Table of Content

Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications

Submission Deadline: 28 February 2021 (closed)

Guest Editors

Dr. MUHAMMAD ADNAN KHAN, Lahore Garrison University, Pakistan.
Dr. RIZWAN ALI NAQVI, Sejong University, Korea.
Dr. MOHAMMED A. ALGHAMDI, Umm Al-Qura University, Saudi Arabia.

Summary

Machine learning has been a subject of increasing concern to scholars, both from academia and business, over the past few years. Unlike conventional learning methods, machine learning methods suggest the potential to learn and develop very broad sets of data. Machine learning methods in computer vision, natural language analysis, robots, and other fields have gained considerable popularity in numerous activities. Recent years have seen a tremendous advancement of the principle of machine learning and numerous implementations in the general area of artificial intelligence, including neural network architecture, automation, statistical analysis and deep learning.

Though machine learning has been extensively explored in recent decades, the use of machine learning strategies in intelligent systems faces several complexities. Well first of all, machine learning methods need a vast and varied amount of data as input to frameworks and provide a wide range of training requirements. Secondly, the teaching of machine learning models is quick to slip into overfitting issues. Furthermore, because machine learning systems have uncertainty or backbox problems, it is challenging to consider how a given algorithm makes a judgment, which is essential in certain fields such as financial trading or medical diagnosis.

Suggested topics include, but are not limited to, the following:

• Agent and Multi-Agent Systems

• Artificial Intelligence Applications

• Artificial Neural Networks

• Autonomous and Ubiquitous Computing

• Biomedical systems

• Colour/Image Analysis

• Computational Intelligence

• Computer Vision

• Cybersecurity and AI

• Distributed AI Systems and Architectures

• eBusiness, eCommerce, eHealth, eLearning

• Finance and AI

• Extreme Machine Learning

• Forensic Science

• Grid-Based Computing

• Internet of Things (IoT), IoMT, AIoT & AIoMT

• Medical Informatics and Biomedical

• Natural Language Processing

• Object and Face Recognition

• Pattern Recognition

• Robotics and Virtual Reality

• Signal and Image Processing

• Signal Processing Techniques

• Knowledge Extraction

• Smart Grids

• Smart City

• Time Series and Forecasting


Keywords

• AIoT
• IoMT
• MUD
• Fuzzy
• Swarm Intelligence
• Evolutionary Algorithms
• Neural Networks
• Extreme machine learning
• Smart Health
• Smart Traffic
• Intelligent Bussiness
• Image Processing

Published Papers


  • Open Access

    ARTICLE

    Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

    Nasir Mahmood, Muhammad Usman Ghani Khan
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4411-4428, 2022, DOI:10.32604/cmc.2022.013956
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings. By linking ‘dots’ of police data comprising scattered information of people, groups, logistics, locations, communication, and spatiotemporal characters on different social media groups, the proposed architecture will spawn beneficial information. This useful information will, in turn, help the police both in predicting potential terrorist events and in investigating previous events. Furthermore, this architecture will aid in the identification of criminals and their associates and handlers. Terrorism is psychological warfare, which, in the broadest sense, can be defined as the utilisation of deliberate violence for… More >

  • Open Access

    ARTICLE

    Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

    Abdullah Ali Salamai, El-Sayed M. El-kenawy, Ibrahim Abdelhameed
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3749-3766, 2021, DOI:10.32604/cmc.2021.018179
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems, which integrate the supply chain’s manufacturing operations, information technology, and telecommunication processes. Although supply chain 4.0 aims to improve supply chains’ production systems and profitability, it is subject to different operational and disruptive risks. Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems. This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group (SCDG) algorithm.… More >

  • Open Access

    ARTICLE

    An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

    Abid Sohail, Ammar Haseeb, Mobashar Rehman, Dhanapal Durai Dominic, Muhammad Arif Butt
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3603-3618, 2021, DOI:10.32604/cmc.2021.017795
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them. However, neither a benchmark… More >

  • Open Access

    ARTICLE

    An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

    Qasim M. Zainel, Murad B. Khorsheed, Saad Darwish, Amr A. Ahmed
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3813-3828, 2021, DOI:10.32604/cmc.2021.014759
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Convolutional Neural Networks (CNNs) models succeed in vast domains. CNNs are available in a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture. Our proposed framework to automated design is aimed at resolving this problem. The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit. In comparison to the co-authored work, our proposed framework is concerned… More >

