Jinghua Chen^1,#, Zihe Wang^2,#, Fabian Herold³, Alyx Taylor⁴, Jin Kuang¹, Ting Wang¹, Arthur F. Kramer^5,6, Liye Zou^1,*

International Journal of Mental Health Promotion, Vol.25, No.11, pp. 1209-1217, 2023, DOI:10.32604/ijmhp.2023.030539

Abstract A significant portion of emerging adults do not achieve recommended levels of physical activity (PA). Previous studies observed associations between features of emerging adulthood and PA levels, while the potential psychological mechanisms that might explain this phenomenon are not fully understood. In this context, there is some evidence that situated decisions toward physical activity (SDPA) and exercise-intensity tolerance might influence PA level. To provide empirical support for this assumption, the current study investigated whether (i) features of emerging adulthood are linked to SDPA, which, in turn, might affect PA engagement; (ii) exercise-intensity tolerance moderate the relationship between SDPA and PA… More >

JIN-HO CHOI, JOO DONG PARK, SEUNG HEE CHOI, EUN-SU KO, HYE JUNG JANG, KYUNG-SOON PARK^*

Oncology Research, Vol.32, No.1, pp. 127-138, 2024, DOI:10.32604/or.2023.042945

Abstract Purpose: Cancer cell metastasis is a multistep process, and the mechanism underlying extravasation remains unclear. ELK3 is a transcription factor that plays a crucial role in regulating various cellular processes, including cancer metastasis. Based on the finding that ELK3 promotes the metastasis of triple-negative breast cancer (TNBC), we investigated whether ELK3 regulates the extravasation of TNBC by forming the ELK3-ID4 axis. ID4 functions as a transcriptional regulator that interacts with other transcription factors, inhibiting their activity and subsequently influencing various biological processes associated with cell differentiation, survival, growth, and metastasis. Methods: We assessed the correlation between the expression of ELK3… More > Graphic Abstract

Muhammad Hood Khan¹, Bilal Khan^1,*, Saifullah Jan¹, Muhammad Imran Chughtai²

Journal on Big Data, Vol.5, pp. 41-56, 2023, DOI:10.32604/jbd.2022.044060

Abstract Author’s Profile (AP) may only be displayed as an article, similar to text collection of material, and must differentiate between gender, age, education, occupation, local language, and relative personality traits. In several information-related fields, including security, forensics, and marketing, and medicine, AP prediction is a significant issue. For instance, it is important to comprehend who wrote the harassing communication. In essence, from a marketing perspective, businesses will get to know one another through examining items and websites on the internet. Accordingly, they will direct their efforts towards a certain gender or age restriction based on the kind of individuals who… More >

Mohammed Abdalsalam^1,*, Chunlin Li¹, Abdelghani Dahou², Natalia Kryvinska³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1427-1467, 2024, DOI:10.32604/cmes.2023.029911

Abstract One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database… More >

Ahmed Abdu¹, Zhengjun Zhai^1,2, Hakim A. Abdo³, Redhwan Algabri^4,*, Sungon Lee^5,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 161-180, 2023, DOI:10.32604/cmc.2023.043680

Abstract Cross-project software defect prediction (CPDP) aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects. The existing CPDP approaches rely on static metrics or dynamic syntactic features, which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties, such as complex design patterns, relationships between multiple functions, and dependencies in different software projects, that are important for CPDP. This paper introduces a novel approach, a graph-based feature learning model for CPDP (GB-CPDP), that utilizes NetworkX to extract features and learn representations of program entities… More >

Wenkai Qin¹, Tianliang Lu^1,*, Lu Zhang², Shufan Peng¹, Da Wan¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417

Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details… More >

Arslan Akram^1,2, Javed Rashid^2,3,4, Fahima Hajjej⁵, Sobia Yaqoob^1,6, Muhammad Hamid⁷, Asma Irshad⁸, Nadeem Sarwar^9,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1081-1101, 2023, DOI:10.32604/cmc.2023.041558

Abstract Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this technique. The proposed method comprises… More >

Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721

Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to classify Android applications into malware… More >

Chengyu Mo^1,2, Fenlin Liu^1,2, Ma Zhu^1,2,*, Gengcong Yan³, Baojun Qi^1,2, Chunfang Yang^1,2

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540

Abstract The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.… More >

Bahera H. Nayef^1,*, Siti Norul Huda Sheikh Abdullah², Rossilawati Sulaiman², Ashwaq Mukred Saeed³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3549-3567, 2023, DOI:10.32604/cmc.2023.039528

Abstract Text extraction from images using the traditional techniques of image collecting, and pattern recognition using machine learning consume time due to the amount of extracted features from the images. Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results. This study proposes using Dual Maxpooling and concatenating convolution Neural Networks (CNN) layers with the activation functions Relu and the Optimized Leaky Relu (OLRelu). The proposed method works by dividing the word image into slices that contain characters. Then pass them to deep… More >