Qian Tang, Hao Chen, Yifei Wei^*

Journal on Big Data, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jbd.2022.026850

Abstract The machine learning model has advantages in multi-category credit rating classification. It can replace discriminant analysis based on statistical methods, greatly helping credit rating reduce human interference and improve rating efficiency. Therefore, we use a variety of machine learning algorithms to study the credit rating of telecom users. This paper conducts data understanding and preprocessing on Operator Telecom user data, and matches the user’s characteristics and tags based on the time sliding window method. In order to deal with the deviation caused by the imbalance of multi-category data, the SMOTE oversampling method is used to balance the data. Using the… More >

S. Prakash^1,*, P. Vishnu Raja², A. Baseera³, D. Mansoor Hussain⁴, V. R. Balaji⁵, K. Venkatachalam⁶

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1273-1287, 2022, DOI:10.32604/csse.2022.021784

Abstract Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, the iterative weighted… More >

Yue Jiang, Xinyu Zhang, Wohuan Jia, Li Xu^*

Journal on Artificial Intelligence, Vol.3, No.4, pp. 163-169, 2021, DOI:10.32604/jai.2021.027590

Abstract As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and bad or irrelevant (bad, dialog,… More >

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

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 data-driven machine learning methods. The… 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

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 the purchasing decisions made by… 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

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 to fit the sensors. Many… 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

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 and T_mean),… 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

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 to the calculated feature importance… 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

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 responsible for dopamine production. In… 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

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 and compared it with Poisson… More >