of bandwidth. 5G also has the potential to provide additional bandwidth in terms of AI support, further increasing the benefits to the IT and telecom sectors. There are many security threats and organizational vulnerabilities that can be exploited by fraudsters to take over or damage corporate data. This research addresses cybersecurity issues and vulnerabilities in 4G(LTE) and 5G technology. The findings in this research were obtained by using primary… More >
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Open Access
ARTICLE
Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images
Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 621-634, 2022, DOI:10.32604/iasc.2022.019117
(This article belongs to the Special Issue: Intelligence 4.0: Concepts and Advances in Computational Intelligence)
Abstract In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts… More >
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Open Access
ARTICLE
Generative Deep Belief Model for Improved Medical Image Segmentation
Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1-14, 2023, DOI:10.32604/iasc.2023.026341
Abstract Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an important role in terms of resources used, accuracy, and the time taken. The noise in the raw medical image are processed using More >
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Open Access
ARTICLE
A Big Data Approach to Black Friday Sales
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 785-797, 2021, DOI:10.32604/iasc.2021.014216
Abstract Retail companies recognize the need to analyze and predict their sales and customer behavior against their products and product categories. Our study aims to help retail companies create personalized deals and promotions for their customers, even during the COVID-19 pandemic, through a big data framework that allows them to handle massive sales volumes with more efficient models. In this paper, we used Black Friday sales data taken from a dataset on the Kaggle website, which contains nearly 550,000 observations analyzed with 10 features: qualitative and quantitative. The class label is purchases and sales (in U.S.… More >
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Open Access
ARTICLE
Roughsets-based Approach for Predicting Battery Life in IoT
Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 453-469, 2021, DOI:10.32604/iasc.2021.014369
(This article belongs to the Special Issue: Soft Computing Methods for Innovative Software Practices)
Abstract Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from… More >
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Open Access
ARTICLE
Exact Analysis of Second Grade Fluid with Generalized Boundary Conditions
Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 547-559, 2021, DOI:10.32604/iasc.2021.015982
(This article belongs to the Special Issue: Recent Trends in Computational Methods for Differential Equations)
Abstract Convective flow is a self-sustained flow with the effect of the temperature gradient. The density is non-uniform due to the variation of temperature. The effect of the magnetic flux plays a major role in convective flow. The process of heat transfer is accompanied by mass transfer process; for instance condensation, evaporation and chemical process. Due to the applications of the heat and mass transfer combined effects in different field, the main aim of this paper is to do comprehensive analysis of heat and mass transfer of MHD unsteady second-grade fluid in the presence of time… More >
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Open Access
ARTICLE
Parallel Equilibrium Optimizer Algorithm and Its Application in Capacitated Vehicle Routing Problem
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 233-247, 2021, DOI:10.32604/iasc.2021.014192
(This article belongs to the Special Issue: Machine Learning and Deep Learning for Transportation)
Abstract The Equilibrium Optimizer (EO) algorithm is a novel meta-heuristic algorithm based on the strength of physics. To achieve better global search capability, a Parallel Equilibrium Optimizer algorithm, named PEO, is proposed in this paper. PEO is inspired by the idea of parallelism and adopts two different communication strategies between groups to improve EO. The first strategy is used to speed up the convergence rate and the second strategy promotes the algorithm to search for a better solution. These two kinds of communication strategies are used in the early and later iterations of PEO respectively. To More >
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Open Access
ARTICLE
Parameter Estimation of Alpha Power Inverted Topp-Leone Distribution with Applications
Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 353-371, 2021, DOI:10.32604/iasc.2021.017586
Abstract We introduce a new two-parameter lifetime model, referred to alpha power transformed inverted Topp-Leone, derived by combining the alpha power transformation-G family with the inverted Topp-Leone distribution. Structural properties of the proposed distribution are implemented like; quantile function, residual and reversed residual life, Rényi entropy measure, moments and incomplete moments. The maximum likelihood, weighted least squares, maximum product of spacing, and Bayesian methods of estimation are considered. A simulation study is worked out to evaluate the restricted sample properties of the proposed distribution. Numerical results showed that the Bayesian estimates give more accurate results than… More >
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Open Access
ARTICLE
Computational Methods for Non-Linear Equations with Some Real-World Applications and Their Graphical Analysis
Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 805-819, 2021, DOI:10.32604/iasc.2021.019164
(This article belongs to the Special Issue: Recent Trends in Computational Methods for Differential Equations)
Abstract In this article, we propose some novel computational methods in the form of iteration schemes for computing the roots of non-linear scalar equations in a new way. The construction of these iteration schemes is purely based on exponential series expansion. The convergence criterion of the suggested schemes is also given and certified that the newly developed iteration schemes possess quartic convergence order. To analyze the suggested schemes numerically, several test examples have been given and then solved. These examples also include some real-world problems such as van der Wall’s equation, Plank’s radiation law and kinetic More >
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Open Access
ARTICLE
Device Security Assessment of Internet of Healthcare Things
Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 593-603, 2021, DOI:10.32604/iasc.2021.015092
Abstract Security of the Internet of Healthcare Things (IoHT) devices plays a vital role in e-healthcare today and there has been a rapid increase in the use of networked devices of IoHT in the present healthcare services. However, these networked devices are also highly vulnerable to attackers who constantly target the security of devices and their components to gain access to the patients’ data. Infringement of patients’ data is not only a violation of privacy but can also jeopardize patients’ health if the health records are tampered with. Once the device has been intruded upon, attackers… More >
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Open Access
ARTICLE
Expert System for Stable Power Generation Prediction in Microbial Fuel Cell
Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 17-30, 2021, DOI:10.32604/iasc.2021.018380
Abstract Expert Systems are interactive and reliable computer-based decision-making systems that use both facts and heuristics for solving complex decision-making problems. Generally, the cyclic voltammetry (CV) experiments are executed a random number of times (cycles) to get a stable production of power. However, presently there are not many algorithms or models for predicting the power generation stable criteria in microbial fuel cells. For stability analysis of Medicinal herbs’ CV profiles, an expert system driven by the augmented K-means clustering algorithm is proposed. Our approach requires a dataset that contains voltage-current relationships from CV experiments on the More >
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Open Access
ARTICLE
Managing Software Security Risks through an Integrated Computational Method
Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 179-194, 2021, DOI:10.32604/iasc.2021.016646
Abstract Security risk evaluation of web-based healthcare applications is important from a design perspective. The developers as well as the users need to make sure that the applications must be secure. Citing the disastrous effects of unsecured web applications, Accuntix Online states that the IT industry has lost millions of dollars due to security theft and malware attacks. Protecting the integrity of patients’ health data is of utmost importance. Thus, assessing the security risk of web-based healthcare applications should be accorded the highest priority while developing the web applications. To fulfill the security requirements, the developers must… More >
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Open Access
ARTICLE
Performances of K-Means Clustering Algorithm with Different Distance Metrics
Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 735-742, 2021, DOI:10.32604/iasc.2021.019067
Abstract Clustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be smaller enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and More >
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