N. B. Ghazali¹, F. C. Seman^1,*, K. Isa¹, K. N. Ramli¹, Z. Z. Abidin¹, S. M. Mustam¹, M. A. Haek², A. N. Z. Abidin², A. Asrokin²

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5427-5440, 2022, DOI:10.32604/cmc.2022.023211 - 14 January 2022

Abstract Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System. The network performance depends on the occurrence of cable fault along the copper cable. Currently, most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and… More >

Kumail Javaid¹, Ayesha Siddiqa², Syed Abbas Zilqurnain Naqvi², Allah Ditta³, Muhammad Ahsan², M. A. Khan⁴, Tariq Mahmood⁵, Muhammad Adnan Khan^6,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4425-4442, 2022, DOI:10.32604/cmc.2022.022984 - 14 January 2022

Abstract Artificial intelligence (AI) and machine learning (ML) help in making predictions and businesses to make key decisions that are beneficial for them. In the case of the online shopping business, it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business. In this research, a dataset of 12,330 records of customers has been analyzed who visited an online shopping website over a period of one year. The main objective of this research is to find features that are relevant in terms of correctly predicting… More >

R. Saravana Ram^1,*, M. Vinoth Kumar², N. Krishnamoorthy³, A. Baseera⁴, D. Mansoor Hussain⁵, N. Susila⁶

Computer Systems Science and Engineering, Vol.42, No.2, pp. 545-460, 2022, DOI:10.32604/csse.2022.021681 - 04 January 2022

Abstract Major fields such as military applications, medical fields, weather forecasting, and environmental applications use wireless sensor networks for major computing processes. Sensors play a vital role in emerging technologies of the 20th century. Localization of sensors in needed locations is a very serious problem. The environment is home to every living being in the world. The growth of industries after the industrial revolution increased pollution across the environment. Owing to recent uncontrolled growth and development, sensors to measure pollution levels across industries and surroundings are needed. An interesting and challenging task is choosing the place… More >

Fuqi Yao¹, Jinwei Sun¹, Jianhua Dong^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 671-700, 2022, DOI:10.32604/cmes.2022.018450 - 13 December 2021

Abstract Accurate estimation of dew point temperature (T_dew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local T_dew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily T_dew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (T_max, T_min… More >

Suzhen Wang¹, Zhanfeng Zhang^1,*, Shanshan Geng¹, Chaoyi Pang²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3721-3731, 2022, DOI:10.32604/cmc.2022.015378 - 07 December 2021

Abstract As society has developed, increasing amounts of data have been generated by various industries. The random forest algorithm, as a classification algorithm, is widely used because of its superior performance. However, the random forest algorithm uses a simple random sampling feature selection method when generating feature subspaces which cannot distinguish redundant features, thereby affecting its classification accuracy, and resulting in a low data calculation efficiency in the stand-alone mode. In response to the aforementioned problems, related optimization research was conducted with Spark in the present paper. This improved random forest algorithm performs feature extraction according More >

Imran Ahmed¹, Sultan Aljahdali², Muhammad Shakeel Khan¹, Sanaa Kaddoura^3,*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 705-722, 2022, DOI:10.32604/iasc.2022.022037 - 17 November 2021

Abstract Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra… More >

Savita Khurana¹, Gaurav Sharma², Neha Miglani³, Aman Singh⁴, Abdullah Alharbi⁵, Wael Alosaimi⁵, Hashem Alyami⁶, Nitin Goyal^7,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 629-649, 2022, DOI:10.32604/cmc.2022.021884 - 03 November 2021

Abstract COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases… More >

Milos Mravik¹, T. Vetriselvi², K. Venkatachalam^3,*, Marko Sarac¹, Nebojsa Bacanin¹, Sasa Adamovic¹

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 457-471, 2022, DOI:10.32604/cmc.2022.020687 - 03 November 2021

Abstract At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a… More >

R. Sujatha¹, B. Venkata Siva Krishna¹, Jyotir Moy Chatterjee², P. Rahul Naidu¹, NZ Jhanjhi^3,*, Challa Charita¹, Eza Nerin Mariya¹, Mohammed Baz⁴

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 525-541, 2022, DOI:10.32604/iasc.2022.021216 - 26 October 2021

Abstract In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. However, not every vaccine will be perfect or will get success for everyone. In the present work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables… More >

Ayesha Siddiqa¹, Syed Abbas Zilqurnain Naqvi¹, Muhammad Ahsan¹, Allah Ditta², Hani Alquhayz³, M. A. Khan⁴, Muhammad Adnan Khan^5,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5519-5536, 2022, DOI:10.32604/cmc.2022.021666 - 11 October 2021

Abstract Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration… More >