XUELIANG WU^1,#, SHAOYU GUAN^2,#, YONGGANG LU³, JUN XUE⁴, XIANGYANG YU¹, QI ZHANG^5,*, XIMO WANG^1,5,*, TIAN LI⁶

Oncology Research, Vol.31, No.2, pp. 125-139, 2023, DOI:10.32604/or.2023.028657 - 10 April 2023

Abstract This research aimed to explore the influence of Src homology-2 containing protein tyrosine phosphatase (SHP- 2) on the functions of tyrosine kinase receptors with immunoglobulin and EGF homology domains 2 (Tie2)-expressing monocyte/macrophages (TEMs) and the influence of the angiopoietin(Ang)/Tie2-phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) (Ang/Tie2-PI3K/Akt/mTOR) signaling pathway on the tumor microvascular remodeling in an immunosuppressive microenvironment. In vivo, SHP-2- deficient mice were used to construct colorectal cancer (CRC) liver metastasis models. SHP-2-deficient mice had significantly more metastatic cancer and inhibited nodules on the liver surface than wild-type mice, and the high-level expression… More > Graphic Abstract

Srikanth Cherukuvada, R. Kayalvizhi^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207 - 31 March 2023

Abstract The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of… More >

Senquan Yang¹, Fan Ding¹, Jianjun Liu¹, Pu Li^1,2, Songxi Hu^1,2,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3309-3323, 2023, DOI:10.32604/iasc.2023.035051 - 15 March 2023

Abstract Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix 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 >

Jin Wang¹, Zhen Liu^1,*, Ying Wang¹, Caifeng Wen^2,3, Jianwen Wang^2,3

Energy Engineering, Vol.120, No.5, pp. 1149-1162, 2023, DOI:10.32604/ee.2023.025209 - 20 February 2023

Abstract Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade, so as to ensure the safe and stable operation of wind turbine in natural environment. The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics. The strain signal contains a lot of noise, which makes the analysis error. Therefore, it is very important to denoise and extract features of measured signals before signal analysis. In this paper, the joint algorithm of ensemble empirical mode decomposition (EEMD) and wavelet… More >

Hoon Ko¹, Chul Park², Wu Seong Kang³, Yunyoung Nam⁴, Dukyong Yoon⁵, Jinseok Lee^1,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2369-2381, 2023, DOI:10.32604/csse.2023.035641 - 09 February 2023

Abstract Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For More >

Xiangwei Chen¹, Zhijin Zhao^1,2,*, Xueyi Ye¹, Shilian Zheng², Caiyi Lou², Xiaoniu Yang²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1385-1400, 2023, DOI:10.32604/csse.2023.034543 - 09 February 2023

Abstract Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning. In the second phase, the original dataset’s k-nearest… More >

Anwer Mustafa Hilal^1,*, Amal Al-Rasheed², Jaber S. Alzahrani³, Majdy M. Eltahir⁴, Mesfer Al Duhayyim⁵, Nermin M. Salem⁶, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1249-1263, 2023, DOI:10.32604/csse.2023.030603 - 03 November 2022

Abstract Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts’ knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC)… More >

V. Harshini¹, T. Dhanwin¹, A. Shahina^1,*, N. Safiyyah², A. Nayeemulla Khan²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1851-1867, 2023, DOI:10.32604/csse.2023.028136 - 03 November 2022

Abstract Biometrics, which has become integrated with our daily lives, could fall prey to falsification attacks, leading to security concerns. In our paper, we use Transient Evoked Otoacoustic Emissions (TEOAE) that are generated by the human cochlea in response to an external sound stimulus, as a biometric modality. TEOAE are robust to falsification attacks, as the uniqueness of an individual’s inner ear cannot be impersonated. In this study, we use both the raw 1D TEOAE signals, as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform (CWT). We use 1D and 2D… More >

Marwa Obayya¹, Nadhem NEMRI², Lubna A. Alharbi³, Mohamed K. Nour⁴, Mrim M. Alnfiai⁵, Mohammed Abdullah Al-Hagery⁶, Nermin M. Salem⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3151-3166, 2023, DOI:10.32604/cmc.2023.032765 - 31 October 2022

Abstract With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of… More >