Jaeuk Moon, Seungwon Jung, Sungwoo Park, Eenjun Hwang^*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2945-2959, 2021, DOI:10.32604/cmc.2021.017435

Abstract One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection… More >

Shaheen Khatoon^1,*, Majed A. Alshamari¹, Amna Asif¹, Md Maruf Hasan¹, Sherif Abdou², Khaled Mostafa Elsayed³, Mohsen Rashwan⁴

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3079-3100, 2021, DOI:10.32604/cmc.2021.017371

Abstract Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze… More >

Pushpendra Kumar^1,2,*, Ramjeevan Singh Thakur³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.016957

Abstract The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this paper, a model has been… More >

Sajid Habib Gill¹, Noor Ahmed Sheikh¹, Samina Rajpar¹, Zain ul Abidin², N. Z. Jhanjhi^3,*, Muneer Ahmad⁴, Mirza Abdur Razzaq¹, Sultan S. Alshamrani⁵, Yasir Malik⁶, Fehmi Jaafar⁷

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3773-3787, 2021, DOI:10.32604/cmc.2021.016001

Abstract Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a… More >

Qura-Tul-Ain Khan¹, Sagheer Abbas¹, Muhammad Adnan Khan^2,*, Areej Fatima³, Saad Alanazi⁴, Nouh Sabri Elmitwally^4,5

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3061-3077, 2021, DOI:10.32604/cmc.2021.015441

Abstract In vehicular systems, driving is considered to be the most complex task, involving many aspects of external sensory skills as well as cognitive intelligence. External skills include the estimation of distance and speed, time perception, visual and auditory perception, attention, the capability to drive safely and action-reaction time. Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments. The parameters for understanding intelligent behaviour are knowledge, reasoning, decision making, habit and cognitive skill. Modelling intelligent behaviour reveals that many of these parameters… More >

Ibrahim B.M. Taha¹, Diaa-Eldin A. Mansour^2,*

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 739-752, 2021, DOI:10.32604/iasc.2021.017703

Abstract Power transformer is one of the more important components of electrical power systems. The early detection of transformer faults increases the power system reliability. Dissolved gas analysis (DGA) is one of the most favorite approaches used for power transformer fault prediction due to its easiness and applicability for online diagnosis. However, the imbalanced, insufficient and overlap of DGA dataset impose a challenge towards powerful and accurate diagnosis. In this work, a novel fault diagnosis for power transformers is introduced based on DGA by using data transformation and six optimized machine learning (OML) methods. Four data transformation techniques are used with… More >

Anshu Parashar^*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.017478

Abstract Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input… More >

Neeraj Kumar Sirohi, Mamta Bansal, S. N. Rajan

Journal on Big Data, Vol.3, No.2, pp. 97-97, 2021, DOI:10.32604/jbd.2021.041299

Abstract This article has no abstract. More >

Mohamed F. El-Amin^1,5, Abdulhamit Subasi², Mahmoud M. Selim^3,*, Awad Mousa⁴

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2349-2362, 2021, DOI:10.32604/cmc.2021.017102

Abstract In the process of oil recovery, experiments are usually carried out on core samples to evaluate the recovery of oil, so the numerical data are fitted into a non-dimensional equation called scaling-law. This will be essential for determining the behavior of actual reservoirs. The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data. This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity, which dynamics of the process. One of the practical machine learning (ML)… More >

Nancy Awadallah Awad^1,*, Amena Mahmoud²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1519-1530, 2021, DOI:10.32604/cmc.2021.016974

Abstract The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in communication. In-text mining, several techniques are available such as information clustering, extraction, summarization, classification. In this study, a text mining framework was presented which consists of 4 phases retrieving, processing, indexing, and mine association rule phase. It is applied by using the association rule mining technique to check the associated term with the Huawei P30 Pro phone. Customer reviews are extracted from many websites and Facebook groups, such as re-view.cnet.com, CNET. Facebook and amazon.com technology, where customers from… More >