Muhammad Hasnain¹, Imran Ghani², Seung Ryul Jeong^3,*, Aitizaz Ali¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 327-339, 2022, DOI:10.32604/csse.2022.018300

Abstract This paper presents a review of the ensemble learning models proposed for web services classification, selection, and composition. Web service is an evolutionary research area, and ensemble learning has become a hot spot to assess web services’ earlier mentioned aspects. The proposed research aims to review the state of art approaches performed on the interesting web services area. The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as a research method. The study reveals an increasing trend of using ensemble learning in the chosen papers within the last ten years.… More >

A. Sumathi^*, S. Meganathan

Computer Systems Science and Engineering, Vol.40, No.1, pp. 313-325, 2022, DOI:10.32604/csse.2022.017484

Abstract Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels namely, format conversion, class labeling,… More >

Shraddha R. Khonde^1,2,*, Venugopal Ulagamuthalvi¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 37-51, 2022, DOI:10.32604/csse.2022.017130

Abstract Exchange of data in networks necessitates provision of security and confidentiality. Most networks compromised by intruders are those where the exchange of data is at high risk. The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network. Malicious activities are monitored and detected by the Intrusion Detection System (IDS) that operates with nodes connected to a distributed network. The IDS operates in two phases, where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest, Convolutional neural network,… More >

Qasim M. Zainel¹, Murad B. Khorsheed², Saad Darwish^3,*, Amr A. Ahmed⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3813-3828, 2021, DOI:10.32604/cmc.2021.014759

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 >

Muhammad Azam¹, Tanvir Ahmed¹, Rehan Ahmad², Ateeq Ur Rehman³, Fahad Sabah¹, Rao Muhammad Asif^4,*

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 853-867, 2021, DOI:10.32604/iasc.2021.019101

Abstract Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is… More >

Qin Zhang¹, Xuyu Xiang^1,*, Jiaohua Qin¹, Yun Tan¹, Qiang Liu¹, Neal N. Xiong²

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 227-241, 2021, DOI:10.32604/iasc.2021.017648

Abstract With the rapid development of network media, the short text has become the main cover of information dissemination by quickly disseminating relevant entity information. However, the lack of context in the short text can easily lead to ambiguity, which will greatly reduce the efficiency of obtaining information and seriously affect the user’s experience, especially in the financial field. This paper proposed an entity disambiguation algorithm based on multi-word vector ensemble and decision to eliminate the ambiguity of entities and purify text information in information processing. First of all, we integrate a variety of unsupervised pre-trained word vector models as vector… More >

Ahsan Aziz¹, Muhammad Attique¹, Usman Tariq², Yunyoung Nam^3,*, Muhammad Nazir¹, Chang-Won Jeong⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2653-2670, 2021, DOI:10.32604/cmc.2021.018606

Abstract Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of… More >

Prince Waqas Khan, Yung-Cheol Byun^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1893-1913, 2021, DOI:10.32604/cmc.2021.018523

Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating… More >

Abdul Majid¹, Muhammad Attique Khan¹, Yunyoung Nam^2,*, Usman Tariq³, Sudipta Roy⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 319-337, 2021, DOI:10.32604/cmc.2021.016816

Abstract The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the… More >

Naveen Kumar Seerangan^1,*, S. Vijayaragavan Shanmugam²

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 819-830, 2021, DOI:10.32604/cmc.2021.012135

Abstract Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations. Aspect extraction and sentiment extraction plays a vital role in identifying the root-causes. This paper proposes the Ensemble based temporal weighting and pareto ranking (ETP) model for Root-cause identification. Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model. The obtained aspects are validated and ranked using the proposed aspect weighing scheme. Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making. Experiments… More >