To help users to determine the most appropriate visualization type is a useful feature of business visualization tools. Existing systems often give preliminary suggestions based on data sources but usually cannot make practical final decision. User stories are generalizations of user requirements. To recommend visualization type based on user stories can make better use of human experience to achieve automated decision making. One approach discussed in the paper is using machine learning techniques to model existing visualization types with corresponding user stories, and then use this model to predict recommended visualization type for new user story. This paper designs and implements a recommendation system prototype ReViz to verify the feasibility of this approach. As a typical web application, Modeling, Input Processing and Predicting components of ReViz are programmed using Python with Flask framework and Anaconda package set, and user interface is implemented using HTML, JavaScript and CSS with Bootstrap front-end library. The evaluation results show that ReViz can give recommended visualization type based on user story keywords. As a data-based intelligent software development technology achievement, visualization type recommendation system can also be integrated into larger business information management systems.

Selecting proper crops for farmland involves a sequence of activities. These activities and the entire process of farming require a help of expert knowledge. However, there is a shortage of skilled experts who provide advice for farmers at district level in developing countries.
This study proposed designing knowledge based solution through the collaboration of experts’ knowledge with the machine learning knowledge base to recommending suitable agricultural crops for a farm land. To design the collaborative approach the knowledge was acquired from document analysis, domain experts’ interview and hidden knowledge were extracted from Ethiopia national meteorology agency weather dataset and from central statistics agency crop production dataset by using machine learning algorithms. The study follows the design science research methodology, with CommonKADS and HYBRID models; and WEKA, SWI-Prolog 7.32 and Java NetBeans tools for the whole process of extracting knowledge, develop the knowledge base and for developing graphical user interface respectively.
Based on the objective measurement PART rule induction have the highest classifier algorithm which classified correctly 82.6087% among 9867 instances. The designed collaborative approach of experts’ knowledge with the knowledge discovery for agricultural crop selections based on the domain expert, farmers and agriculture extension evaluation 95.23%, 82.2 % and 88.5 % overall performance respectively.

Disaster caused by sudden uncertain fire is one of the main reasons for a great loss of properties and human lives. In our paper, we have developed a smart and cost-effective fire detection system based on the IoT that can detect the sudden uncertain fire in a quick succession to reduce the significant loss. The device houses a sensor-based smoke detection system and a camera which could be accessed by the user from anywhere through the use of internet for taking necessary preventive actions based on the reliable assessment. The notification system takes advantage of an online short message service which is connected to the Raspberry Pi module that gets triggered when the smoke sensors detect the smoke and informs the users about the predicament. The device also has a buzzer connected to central module to notify the nearby users.

This paper presents that the simulation of control of three phase Brushless Direct Current (BLDC) motor in all four quadrants with PI and Fuzzy Logic controllers (FLC). Traditionally the speed control of motors is carried out by conventional motors with using P, PI, PID and some other control techniques [5]. But it provides a chance to occurrence of nonlinearity & uncertainties that causes some internal and external parameter errors. The efficient speed control in four quadrant operation can be achieved by using a fuzzy logic controller. The improvisation of Brushless Direct current motor drive through fuzzy logic controller in all four quadrants is done using simulink/MATLAB [7].

The quantity of information on the internet is massively increasing and gigantic volume of data with numerous compositions accessible openly online become more widespread. It is challenging nowadays for a user to extract the information efficiently and smoothly. As one of the methods to tackle this challenge, text summarization process diminishes the redundant information and retrieves the useful and relevant information from a text document to form a compressed and shorter version which is easy to understand and time-saving while reflecting the main idea of the discussed topic within the document. The approaches of automatic text summarization earn a keen interest within the Text Mining and NLP (Natural Language Processing) communities because it is a laborious job to manually summarize a text document. Mainly there are two types of text summarization, namely extractive based and abstractive based. This paper focuses on the extractive based summarization using K-Means Clustering with TF-IDF (Term Frequency-Inverse Document Frequency) for summarization. The paper also reflects the idea of true K and using that value of K divides the sentences of the input document to present the final summary. Furth more, we have combined the K-means, TF-IDF with the issue of K value and predict the resulting system summary which shows comparatively best results.

Wireless communication system is of paramount importance in the world of telecommunication infrastructure and is expected to be a leading role in the development of a nation. However, the system is characterized by multipath propagation effects that lead to variability of the received signal thereby degrading the performance. Equal Gain Combiner (EGC) being used to address this problem is associated with hardware complexity that results in long processing time, while Threshold Combiner (TC) with low processing time has poor performance. Hence, in this paper, a hybridized Diversity Combiner (DC) consisting of EGC and TC, (TC-EGC) with a closed form expression over Nakagami fading channel is developed. TC-EGC is derived using the conventional EGC and TC at the receiver. Randomly generated bits used as source data are modulated using M-ary Quadrature Amplitude Modulation (M-QAM) and transmitted over Nakagami channel after filtering. The faded signals generated at varying paths ‘L’ (2, 3, 4) are scanned by TC to select the strongest paths. The outputs from the three TCs are combined by EGC to obtain the received signal which is converted to baseband through demodulation. A mathematical expression using the Probability Density Function (PDF) of Nakagami fading channel at varying paths ‘L’ for Outage Probability (OP) is also derived. The technique is simulated using Matrix Laboratory (version 7.2). The performance is evaluated using Signal-to-Noise Ratio (SNR), Outage Probability (OP) and Processing Time (PT). The study shows that the TC-EGC gives lower OP and PT values when compared with conventional EGC and TC, with reduction in hardware complexity. The TC-EGC developed can be used to enhance the performance of wireless communication system.