When developing software, the selection of an appropriate software development methodology (SDM) is an essential decision. There are many SDM exist that are used to control the process of developing a software process. No exact system was found which could guide software engineers for selection of a proper methodology during software development. This paper show factor affecting the selection of SDM and the consistency in which the methodology selection carried out. Based on Fuzzy AHP, we evaluate the consistency in SDM selection. This paper presents a framework of Fuzzy AHP approach for selection of three different SDM with their three conflicting selecting criteria alternatives.
Internet of Things (IoT) is a combination of hardware and software technology that produces trillions of data by connecting multiple devices and sensors with the cloud and computing and accessing the required data through intelligent means of connecting and utilizing various tools. With numerous connected devices and appliances, the smart home is one of the emphasized areas of IoT. Smart home concept deals with inter connecting the working of multiple devices using IoT. In view of this, multiple home appliances are operated using the common remote controller access. When IoT devices are meant to operate based on remote control there is an immense role of identifying the state dependencies of various devices. If one device is in ON state, then it will show the status of other devices also whether to be in ON state or OFF state or choice of none of devices to be in state of ON or OFF. Till now, state dependencies of devices in home appliances are manually identified. Therefore, in order to control devices of home appliances with in a precise location, a design issue based on identification of state dependencies by using graph matrices for multiple devices can be made for better utilization to save energy and also to restrict the unnecessary access of devices.
Data analytics has an interesting variant that aims to understand an entity's behavior. It is termed as diagnostic analytics, which answers "why type questions". "Why type questions" find their applications in emotion classification, brand analysis, drug review modeling, customer complaints classification etc. Labeled data form the core of any analytics' problem, leave alone diagnostic analytics; however, labeled data is not always available. In some cases, it is required to assign labels to unknown entities and understand its behavior. For such scenarios, the proposed model unites topic modeling and text classification techniques. This combined data model will help to solve diagnostic issues and obtain meaningful insights from data by treating the procedure as a classification problem. The proposed model uses Improved Latent Drichlet Allocation for topic modeling and sentiment analysis to understand an entity's behavior and represent it as an Improved Multinomial Naïve Bayesian data model to achieve automated classification. The model is tested using drug review dataset obtained from UCI repository. The health conditions with their associated drug names were extracted from the reviews and sentiment scores were assigned. The sentiment scores reflected the behavior of various drugs for a particular health condition and classified them according to their quality. The proposed model performance is compared with existing baseline models and it is proved that our model exhibited better than other models.
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