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  • Open Access

    ARTICLE

    Classification and Categorization of COVID-19 Outbreak in Pakistan

    Amber Ayoub1, Kainaat Mahboob1, Abdul Rehman Javed2, Muhammad Rizwan1, Thippa Reddy Gadekallu2, Mustufa Haider Abidi3,*, Mohammed Alkahtani4,5

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1253-1269, 2021, DOI:10.32604/cmc.2021.015655 - 04 June 2021

    Abstract Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide More >

  • Open Access

    ARTICLE

    Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

    Taher M. Ghazal1,2, Marrium Anam3, Mohammad Kamrul Hasan1, Muzammil Hussain4,*, Muhammad Sajid Farooq5, Hafiz Muhammad Ammar Ali4, Munir Ahmad6, Tariq Rahim Soomro7

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 191-203, 2021, DOI:10.32604/cmc.2021.015436 - 04 June 2021

    Abstract Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients More >

  • Open Access

    ARTICLE

    Supervised Machine Learning-Based Prediction of COVID-19

    Atta-ur-Rahman1, Kiran Sultan3, Iftikhar Naseer4, Rizwan Majeed5, Dhiaa Musleh1, Mohammed Abdul Salam Gollapalli2, Sghaier Chabani2, Nehad Ibrahim1, Shahan Yamin Siddiqui6,7, Muhammad Adnan Khan8,*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 21-34, 2021, DOI:10.32604/cmc.2021.013453 - 04 June 2021

    Abstract COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and overwhelming spread has become one of the greatest challenges for the world. As yet, no satisfactory vaccine or medication has been developed that could guarantee its mitigation, though several efforts and trials are underway. Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment. In this regard, healthcare experts, researchers and scientists have delved into the investigation of existing as well as new technologies. The situation… More >

  • Open Access

    ARTICLE

    Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers

    Mingdao Lu, Peng Wei, Mingshu He*, Yinglei Teng

    Journal of Quantum Computing, Vol.3, No.1, pp. 1-12, 2021, DOI:10.32604/jqc.2021.016315 - 20 May 2021

    Abstract With the increasing of civil aviation business, flight delay has become a key problem in civil aviation field in recent years, which has brought a considerable economic impact to airlines and related industries. The delay prediction of specific flights is very important for airlines’ plan, airport resource allocation, insurance company strategy and personal arrangement. The influence factors of flight delay have high complexity and non-linear relationship. The different situations of various regions and airports, and even the deviation of airport or airline arrangement all have certain influence on flight delay, which makes the prediction more… More >

  • Open Access

    ARTICLE

    Emotional Analysis of Arabic Saudi Dialect Tweets Using a Supervised Learning Approach

    Abeer A. AlFutamani, Heyam H. Al-Baity*

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 89-109, 2021, DOI:10.32604/iasc.2021.016555 - 12 May 2021

    Abstract Social media sites produce a large amount of data and offer a highly competitive advantage for companies when they can benefit from and address data, as data provides a deeper understanding of clients and their needs. This understanding of clients helps in effectively making the correct decisions within the company, based on data obtained from social media websites. Thus, sentiment analysis has become a key tool for understanding that data. Sentiment analysis is a research area that focuses on analyzing people’s emotions and opinions to identify the polarity (e.g., positive or negative) of a given… More >

  • Open Access

    ARTICLE

    Mobile Memory Management System Based on User’s Application Usage Patterns

    Jaehwan Lee, Sangoh Park*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4031-4050, 2021, DOI:10.32604/cmc.2021.017872 - 06 May 2021

    Abstract Currently, the number of functions to improve user convenience in smartphone applications is increasing. In addition, more mobile applications are being loaded into mobile operating system memory for faster launches, thus increasing the memory requirements for smartphones. The memory used by applications in mobile operating systems is managed using software; allocated memory is freed up by either considering the usage state of the application or terminating the least recently used (LRU) application. As LRU-based memory management schemes do not consider the application launch frequency in a low memory situation, currently used mobile operating systems can… More >

  • Open Access

    ARTICLE

    Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

    Abdul Rehman1,*, Mohd Fadzil Hassan1, Yew Kwang Hooi1, Muhammad Aasim Qureshi2, Tran Duc Chung3, Rehan Akbar4, Sohail Safdar5

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4125-4142, 2021, DOI:10.32604/CMC.2021.017620 - 06 May 2021

    Abstract Trust is one of the core components of any ad hoc network security system. Trust management (TM) has always been a challenging issue in a vehicular network. One such developing network is the Internet of vehicles (IoV), which is expected to be an essential part of smart cities. IoV originated from the merger of Vehicular ad hoc networks (VANET) and the Internet of things (IoT). Security is one of the main barriers in the on-road IoV implementation. Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements. Trust plays a… More >

  • Open Access

    ARTICLE

    Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

    Fawaz Jaber Alsolami1, Abdullah Saad Al-Malaise ALGhamdi2, Asif Irshad Khan1,*, Yoosef B. Abushark1, Abdulmohsen Almalawi1, Farrukh Saleem2, Alka Agrawal3, Rajeev Kumar3,4, Raees Ahmad Khan3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2895-2912, 2021, DOI:10.32604/cmc.2021.017469 - 06 May 2021

    Abstract Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

    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 - 06 May 2021

    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… More >

  • Open Access

    ARTICLE

    Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

    Shaheen Khatoon1,*, 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 - 06 May 2021

    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… More >

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