Sandeep Kumar¹, MohdAnul Haq², Arpit Jain³, C. Andy Jason⁴, Nageswara Rao Moparthi¹, Nitin Mittal⁵, Zamil S. Alzamil^2,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1523-1540, 2023, DOI:10.32604/cmc.2023.028631

Abstract Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate… More >

Tran-Hieu Nguyen^*, Anh-Tuan Vu

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 429-458, 2023, DOI:10.32604/cmes.2022.020819

Abstract Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses that must be conducted. Building a surrogate model to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem. However, most existing surrogate models have been designed based on regression techniques. This paper proposes a novel method, called CaDE, which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution (DE) optimization. The proposed method is separated into two stages. During the first optimization stage, the… More >

P. Nithya^*, A. M. Kalpana

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3743-3755, 2023, DOI:10.32604/iasc.2023.030394

Abstract Agriculture plays a vital role in economic development. The major problem faced by the farmers are the selection of suitable crops based on environmental conditions such as weather, soil nutrients, etc. The farmers were following ancestral patterns, which could sometimes lead to the wrong selection of crops. In this research work, the feature selection method is adopted to improve the performance of the classification. The most relevant features from the dataset are obtained using a Probabilistic Feature Selection (PFS) approach, and classification is done using a Neural Fuzzy Classifier (NFC). Scaling Conjugate Gradient (SCG) optimization method is used to update… More >

S. J. K. Jagadeesh Kumar¹, P. Parthasarathi², Mehedi Masud³, Jehad F. Al-Amri⁴, Mohamed Abouhawwash^5,6,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2909-2924, 2023, DOI:10.32604/iasc.2023.030335

Abstract The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is evaluated with parametric metric measures… More >

P. P. Fathimathul Rajeena^1,*, R. Sivakumar²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2837-2855, 2023, DOI:10.32604/iasc.2023.030144

Abstract An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in recent years. This paper intends… More >

C. Santhosh Kumar¹, K. Vishnu Kumar^2,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2893-2907, 2023, DOI:10.32604/iasc.2023.029669

Abstract Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy and delay aware computational offloading… More >

P. Ganesan^1,*, S. Arockia Edwin Xavier²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2979-2996, 2023, DOI:10.32604/iasc.2023.029264

Abstract Typically, smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks. These vulnerabilities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems. Thus, for this purpose, Intrusion detection system (IDS) plays a pivotal part in offering a reliable and secured range of services in the smart grid framework. Several existing approaches are there to detect the intrusions in smart grid framework, however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of… More >

Mohammed Alarfaj¹, Mahwish Pervaiz², Yazeed Yasin Ghadi³, Tamara al Shloul⁴, Suliman A. Alsuhibany⁵, Ahmad Jalal⁶, Jeongmin Park^7,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2655-2671, 2023, DOI:10.32604/iasc.2023.027205

Abstract With the dramatic increase in video surveillance applications and public safety measures, the need for an accurate and effective system for abnormal/suspicious activity classification also increases. Although it has multiple applications, the problem is very challenging. In this paper, a novel approach for detecting normal/abnormal activity has been proposed. We used the Gaussian Mixture Model (GMM) and Kalman filter to detect and track the objects, respectively. After that, we performed shadow removal to segment an object and its shadow. After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method… More >

N. Karthikeyan², P. Sivaprakash^1,*, S. Karthik²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 671-685, 2023, DOI:10.32604/csse.2023.029984

Abstract Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats.… More >

M. Valavan, S. Rita^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 231-245, 2023, DOI:10.32604/csse.2023.026508

Abstract Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.… More >