Dr. Nilamadhab, PhD - Debre Berhan University - Nilamadhab Mishra, Debre Berhan University

Dr. Nilamadhab


Debre Berhan University

Nilamadhab Mishra, Debre Berhan University

Addis Ababa, ETHIOPIA | Ethiopia

Main Specialties: Other


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Dr. Nilamadhab, PhD - Debre Berhan University - Nilamadhab Mishra, Debre Berhan University

Dr. Nilamadhab



Dr. Mishra received his, Master degree in Computer Applications (MCA) from Utkal University, Orissa, India, Master of Technology (M. Tech) in Computer Science & Engineering from Bijupatnaik University of Technology, Orissa, India, and Doctor of Philosophy (PhD) in Computer Science & Information Engineering from Graduate Institute of Electrical Engineering, College of Engineering, Chang Gung University, Taiwan, in 2002, 2011, and 2016 respectively.
More than 14 years of experience in Academic Teaching & Research. Published numerous peer reviewed researches in Thomson Reuters ranked SCI journals & IEEE conference proceedings.
Dr. Mishra Worked, as an Asst. Prof. in Gandhi Engineering College, Orissa, India ; as a Teaching and Research Assistant in Computer Science & Information Engineering department, Chang Gung University, Taiwan ; as a Lecturer/Assistant professor in Computer Engineering and Application department, Krupajal Engineering College and Krupajal Computer Academy, Orissa, India, respectively ; as a Lecturer in Computer Applications department, Academy of Business Administration, Orissa, India ; as a Lecturer in Computer Science department, Professional Academy of Computer Education, Orissa, India ; and as a Programmer in IDCOL Software, Orissa, India.
The Work for Doctor of philosophy is in the field of “Progressive Data Science and Knowledge Analytic Frameworks on IoT Data for Intellectual Domain Applications”. The Research targets to explore several cognitive, conceptual, theoretical, analytical, and logical data science and analytic frameworks on IoE environment aiming to intellectual domain applications.
Presently, Dr. Mishra works in Post Graduate Teaching & Research Dept., School of Computing, Debre Berhan University, Ministry of Education, Government of Ethiopia, and involves in Academic Research in the field of Computer Science and Information Engineering. The research encompasses Network Centric Data Management, Data Science Analytics and Applications, CIoT Big-Data System, and Cognitive Apps Design & Explorations.

Primary Affiliation: Debre Berhan University - Addis Ababa, ETHIOPIA , Ethiopia


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Jun 2016
Chang Gung University, Taiwan
Computer Science


Oct 2016
Debre Berhan University, Ethiopia
Asst. Prof
School Of Computing




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1PubMed Central Citations

Sensor Data Distribution and Knowledge Inference Framework for a Cognitive-Based Distributed Storage Sink Environment

Mishra, Nilamadhab, Hsien-Tsung Chang, and Chung-Chih Lin. "Sensor data distribution and knowledge i

International Journal of Sensor Networks

Large-scale sensor data distributions and knowledge inferences are major challenges for cognitive-based distributed storage environments. Cognitive storage sinks play an essential role in addressing these challenges. In a data-concentrated distributed cognitive sensor environment, cognitive storage sinks regulate the data distribution operations and infer knowledge from the large amounts of sensor data that are distributed across the conventional sensors. Embedding cognitive functions in conventional sensors is unreasonable, and the knowledge-processing limitations of conventional sensors create a serious problem. To overcome this problem, we propose a cognitive co-sensor platform across a large-scale distributed environment. Further, we propose a distributed data distribution framework (DDD-framework) for effective data distributions and a distributed knowledge inference framework (DKI-framework) that infers useful patterns for building knowledge intelligence. The analysis and discussion demonstrate that these frameworks can be adequately instigated for the purpose of optimal data distribution and knowledge inference within the horizon of a real-time distributed environment.

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January 2018
21 Reads

mCAF: a multi-dimensional clustering algorithm for friends of social network services.

Springerplus 2016 17;5(1):757. Epub 2016 Jun 17.

Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.