  • Open Access

    ARTICLE

    Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost

    Mohamed Ezz, Murtada K. Elbashir, Hosameldeen Shabana
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2077-2092, 2021, DOI:10.32604/cmc.2021.018155
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited. Delay in ICU admissions is associated with negative outcomes such as mortality and cost. Therefore, early identification of patients with a high risk of respiratory failure can prevent complications, enhance risk stratification, and improve the outcomes of severely-ill hospitalized patients. In this paper, we develop a model that uses the characteristics and information collected at the time of patients’ admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions. We use the… More >

  • Open Access

    ARTICLE

    Brain Tumour Detection by Gamma DeNoised Wavelet Segmented Entropy Classifier

    Simy Mary Kurian, Sujitha Juliet Devaraj, Vinodh P. Vijayan
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2093-2109, 2021, DOI:10.32604/cmc.2021.018090
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Magnetic resonance imaging (MRI) is an essential tool for detecting brain tumours. However, identification of brain tumours in the early stages is a very complex task since MRI images are susceptible to noise and other environmental obstructions. In order to overcome these problems, a Gamma MAP denoised Strömberg wavelet segmentation based on a maximum entropy classifier (GMDSWS-MEC) model is developed for efficient tumour detection with high accuracy and low time consumption. The GMDSWS-MEC model performs three steps, namely pre-processing, segmentation, and classification. Within the GMDSWS-MEC model, the Gamma MAP filter performs the pre-processing task and achieves a significant increase in… More >

  • Open Access

    ARTICLE

    Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

    Mohanad Al-Ghobari, Amgad Muneer, Suliman Mohamed Fati
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1553-1570, 2021, DOI:10.32604/cmc.2021.016348
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly, this study proposes location-aware personalized… More >

  • Open Access

    ARTICLE

    An E-Business Event Stream Mechanism for Improving User Tracing Processes

    Ayman Mohamed Mostafa, Saleh N. Almuayqil, Wael Said
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 767-784, 2021, DOI:10.32604/cmc.2021.018236
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record… More >

  • Open Access

    ARTICLE

    Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

    Shabir Ahmad, Jehad Ali, Faisal Jamil, Taeg Keun Whangbo, DoHyeun Kim
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1271-1288, 2021, DOI:10.32604/cmc.2021.017773
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Smart cities have different contradicting goals having no apparent solution. The selection of the appropriate solution, which is considered the best compromise among the candidates, is known as complex problem-solving. Smart city administrators face different problems of complex nature, such as optimal energy trading in microgrids and optimal comfort index in smart homes, to mention a few. This paper proposes a novel architecture to offer complex problem solutions as a service (CPSaaS) based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city. Predictive model optimization uses a machine learning module and… More >

  • Open Access

    ARTICLE

    Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

    Thavavel Vaiyapuri, Adel Binbusayyis
    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More >

  • Open Access

    ARTICLE

    UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting

    Chung-Il Kim, Jehyeok Rew, Yongjang Cho, Eenjun Hwang
    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel reconstruction failure and unintended filling.… More >

  • Open Access

    ARTICLE

    Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

    Abdul Rehman, Mohd Fadzil Hassan, Yew Kwang Hooi, Muhammad Aasim Qureshi, Tran Duc Chung, Rehan Akbar, Sohail Safdar
    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4125-4142, 2021, DOI:10.32604/CMC.2021.017620
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Trust is one of the core components of any ad hoc network security system. Trust management (TM) has always been a challenging issue in a vehicular network. One such developing network is the Internet of vehicles (IoV), which is expected to be an essential part of smart cities. IoV originated from the merger of Vehicular ad hoc networks (VANET) and the Internet of things (IoT). Security is one of the main barriers in the on-road IoV implementation. Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements. Trust plays a vital role in ensuring security,… More >

  • Open Access

    ARTICLE

    Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

    Shaheen Khatoon, Majed A. Alshamari, Amna Asif, Md Maruf Hasan, Sherif Abdou, Khaled Mostafa Elsayed, Mohsen Rashwan
    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3079-3100, 2021, DOI:10.32604/cmc.2021.017371
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze… More >

  • Open Access

    ARTICLE

    Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model

    Mohamed F. El-Amin, Abdulhamit Subasi, Mahmoud M. Selim, Awad Mousa
    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2349-2362, 2021, DOI:10.32604/cmc.2021.017102
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract In the process of oil recovery, experiments are usually carried out on core samples to evaluate the recovery of oil, so the numerical data are fitted into a non-dimensional equation called scaling-law. This will be essential for determining the behavior of actual reservoirs. The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data. This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity, which dynamics of the process. One of the practical machine learning (ML)… More >

