Shahid Ali Mahar¹, Mumtaz Hussain Mahar¹, Javed Ahmed Mahar¹, Mehedi Masud², Muneer Ahmad³, NZ Jhanjhi^4,*, Mirza Abdur Razzaq¹

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 183-197, 2021, DOI:10.32604/iasc.2021.015755

Abstract Speech signal analysis for the extraction of speech elements is viable in natural language applications. Rhythm, intonation, stress, and tone are the elements of prosody. These features are essential in emotional speech, speech to speech, speech recognition, and other applications. The current study attempts to extract the pitch and duration from historical Sindhi sound clips using the functional contours model’s superposition. The sampled sound clips contained the speech of 273 undergraduates living in 5 districts of the Sindhi province. Several Python libraries are available for the application of this model. We used these libraries for the extraction of prosodic data… More >

D. Venugopal¹, T. Jayasankar², Mohamed Yacin Sikkandar³, Mohamed Ibrahim Waly³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar^6,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2877-2893, 2021, DOI:10.32604/cmc.2021.015480

Abstract Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In… More >

Zheng Zhang^*, Shu Zhou

Journal of New Media, Vol.3, No.2, pp. 73-80, 2021, DOI:10.32604/jnm.2021.018923

Abstract The development of various applications based on social network text is in full swing. Studying text features and classifications is of great value to extract important information. This paper mainly introduces the common feature selection algorithms and feature representation methods, and introduces the basic principles, advantages and disadvantages of SVM and KNN, and the evaluation indexes of classification algorithms. In the aspect of mutual information feature selection function, it describes its processing flow, shortcomings and optimization improvements. In view of its weakness in not balancing the positive and negative correlation characteristics, a balance weight attribute factor and feature difference factor… More >

Masoud Muhammed Hassan^1,*, Haval Ismael Hussein¹, Adel Sabry Eesa¹, Ramadhan J. Mstafa^1,2

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1637-1659, 2021, DOI:10.32604/cmc.2021.016467

Abstract Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis… More >

Salisu Muhammed^*, Erbuğ Çelebi

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 397-416, 2021, DOI:10.32604/iasc.2021.016574

Abstract As the models with fewer operations help realize the performance of intelligent computing systems, we propose a novel deep network for DeepGait feature extraction with less operation for video sensor-based gait representation without dimension decomposition. The DeepGait has been known to have outperformed the hand-crafted representations, such as the frequency-domain feature (FDF), gait energy image (GEI), and gait flow image (GFI), etc. More explicitly, the channel-activated mapping network (CAMNet) is composed of three progressive triplets of convolution, batch normalization, max-pooling layers, and an external max pooling to capture the Spatio-temporal information of multiple frames in one gait period. We conducted… More >

Charles Arputham^1,*, Krishnaraj Nagappan², Lenin Babu Russeliah³, AdalineSuji Russeliah⁴

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 747-759, 2021, DOI:10.32604/iasc.2021.012077

Abstract Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram images that are highly sensitive… More >

Neelum Noreen^1,*, Sellapan Palaniappan¹, Abdul Qayyum², Iftikhar Ahmad³, Madini O. Alassafi³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3967-3982, 2021, DOI:10.32604/cmc.2021.014158

Abstract Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high… More >

Mustafa M. M. Al Qudah, Ahmad S. A. Mohamed^*, Syaheerah L. Lutfi

Computer Systems Science and Engineering, Vol.37, No.1, pp. 47-62, 2021, DOI:10.32604/csse.2021.015222

Abstract The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects due to its ability to measure the facial transient temperature, which is correlated with human affects and robustness against illumination changes. Therefore, studies have increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation of light conditions and revealing original human affect. Moreover, the thermal-based imaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and… More >

Abderrahmane Saidi^*, Abdelmouneim Moulay Lakhdar, Mohammed Beladgham

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2875-2889, 2021, DOI:10.32604/cmc.2021.013744

Abstract Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages. Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts. The present paper suggests a feature extraction technique for offline Arabic handwriting recognition. A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function (RBF) neural… More >

Diyar Qader Zeebaree¹, Adnan Mohsin Abdulazeez², Dilovan Asaad Zebari^3,*, Habibollah Haron⁴, Haza Nuzly Abdull Hamed⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314

Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level… More >