Ierin Babu^1,*, R. MathuSoothana², S. Kumar²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791

Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately… More >

Bing Qu^*, Ping Liao, Yaolong Huang

Structural Durability & Health Monitoring, Vol.16, No.4, pp. 323-341, 2022, DOI:10.32604/sdhm.2022.021446

Abstract The method of time series analysis, applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior, stands out as a novel and viable research direction for bridge state assessment. However, outliers inevitably exist in the monitoring data due to various interventions, which reduce the precision of model fitting and affect the forecasting results. Therefore, the identification of outliers is crucial for the accurate interpretation of the monitoring data. In this study, a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory, and the forecasting… More >

R. Dhanya^1,*, R. Kalaiselvi²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1577-1593, 2023, DOI:10.32604/csse.2023.031319

Abstract A severe problem in modern information systems is Digital media tampering along with fake information. Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel. Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments. However, high complexity affects the developed methods. Presently, it is complicated to resolve the issue of the speed-accuracy trade-off. For tackling these challenges, this article put forward a quick and effective… More >

Hassen Sallay^*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 429-444, 2023, DOI:10.32604/iasc.2023.030791

Abstract Security breaches can seriously harm the Internet of Things (IoT) and Industrial IoT (IIoT) environments. The damage can exceed financial and material losses to threaten human lives. Overcoming these security risks is challenging given IoT ubiquity, complexity, and restricted resources. Security intrusion management is a cornerstone in fortifying the defensive security process. This paper presents an integrated multilayered framework facilitating the orchestration of the security intrusion management process and developing security decision support systems. The proposed framework incorporates four layers with four dedicated processing phases. This paper focuses mainly on the analytical layer. We present the architecture and models for… More >

R. Rajakumar^1,*, S. Sathiya Devi²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2101-2116, 2023, DOI:10.32604/iasc.2023.028889

Abstract Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices. The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest. At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important. This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique. Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal. In addition, Local Correlation Integral (LOCI)… More >

Khudhayr A. Rashedi^1,2,*, Mohd Tahir Ismail¹, Abdeslam Serroukh³, S. Al wadi⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3835-3847, 2022, DOI:10.32604/cmc.2022.026476

Abstract We introduce a new wavelet based procedure for detecting outliers in financial discrete time series. The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models. We apply the Maximal-Overlap Discrete Wavelet Transform (MODWT) to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers. Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform (DWT). The series sample size does not need to be a power of 2 and the… More >

Amgad Muneer^1,2,*, Shakirah Mohd Taib^1,2, Suliman Mohamed Fati³, Abdullateef O. Balogun¹, Izzatdin Abdul Aziz^1,2

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5363-5381, 2022, DOI:10.32604/cmc.2022.021113

Abstract Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a… More >

Farzaneh Khorasani¹, Morteza Mohammadi Zanjireh^1,*, Mahdi Bahaghighat¹, Qin Xin²

Computer Systems Science and Engineering, Vol.40, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.016003

Abstract With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected… More >

Ahmad A. A. Alkhatib^*, Qusai Abed-Al

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461

Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the proposed OD-DSAE model is to… More >