Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

Multicriteria decision-making (MCDM) is one of the most common methods used to select the best alternative from a set of available alternatives. Many methods in MCDM are presented in the academic literature, with the latest being the Fuzzy Decision by Opinion Score Method (FDOSM). The FDOSM can solve many challenges that are present in other MCDM methods. However, several problems still exist in the FDOSM and its extensions, such as uncertainty. One of the most significant problems in the use of the FDOSM is the loss of information during the conversion of a decision matrix into an opinion decision matrix. In this paper, the authors expanded the FDOSM into the 2-tuple-FDOSM to solve this problem. The methodology behind the development of the 2-tuple-FDOSM was presented. Within the methodology, definitions of the 2-tuple linguistic fuzzy method, which was used to solve the loss-of-information problem that is present in the FDSOM method, are presented. A network case study was used in the application of the 2-tuple-FDOSM. The final results show that the 2-tuple-FDOSM can be used to address the problem of loss of information. Finally, a comparison between the basic FDOSM, TOPSIS, and 2-tuple-FDOSM was presented.

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Health-care-sector-related activities are more accessible and faster as a result of technological development. Technology such as the Internet of Things (IoT) can work with blood bank services to manage and provide healthy blood in emergencies. However, there are many problems in blood bank management and inventory monitoring, especially in developing countries as compared to developed ones. The lack of an adequate and safe blood supply is a major limitation to health care in the developing world. The instability of the electric power in developing countries may lead to a temperature departure from the recommended for keeping blood inventory, and the use of manual systems, which are characterized by time and resource exhaustion and human mistakes, augments the management problems. This study aims to introduce a reliable, practical application to manage and organize the blood bank, manage donor information, monitor inventory, and obtain matching blood types as quickly as possible. The proposed system was designed and implemented in two parts: using Web technology for enhanced data management and using an IoT sensor for blood inventory temperature monitoring in real time. The test stage helped us to measure the Web application’s functionality with sensors, and the results were encouraging. Obtaining and monitoring blood bank data were made easier in real time by using the black box method for functionalities testing. The evaluation step was performed using a questionnaire instrument based on three parameters: Satisfaction, Effectiveness, and Efficiency. The questionnaire was answered by 22 participants working in the blood bank management field. The results indicated that end users generally responded positively to the system which improved blood bank administration and services. This indicated efficiency of the application and the desire to adopt it. Integrating the two technologies can enhance usability and applicability in the health care sector.