Mohammad Wedyan¹, Omar Elshaweesh², Enas Ramadan³, Ryan Alturki^4,*

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1189-1206, 2022, DOI:10.32604/csse.2022.024210

Abstract Big data is a term that refers to a set of data that, due to its largeness or complexity, cannot be stored or processed with one of the usual tools or applications for data management, and it has become a prominent word in recent years for the massive development of technology. Almost immediately thereafter, the term “big data mining” emerged, i.e., mining from big data even as an emerging and interconnected field of research. Classification is an important stage in data mining since it helps people make better decisions in a variety of situations, including scientific endeavors, biomedical research, and… More >

R. Gopi^1,*, S. Veena², S. Balasubramanian³, D. Ramya⁴, P. Ilanchezhian⁵, A. Harshavardhan⁶, Zatin Gupta⁷

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1215-1230, 2022, DOI:10.32604/iasc.2022.025792

Abstract In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN³ techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM)… More >

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Ibrahim Abunadi³, Fahd N. Al-Wesabi^2,4, Hadeel Alsolai⁵, Anwer Mustafa Hilal¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2581-2596, 2022, DOI:10.32604/cmc.2022.024764

Abstract Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique to choose an optimal subset… More >

D. Dhinakaran^1,*, P. M. Joe Prathap²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1877-1892, 2022, DOI:10.32604/iasc.2022.024509

Abstract These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals’ crude information. We devised two unique protocols for frequent mining itemsets in horizontally partitioned… More >

Majdy Mohamed Eltayeb Eltahir^1,*, Tarig Mohammed Ahmed^2,3

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1257-1272, 2022, DOI:10.32604/csse.2022.022942

Abstract In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing life expectancy, increasing urbanization and embracing Western lifestyles, the high prevalence of this cancer is noted in the developed world. This paper aims to develop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors to make the right decision in diagnosing breast cancer patients. The proposed model is based on three datasets to develop three sub-models. Each sub-model works independently. The final… More >

T. Viveka^1,*, C. Christopher Columbus², N. Senthil Velmurugan³

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 535-547, 2022, DOI:10.32604/iasc.2022.022976

Abstract Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of this study is to determine… More >

Surbhi Bhatia^*, Mohammed AlOjail

Computer Systems Science and Engineering, Vol.42, No.2, pp. 779-794, 2022, DOI:10.32604/csse.2022.022579

Abstract Opinion summarization recapitulates the opinions about a common topic automatically. The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text. The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining. This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using seq2seq model. It presents a… More >

Mesfer Al Duhayyim¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi⁴, Maram Alrajhi⁵, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1233-1247, 2022, DOI:10.32604/cmc.2022.021652

Abstract Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At the same time, in DM, the feature selection process becomes necessary to generate subset of features and can be solved by the use… More >

KATARINA BARALIć^1,*, KATARINA ŽIVANčEVIć¹, DRAGICA BoŽIĆ¹, DANYEL JENNEN², ALEKSANDRA BUHA DJORDJEVIC¹, EVICA ANTONIJEVIć MILJAKOVIć¹, DANIJELA ĐUKIć-ĆOSIć¹

BIOCELL, Vol.46, No.2, pp. 519-533, 2022, DOI:10.32604/biocell.2022.018271

Abstract This in silico toxicogenomic study aims to explore the relationship between phthalates and bisphenol A (BPA) co-exposure and obesity, as well as its comorbid conditions, in order to construct a possible set of genomic biomarkers. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) was used as the main data mining tool, along with GeneMania (https://genemania.org), ToppGene Suite (https://toppgene.cchmc.org) and DisGeNET (http://www.disgenet.org). Among the phthalates, bis(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) were chosen as the most frequently curated phthalates in CTD, which also share similar mechanisms of toxicity. DEHP, DBP and BPA interacted with 84, 90 and 194 obesity-related genes/proteins, involved in… More >

Sunil Kr. Jha^1,*, Zulfiqar Ahmad²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 567-594, 2021, DOI:10.32604/cmes.2021.016817

Abstract Health care data mining is noteworthy in disease diagnosis and recognition procedures. There exist several potentials to further improve the performance of machine learning based-classification methods in healthcare data analysis. The selection of a substantial subset of features is one of the feasible approaches to achieve improved recognition results of classification methods in disease diagnosis prediction. In the present study, a novel combined approach of feature generation using latent semantic analysis (LSA) and selection using ranker search (RAS) has been proposed to improve the performance of classification methods in lymph disease diagnosis prediction. The performance of the proposed combined approach… More >