Mamdouh El Haj Assad¹, Ibrahim Mahariq², Raymond Ghandour², Mohammad Alhuyi Nazari³, Thabet Abdeljawad^4,5,6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 361-374, 2022, DOI:10.32604/cmc.2022.019048

Abstract Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their… More >

Jurgita Markevičiūtė^1,*, Jolita Bernatavičienė², Rūta Levulienė¹, Viktor Medvedev², Povilas Treigys², Julius Venskus²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 695-714, 2022, DOI:10.32604/cmc.2022.018735

Abstract The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA… More >

Mohamed Esmail Karar^1,2, Marwa Ahmed Shouman³, Claire Chalopin^4,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1683-1697, 2022, DOI:10.32604/cmc.2022.018564

Abstract The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes… More >

Harshita Patel¹, Dharmendra Singh Rajput^1,*, Ovidiu Petru Stan², Liviu Cristian Miclea²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 73-89, 2022, DOI:10.32604/cmc.2022.017114

Abstract Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify… More >

K. M. Ameen Suhail¹, Syam Sankar¹, Ashok S. Kumar², Tsafack Nestor³, Naglaa F. Soliman^4,*, Abeer D. Algarni⁴, Walid El-Shafai⁵, Fathi E. Abd El-Samie^4,5

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 935-950, 2022, DOI:10.32604/cmc.2022.017069

Abstract Stock market is a place, where shares of different companies are traded. It is a collection of buyers’ and sellers’ stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends… More >

P. S. Anoop¹, Pranav Nair², V. Sugumaran^1,*

Structural Durability & Health Monitoring, Vol.15, No.3, pp. 261-279, 2021, DOI:10.32604/sdhm.2021.06656

Abstract Tyre Pressure Monitoring Systems (TPMS) are installed in automobiles to monitor the pressure of the tyres. Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers. Many methods have been researched and reported for TPMS. Amongst them, vibration-based indirect TPMS using machine learning techniques are the recent ones. The literature reported the results for a perfectly balanced wheel. However, if there is a small unbalance, which is very common in automobile wheels, ‘What will be the effect on the classification accuracy?’ is the question on hand. This paper attempts to study the… More >

R. Kingsy Grace^*, K. Induja, M. Lincy

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 279-296, 2022, DOI:10.32604/iasc.2022.019947

Abstract Strong associations exist between the crop productivity and the seasonal, biological, economical causes in natural ecosystems. The linkages like climatic conditions, health of a soil, growth of crop, irrigation, fertilizers, temperature, rainwater, pesticides desired to be preserved in comprehensively managed crop lands which impacts the crop potency. Crop yield prognosis plays a vibrant part in agricultural planning, administration and environs sustainability. Advancements in the field of Machine Learning have perceived novel expectations to improve the prediction performance in Agriculture. Highly gratifying prediction of crop yield helps the majority of agronomists for their rapid decision-making in the choice of crop to… More >

Taher M. Ghazal^1,2, Sajida Noreen³, Raed A. Said⁴, Muhammad Adnan Khan^5,*, Shahan Yamin Siddiqui^3,6, Sagheer Abbas³, Shabib Aftab³, Munir Ahmad³

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658

Abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to… More >

Marium Malik¹, Muhammad Waseem Iqbal^1,*, Syed Khuram Shahzad², Muhammad Tahir Mushtaq², Muhammad Raza Naqvi^3,4, Maira Kamran¹, Babar Ayub Khan⁴, Muhammad Usman Tahir⁴

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 207-222, 2022, DOI:10.32604/iasc.2022.018753

Abstract Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential… More >

Jeongcheol Lee, Sunil Ahn^*, Hyunseob Kim, Jongsuk Ruth Lee

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 255-277, 2022, DOI:10.32604/iasc.2022.018558

Abstract Automated hyperparameter optimization (HPO) is a crucial and time-consuming part in the automatic generation of efficient machine learning models. Previous studies could be classified into two major categories in terms of reducing training overhead: (1) sampling a promising hyperparameter configuration and (2) pruning non-promising configurations. These adaptive sampling and resource scheduling are combined to reduce cost, increasing the number of evaluations done on more promising configurations to find the best model in a given time. That is, these strategies are preferred to identify the best-performing models at an early stage within a certain deadline. Although these time and resource constraints… More >