Cotton crop cultivation oriented semantic framework based on IoT smart farming application

The fact that each technological concept comes from the advances in the research and development, Internet of Things (IoT) grows and touches virtually every area of human activities. This has yielded the possibility of analyzing various types of sensors-environment from any kind of IoT platform. The existing IoT platforms focuses more on the area related to urban infrastructure, smart cities, healthcare, smart industry, smart mobility and much more. In this paper, we are focusing on the architecture of designing the application of IoT based solution in agriculture with more specific to Cotton farming. Our specific approach on farming is relevant to cotton crops cultivation, irrigation and harvesting of yields. In the context of cotton crops cultivation, there are many factors that should be concerned which includes weather, legal regulation, market conditions and resource availability. As a result, this paper presents a cotton crops cultivation oriented semantic framework based on IoT smart farming application which supports smart reasoning over multiple heterogenous data streams associated with the sensors providing a comprehensive semantic pipeline. This framework will support large scale data analytic solution, rapid event recognition, seamless interoperability, operations, sensors and other relevant features covering online web based semantic ontological solution in an agriculture context

Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively

Motion capture using the internet of things technology: A tutorial

© 2017 Association for Computing Machinery. This tutorial explores a variety of applications for Internet of fiings (IoT) aided motion capture, drawing comparisons from different technologies and implementations. Various uses for these implementations will be discussed, as well as weighted against one another to analyse their advantages and disadvantages

Motion capture using the internet of things technology: A tutorial

© 2017 Association for Computing Machinery. This tutorial explores a variety of applications for Internet of fiings (IoT) aided motion capture, drawing comparisons from different technologies and implementations. Various uses for these implementations will be discussed, as well as weighted against one another to analyse their advantages and disadvantages

A Concurrence Study on Interoperability Issues in IoT and Decision Making Based Model on Data and Services being used during Inter-Operability

The Internet-of-Things (IoT) has become an important topic among researchers owing to its potential to change the way we live and use smart devices. In recent years, many research work found in the world are interrelated and convey via the existing web structure which makes a worldwide system called IoT. This study focused on the significant improvement of answers for a wider scope of gadgets and the Internet of Things IoT stages in recent years. In any case, each arrangement gives its very own IoT framework, gadgets, APIs, and information configurations promoting interoperability issues. These issues are the outcome of numerous basic issues, difficulty to create IoT application uncovering cross-stage, and additionally cross-space, trouble in connecting non-interoperable IoT gadgets to various IoT stages, what's more, eventually averts the development of IoT innovation at an enormous scale. To authorize consistent data sharing between various IoT vendors, endeavors by a few academia, industrial, and institutional groups have accelerated to support IoT interoperability. This paper plays out a far-reaching study on the cutting-edge answers for encouraging interoperability between various IoT stages. Likewise, the key difficulties in this theme are introduced

A survey on internet of things enabled smart campus applications

The fictional future home, workspace or city, as predicted by science TV shows of the 1960s, is now a reality. Modern microelectronics and communication technologies offer the type of smart living that looked practically inconceivable just a few decades ago. The Internet of Things (IoT) is one of the main drivers of the future smart spaces. It enables new operational technologies and offers vital financial and environmental benefits. With IoT, spaces are evolving from being just 'smart' to become intelligent and connected. This survey paper focuses on how to leverage IoT technologies to build a modular approach to smart campuses. The paper identifies the key benefits and motivation behind the development of IoT-enabled campus. Then, it provides a comprehensive view of general types of smart campus applications. Finally, we consider the vital design challenges that should be met to realise a smart campus

IoT-based students interaction framework using attention-scoring assessment in eLearning

Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies

Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions

In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 s, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed

Developing ontology-based decision-making framework for Middle Eastern region HEIs

Decision making is one of the most challenging processes that higher education institutions continuously experience worldwide. Most educational decisions rely mainly on evaluating the academic profile of staff members, which usually includes the academic and research activities of the teacher. The massive amount of scattered educational data, if represented in traditional forms, causes the problem of ambiguity and inaccuracy of decisions. Educational institutions have recently been attempting to apply emerging technologies in the data engineering field to solve as many challenges as possible. In addition, online libraries continuously produce an enormous amount of open scholarly data, including publications, citations, and other research activity records, which could effectively improve the quality of academic decisions when linked with the local data of universities. This thesis presents the academic profiles and course records semantically, and employs them with a scientific knowledge graph as linked data to enrich the internal data and support the decision-making process within universities. The proposed approach is applied to assign courses to the most qualified academic staff as a proof-of-concept experiment. Traditionally, this process is performed manually by heads of departments and is considered time-consuming, especially when the data are in textual format. This research aims to address this challenge. To this end, courses and academic profiles are represented semantically in RDF format, in order to improve the quality of the institutional data. To ensure the efficiency of this process, a survey is conducted to identify the key factors that influence decision making during the distribution of courses among staff members, which was successfully distributed to the heads of departments who actively participated and provided their variable insights into this matter. The survey results indicated that the research areas of academic staff and whether they had taught the course before are the most important factors that are usually considered in this type of decision. Furthermore, this study proves the importance of generating links between local data and external repositories with updated research records to improve the course–teacher assignment process. Linked data technology is applied to combine all the possible information affecting the course–teacher assignment decision from different resources, and the sufficiency of the linked data and the selection of external data are examined using data mining techniques. Two prediction models are developed to predict the most qualified academic teacher to teach each course, with the results being associated with 314 academic teachers and 119 courses from the Faculty of Computing and Information Technology at King Abdulaziz University. According to the obtained accuracy of the models, it is suggested that the performance is improved when the data are enriched with external scholarly open data using LD, with the accuracy increasing from 80.95% to 93.26% after applying LD techniques. Additionally, adding research records of the academic member improved the sensitivity of the models to 89.11% and 97.76%. These improvements demonstrate the importance of considering the research activities of academic members when distributing courses, especially when extracted from external repositories using LD