Mrim M. Alnfiai^1,2, Nabil Almalki^1,3, Fahd N. Al-Wesabi^4,*, Mesfer Alduhayyem⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2639-2652, 2023, DOI:10.32604/iasc.2023.034069 - 15 March 2023

Abstract Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers… More >

Aqsa Aslam, Saima Farhan^*, Momina Abdul Khaliq, Fatima Anjum, Ayesha Afzaal, Faria Kanwal

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2607-2622, 2023, DOI:10.32604/iasc.2023.034041 - 15 March 2023

Abstract Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique. The increase in retinal diseases is alarming as it may lead to permanent blindness if left untreated. Automation of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also. Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted feature selection or binary classification. This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research,… More >

Najm Alotaibi¹, Badriyya B. Al-onazi², Mohamed K. Nour³, Abdullah Mohamed⁴, Abdelwahed Motwakel^5,*, Gouse Pasha Mohammed⁵, Ishfaq Yaseen⁵, Mohammed Rizwanullah⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3121-3137, 2023, DOI:10.32604/iasc.2023.033915 - 15 March 2023

Abstract Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in… More >

Jin Wang^1,2, Yongsong Zou¹, Se-Jung Lim^3,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2743-2755, 2023, DOI:10.32604/iasc.2023.033869 - 15 March 2023

Abstract Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel… More >

Ierin Babu^1,*, R. MathuSoothana², S. Kumar²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791 - 15 March 2023

Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model,… More >

Bowen Zhang^1,2,3, Yanbo Shi⁴, Jianming Zhao^1,2,3,*, Tianyu Wang^1,2,3, Kaidi Wang⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2703-2717, 2023, DOI:10.32604/iasc.2023.033762 - 15 March 2023

Abstract Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the… More >

Umashankar Ganesan^1,*, A. Vimala Juliet², R. Amala Jenith Joshi³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.033759 - 15 March 2023

Abstract Brain signal analysis plays a significant role in attaining data related to motor activities. The parietal region of the brain plays a vital role in muscular movements. This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements; perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm. This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease (PD).… More >

P. Kavitha Rani¹, Hee-Kwon Chae², Yunyoung Nam^2,*, Mohamed Abouhawwash^3,4

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2591-2605, 2023, DOI:10.32604/iasc.2023.033697 - 15 March 2023

Abstract Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data. WSNs use sensor nodes (SNs) to collect and transmit data. However, the power supplied by the sensor network is restricted. Thus, SNs must store energy as often as to extend the lifespan of the network. In the proposed study, effective clustering and longer network lifetimes are achieved using multi-swarm optimization (MSO) and game theory based on locust search (LS-II). In this research, MSO is used to improve the… More >

Mesfer Al Duhayyim^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2473-2489, 2023, DOI:10.32604/iasc.2023.033585 - 15 March 2023

Abstract Falls are the most common concern among older adults or disabled people who use scooters and wheelchairs. The early detection of disabled persons’ falls is required to increase the living rate of an individual or provide support to them whenever required. In recent times, the arrival of the Internet of Things (IoT), smartphones, Artificial Intelligence (AI), wearables and so on make it easy to design fall detection mechanisms for smart homecare. The current study develops an Automated Disabled People Fall Detection using Cuckoo Search Optimization with Mobile Networks (ADPFD-CSOMN) model. The proposed model’s major aim… More >

Xiaorui Zhang^1,2,3,4,*, Yingying Wang¹, Wei Sun^4,5, Qi Cui⁶, Xindong Wei⁷

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3057-3071, 2023, DOI:10.32604/iasc.2023.033578 - 15 March 2023

Abstract Signature verification, which is a method to distinguish the authenticity of signature images, is a biometric verification technique that can effectively reduce the risk of forged signatures in financial, legal, and other business environments. However, compared with ordinary images, signature images have the following characteristics: First, the strokes are slim, i.e., there is less effective information. Second, the signature changes slightly with the time, place, and mood of the signer, i.e., it has high intraclass differences. These challenges lead to the low accuracy of the existing methods based on convolutional neural networks (CNN). This study… More >