  • Open Access

    ARTICLE

    Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

    Muhammad Usman Ghani Khan, Zubaira Naz, Javeria Khan, Tanzila Saba, Ibrahim Abunadi, Amjad Rehman, Usman Tariq
    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2363-2376, 2021, DOI:10.32604/cmc.2021.014550
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract The National Health Interview Survey (NHIS) shows that there are 13.2% of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder (ADHD), globally. The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy. These traditional methods, namely therapy, need a large number of visits to hospitals and include medication. Neurogames could be used for the effective treatment of ADHD. It could be a helpful tool in improving children and ADHD patients’ cognitive skills by using Brain–Computer Interfaces (BCI). BCI enables the user to interact with the computer through brain activity using… More >

  • Open Access

    ARTICLE

    A Genetic Based Leader Election Algorithm for IoT Cloud Data Processing

    Samira Kanwal, Zeshan Iqbal, Aun Irtaza, Rashid Ali, Kamran Siddique
    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2469-2486, 2021, DOI:10.32604/cmc.2021.014709
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract In IoT networks, nodes communicate with each other for computational services, data processing, and resource sharing. Most of the time huge data is generated at the network edge due to extensive communication between IoT devices. So, this tidal data is transferred to the cloud data center (CDC) for efficient processing and effective data storage. In CDC, leader nodes are responsible for higher performance, reliability, deadlock handling, reduced latency, and to provide cost-effective computational services to the users. However, the optimal leader selection is a computationally hard problem as several factors like memory, CPU MIPS, and bandwidth, etc., are needed to… More >

  • Open Access

    ARTICLE

    A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

    Kamran Sadiq Awan, Tahir Mahmood, Mohammad Shorfuzzaman, Rashid Ali, Raja Majid Mehmood
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 29-43, 2021, DOI:10.32604/cmc.2021.014824
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions,… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Hybrid Intelligent Intrusion Detection System

    Muhammad Ashfaq Khan, Yangwoo Kim
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 671-687, 2021, DOI:10.32604/cmc.2021.015647
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop… More >

  • Open Access

    ARTICLE

    COVID19: Forecasting Air Quality Index and Particulate Matter (PM2.5)

    R. Mangayarkarasi, C. Vanmathi, Mohammad Zubair Khan, Abdulfattah Noorwali, Rachit Jain, Priyansh Agarwal
    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3363-3380, 2021, DOI:10.32604/cmc.2021.014991
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Urbanization affects the quality of the air, which has drastically degraded in the past decades. Air quality level is determined by measures of several air pollutant concentrations. To create awareness among people, an automation system that forecasts the quality is needed. The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India. The overall air quality index (AQI) at any particular time is given as the maximum band for any pollutant. PM2.5 is a fine particulate matter of a size less than 2.5 micrometers, the inhalation… More >

  • Open Access

    ARTICLE

    Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

    Saleh Albahli, Tahira Nazir, Aun Irtaza, Ali Javed
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More >

  • Open Access

    ARTICLE

    QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos

    Muhammad Arslan Usman, Muhammad Rehan Usman, Gandeva Bayu Satrya, Muhammad Ashfaq Khan, Christos Politis, Nada Philip, Soo Young Shin
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1697-1712, 2021, DOI:10.32604/cmc.2021.014696
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract With the advent in services such as telemedicine and telesurgery, provision of continuous quality monitoring for these services has become a challenge for the network operators. Quality standards for provision of such services are application specific as medical imagery is quite different than general purpose images and videos. This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy (WCE) videos containing bleeding regions. Bleeding regions in gastrointestinal tract have been focused in this research, as bleeding is one of the major reasons behind several diseases within the tract. The… More >

  • Open Access

    ARTICLE

    Performance of Lung Cancer Prediction Methods Using Different Classification Algorithms

    Yasemin Gültepe
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More >

  • Open Access

    ARTICLE

    Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

    Ameen Banjar, Zohair Ahmed, Ali Daud, Rabeeh Ayaz Abbasi, Hussain Dawood
    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2203-2225, 2021, DOI:10.32604/cmc.2021.014226
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if… More >

