The Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things.
Electronic Assessment (E-Assessment) also known as computer aided assessment for the purposes involving diagnostic, formative or summative examining using data analysis. Digital assessments come commonly from social, academic, and adaptive learning in machine readable forms to deliver the machine scoring function. To achieve real-time and smart e-assessment, data modeling needs dramatic improvements at the level of representation which will improve examinees to gain prompt response instantly after attempting exams. Whereas, computer based inference to gain intelligence in assessing results through computations is becoming a useful feature in todays' testing systems. Induction of rule base linked data is desired to be reformed from the old tradition data model found either in spread sheet or relational database used for data storage. These data forms are essential to be converted into semantical annotated form to support Artificial Intelligence. This can be done with the use of Semantic Web data model Resource Description Framework (RDF) built-up using hierarchal and linked data representation. Updating assessment source data later for results is one of the hardest problem of all viabilities in traditional and semantically augmented systems when combined for evaluating. This study purposes a methodology of bidirectional data transformation back and forth from Relational Database (RDB) and RDF. A case study representing qualitative analysis of transforming student's results information into RDF store reforming data as ready to be analyzed. At the end of this study outcomes show how data updating becomes feasible by following proposed data transformation procedure.
Human feelings are fundamental to perceive the conduct and state of mind of an individual. A healthy emotional state is one significant highlight to improve personal satisfaction. On the other hand, bad emotional health can prompt social or psychological well-being issues. Recognizing or detecting feelings in online health care data gives important and helpful information regarding the emotional state of patients. To recognize or detection of patient’s emotion against a specific disease using text from online sources is a challenging task. In this paper, we propose a method for the automatic detection of patient’s emotions in healthcare data using supervised machine learning approaches. For this purpose, we created a new dataset named EmoHD, comprising of 4,202 text samples against eight disease classes and six emotion classes, gathered from different online resources. We used six different supervised machine learning models based on different feature engineering techniques. We also performed a detailed comparison of the chosen six machine learning algorithms using different feature vectors on our dataset. We achieved the highest 87% accuracy using MultiLayer Perceptron as compared to other state of the art models. Moreover, we use the emotional guidance scale to show that there is a link between negative emotion and psychological health issues. Our proposed work will be helpful to automatically detect a patient’s emotion during disease and to avoid extreme acts like suicide, mental disorders, or psychological health issues. The implementation details are made publicly available at the given link: https://bit.ly/2NQeGET.
The rapid generation of data from various sources by the public sector, private corporations, business associations, and local communities is referred to as big data. This large and complex dataset is often regarded as the ‘new oil’ by public administrations (PAs), and data-driven approaches are employed to transform it into valuable insights that can improve governance, transparency, digital services, and public engagement. The government’s big-data ecosystem (GBDE) is a result of this initiative. Effective data management is the first step towards large-scale data analysis, which yields insights that benefit your work and your customers. However, managing big data throughout its life cycle is a daunting challenge for public agencies. Despite its widespread use, big data management is still a significant obstacle. To address this issue, this study proposes a hybrid approach to secure the data management life cycle for GBDE. Specifically, we use a combination of the ECC algorithm with AES 128 BITS encryption to ensure that the data remain confidential and secure. We identified and analyzed various data life cycle models through a systematic literature review to create a data management life cycle for data-driven governments. This approach enhances the security and privacy of data management and addresses the challenges faced by public agencies.
This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.
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