Mahmood Alsaadi, Mohammed E. Seno^*, Mohammed I. Khalaf

Intelligent Automation & Soft Computing, Vol.40, pp. 29-52, 2025, DOI:10.32604/iasc.2024.060390 - 10 January 2025

Abstract The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, More >

Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >

XIANG LI^1,#, WENKE JIN^2,#, LIFENG WU², HUAN WANG¹, XIN XIE¹, WEI HUANG^1,*, BO LIU^2,*

Oncology Research, Vol.33, No.1, pp. 67-81, 2025, DOI:10.32604/or.2024.055921 - 20 December 2024

Abstract Background: Triple-negative breast cancer (TNBC), characterized by its lack of traditional hormone receptors and HER2, presents a significant challenge in oncology due to its poor response to conventional therapies. Autophagy is an important process for maintaining cellular homeostasis, and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors. In contrast to targeting protein activity, intervention with protein-protein interaction (PPI) can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways. Methods: Here, we employed Naive Bayes, Decision Tree, and k-Nearest Neighbors to elucidate the complex PPI… More >

S. Ramacharan^1,*, Martin Margala¹, Amjan Shaik², Prasun Chakrabarti³, Tulika Chakrabarti⁴

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3731-3760, 2024, DOI:10.32604/cmc.2024.058488 - 19 December 2024

Abstract Breast cancer remains a significant global health concern, with early detection being crucial for effective treatment and improved survival rates. This study introduces HERA-Net (Hybrid Extraction and Recognition Architecture), an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging modalities. The HERA-Net model integrates powerful deep learning architectures, including VGG19, U-Net, GRU (Gated Recurrent Units), and ResNet-50, to capture multi-dimensional features that support robust image segmentation, feature extraction, and temporal analysis. For thermographic imaging, a comprehensive dataset of 3534 infrared (IR) images from the… More >

Umapathi Krishnamoorthy^1,*, Shanmugam Jagan², Mohammed Zakariah³, Abdulaziz S. Almazyad^4,*, K. Gurunathan⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3903-3926, 2024, DOI:10.32604/cmc.2024.055910 - 19 December 2024

Abstract Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure… More >

Zhao Wang¹, Jun Liu¹, Xiaoying Qi², Chadur Venkatesan², Sharon Nai², David W. Rosen^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011890

Abstract Shape memory polymers (SMP) have many applications as actuators in soft robotics. However, predicting their shape change behavior is challenging, which makes designing suitable actuators difficult. For thermally stimulated shape memory polymers, constitutive models of shape change behavior show promise in enabling predictable shape changes, which is necessary for actuator design. These models are usually classified as either rheological or phase transition, with the former being more general, although non-physical in nature, and the latter being more physically significant [1]. Of interest in this work is 2-state shape change transitions for single-material actuators; that is,… More >

Peichao Xie^1,#, Kexu Chen^1,#, Yuxuan Ji¹, Qi Wang¹, Kaiyun Li^1,*, Fanlu Jia¹, Ting Peng²

International Journal of Mental Health Promotion, Vol.26, No.11, pp. 887-895, 2024, DOI:10.32604/ijmhp.2024.056305 - 28 November 2024

Abstract Background: Coping self-efficacy can help individuals mitigate the adverse emotional impacts of stress, anxiety, and other negative emotions, and it also influences individuals’ academic performance, including school adjustment and academic burnout. It is an important factor affecting the mental health of adolescents. However, there is no measurement tool specifically designed for adolescent populations in China. Therefore, the purpose of this study is to assess the applicability of the Coping Self-Efficacy Scale (CSES) among Chinese adolescents. Methods: In September 2023, this study collected data through online questionnaires and ultimately conducted item analysis, exploratory factor analysis, confirmatory… More >

Mohamed Karray^1,*, Nesrine Triki^2,*, Mohamed Ksantini²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562 - 18 July 2024

Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

Naif Al Mudawi^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4945-4962, 2024, DOI:10.32604/cmc.2024.048967 - 20 June 2024

Abstract Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply… More >

ZHILI ZHUO^#, DONGNI ZHANG^#, WENPING LU^*, XIAOQING WU, YONGJIA CUI, WEIXUAN ZHANG, MENGFAN ZHANG

Oncology Research, Vol.32, No.6, pp. 1093-1107, 2024, DOI:10.32604/or.2024.047257 - 23 May 2024

Abstract Breast cancer is the leading cause of cancer-related deaths in women worldwide, with Hormone Receptor (HR)+ being the predominant subtype. Tamoxifen (TAM) serves as the primary treatment for HR+ breast cancer. However, drug resistance often leads to recurrence, underscoring the need to develop new therapies to enhance patient quality of life and reduce recurrence rates. Artemisinin (ART) has demonstrated efficacy in inhibiting the growth of drug-resistant cells, positioning art as a viable option for counteracting endocrine resistance. This study explored the interaction between artemisinin and tamoxifen through a combined approach of bioinformatics analysis and experimental… More > Graphic Abstract