Vineetha Mathai^*, P. Indumathi

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1729-1746, 2022, DOI:10.32604/iasc.2022.024234 - 24 March 2022

Abstract An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other… More >

P. R. Varshini^*, S. Baskar, M. Varatharajan, S. Sadhana

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1787-1799, 2022, DOI:10.32604/iasc.2022.024192 - 24 March 2022

Abstract

Flexibility and robust performance have made the FOPID (Fractional Order PID) controllers a better choice than PID (Proportional, Integral, Derivative) controllers. But the number of tuning parameters decreases the usage of FOPID controllers. Using synthetic data in available FOPID tuners leads to abnormal controller performances limiting their applicability. Hence, a new tuning methodology involving real-time data and overcomes the drawbacks of mathematical modeling is the need of the hour. This paper proposes a novel FOPID controller tuning methodology using machine learning algorithms. Feed Forward Back Propagation Neural Network (FFBPNN), Multi Least Squares Support Vector Regression

More >

J. Faritha Banu^1,*, S. B. Rajeshwari², Jagadish S. Kallimani², S. Vasanthi³, Ahmed Mateen Buttar⁴, M. Sangeetha⁵, Sanjay Bhargava⁶

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176 - 24 March 2022

Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is… More >

Joe Louis Paul Ignatius^*, Sasirekha Selvakumar

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1941-1959, 2022, DOI:10.32604/iasc.2022.024173 - 24 March 2022

Abstract In today’s digital environment, large volume of digital data is created daily and this data accumulates to unforeseen levels. Industries are finding it increasingly difficult to store data in an effective and trustworthy manner. Distributed storage appears to be the greatest approach for meeting current data storage demands at the moment. Furthermore, due to disc crashes or failures, efficient data recovery is becoming an issue. At present, new data storage techniques are required in order to restore data effectively even if some discs or servers are crashed. Hence, this proposed work aims to improve the… More >

B. Gomathi¹, B. Saravana Balaji², V. Krishna Kumar³, Mohamed Abouhawwash^4,5,*, Sultan Aljahdali⁶, Mehedi Masud⁶, Nina Kuchuk⁷

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1771-1785, 2022, DOI:10.32604/iasc.2022.024052 - 24 March 2022

Abstract Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model. However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment. Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance. In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in… More >

R. Saravana Ram¹, K. Venkatachalam², Mehedi Masud³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1639-1652, 2022, DOI:10.32604/iasc.2022.023962 - 24 March 2022

Abstract In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT… More >

M. Vinoth Kumar¹, K. Venkatachalam², Mehedi Masud³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1547-1559, 2022, DOI:10.32604/iasc.2022.023961 - 24 March 2022

Abstract Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim… More >

M. Gomathy Nayagam^1,*, K. Ramar², K. Venkatesh³, S. P. Raja⁴

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1485-1499, 2022, DOI:10.32604/iasc.2022.023953 - 24 March 2022

Abstract Moving object detection, classification and tracking are more crucial and challenging task in most of the computer vision and machine vision applications such as robot navigation, human behavior analysis, traffic flow analysis and etc. However, most of object detection and tracking algorithms are not suitable for real time processing and causes slower processing speed due to the processing and analyzing of high resolution video from high-end multiple cameras. It requires more computation and storage. To address the aforementioned problem, this paper proposes a way of parallel processing of temporal frame differencing algorithm for object detection More >

Ajina Mohamed Ameer^*, M. Victor Jose

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924 - 24 March 2022

Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in… More >

Iqra Abid¹, Sultan Almakdi², Hameedur Rahman³, Ahmed Almulihi⁴, Ali Alqahtani², Khairan Rajab^2,5, Abdulmajeed Alqhatani^2,*, Asadullah Shaikh²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1407-1421, 2022, DOI:10.32604/iasc.2022.023753 - 24 March 2022

Abstract Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment… More >