An automated greenhouse is a framework that utilizes nursery innovations to make a positive ecological condition virtually to develop ideal creation of plants. Controlling climatic conditions are one of the most critical challenges in agribusiness. In like manner, a robotized nursery framework has been developed to accomplish observations and controlling of the climatic parameters which is straightforwardly or in a roundabout way that administers the plants development and generation. In a nursery using greenhouse structure that is made with glass or plastic it warms up from approaching obvious sun oriented radiation which is consumed by the plants and absorbed by the surface. Eventually air is warmed by the warmth from hot inside surfaces and held within the nursery dividers that makes the environment inside it warmer. With the association of present day innovations, this research takes a risk to actualize these frameworks for farming with no human consideration. The motivation behind this exploration is to make an innovation that makes exceedingly exact environment in a work sparing angle. In this work an Atmega328 MCU is utilized to do some predefined tasks, made by some specific codes with the help of an Arduino prototyping board. The system projects the parameters in a display as well as controls four vital parameters like temperature, light, humidity and soil moisture to accomplish viability and functionality of nursery environment control gadgets.

Cooperative, which support the national economy of Indonesia, still has many problems. In 2015 the report by the Ministry of Cooperatives and SMEs shows many cooperatives are inactive because of having incompetence. Some issues such as the value of savings that do not fit between the cooperative and its customers, embezzlement of customer funds by parties who are not responsible, refunds troubled loans so that the cooperative suffered losses. These problems will be used as a critical point to do mapping with COBIT 5 framework. Based on the result of this mapping, it will be prepared internal control based on the framework of possible internal controls such as COBIT(Control Objective for Information & Related Technology), COSO(Committee of Sponsoring Organizations), ITIL(IT Infrastructure Library) and national regulations that are prevailing in Indonesia. Control is expected to discuss the internal problems that occurred in each business process that used in the cooperative.

Maintaining and understanding large software systems is a complex and time consuming process. Understanding the internal structure of such systems is not an easy task due to the invisible nature of software. Source Code Visualization, i.e. measuring the code metrics and then visualizing them, provides an efficient solution to this problem. There are many tools available which gives an insight to the internal structure of the software. But all of them don’t provide visualizations of hierarchical relationships and code complexity of the source code. In this paper, we present a tool named “SourceViz”. This application computes different software metrics, code complexity, class-relations and then visualizes the calculated metrics in different metaphors. This visual analysis assists the project managers and software developers in understanding the static structure of the software by presenting project-level as well as class-level view.

In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today’s era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.

The advancement of the present day technology enables the production of huge amount of information. Retrieving useful information out of these huge collections necessitates proper organization and structuring. Automatic text classification is an inevitable solution in this regard. However, the present approach focuses on the flat classification, where each topic is treated as a separate class, which is inadequate in text classification where there are a large number of classes and a huge number of relevant features needed to distinguish between them. This paper aimed to explore the use of hierarchical structure for classifying a large, heterogeneous collection of Amharic News Text. The approach utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree. An experiment had been conducted using a categorical data collected from Ethiopian News Agency (ENA) using SVM to see the performances of the hierarchical classifiers on Amharic News Text. The findings of the experiment show the accuracy of flat classification decreases as the number of classes and documents (features) increases. Moreover, the accuracy of the flat classifier decreases at an increasing number of top feature set. The peak accuracy of the flat classifier was 68.84 % when the top 3 features were used. The findings of the experiment done using hierarchical classification show an increasing performance of the classifiers as we move down the hierarchy. The maximum accuracy achieved was 90.37% at level-3(last level) of the category tree. Moreover, the accuracy of the hierarchical classifiers increases at an increasing number of top feature set compared to the flat classifier. The peak accuracy was 89.06% using level three classifier when the top 15 features were used. Furthermore, the performance between flat classifier and hierarchical classifiers are compared using the same test data. Thus, it shows that use of the hierarchical structure during classification has resulted in a significant improvement of 29.42 % in exact match precision when compared with a flat classifier.

In general, the applications of robots have shifted rapidly from industrial uses to social uses. This provides robots with the ability to naturally interact with human beings and socially fit into the human environment. The deployment of social robots in the healthcare system is becoming extensive as a result of the shortage of healthcare professionals, rising costs of healthcare and the exponential growth in the number of vulnerable populations such as the sick, the aged and children with developmental disabilities. Consequently, social robots are used in healthcare for providing health education and entertainment for patients in the hospital and for providing aids for the sick and aged. They are also used for dispensing drugs and providing rehabilitation as well as emotional and aging care. Hence, social robots improve the efficiency and quality of healthcare services. The interaction between social robots and human beings is known as human-robot interaction. Human-robot interaction in healthcare is faced with numerous challenges such as the fear of displacement of caregivers by robots, safety, usefulness, acceptability as well as appropriateness. These challenges ultimately lead to a low rate of acceptance of the robotic technology. Consequently, this paper extensively appraises human-robot interaction in healthcare, their applications and challenges. Design, ethical and usability issues such as privacy, trust, safety, users’ attitude, culture, robot morphology as well as emotions and deception arising from the interaction between humans and robots in healthcare are also reviewed in this paper.