GWO Optimized LEACH CRT-Based Forwarding Scheme for Wireless Sensor Networks Energy Optimization (Published)

Wireless sensor networks (WSNs) are intelligent communication systems that assist in the industrial revolution of IoT. Sensor nodes present in WSNs often have limited battery lifetime due to their energy limits. Ability to replace or recharge the battery supply for networks in largescale or remote sites has been a major drawback. Clustering, duty cycling, data aggregation and routing has been major energy efficiency techniques employed to address energy problems. Due to the poor performance of the traditional LEACH, this paper employ the GWO CRT-based forwarding and splitting techniques to enhance the performance of LEACH. The simulation is done on MATLAB 2018a and the obtained results prove that the proposed protocol i.e., GWO CRT-based outperforms the existing models such LEACH, improved LEACH etc., with respect to energy consumption, throughput, and the lifespan of the network.

 

Keywords: Chinese remainder theorem, Clustering., and wireless sensor networks, cluster head, grey wolf optimization, packet splitting and forwarding techniques

Classification de la population de quelques régions à Madagascar par une approche non supervisée à base de K-moyennes (Published)

La méthode k-moyennes ou k-means est un moyen très utilisé pour l’analyse des données car elle peut s’appliquer à des différents secteurs d’activés ou d’étude. L’algorithme k-means s’adapte aux divers changements des données. Cette méthode nécessite de définir le nombre de clusters (k) pour que la classification soit efficace. L’algorithme k-moyennes peut être exécuté dans tous les données numériques et à un grand nombre d’ensembles de données tel que la « Classification de la population de quelques régions à Madagascar par une approche non supervisée à base de K-moyennes ». Il s’agit de la classification non supervisée les régions le plus semblable selon le sexe qui prend en considération les résidents ruraux et les résidents urbains d’après les données de recueillir RGPH 2018, INSTAT Madagascar. K-means génère des descriptions de cluster sous une forme minimisée pour maximiser la compréhension des données. Les données le plus proche sont groupées et le nombre dans les groupes ne sont pas forcément à égale.

Keywords: Analyse des données, Clustering., k-means, non supervisée

Web Data Mining: Views of Criminal Activities (Published)

Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.

Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining

Web Data Mining: Views of Criminal Activities (Published)

Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.

Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining

A HYBRID AND PERSONALIZED ONTOLOGY RANKING MODEL USING U-MEANS CLUSTERING AND HIT COUNT (Published)

Semantic Web is an extension of current Web which offers to add structure to the present Web. Ontologies play an important role in Semantic Web development and retrieval of relevant ontology. Ontology is being represented as a set of concepts and their inter-relationships relevant to some knowledge domain. As the number of Ontology repositories are more on Semantic Web, the problem of retrieving relevant ontologies of the scope arises. Even though there are Semantic Web search engines available, a major problem is that the huge number of results returned and which gives overhead to the searcher to find their need by themselves after going through the long list. This makes time consumption in search and creates dissatisfaction. One solution for this problem is that of maintaining the history of already analyzed, highly relevant and quality results in a log, which can used quickly to respond to the users of the similar type. This places highly relevant results analyzed and stored on the top list when results are presented to the searcher. Personalization and ranking takes care of these approaches. Another solution is the integration of clustering approach which helps in retrieving results from the history or log faster. This paper proposes a hybrid approach that creates the log and retrieves from log when the query is known and there are sufficient entries in the log. This approach imparts convenience to users and reduces the time complexity in finding their relevant needs.

Keywords: Clustering., Ontology, Ontology Ranking, Personalization, Semantic Search, Semantic Web