  • Open Access

    ARTICLE

    Identifying Driver Genes Mutations with Clinical Significance in Thyroid Cancer

    Hyeong Won Yu, Muhammad Afzal, Maqbool Hussain, Hyungju Kwon, Young Joo Park, June Young Choi, Kyu Eun Lee
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1241-1251, 2021, DOI:10.32604/cmc.2021.014910
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Advances in technology are enabling gene mutations in papillary thyroid carcinoma (PTC) to be analyzed and clinical outcomes, such as recurrence, to be predicted. To date, the most common genetic mutation in PTC is in BRAF kinase (BRAF). However, whether mutations in other genes coincide with those in BRAF remains to be clarified. The aim of this study was to find mutations in other genes that co-exist with mutated BRAF, and to analyze their frequency and clinical relevance in PTC. Clinical and genetic data were collected from 213 PTC patients with a total of 36,572 mutation sites in 735 genes.… More >

  • Open Access

    ARTICLE

    Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

    Arslan Musaddiq, Rashid Ali, Jin-Ghoo Choi, Byung-Seo Kim, Sung-Won Kim
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 799-814, 2021, DOI:10.32604/cmc.2021.014751
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic… More >

  • Open Access

    ARTICLE

    Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach

    Muhammad Ashfaq Khan, Yangwoo Kim
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 427-443, 2021, DOI:10.32604/cmc.2021.014682
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a… More >

  • Open Access

    ARTICLE

    A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything

    Masood Ahmad, Fasee Ullah, Ishtiaq Wahid, Atif Khan, M. Irfan Uddin, Abdullah Alharbi, Wael Alosaimi
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 321-336, 2021, DOI:10.32604/cmc.2021.014102
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Internet of Everything (IoE) indicates a fantastic vision of the future, where everything is connected to the internet, providing intelligent services and facilitating decision making. IoE is the collection of static and moving objects able to coordinate and communicate with each other. The moving objects may consist of ground segments and flying segments. The speed of flying segment e.g., Unmanned Ariel Vehicles (UAVs) may high as compared to ground segment objects. The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE (Ue-IoE). The routing maintenance overhead may increase when scaling the Ue-IoE (number of… More >

  • Open Access

    ARTICLE

    Authenblue: A New Authentication Protocol for the Industrial Internet of Things

    Rachid Zagrouba, Asayel AlAbdullatif, Kholood AlAjaji, Norah Al-Serhani, Fahd Alhaidari, Abdullah Almuhaideb, Atta-ur-Rahman
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1103-1119, 2021, DOI:10.32604/cmc.2021.014035
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract The Internet of Things (IoT) is where almost anything can be controlled and managed remotely by means of sensors. Although the IoT evolution led to quality of life enhancement, many of its devices are insecure. The lack of robust key management systems, efficient identity authentication, low fault tolerance, and many other issues lead to IoT devices being easily targeted by attackers. In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network (CPANs) in an Industrial IoT (IIoT) environment. This study proposed Authenblue protocol as a… More >

  • Open Access

    ARTICLE

    Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

    Atif Ishaq Khan, Syed Asad Raza Kazmi, Ayesha Atta, Muhammad Faheem Mushtaq, Muhammad Idrees, Ilyas Fakir, Muhammad Safyan, Muhammad Adnan Khan, Awais Qasim
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 519-528, 2021, DOI:10.32604/cmc.2021.013865
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach… More >

  • Open Access

    ARTICLE

    Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

    Mavra Mehmood, Ember Ayub, Fahad Ahmad, Madallah Alruwaili, Ziyad A. Alrowaili, Saad Alanazi, Mamoona Humayun, Muhammad Rizwan, Shahid Naseem, Tahir Alyas
    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

  • Open Access

    ARTICLE

    Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

    Jaiteg Singh, Tanya Gera, Farman Ali, Deepak Thakur, Karamjeet Singh, Kyung-sup Kwak
    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2655-2670, 2021, DOI:10.32604/cmc.2021.014504
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never… More >

  • Open Access

    ARTICLE

    A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

    Amir Haider, Muhammad Adnan Khan, Abdur Rehman, Muhib Ur Rahman, Hyung Seok Kim
    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1785-1798, 2021, DOI:10.32604/cmc.2020.013910
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection… More >

  • Open Access

    ARTICLE

    Hajj Crowd Management Using CNN-Based Approach

    Waleed Albattah, Muhammad Haris Kaka Khel, Shabana Habib, Muhammad Islam, Sheroz Khan, Kushsairy Abdul Kadir
    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2183-2197, 2021, DOI:10.32604/cmc.2020.014227
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Hajj as the Muslim holy pilgrimage, attracts millions of humans to Mecca every year. According to statists, the pilgrimage has attracted close to 2.5 million pilgrims in 2019, and at its peak, it has attracted over 3 million pilgrims in 2012. It is considered as the world’s largest human gathering. Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided. This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj. The image classification… More >

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