The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
This paper presents a development of a security system based on Internet-of-Things (IoT) technology, where an IoT device, Raspberry Pi has been used. In the developed surveillance system, a camera works as a sensor to detect motion, and automatically capture the video of the view of area where the motion is detected. The motion is detected by image processing techniques; background subtraction technique. The technique is applied by comparing two different captured images using Pi NoIR camera. The system can be controlled from anywhere using Telegram application, and users will receive alert message with video using the application. The user can also play a siren from anywhere once detecting suspicious object can access images and videos using Telegram application. This can frighten the thief if the crime is suspected in home or office. Users can also deactivate and activate the system from anywhere at any time using the Telegram. The functionality tests have been done to ensure the developed product can work properly. Besides, tests to identify a suitable video length to be transmitted to the user and to identify the adequate location of the security in order to minimize false detection as well as false alert have been performed. The project is an IoT-based which significantly in line with the Industrial Revolution 4.0, supporting the infrastructure of Cyber-Physical System.
A wireless smartphone can be designed to process a financial payment efficiently. A user can just swipe his/her credit/debit card over the counter and all the processing needed shall be done seamlessly. A smartphone is a popular device to carry around. It is a hassle to carry and keep track on so many physical debit/credit cards in a wallet. An electronic debit/credit card on a smartphone is a more convenient alternative. This research project will embark on an electronic debit/credit card on a smartphone and migrate to an IoT money. A novel session payment system using IoT money has been introduced to minimise debit/credit card risk. The scope of this paper is confined to the security model for an easy payment system based on Internet of Things (IoT). Previously, each IoT money is unique and used once only on one-time payment. The session payment system will ease the burden on protecting the database of the payment system. This paper will extend the use of one-time payment to a multiple session payment system using an IoT money note.
The development of the technology and connected devices such as internet of things (IoT), internet of vehicles (IoV), and 5G motivate the researchers to give more attention in the field. Clustering is a key factor in vehicular ad-hoc network (VANET) where a number of vehicles join to form a group based on common characteristics. Vehicles are distinguished by their high mobility in ad hoc vehicle networks. Changes frequently occur in the topology of VANET, causing continuous failures in network communication. In such a dynamic environment, the creation and maintenance of a stable cluster are significant challenges. The evaluation of stability in VANET clustering is an important part to evaluate the clustering approaches. In this paper, a mathematical technique (based on the birth-death process) is created to evaluate the clusters stability based on the number of leaving and joining vehicles to each cluster after its creation. The stability of the created clusters is tested by checking the number of vehicles in each cluster at different successive times. These tests indicate the joining and leaving vehicles to each cluster and their effects on the cluster stability. When the results of the technique show that the standard deviation is small for each cluster, it can be concluded that the proposed clustering algorithm is able to achieve stability in cluster maintaining phase.
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