Special Issue "Emerging Applications of Artificial Intelligence, Machine learning and Data Science"

Submission Deadline: 25 October 2021 (closed)

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  ARTICLE
• Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation
• Walid Aydi^1,3,*, Fuad S. Alduais^2,4
• CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4033-4050, 2022, DOI:10.32604/cmc.2022.023119
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases… More
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• Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion
• Abhishek Kumar Shukla^*, Sujoy Das
• CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3557-3570, 2022, DOI:10.32604/cmc.2022.022411
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the… More
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  ARTICLE
• A Study on Classification and Detection of Small Moths Using CNN Model
• Sang-Hyun Lee^*
• CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1987-1998, 2022, DOI:10.32604/cmc.2022.022554
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification… More
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  ARTICLE
• Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing
• Jianbo Xu, Wenxi Chen^*
• CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1807-1819, 2022, DOI:10.32604/cmc.2022.021154
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. The R peaks were… More
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  ARTICLE
• Estimating Usable-Security Through Hesitant Fuzzy Linguistic Term Sets Based Technique
• Abdulaziz Attaallah¹, Raees Ahmad Khan^2,*
• CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5683-5705, 2022, DOI:10.32604/cmc.2022.021643
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The apparent contradiction between usability and security has been discussed in the literature for several years. This continuous trade-off requires be acknowledging and handling whenever security solutions are introduced. However, some progressive analysts point out that present security solutions are usually very difficult for several users, and they have expressed a willingness to simplify the security product user experience. Usable security is still mostly unexplored territory in computer science. Which we are all aware with security and usability on many levels, usable security has received little operational attention. Companies have recently focused primarily on usable security. As consumers prefer to… More
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  ARTICLE
• Intelligent Multilevel Node Authentication in Mobile Computing Using Clone Node
• Neha Malhotra^1,2,*, Manju Bala³
• CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5269-5284, 2022, DOI:10.32604/cmc.2022.020920
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Nodes in a mobile computing system are vulnerable to clone attacks due to their mobility. In such attacks, an adversary accesses a few network nodes, generates replication, then inserts this replication into the network, potentially resulting in numerous internal network attacks. Most existing techniques use a central base station, which introduces several difficulties into the system due to the network's reliance on a single point, while other ways generate more overhead while jeopardising network lifetime. In this research, an intelligent double hashing-based clone node identification scheme was used, which reduces communication and memory costs while performing the clone detection procedure.… More
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• Emerging Applications of Artificial Intelligence, Machine learning and Data Science
• Dharmendra Dangi^*, Amit Bhagat, Dheeraj Kumar Dixit
• CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5399-5419, 2022, DOI:10.32604/cmc.2022.020431
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites. Today, customers throughout the world share their points of view on all kinds of topics through these sources. The massive volume of data created by these customers makes it impossible to analyze such data manually. Therefore, an efficient and intelligent method for evaluating social media data and their divergence needs to be developed. Today, various types of equipment and techniques are available for automatically estimating the classification of sentiments. Sentiment analysis involves determining people's emotions using facial expressions. Sentiment analysis can… More
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  ARTICLE
• Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics
• Nebojsa Bacanin¹, Khaled Alhazmi^2,*, Miodrag Zivkovic¹, K. Venkatachalam³, Timea Bezdan¹, Jamel Nebhen⁴
• CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4199-4215, 2022, DOI:10.32604/cmc.2022.020449
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing… More
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• Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG
• Akanksha Tiwari¹, Ram Bilas Pachori^1,2, Premjit Khanganba Sanjram^1,3,4,*
• CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2347-2363, 2022, DOI:10.32604/cmc.2022.020299
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract In performing a gaming task, mental rotation (MR) is one of the important aspects of visuospatial processing. MR involves dorsal-ventral pathways of the brain. Visual objects/models used in computer-games play a crucial role in gaming experience of the users. The visuospatial characteristics of the objects used in the computer-game influence the engagement of dorsal-ventral visual pathways. The current study investigates how the objects’ visuospatial characteristics (i.e., angular disparity and dimensionality) in an MR-based computer-game influence the cortical activities in dorsal-ventral visual pathways. Both the factors have two levels, angular disparity: convex angle (CA) vs. reflex angle (RA) and dimensionality: 2D… More
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• Generating Synthetic Data to Reduce Prediction Error of Energy Consumption
• Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun^*
• CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a technique used for increasing the… More
•   Views:262       Downloads:253        Download PDF

