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 - 05 January 2022

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… More >

G. Maria Jones^1,*, S. Godfrey Winster², P. Valarmathie³

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 87-102, 2022, DOI:10.32604/iasc.2022.022972 - 05 January 2022

Abstract Rapid advancement of digital technology has encouraged its use in all aspects of life, including the workplace, education, and leisure. As technology advances, so does the number of users, which leads to an increase in criminal activity and demand for a cyber-crime investigation. Mobile phones have been the epicenter of illegal activity in recent years. Sensitive information is transferred due to numerous technical applications available at one’s fingertips, which play an essential part in cyber-crime attacks in the mobile environment. Mobile forensic is a technique of recovering or retrieving digital evidence from mobile devices so… More >

Mohd Anul Haq¹, Mohd Abdul Rahim Khan^1,*, Mohammed Alshehri²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 619-635, 2022, DOI:10.32604/iasc.2022.021430 - 05 January 2022

Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which… More >

Gulshan Kumar^1,*, Hamed Alqahtani²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1271-1307, 2022, DOI:10.32604/cmes.2022.018418 - 30 December 2021

Abstract Cancer is one of the most critical diseases that has caused several deaths in today’s world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual… More >

Adil Hussain Seh¹, Jehad F. Al-Amri², Ahmad F. Subahi³, Alka Agrawal¹, Nitish Pathak⁴, Rajeev Kumar^5,6,*, Raees Ahmad Khan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1387-1422, 2022, DOI:10.32604/cmes.2022.018163 - 30 December 2021

Abstract The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’ data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital… More >

Jiandong Huang¹, Jia Zhang¹, Yuan Gao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 805-821, 2022, DOI:10.32604/cmes.2022.017792 - 13 December 2021

Abstract

Pervious concrete (PC) is at risk of clogging due to the continuous blockage of sand into it during its service time. This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning techniques. Based on the 84 groups of the dataset developed in the earlier study, the clogging behavior of the PC was determined by the algorithm combing the SVM (support vector machines) and particle swarm optimization (PSO) methods. The PSO algorithm was employed to adjust the hyperparameters of the SVM and verify the performance using 10-fold cross-validation. The predicting results

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Ritam Sharma¹, Janki Ballabh Sharma¹, Ranjan Maheshwari¹, Praveen Agarwal^2,3,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 923-947, 2022, DOI:10.32604/cmes.2022.016065 - 13 December 2021

Abstract In this paper, a novel hybrid texture feature set and fractional derivative filter-based breast cancer detection model is introduced. This paper also introduces the application of a histogram of linear bipolar pattern features (HLBP) for breast thermogram classification. Initially, breast tissues are separated by masking operation and filtered by Grmwald–Letnikov fractional derivative-based Sobel mask to enhance the texture and rectify the noise. A novel hybrid feature set using HLBP and other statistical feature sets is derived and reduced by principal component analysis. Radial basis function kernel-based support vector machine is employed for detecting the abnormality… More >

Sourav Kumar Bhoi¹, Sanjaya Kumar Panda², Kalyan Kumar Jena¹, Kshira Sagar Sahoo³, N. Z. Jhanjhi^4,*, Mehedi Masud⁵, Sultan Aljahdali⁵

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1493-1507, 2022, DOI:10.32604/iasc.2022.022833 - 09 December 2021

Abstract Environment monitoring is an important area apart from environmental safety and pollution control. Such monitoring performed by the physical models of the atmosphere is unstable and inaccurate. Machine Learning (ML) techniques on the other hand are more robust in capturing the dynamics in the environment. In this paper, a novel approach is proposed to build a cost-effective standardized environment monitoring system (IoT-EMS) in volunteer computing environment. In volunteer computing, the volunteers (people) share their resources for distributed computing to perform a task (environment monitoring). The system is based on the Internet of Things and is… More >

J. Haritha^1,*, R. S. Valarmathi², M. Kalamani³

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1867-1880, 2022, DOI:10.32604/iasc.2022.022547 - 09 December 2021

Abstract Urea is the most common fertilizer used by the farmers. In this study, the variation of mid-infrared transmittance spectra with addition of urea in soil was studied for five different concentrations of urea. 150 gm of soil is taken and dried in a hot air oven for 5 h at 80°C and then samples are prepared by adding urea and water to it. The spectral signature of soil with urea is obtained by using an Infrared Spectrometer that reads the spectra in the mid infra-red region. The analysis is done using Partial Least Square Regression… More >

D. Arivudainambi¹, S. Mangairkarasi^1,*, K. A. Varun Kumar²

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1525-1540, 2022, DOI:10.32604/iasc.2022.020463 - 09 December 2021

Abstract Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce More >