The paper presents the conceptual framework for the factors influencing adoption of e-government. By conducting review, it is revealed that there are few footsteps found on the citizen's perspective about the government to citizen's relations. Therefore; researchers focused upon the scenario of government to citizen services. Moreover, the paper contains the theoretical framework of the study that includes the TAM model which is mostly suitable and renowned for the assessment of technology. The study also focused upon the models, such as TAM and TOE along with the trust to be used in the study. Later the study provided a new conceptual framework and developed five construct for the study which is the extended form of TAM. These constructs are usefulness, ease of use, IT knowledge, technology factors, trust and behavioral intentions.
The High Resolution Images (HRI) are more attractive in order to process the tasks like patient health monitoring, Machine inspection in industry, identify the missing objects location with the help of satellite images, missing number plate reorganization, criminal fingerprint identification, and many more. Images are degraded because of insufficient sensor resolution of the acquisition device, moving of object or camera and during transmission, processing and storing. Over the decades many researchers have proposed different approaches to solve these problems. The process of constructing High Resolution image from cluster of low resolution pictures or single low resolution image is named image super resolution. In this paper, the authors have evaluated various classes of super resolution algorithms, such as Iterative Back Projection (IBP), Sparse Representation (SR), and Convolution Neural Networks (CNN) with their performances. After evaluation Super Resolution with Deep Convolution networks can give best results over the state of the methods in terms of PSNR and quality of the image. This paper also gives some directions to future researchers to solve more ill posed problems.
Information Retrieval (IR) is an activity of searching and extracting information from web resources based on the information need of user. There are various domains like legal domain where information being searched is stored in large databases and is available as documents written in natural languages. Due to the huge amount of information being available as text documents, there is a paradigm shift towards knowledge based information retrieval. Knowledge management requirements of legal domain are very challenging due to the complex structure of legal documents like acts, judgments, petitions, etc. Citations across these documents thus can be considered as very important component in legal processes. Citation analysis in legal domain is used to examine the patterns to find the relationship between the legal documents. Citations can be represented as network of legal documents where every document represents a legal concept. In this study, similarities between legal documents are analyzed and visualized using Network Analysis. Unlike other techniques where similarity is defined between two objects directly, network analysis allows to analyze relatedness with the help of betweenness and paths. Citations in the judgements of Indian courts are used to build the network structure which is then evaluated using network metrics.
The applications of data mining in the recent decade are increased exponentially. The devices through which data is collected and analyzed are different from the previous data collected. One of the data emerged in the recent years for knowledge discovery is class imbalance data. Class imbalance data can be defined as the data with extreme imbalance in the ratio of the class instances. In this paper, the authors have presented different scenarios of clustering algorithms for tackling such type of data; especially k-means algorithm towards class imbalance data. The survey provided a shortcoming of the k-means algorithm towards class imbalance data known as 'uniform effect'. The different causes and reasons for such behavior are analyzed with different benchmark imbalance data with different evaluation criterias.
Source code size is exercised as input to numerous parametric software estimation models. But it is rarely presented at the initial phase of software development. For software project planning, accurately determining the software size estimation at the early stage is a very important parameter. This paper aims to provide a basis for estimating the software size at the early stage of the software development process through a systematic review of previous works. The authors have reviewed the current techniques of size estimation to identify their strengths and weaknesses.