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  ARTICLE
• DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code
• Walaa Gad^1,*, Anas Alokla¹, Waleed Nazih², Mustafa Aref¹, Abdel-badeeh Salem¹
• CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3117-3132, 2022, DOI:10.32604/cmc.2022.019884
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language… More
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• Deep Learning Approach for Analysis and Characterization of COVID-19
• Indrajeet Kumar¹, Sultan S. Alshamrani², Abhishek Kumar³, Jyoti Rawat⁴, Kamred Udham Singh¹, Mamoon Rashid^5,*, Ahmed Saeed AlGhamdi⁶
• CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 451-468, 2022, DOI:10.32604/cmc.2022.019443
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images… More
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  ARTICLE
• A Hybrid Approach for Network Intrusion Detection
• Mavra Mehmood¹, Talha Javed², Jamel Nebhen³, Sidra Abbas^2,*, Rabia Abid¹, Giridhar Reddy Bojja⁴, Muhammad Rizwan¹
• CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 91-107, 2022, DOI:10.32604/cmc.2022.019127
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination… More
•   Views:503       Downloads:496        Download PDF

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• A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data
• Harshita Patel¹, Dharmendra Singh Rajput^1,*, Ovidiu Petru Stan², Liviu Cristian Miclea²
• CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 73-89, 2022, DOI:10.32604/cmc.2022.017114
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify… More
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  ARTICLE
• Empirical Assessment of Bacillus Calmette-Guérin Vaccine to Combat COVID-19
• Nikita Jain¹, Vedika Gupta^1,*, Chinmay Chakraborty², Agam Madan¹, Deepali Virmani³, Lorenzo Salas-Morera⁴, Laura Garcia-Hernandez⁴
• CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 213-231, 2022, DOI:10.32604/cmc.2022.016424
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract COVID-19 has become one of the critical health issues globally, which surfaced first in latter part of the year 2019. It is the topmost concern for many nations’ governments as the contagious virus started mushrooming over adjacent regions of infected areas. In 1980, a vaccine called Bacillus Calmette-Guérin (BCG) was introduced for preventing tuberculosis and lung cancer. Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory. This paper’s initial research shows that the countries with a long-term compulsory BCG vaccination system are less affected by… More
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• Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram
• Vinay Arora¹, Karun Verma¹, Rohan Singh Leekha², Kyungroul Lee³, Chang Choi^4,*, Takshi Gupta⁵, Kashish Bhatia⁶
• CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4151-4168, 2021, DOI:10.32604/cmc.2021.019178
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual… More
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• Robust and Efficient Reliability Estimation for Exponential Distribution
• Muhammad Aslam Mohd Safari¹, Nurulkamal Masseran^2,*, Muhammad Hilmi Abdul Majid²
• CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2807-2824, 2021, DOI:10.32604/cmc.2021.018815
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract In modeling reliability data, the exponential distribution is commonly used due to its simplicity. For estimating the parameter of the exponential distribution, classical estimators including maximum likelihood estimator represent the most commonly used method and are well known to be efficient. However, the maximum likelihood estimator is highly sensitive in the presence of contamination or outliers. In this study, a robust and efficient estimator of the exponential distribution parameter was proposed based on the probability integral transform statistic. To examine the robustness of this new estimator, asymptotic variance, breakdown point, and gross error sensitivity were derived. This new estimator offers… More
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• Towards Machine Learning Based Intrusion Detection in IoT Networks
• Nahida Islam¹, Fahiba Farhin¹, Ishrat Sultana¹, M. Shamim Kaiser¹, Md. Sazzadur Rahman¹, Mufti Mahmud², A. S. M. Sanwar Hosen³, Gi Hwan Cho^3,*
• CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1801-1821, 2021, DOI:10.32604/cmc.2021.018466
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The Internet of Things (IoT) integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention. In recent years, IoT based systems have been used in improving the experience in many applications including healthcare, agriculture, supply chain, education, transportation and traffic monitoring, utility services etc. However, node heterogeneity raised security concern which is one of the most complicated issues on the IoT. Implementing security measures, including encryption, access control, and authentication for the IoT devices are ineffective in achieving security. In this paper, we identified various types of IoT threats and shallow (such as decision… More
•   Views:1425       Downloads:1130        Download PDF

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• An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease
• Pushpendra Kumar^1,2,*, Ramjeevan Singh Thakur³
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.016957
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this paper, a model has been… More
•   Views:707       Downloads:718        Download PDF

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• Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction
• Kathiravan Srinivasan¹, Lalit Garg^2,*, Debajit Datta³, Abdulellah A. Alaboudi⁴, N. Z. Jhanjhi⁵, Rishav Agarwal³, Anmol George Thomas¹
• CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4109-4124, 2021, DOI:10.32604/cmc.2021.016736
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research… More
•   Views:921       Downloads:823        Download PDF

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• Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model
• Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan¹, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵
• CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2413-2429, 2021, DOI:10.32604/cmc.2021.016698
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data… More
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• Multi Sensor-Based Implicit User Identification
• Muhammad Ahmad^1,*, Rana Aamir Raza², Manuel Mazzara³, Salvatore Distefano⁴, Ali Kashif Bashir⁵, Adil Khan³, Muhammad Shahzad Sarfraz¹, Muhammad Umar Aftab¹
• CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1673-1692, 2021, DOI:10.32604/cmc.2021.016232
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner’s personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors. A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized… More
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