This paper presents an intelligent health monitoring system for post management of stroke. Fitbit sensor was used to take the reading of four stroke patients in Federal Teaching Hospital, Gombe State. The vital readings taken were heartbeat rate, sleeping rate, and the number of steps taken, and this was done for a period of four weeks. The developed AppFabric, Web service and AppFeedBack synchronized the operation of the sensor, the user mobile device and the medical diagnostic platform. The readings taken by the sensor were made available to the medical experts and the monitoring team using web service. The evaluation of the system in terms of efficiency and reliability using t-Test were (82.3, 85.9) and (1.729133, 2.093024) respectively. The results show that the developed system performs better than the existing manual method for monitoring stroke patients.

We live in the era of digital technologies where data is increasing day by day at a very high rate. The data is further popularly classified as ‘Big Data’ because of its velocity, veracity, variety and its huge volume. This data could be unstructured, semi-structured or structured as it is divergent in nature. In this work, we would assess various categories of Amazon Product Reviews, the large datasets that contain around 144 million reviews in total. The datasets consists of Product reviews collected from Amazon, each having various numbers of attributes of 11 different categories. The motive of this work is to find and compare the ratings of the products during the lifespan of the product reviews. Another goal of this work is to help Amazon regarding the listing of the products in their database.
This work aims to relate user’s ratings and reviews to discover how beneficial and good a product is [6]. User ratings are collected and are analyzed based on different categories (datasets) which gives an insight as to which product performs good and what are the problems associated to a certain non-performing product.

The current era is generally treated as the era of data, Users of computer are gradually increasing day by day and vast amount of data is generated from multiple domains such as healthcare- domain, Business related domains etc. The terminology Business Intelligence (BI) generally refers different technologies, applications and practices used for the collection, integration, analysis, and presentation of information of business related domain. The main motive for Business intelligence and analytics are to help in decision making process and to enhance the profit of the organisation. Various business related tools are used to analyze & visualize different types of data which are generated frequently. Tableau prepared its mark on the Field of BI by being one of the first companies to permit business customers the ability to achieve equitably arduous data visualization in a very interesting, drag and drop manner. Tableau will enhance decision making, add operational awareness, and increase performance throughout the organization The presented paper describes different tools used for business intelligence field and provides a depth knowledge regarding the tableau tool. It also describes why tableau is widely used for data visualization purpose in different organization day by day. The main aim of this paper is to describe how easily forecasting and analysis can be done by using this tool ,this paper has explained how easily prediction can be done through tableau by taking the dataset of a superstore and predict the forthcoming sales and profit for the next four quarters of the forthcoming year. In the collected dataset sales and profit details of different categories of goods are given and by using the forecasting method in tableau platform these two measures are calculated for the forthcoming year and represented in a fruitful way. Finally, the paper has compared all the framework used for business intelligence and analytics on the basis of various parameters such as complexity, speed etc.

The most widely accepted method of monitoring the fetal heart rate and uterine activity of the mother is using Cardiotocograph (CTG). It simultaneously captures these two signals and correlate them to find the status of the fetus. This method is preferred by obstetricians since it is non-invasive as well as cost-effective. Though used widely, the specificity and predictive precision has not been undisputable. The main reason behind this is due to the contradiction in clinicians opinions. The two main components of CTG are Baseline and Variability which provide a thorough idea about the state of the fetal-health when CTG signals are inspected visually. These parameters are indicative of the oxygen saturation level in the fetal blood. Automated detection and analysis of these parameters is necessary for early and accurate detection of hypoxia, thus avoiding further compromise. Results of the proposed algorithm were compared with the visual assessment performed by three clinicians in this field using various statistical techniques like Confidence Interval (CI), paired sample t-test and Bland-Altman plot. The agreement between the proposed method and the clinicians’ evaluation is strong.

Since organizational decisions are vital to organizational development, customers’ views and feedback are equally important to inform good decisions. Given this relevance, this paper seeks to automate a sentiment analysis system - SentDesk- that can aid tracking sentiments in customers’ reviews and feedback. The study was contextualised in some business organisations in Ghana. Three business organizational marketers were made to annotate emotions and as well tag sentiments to each instance in the corpora. Kappa and Krippendoff coefficients were computed to obtain the annotation agreement in the corpora. The SentDesk system was evaluated in the environment alongside comparing the output to that of the average sentiments tagged by the marketers. Also, the SentDesk system was evaluated in the environment by the selected marketers after they had tested the platform. By finding the average kappa value from the corpora (CFR + ISEAR), the average kappa coefficient was found to be 0.40 (40%). The results of evaluating the SentDesk system with humans shows that the system performed as better as humans. The study also revealed that, while annotating emotions and sentiments in the datasets, counsellor’s own emotions influences their perception of emotions.

This paper bestows the newly developed Grey Wolf Optimization (GWO) method to solve the Economic Dispatch (ED) problem with multiple fuels. The GWO method imitates the superiority ranking and feeding mechanism of grey wolves in nature. For simulating the superiority ranking follows as alpha, beta, omega and delta. For feeding the prey grey wolves follows three steps, in the order of searching, encircling and attacking, are carry out to perform optimization. While searching for a better solution, GWO does not obligate any statistics about the gradient of the fitness function. The intention of ED is to curtail the fuel cost for any viable load demand and at the same time to determine the optimal power generation. The ED is modeled as a complex problem by considering multiple fuels, valve-point loading and transmission losses. The potency of the GWO method has been examined on ten units system with four different load demands by considering four different case studies. The result of the test systems shows, for practical power systems, that the GWO is a better option to solve the ED problems. Both the optimality of the solution to test system and the convergence speed of the GWO algorithm are promising.