Batyrkhan Omarov^1,2,*, Meirzhan Baikuvekov¹, Daniyar Sultan¹, Nurzhan Mukazhanov³, Madina Suleimenova², Maigul Zhekambayeva³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 341-359, 2024, DOI:10.32604/cmc.2024.052437 - 18 July 2024

Abstract This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model’s focus on critical segments… More >

Popuri Srinivasarao, Aravapalli Rama Satish^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1527-1547, 2023, DOI:10.32604/csse.2023.038951 - 28 July 2023

Abstract Responding to complex analytical queries in the data warehouse (DW) is one of the most challenging tasks that require prompt attention. The problem of materialized view (MV) selection relies on selecting the most optimal views that can respond to more queries simultaneously. This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs. The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique (ECHT). The constraints such as self-adaptive… More >

Shahid Mohammad Ganie¹, Pijush Kanti Dutta Pramanik², Majid Bashir Malik³, Anand Nayyar⁴, Kyung Sup Kwak^5,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244 - 03 April 2023

Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and… More >

Ushaa Eswaran^1,*, S. Anand²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1917-1928, 2023, DOI:10.32604/csse.2023.032523 - 09 February 2023

Abstract Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance… More >

J. Harikiran^1,*, B. Sai Chandana¹, B. Srinivasarao¹, B. Raviteja², Tatireddy Subba Reddy³

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2313-2331, 2023, DOI:10.32604/csse.2023.029689 - 21 December 2022

Abstract Software systems have grown significantly and in complexity. As a result of these qualities, preventing software faults is extremely difficult. Software defect prediction (SDP) can assist developers in finding potential bugs and reducing maintenance costs. When it comes to lowering software costs and assuring software quality, SDP plays a critical role in software development. As a result, automatically forecasting the number of errors in software modules is important, and it may assist developers in allocating limited resources more efficiently. Several methods for detecting and addressing such flaws at a low cost have been offered. These… More >

S. Subha^1,*, S. Baghavathi Priya²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2081-2093, 2023, DOI:10.32604/csse.2023.028451 - 01 August 2022

Abstract This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using min-max normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is… More >

K. Poongodi^1,*, A. Sabari²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038 - 08 February 2022

Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which 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 - 26 August 2021

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… More >

Hashem Alyami¹, Mohd Faizan², Wael Alosaimi³, Abdullah Alharbi³, Abhishek Kumar Pandey², Md Tarique Jamal Ansari⁴, Alka Agrawal², Raees Ahmad Khan^2,*

Computer Systems Science and Engineering, Vol.38, No.2, pp. 141-149, 2021, DOI:10.32604/csse.2021.017039 - 23 April 2021

Abstract The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online. The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it. To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets, this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets. In this work, we have used part-of-speech (PoS) tagged features in conjunction with n-gram models to construct the feature set for the ensemble model. We studied More >

Haikel Alhichri^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3581-3599, 2021, DOI:10.32604/cmc.2021.015399 - 01 March 2021

Abstract In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep… More >