Minxi Rong¹, Yong Li^1,*, Xiaoli Guo^1,*, Tao Zong², Zhiyuan Ma², Penglei Li²

Oncologie, Vol.24, No.2, pp. 309-327, 2022, DOI:10.32604/oncologie.2022.021256

Abstract Objectives: The ERα biological activity prediction model is constructed by the compound molecular data of the anti-breast cancer therapeutic target ERα and its biological activity data, which improves the screening efficiency of anti-breast cancer drug candidates and saves the time and cost of drug development. Methods: In this paper, Ridge model is used to screen out molecular descriptors with a high degree of influence on the biological activity of Erα and divide datasets with different numbers of the molecular descriptors by screening results. Random Forest (RF) is trained by Root Mean Square Error (RMSE) and Coefficient of determination (R²) to… More >

Wen Yee Wong¹, Ayman Khallel Ibrahim Al-Ani¹, Khairunnisa Hasikin^1,*, Anis Salwa Mohd Khairuddin², Sarah Abdul Razak³, Hanee Farzana Hizaddin⁴, Mohd Istajib Mokhtar⁵, Muhammad Mokhzaini Azizan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 1011-1038, 2022, DOI:10.32604/cmes.2022.019244

Abstract Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH₃-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features… More >

Hadil Shaiba¹, Radwa Marzouk², Mohamed K Nour³, Noha Negm^4,5, Anwer Mustafa Hilal^6,*, Abdullah Mohamed⁷, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶, Mohammed Rizwanullah⁶

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3367-3382, 2022, DOI:10.32604/cmc.2022.030067

Abstract The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues, this paper proposes an enhanced… More >

Tao Li¹, Di Wu¹, Rui Han², Jinyue Xia³, Yongjun Ren^4,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3721-3739, 2022, DOI:10.32604/cmc.2022.029619

Abstract As an important maritime hub, Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter, which greatly affects the socio-economic and development of the region. Therefore, this paper uses FY-4A (a weather satellite) data to study sea ice in the Bohai Sea. After processing the data for land removal and cloud detection, it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions. The random forests classification algorithm is introduced in sea ice identification, which can achieve a certain effect of… More >

Majida Kazmi^1,*, Hashim Raza Khan^1,2, Lubaba², Mohammad Hashir Bin Khalid², Saad Ahmed Qazi^1,2

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2351-2370, 2022, DOI:10.32604/cmc.2022.026265

Abstract Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers’ interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology… More >

G. Logeswari^*, S. Bose, T. Anitha

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 867-880, 2023, DOI:10.32604/iasc.2023.026769

Abstract Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flexibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with substantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on organizations, enterprises, and even economies. Accuracy, high performance, and real-time systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System (IDS) has stimulated… More >

Polin Rahman¹, Ahmed Rifat¹, MD. IftehadAmjad Chy¹, Mohammad Monirujjaman Khan^1,*, Mehedi Masud², Sultan Aljahdali²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.021469

Abstract Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and CatBoost are… More >

Miao Xu¹, Bo Zhu^1,*, Chunmei Chen¹, Yuwei Wan²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1707-1722, 2022, DOI:10.32604/cmc.2022.027708

Abstract It is not uncommon that two or more related process quality characteristics are needed to be monitored simultaneously in production process for most of time. Meanwhile, the observations obtained online are often serially autocorrelated due to high sampling frequency and process dynamics. This goes against the statistical I.I.D assumption in using the multivariate control charts, which may lead to the performance of multivariate control charts collapse soon. Meanwhile, the process control method based on pattern recognition as a non-statistical approach is not confined by this limitation, and further provide more useful information for quality practitioners to locate the assignable causes… More >

Heba A. Elzeheiry^*, Sherief Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1283-1305, 2022, DOI:10.32604/cmc.2022.027345

Abstract Electronic Health Records (EHRs) are the digital form of patients’ medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient with different datasets. The MDRL… More >

Liang Xie^1,2,5, Peihao Peng^1,*, Haijun Wang^1,3, Shengbin Chen⁴

Phyton-International Journal of Experimental Botany, Vol.91, No.9, pp. 2069-2086, 2022, DOI:10.32604/phyton.2022.018807

Abstract Identifying the ecological environment suitable for the growth of Thuja sutchuenensis and predicting other potential distribution areas are essential to protect this endangered species. After selecting 24 environmental factors that could affect the distribution of T. sutchuenensis, including climate, topography, soil and Normalized Difference Vegetation Index (NDVI), we adopted the Random Forest-MaxEnt integrated model to analyze our data. Based on the Random Forest study, the contribution of the mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual mean temperature and mean temperature of the driest quarter was large. Based on MaxEnt model prediction outputs, the potential… More >