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http://dx.doi.org/10.1186/s40064-016-2420-1DOI Listing
July 2016
11 Reads

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

PLoS One 2015 24;10(11):e0141980. Epub 2015 Nov 24.

Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan, ROC.

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June 2016
25 Reads
3.234 Impact Factor

mCAF: a multi-dimensional clustering algorithm for friends of social network services

SpringerPlus20165:757 DOI: 10.1186/s40064-016-2420-1


In recent years, social network services have grown rapidly. The number of friends of each user using social network services has also increased significantly and is so large that clustering and managing these friends has become difficult. In this paper, we propose an algorithm called mCAF that automatically clusters friends. Additionally, we propose methods that define the distance between different friends based on different sets of measurements. Our proposed mCAF algorithm attempts to reduce the effort and time required for users to manage their friends in social network services. The proposed algorithm could be more flexible and convenient by implementing different privacy settings for different groups of friends. According to our experimental results, we find that the improved ratios between mCAF and SCAN are 35.8 % in similarity and 84.9 % in F 1 score.

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June 2016
13 Reads

Cognitive inference device for activity supervision in the elderly.

ScientificWorldJournal 2014 27;2014:125618. Epub 2014 Oct 27.

Department of Computer Science and Information Engineering, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan Taoyuan 333, Taiwan.

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http://dx.doi.org/10.1155/2014/125618DOI Listing
November 2015
19 Reads
1 Citation
1.220 Impact Factor

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

Chang H-T, Mishra N, Lin C-C (2015) IoT Big-Data Centred Knowledge Granule Analytic and Cluster Fram


The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

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November 2015
23 Reads

A Tracking and Summarization System for Online Chinese News Topics

Aslib Journal of Information Management

Purpose The purpose of this work is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, we extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen. Design/methodology/approach This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a TF-density algorithm to empirically compute three target parameters, i.e., recall, precision, and F-measure. Findings The proposed TF–density algorithm is implemented and compared with the well-known algorithms TF-IWF and TF-IDF. Three test data sets are configured from Chinese news websites for use during the investigation, and two important findings are obtained that help us provide more precision and efficiency when recognizing the important words in the text. First, we evaluate three topic tracking algorithms, i.e., TF-Density, TF-IDF, and TF-IWF, with the said target parameters and find that the recall, precision and F-measure of the proposed TF-Density algorithm is better than those of the TF-IWF and TF-IDF algorithms. In the context of the second finding, we implement a blind test approach to obtain the results of topic summarizations and find that the proposed Dynamic Centroid Summarization process can more accurately select topic sentences than the LexRank process. Research limitations/implications The results show that our tracking and summarization algorithms for news topics can provide more precise and convenient results for users tracking the news. Our analysis and implications are limited to Chinese news content from Chinese news websites such as Apple Library, UDN, and well-known portals like Yahoo and Google. Originality/value The research provides an empirical analysis of Chinese news content through the proposed TF-density and Dynamic Centroid Summarization algorithms. It focuses on improving our means of summarizing a set of news stories to appear for browsing on a single screen and carries implications for innovative word measurements in practice.

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September 2015
22 Reads

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

Mathematical Problems in Engineering

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications.

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May 2015
12 Reads

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

International Journal of Distributed Sensor Networks

In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.

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November 2014
19 Reads

Data-Centric Knowledge Discovery Strategy for a Safety-Critical Sensor Application

International Journal of Antennas and Propagation

In an indoor safety-critical application, sensors and actuators are clustered together to accomplish critical actions within a limited time constraint. The cluster may be controlled by a dedicated programmed autonomous microcontroller device powered with electricity to perform in-network time critical functions, such as data collection, data processing, and knowledge production. In a data-centric sensor network, approximately 3–60% of the sensor data are faulty, and the data collected from the sensor environment are highly unstructured and ambiguous. Therefore, for safety-critical sensor applications, actuators must function intelligently within a hard time frame and have proper knowledge to perform their logical actions. This paper proposes a knowledge discovery strategy and an exploration algorithm for indoor safety-critical industrial applications. The application evidence and discussion validate that the proposed strategy and algorithm can be implemented for knowledge discovery within the operational framework.

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June 2014
24 Reads