Family welfare is a family formed in legitimate marriage, spiritual needs and material worthy, devoted to God YME, have a harmonious relationship, harmonious and balanced with society and the environment. The government has implemented various family development programs prosperous. To support this, every year the government implements the family data collection process. Family data collection is considered an important step because it has many functions, primarily to understand the target group and to determine solutions to solve the problems of each target group. The search or discovery process of information and knowledge contained in the number of data can be done with data mining technology. Data mining is a term used to describe the discovery of knowledge in a database. In this case data mining can be used to determine the status of the prosperous family stage. The K-Nearest Neighbor (KNN) method, the Naive Bayes method and the Principal Component Analysis (PCA) are used for the proper classification of status stages. Based on the test results, the performance test of classification algorithm for case of determining status of prosperous family of Sidareja District for Naïve Bayes method using confusion matrix obtained 98.12% accuracy after added PCA feature selection to 97.73% while KNN method obtained accuracy of 98.86%, then after added PCA feature selection increased to 98.96%.

Maximizing the network lifetime is one of the major challenges in Low Power and Lossy Networks (LLN). Routing plays a major role in LLN, for minimizing the energy consumption across the network nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for LLN. Though, RPL fulfilled the necessity of LLN, several issues like increasing the energy efficiency, quality of service and the network lifetime are to be focused. In LNN, the inefficient route selection results in increased network traffic, energy depletion and packet loss ratio across the network. In this paper, we propose a fuzzy logic based energy aware routing protocol (FLEA-RPL), which considers the routing metrics load, residual energy (RER) and expected transmission count (ETX) for the best route selection. FLEA-RPL applies fuzzy logic over these metrics, to select the best route to transfer the network data efficiently. The COOJA simulator is used to assess the efficiency of the proposed FLEA-RPL. The FLEA-RPL protocol is compared with similar protocol standard RPL, MRHOF (ETX) based RPL (MRHOF-RPL) and FL-RPL. The simulation result shows that FLEA-RPL improves the network lifetime by 10-12% and packet delivery ratio by 2-5%.

Dynamic behavior of a heavy homogeneous sphere in a spherical cavity of a supporting body that performs specified translational movements in space has been studied. Using the Appel formalism, the equations of ball motion in a moving spherical cavity without slip are constructed and a numerical analysis of the evolution of the ball motion is carried out.

Migration of legacy system is not a single step activity but a process that comprises of several phases of which Legacy System Understanding (LSU) is the first step. The intent of this work is to carry out a detailed study on the Legacy System Understanding in terms of Techniques and Tools used and to identify the potential gaps in them. The understanding of the legacy system has to be at the perspective of system level rather than the code level which has a narrow perspective, because the migration at code level may have a cascading impact to different aspects of the Legacy System. These findings have enabled us to formulate a process for building up an artefact repository and artefact dependency repository. These repositories along with the legacy system have aided us in understanding the legacy system in a comprehensive manner in terms of migrating artefacts in the context of migration of legacy systems. A formal mathematical model for representing the status of LSU and application of the same on a case study has been presented.

Mobile tourism or m-tourism can assist and help tourists anywhere and anytime face the overload of information that may appear in their smartphones. Indeed, these mobile users find difficulties in the choice of points of interest (POIs) that may interest them during their discovery of a new environment (a city, a university campus ...). In order to reduce the number of POIs to visit, the recommendation systems (RS) represent a good solution to guide each tourist towards personalized paths close to his instantaneous location during his visit. In this article, we focus on (1) the detection of the spatiotemporal context of the tourist to filter the POIs and (2) the use of the previous notations of the places. These two criteria make it possible to integrate the evolutionary context of the visit in order to predict incrementally the most relevant transitions to be borrowed by the tourists without profile. These predictions are calculated using collaborative filtering algorithms that require the collection of traces of tourists to better refine the recommendation of POIs. In our software prototype, we have adapted the SLOPE ONE algorithm to our context of discovering the city of Chlef to avoid problems like data scarcity, cold start and scalability. In order to validate the use of this prototype, we conducted experiments by tourists in order to calculate indicators to assess the relevance of the recommendations provided by our system.

Recommender systems inherently dynamic in nature and exponentially grow with time, in terms of interests and behaviour patterns. Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is constant. This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic community algorithm in order to exploit the temporal changes in the community structure to enhance the existing system. Our framework also provides solutions to common inherent issues of collaborative filtering approach such as cold-start, sparsity and compared against static and traditional collaborative systems. The outcomes indicate that the proposed system yields higher values in quality standards and minimizes the drawbacks of the traditional recommender system.

Extraction of complex head and hand movements along with their constantly changing shapes for recognition of sign language is considered a difficult problem in computer vision. This paper proposes the recognition of Indian sign language gestures using a powerful artificial intelligence tool, convolutional neural networks (CNN). Selfie mode continuous sign language video is the capture method used in this work, where a hearing-impaired person can operate the Sign language recognition (SLR) mobile application independently. Due to non-availability of datasets on mobile selfie sign language, we initiated to create the dataset with five different subjects performing 200 signs in 5 different viewing angles under various background environments. Each sign occupied for 60 frames or images in a video. CNN training is performed with 3 different sample sizes, each consisting of multiple sets of subjects and viewing angles. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our selfie sign language data to obtain better accuracy in recognition. We achieved 92.88 % recognition rate compared to other classifier models reported on the same dataset.

In this paper, power quality at the distribution system has been examined by introducing an observer based control technique with fuzzy logic controller for wind energy conversion systems with constant wind velocity, for combination of linear, nonlinear loads and with load removal in one of the phases. The power quality improvement, including voltage regulation and reactive power management on the distribution side is achieved and the device used here is a distributed static compensator (DSTATCOM) a voltage source converter(VSC) based power electronic device. The performance is found to be satisfactory with the implementation of DSTATCOM for better voltage regulation with self-sustained DC link voltage at VSC of DSTATCOM. The fuzzy logic controller is used to generate gate pulses to VSC for power quality improvement and is simulated in MATLAB environment and results are studied.