R. Bhaskaran¹, R. Karuppathal¹, M. Karthick², J. Vijayalakshmi³, Seifedine Kadry⁴, Yunyoung Nam^5,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1593-1606, 2022, DOI:10.32604/iasc.2022.024902

Abstract Industrial Internet of Things (IIoT) and Industry 4.0/5.0 offer several interconnections between machinery, equipment, processes, and personnel in diverse application areas namely logistics, supply chain, manufacturing, transportation, and healthcare. The conventional security-based solutions in IIoT environment get degraded due to the third parties. Therefore, the recent blockchain technology (BCT) can be employed to resolve trust issues and eliminate the need for third parties. Therefore, this paper presents a novel blockchain enabled secure optimal lightweight cryptography based image encryption (BC-LWCIE) technique for industry 4.0 environment. In addition, the BC-LWCIE technique involves the design of an optimal LWC based hash function with… More >

P. E. Irin Dorathy^1,*, M. Chandrasekaran²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1423-1438, 2022, DOI:10.32604/iasc.2022.024815

Abstract In this paper, an Ant Colony Optimization (ACO) based Energy Efficient Shortest Path Routing (AESR) algorithm is developed for Mobile Ad hoc Network (MANET). The Mobile Ad hoc Network consists of a group of mobile nodes that can communicate with each other without any predefined infrastructure. The routing process is critical for this type of network due to its dynamic topology, limited resources and wireless channel. The technique incorporated in this paper for optimizing the routing in a Mobile ad hoc network is Ant Colony Optimization. The ACO algorithm is used to solve network problems related to routing, security, etc.… More >

S. Priyadarsini^1,*, Carlos Andrés Tavera Romero², M. Mrunalini³, Ganga Rama Koteswara Rao⁴, Sudhakar Sengan⁵

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1517-1530, 2022, DOI:10.32604/iasc.2022.024786

Abstract A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region… More >

Worakamol Wisetsri¹, Shamimul Qamar², Gaurav Verma^3,*, Deval Verma⁴, Varun Kumar Kakar⁵, Thanyanant Chansongpol⁶, Chanyanan Somtawinpongsai⁶, Chai Ching Tan⁷

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1473-1483, 2022, DOI:10.32604/iasc.2022.024610

Abstract Industry 4.0 is considered as the fourth revolution in industrial sector that represents the digitization of production process in a smarter way. Industry 4.0 refers to the intelligent networking of machines, their processes, and infrastructure, as well as the use of information and computer technology to transform industry. The technologies like industrial internet of things (IIoT), big data analytics, cloud computing, augmented reality and cyber security are the main pillars of industry 4.0. Industry 4.0, in particular, is strongly reliant on the IIoT that refers to the application of internet of things (IoT) in industrial sector like smart grids (SG).… 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 >

D. Devina Merin^1,*, P. Jagatheeswari²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1849-1860, 2022, DOI:10.32604/iasc.2022.024495

Abstract The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the networking system and the DL… More >

V. Rajalakshmi^1,*, S. Ganesh Vaidyanathan²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1335-1348, 2022, DOI:10.32604/iasc.2022.024310

Abstract Traffic flow forecasting is the need of the hour requirement in Intelligent Transportation Systems (ITS). Various Artificial Intelligence Frameworks and Machine Learning Models are incorporated in today’s ITS to enhance forecasting. Tuning the model parameters play a vital role in designing an efficient model to improve the reliability of forecasting. Hence, the primary objective of this research is to propose a novel hybrid framework to tune the parameters of Multilayer Perceptron (MLP) using the Swarm Intelligence technique called Particle Swarm Optimization (PSO). The proposed MLP-PSO framework is designed to adjust the weights and bias parameters of MLP dynamically using PSO… More >

Vineetha Mathai^*, P. Indumathi

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

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 fading models considered. To accomplish… 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

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 (MLSSVR) chosen to design Machine… 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

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 a huge difficulty, and as… More >