Browsing by Author "Chaudhary, Pramod Kumar"

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    Cost volume and profit analysis: A tool of profit planning and control of NEBICO Pvt. Ltd.
    (2012) Chaudhary, Pramod Kumar; Yadav Raj Koirala
    Not available
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    Personal Pronouns and Time Adverbial in Tharu and English
    (Faculty of English Education, 2015) Chaudhary, Pramod Kumar
    The present study entitled "Personal Pronoun and Time Adverbial in Tharu and English" was carried out in order to find out the similarities and differences between English and Tharu deictic expressions. The researcher utilized both primary and secondary data to complete the study. The primary data were collected from native speakers of Tharu, who were selected by judgmental sampling procedure. The interview questions were adopted as a research tool to elicit primary data from both literate and illiterate informants. Then data were analyzed, interpreted and presented descriptively as well as statistically byusing table, charts and illustrations. The major findings of the study was thatTharu person deictic pronouns are more in number than those of English interms of honorific distinction, number, case, gender and Tharu Deictic personal pronouns have suffixation in plural number unlike in English. This thesis consists of four chapters. The first chapter deals with the introduction of the study which includes general background, literature review,objectives and significance of the study. It also includes the definition of the specific terms used in the research. The second chapter deals with the methodological processes of the study which consists of source of data collection and delimitations of the study. The third chapter is concerned with the analysis and interpretation of the collected data along with the comparison of similar and different cases between English and Tharu adverbials. The four chapter presents findings as wellas some pedagogical implications.
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    Sensor Network Anomaly Detection Model by cascading Inverse Weight Clustering and C5.0 Decision Tree
    (Pulchowk Campus, 2019-11) Chaudhary, Pramod Kumar
    Wireless Sensor Network is a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that does not conform to the normal behavior of the network communication. Anomaly detection in wireless sensor network using Inverse Weighted Clustering and C5.0 Decision tree, a method for classifying anomalous and normal activities have been proposed. The IWC clustering method is first used to partition the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, decision trees are built using C5.0 decision tree algorithm. The decision tree on each cluster refined the decision boundaries by learning the subgroups within the cluster. The experiment was carried out on three datasets (University of North Carolina Greensboro (UNCG), Intel Berkeley Research Lab (IBRL) and Bharatpur Airport WSN). The results show that proposed method achieved detection rate of 98.9% at false alarm-rate of 0.31% on IBRL; detection rate of 99.57 % at false alarm-rate of 0.35% on Bharatpur Airport.