The task of Allocating courses to lecturers in many tertiary institutions is done manually by typing using word processor application. Motivated by the widespread application of knowledge graphs in different domains, we present automated approach based on knowledge graph to address the problem of manual course allocation to, a task usually carried out at the beginning of every semester or academic year by departments in tertiary institutions. The development of knowledge graph in a way that enables easy manipulation and automatic generation of course allocation schedule is the core contribution of this paper. Rather than storing the data in relational database tables, the system stores data in a knowledge graph which is in RDF/XML format and refer to it to support intelligent knowledge services. In addition to automatic generation of course allocation schedule, another important feature of the system proposed in this paper is its ability to enable easy implementation of tasks similar to Question Answering that are very important to education administrators, which the existing manual approach does not provide. Testing of the proposed system reveals its ability to perform effectively. Our approach of using Knowledge graph offers advantages such as flexibility and security.

For any educational institution course or teacher evaluation is an integral part for the betterment and effective education system. Student’s feedback could be taken as one of parts of teacher evaluation. This paper has tried to evaluate the effectiveness of the course teacher evaluation system from the students’ feedback of their corresponding courses. At Stamford University Bangladesh currently the teacher’s evaluation task is running by manually which is very time consuming, slow and a lengthy process. It also needs number of human resources for completing this task. This paper has presented a web based automated tool for Course Teacher Evaluation System (TTE). In this technique student’s opinions are taken from some predefined questions in a web based platform for evaluating a teacher of any particular course. And the result from the data analysis is automatically generated along with a graphical representation. From the generated report it becomes very easy for the teacher to understand and focus on the area where they need to emphasize for their personal and professional growth. As the results are generated automatically from the survey it saves time as well as man power.

Cloud computing is the study of using remote servers which are hosted on the internet to deliver on-demand computing resources on pay-for-use basis rather than a local server. Due to high energy consumption in cloud computing environments, it has become the main area of research. The amount of energy consumption becomes difficult to determine in real cloud computing infrastructure. In recent years some simulation tools have been developed to analyze the energy consumption in cloud computing environments for the researchers. This paper evaluates three different popular cloud energy consumption measuring simulators: CloudSim, CloudAnalyst and GreenCloud. All of these simulators can determine energy consumption according to their ability and capacity. The comparative study indicates that in a cloud computing environment, GreenCloud simulator is better than CloudSim and CloudAnalyst due to reliable measurement of energy consumption. The significance of this comparative study is to help out the researchers who are working on energy consumption in cloud computing environments that how GreenCloud simulator make their work efficient rather than other simulators.

With the development of technology the use of Computer Aided Diagnosis has become a key for breast cancer diagnosis. It is important to increase the accuracy and effective of such systems. The concept of data mining can be applied on the data gathered through such systems for prediction and prevention of breast cancer. In this research, we have conducted the comparison between seven classification algorithms with the help of WEKA (The Waikato Environment for Knowledge Analysis) tool on the 569 instances (10 nucleus attributes) of data with two classes Malignant(M) and Benign (B) of breast cancer aspirate cells. Furthermore the influence of each attribute on prediction was evaluated. The accuracy of these algorithms was above 91% with the highest value of 94.02% for random forest and the predictive power of conclave points was highest whereas lowest was of Fractal Dimension.

The incorporation of technology in waste management of smart cities will immensely contribute to the reduction of cost and complexities of current waste management systems by improving their efficiency, health safety, and productivity. It will also help to lessen the negative impact of waste management activities on the environment, thus contributing to the smart city scenario. Present designs are not refined enough to accomplish a liberal, generous and viable waste organization segment.Various solutions have been offered for implementing smart collection, transportation, and disposal of waste, yet efficient waste management has not been achieved. . It is basic to have a sharp technique for directing and coordinating waste and also the garbage level should be educated in-time when to be taken, and likewise, all the accomplices should know that what kind of waste in what sum needs to be collected.To ensure such efficient collection and management of waste, we propose a “Smart Bin for Waste Management System using Communication Platform as a Service Clouds & Internet of Things (IoT)”. In this prototype system, a unique ID is assigned to each garbage bin. The bins are equipped with an embedded system to measure the garbage level and other indicators such as temperature and tilt in the garbage bin. These sensors detect different events such as the bin getting filled up to its maximum level or tilting beyond a certain threshold and the system sends an alert message to the municipal authorities or central monitoring station using SMS, E-mail, & Voice-call. It also provides route optimization to the pickup vehicle, responsible for garbage collection in the concerned area.