In recent years, rapid developing information and communication technology has been changing swiftly our insight of management perspective in our country and the World. Generally, dynamic and visionary private sector enterprises could easily adapt to these changes. From the point of information systems view, local governments just like the private sector which produce services. Local governments provide services to people living within the boundaries, but they never think or act as profit-oriented private sector. In new economy, cities are becoming more interconnected economically, culturally, and infrastructural through the parallel development of global telecommunication and transportation networks. Firstly, it has been investigated what factors affect the adoption of information technologies in Izmir Metropolitan Municipality. A survey was developed to measure the factors affecting the use of information technology in municipalities. The survey questions were prepared according to the effects of the above organizational factors. Also it was conducted with the managers of the branch manager level in Izmir Metropolitan Municipality. Finally, the results of the study were discussed and give a contribution to the literature.

Disaster recovery is a continuous dilemma in cloud platform. Though sudden scaling up and scaling down of user’s resource requests is available, the problem of servers down still persists getting users locked at vendor’s end. This requires such a monitoring agent which will reduce the chances of disaster occurrence and server downtime. To come up with an efficient approach, previous researchers’ techniques are analyzed and compared regarding prediction and monitoring of outages in cloud computing. A dual functionality Prediction and Monitoring Agent is proposed to intelligently monitor users’ resources requests and to predict coming surges in web traffic using Linear Regression algorithm. This solution will help to predict the user’s future requests’ behavior, to monitor current progress of resources’ usage, server virtualization and to improve overall disaster recovery process in Cloud Computing.

Data publishing plays a major role to establish a path between current world scenarios and next generation requirements and it is desirable to keep the individuals privacy on the released content without reducing the utility rate. Existing KC and KCi models concentrate on multiple categorical sensitive attributes. Both these models have their own merits and demerits. This paper proposes a new method named as novel KCi - slice model, to enhance the existing KCi approach with better utility levels and required privacy levels. The proposed model uses two rounds to publish the data. Anatomization approach is used to separate the sensitive attributes and quasi attributes. The first round uses a novel approach called as enhanced semantic l-diversity technique to bucketize the tuples and also determine the correlation of the sensitive attributes to build different sensitive tables. The second round generates   multiple quasi tables by performing slicing operation on concatenated correlated quasi attributes. It concatenate the attributes of the quasi tables with the ID's of the buckets from the different sensitive tables and perform random permutations on the buckets of quasi tables. Proposed model publishes the data with more privacy and high utility levels when compared to the existing models.

Olympic Games are international field and track events hosted within four years periods. Like other events, sprinting is a track event that requires rigorous and focused training. When training is done with little or no understanding of the possibilities of the games, the competition would leave more to be desired. This paper formulates, evaluates and validates a model for predicting the fastest sprinting time of Olympic athletes of 100m race for a-5 season appearances. Dataset was obtained from the Olympic official records of world best performances, typically Gold medalists in sprint for the male category from the inception in 1896 to the 2016 edition. The model was simulated on MATLAB. Cross-validation was done using residuals for whiteness and independence tests and model outputs. The results were evaluated based on Sum of Square Error (SSE), R-Square, adjusted R-Square, and Root Mean Square Error (RMSE) and benchmarked with existing models. The model outperformed the existing models with higher accuracy and goodness of fit. This prediction is a reasonable guide for predictive training, forecasting and future study on predictive algorithms.

Web service is a software application, which is accessible using platform independent and language neutral web protocols. However, selecting the most relevant services became one of the vital challenges. Quality of services plays very important role in web service selection, as it determines the quality and usability of a service, including its non-functional properties such as scalability, accessibility, integrity, efficiency, etc. When agent application send request with a set of quality attributes, it becomes challenging to find out the best service for satisfying maximum quality requirements. Among the existing approaches, the single value decomposition technique is popular one; however, it suffers for computational complexity. To overcome this limitation, this paper proposed a subset matching based web service selection and ranking by considering the quality of service attributes. This proposed method creates a quality-web matrix to store available web services and associated quality of service attributes. Then, matrix subsets are created using web service repository and requested quality attributes. Finally, web services are efficiently selected and ranked based on calculated weights of corresponding web services to reduce composition time. Experimental results showed that proposed method performs more efficient and scalable than existing several techniques such as single value decomposition.

Snow avalanche is an inevitable issue that is faced by mankind residing near the hilly and the mountainous regions. It is a natural disaster that is frequently observed in such terrains. Prediction of these avalanches is crucial for wellbeing of mankind. The concept of using cosine similarity with nearest neighbour is an innovative idea in nearest neighbour based avalanche forecasting model. The results of the model are encouraging, but a need for a mechanism that will provide additional preference to the significant parameters is observed. Present work focuses on the application of weighting factor to the nearest neighbour model with cosine similarity. Use of weighting factor helps in further tuning of the forecasting model. Selection of weighting factors for each parameter is accomplished by considering the effect of each parameter on the avalanche activity. The accuracy of the model is gauged using performance measures - Critical Success Index and Bias and by the changes reflected in the confusion matrix. An increase of 0.1978 and 0.4167 is observed in the values of Critical Success Index after the application of the weights to the forecasting model for dataset combination I and II respectively. The proposed work is implemented using the snow and meteorological data for the Bahang region of Himachal Pradesh, India.