T. S. Kanchana^*, B. Smitha Evelin Zoraida

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2003-2013, 2023, DOI:10.32604/iasc.2023.029412

Abstract Human Facial expressions exhibits the inner personality. Evaluating the inner personality is performed through questionnaires during recruitment process. However, the evaluation through questionnaires performs less due to anxiety, and stress during interview and prediction of leadership quality becomes a challenging problem. To the above problem, Temporal sequence based SENet architecture (TSSA) is proposed for accurate evaluation of personality trait for employing the correct person for leadership position. Moreover, SENet is integration with modern architectures for performance evaluation. In Proposed TSSA, face book facial images of a particular person for a period of one month and face images collect from different… More >

Saranya Rajeshwaran^1,*, C. Agees Kumar², Kanthaswamy Ganapathy³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1611-1625, 2023, DOI:10.32604/iasc.2023.029299

Abstract PID controllers play an important function in determining tuning parameters in any process sector to deliver optimal and resilient performance for nonlinear, stable and unstable processes. The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller. The Direct Multi Search (DMS) algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model. A Metaheuristics Algorithm such as, SA (Simulated Annealing), MBBO (Modified… More >

V. Balaji, P. Selvaraj^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2015-2027, 2023, DOI:10.32604/iasc.2023.029260

Abstract Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge servers. It facilitates the benefits of the network edge with the maximized probability of offering interaction with the cloud. However, the fog computing characteristics are susceptible to counteract the challenges of security. The issues present with the Physical Layer Security (PLS) aspect in fog computing which included authentication, integrity, and confidentiality has been considered as a reason for the potential issues leading to the security breaches. In this work, the Octonion Algebra-inspired Non- Commutative Ring-based Fully Homomorphic Encryption Scheme… More >

D. Maincer^1,*, Y. Benmahamed², M. Mansour¹, Mosleh Alharthi³, Sherif S. M. Ghonein³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1957-1969, 2023, DOI:10.32604/iasc.2023.029210

Abstract In this paper, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based methods are to be applied on fault diagnosis in a robot manipulator. A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work. For both classifiers, the torque, the position and the speed of the manipulator have been employed as the input vector. However, it is to mention that a large database is needed and used for the training and testing phases. The SVM method used in this paper is based on the Gaussian… More >

Jazem Mutared Alanazi, Ahmad Ali AlZubi^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2289-2308, 2023, DOI:10.32604/iasc.2023.029181

Abstract Accounting Information System (AIS), which is the foundation of any enterprise resource planning (ERP) system, is often built as centralized system. The technologies that allow the Internet-of-Value, which is built on five aspects that are network, algorithms, distributed ledger, transfers, and assets, are based on blockchain. Cryptography and consensus protocols boost the blockchain platform implementation, acting as a deterrent to cyber-attacks and hacks. Blockchain platforms foster innovation among supply chain participants, resulting in ecosystem development. Traditional business processes have been severely disrupted by blockchains since apps and transactions that previously required centralized structures or trusted third-parties to authenticate them may… More >

Shyla^1,*, Vishal Bhatnagar²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2431-2447, 2023, DOI:10.32604/iasc.2023.029126

Abstract The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of… More >

G. Rajasekaran^1,*, J. Raja Sekar²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2399-2412, 2023, DOI:10.32604/iasc.2023.029119

Abstract Abnormal behavior detection is challenging and one of the growing research areas in computer vision. The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events. In this work, Pyramidal Lucas Kanade algorithm is optimized using EMEHOs to achieve the objective. First stage, OPLKT-EMEHOs algorithm is used to generate the optical flow from MIIs. Second stage, the MIIs optical flow is applied as input to 3 layer CNN for detect the abnormal crowd behavior. University of Minnesota (UMN) dataset is used to evaluate the proposed system. The experimental result shows… More >

S. Prabu, K. Joseph Abraham Sundar^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2071-2086, 2023, DOI:10.32604/iasc.2023.029105

Abstract Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain… More >

Thien Khai Tran¹, Kha Tu Huynh^2,*, Dac-Nhuong Le³, Muhammad Arif⁴, Hoa Minh Dinh¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2479-2491, 2023, DOI:10.32604/iasc.2023.029078

Abstract Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The… More >

Kanupriya Mittal^*, V. Mary Anita Rajam

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1907-1921, 2023, DOI:10.32604/iasc.2023.029037

Abstract An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the… More >