Guest Editors
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Mohammad Ayoub Khan, University of Bisha, Saudi Arabia.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.
Dr. Kapal Dev, CONNECT Centre, Trinity College Dublin, Ireland.
Summary
Data Science is gaining tremendous popularity in cyber world. Currently It is very active topic and has extensive scope, both in term of theory and applications. It has an enormous effect on improving business productivity and performance. Data science can be defined as an interdisciplinary field involving techniques to collect, store, analyze, manage and publish data.
Machine Learning is one of the core components of its foundation, which addressed the different important challenges of data science by using different innovative machine learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the integration between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science.
Keywords
• Data science and analytics
• Data mining and big data analysis
• Intelligent systems
• Machine and deep learning
Published Papers
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Open Access
An Access Control Scheme Using Heterogeneous Signcryption for IoT Environments
Insaf Ullah, Hira Zahid
, Fahad Algarni, Muhammad Asghar Khan
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4307-4321, 2022, DOI:10.32604/cmc.2022.017380
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature,… More >
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Open Access
ARTICLE
Graphical Transformation of OWL Ontologies to Event-B Formal Models
Eman H. Alkhammash
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3733-3750, 2022, DOI:10.32604/cmc.2022.015987
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract Formal methods use mathematical models to develop systems. Ontologies are formal specifications that provide reusable domain knowledge representations. Ontologies have been successfully used in several data-driven applications, including data analysis. However, the creation of formal models from informal requirements demands skill and effort. Ambiguity, inconsistency, imprecision, and incompleteness are major problems in informal requirements. To solve these problems, it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications. The purpose of this paper is to present an approach that addresses this challenge by using the Web Ontology Language (OWL) to construct Event-B formal…
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Open Access
ARTICLE
A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management
Jari Jussila, Eman Alkhammash, Norah Saleh Alghamdi, Prashanth Madhala, Mohammad Ayoub Khan
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1845-1856, 2022, DOI:10.32604/cmc.2022.017284
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’ stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and arousal to elicit customer insights.…
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Open Access
ARTICLE
ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network
Sundresan Perumal, Mujahid Tabassum, Ganthan Narayana, Suresh Ponnan, Chinmay Chakraborty, Saju Mohanan, Zeeshan Basit, Mohammad Tabrez Quasim
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1447-1462, 2021, DOI:10.32604/cmc.2021.014854
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract A wireless sensor network (WSN) consists of several tiny sensor nodes to monitor, collect, and transmit the physical information from an environment through the wireless channel. The node failure is considered as one of the main issues in the WSN which creates higher packet drop, delay, and energy consumption during the communication. Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets. In this paper, Artificial Neural Network (ANN) based Node Failure Detection (NFD) is developed with cognitive radio for detecting the location of the node failure. The
ad hoc on-demand distance vector (AODV)…
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Open Access
ARTICLE
A Novel Method Based on UNET for Bearing Fault Diagnosis
Dileep Kumar Soother, Imtiaz Hussain Kalwar, Tanweer Hussain, Bhawani Shankar Chowdhry, Sanaullah Mehran Ujjan, Tayab Din Memon
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 393-408, 2021, DOI:10.32604/cmc.2021.014941
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model is applied to the 2D…
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Open Access
ARTICLE
Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN
Sehar Shahzad Farooq, Mustansar Fiaz, Irfan Mehmood, Ali Kashif Bashir, Raheel Nawaz, KyungJoong Kim, Soon Ki Jung
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4087-4108, 2021, DOI:10.32604/cmc.2021.015612
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In…
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Open Access
ARTICLE
Computer Vision-Control-Based CNN-PID for Mobile Robot
Rihem Farkh, Mohammad Tabrez Quasim, Khaled Al jaloud, Saad Alhuwaimel, Shams Tabrez Siddiqui
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1065-1079, 2021, DOI:10.32604/cmc.2021.016600
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed…
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Open Access
REVIEW
A Comprehensive Review on Medical Diagnosis Using Machine Learning
Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali AlZubi, Ali Kashif Bashir, Nikita
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1997-2014, 2021, DOI:10.32604/cmc.2021.014943
（This article belongs to this Special Issue:
Machine Learning for Data Analytics)
Abstract The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine…
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