Physical sensors are used mostly to detect sludge and odour in wastewater. Black box modelling or data-derived model using the correlation of input-output parameters is the preferred method as we have assessed. This is due to the non-complex approach of such models as opposed to model-driven, mechanistic models. The latter is hard to be adopted for soft-sensor development due to the inherent complexities and uncertainties. The commonest methods for soft sensor model development are ANN and ANFIS. Many other improvements of these methods are achieved by combining with other techniques to enhance the prediction performance of the soft sensors. Accuracy and precision of data collected for soft sensor modelling has become a vital concern at present to ensure the reliability of wastewater quality indices predicted by the soft sensors. Reduction of the level of reliability of the sensor system in monitoring and controlling of WWTPs would lead to serious lapses in the wastewater quality management. In this backdrop we recommend SEVA soft sensor as one of the best potential solutions which could be offered by the existing technologies.

The rapid increase in student population has resulted in expansion of educational facilities at all level. Nowadays, responsibilities of teachers are many. It is the responsibilities of teachers to guide the students to choose their carrier field according to their abilities and aptitudes. The Data Mining field mines the educational data from large volumes of data to improve the quality of educational processes. Today’s need of educational system is to develop the individual to enhance problem solving and decision making skills in addition to build their social skills. Educational Data Mining is one of the applications of Data Mining to find out the hidden patterns and knowledge in Educational Institutions. There are three important groups of students have been identified: Fast Learners, Average Learners, and Slow Learners. In fact, students are probably struggles in many factors. This work focuses on finding the high potential factors that affects the performance of college students. This finding will improve the students’ academic performance positively.

Article proposes an architecture based on new Internet of Things (IoT) for easy, safe, reliable and rapid data collection from sensors installed in oil and gas industry. Use of several Wireless Sensor Networks in management of oil and gas platforms is researched. New opportunities created by processing of data collected via sensors for improvement of safety of oil platforms (deposits), optimization of operations, prevention of problems, troubleshooting and reduction of exploitation costs in oil and gas industry. At the same time, the article analyses safety issues of different layers of monitoring system with IoT architecture.

This paper focuses on an approach that could be effective for teaching programming languages such as advanced Java Programming, and involves a new framework implemented using the practical approach along with new framework. Assessment tools were designed to facilitate this kind of approach toward teaching programming. The new approach is implemented by conducting lectures in the lab or laptop facilitated classroom. The subject assessments and delivery methods were modified to include projects and class works. Students were encouraged to apply concepts learnt in the class in an incremental manner leading to a complete software application and also write a reflective report. It adapted the Kolb’s experimental learning style theory. Effectiveness of this approach is evident through a comparison of students’ results obtained after implementation of this approach, with results obtained using the traditional style of teaching.

Data Warehousing, data mining and analysis plays a very important role in decision support. Various commercial organisations are using tools based on these techniques to be used for decision support system. Apriori algorithm is a classic algorithm which works on a set of data in the database and provides us with the set of most frequent itemsets. It is used to find the association rules and mines the most frequent itemsets in a set of transactions. Here the frequent subsets are extended one item at a time. In this paper a hash-based technique with Apriori algorithm has been designed to work on data analysis. Hashing helps in improving the spatial requirements as well as makes the process faster. The main purpose behind the work is to help in decision making. The user will select an item which he/she wishes to purchase, and his/her item selection is analysed to give him/her an option of two and three item sets. He/she can consider choosing a combination of two item sets or three item sets, or he/she can choose to go with his/her own purchase. Either ways, the algorithm helps him in making a decision.

There are different life cycle models available for developing various types of software. Every Software Development Life Cycle (SDLC) model has some advantages and some limitations. In that case software developers decide which SDLC model is suitable for their product. Further, we need development of software in a systematic and disciplined manner. This is advantage of using a life cycle model. A life cycle model forms a common understanding of the activities among the software engineers and helps to develop software in a proper manner, so that time can be reduced. The objective of this paper is to compare all traditional or existing SDLC model with our Proposed SDLC model for development of software in effective and efficient manner.