Top 5 Most Viewed Articles From Academia in 2019

TTOOPP 55 MMOOSSTT VVIIEEWWEEDD
AARRTTIICCLLEESS FFRROOMM AACCAADDEEMMIIAA
IINN 22001199
International Journal of Artificial
Intelligence & Applications (IJAIA)
ISSN: 0975-900X (Online); 0976-2191 (Print)
http://www.airccse.org/journal/ijaia/ijaia.html

A HYBRID LEARNING ALGORITHM IN AUTOMATED TEXT
CATEGORIZATION OF LEGACY DATA
Dali Wang1
, Ying Bai2
, and David Hamblin1
1
Christopher Newport University, Newport News, VA, USA
2
Johnson C. Smith University, Charlotte, NC, USA
ABSTRACT
The goal of this research is to develop an algorithm to automatically classify measurement
types from NASA’s airborne measurement data archive. The product has to meet specific
metrics in term of accuracy, robustness and usability, as the initial decision-tree based
development has shown limited applicability due to its resource intensive characteristics. We
have developed an innovative solution that is much more efficient while offering comparable
performance. Similar to many industrial applications, the data available are noisy and
correlated; and there is a wide range of features that are associated with the type of
measurement to be identified. The proposed algorithm uses a decision tree to select features
and determine their weights. A weighted Naive Bayes is used due to the presence of highly
correlated inputs. The development has been successfully deployed in an industrial scale, and
the results show that the development is well-balanced in term of performance and resource
requirements.
KEYWORDS
Classification, machine learning, atmospheric measurement.
For More Details: http://aircconline.com/ijaia/V10N5/10519ijaia04.pdf
Volume Link: http://airccse.org/journal/ijaia/current2019.html

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[8] David Hamblin, Dali Wang, Gao Chen and Ying Bai, “Investigation of Machine Learning
Algorithms for the Classification of Atmospheric Measurements”, Proceedings of the 2017
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AN APPLICATION OF CONVOLUTIONAL NEURAL
NETWORKS ON HUMAN INTENTION PREDICTION
Lin Zhang1
, Shengchao Li2
, Hao Xiong2
, Xiumin Diao2
and Ou Ma1
1
Department of Aerospace Engineering and Engineering Mechanics, University of
Cincinnati, Cincinnati, Ohio, USA
2
School of Engineering Technology, Purdue University, West Lafayette, Indiana, USA
ABSTRACT
Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots
are in demand. However, the vast majority of robots can only follow strict instructions, which
seriously restricts their flexibility and versatility. A critical fact that strongly negates the
experience of HRI is that robots cannot understand human intentions. This study aims at
improving the robotic intelligence by training it to understand human intentions. Different
from previous studies that recognizing human intentions from distinctive actions, this paper
introduces a method to predict human intentions before a single action is completed. The
experiment of throwing a ball towards designated targets are conducted to verify the
effectiveness of the method. The proposed deep learning based method proves the feasibility
of applying convolutional neural networks (CNN) under a novel circumstance. Experiment
results show that the proposed CNN-vote method out competes three traditional machine
learning techniques. In current context, the CNN-vote predictor achieves the highest testing
accuracy with relatively less data needed.
KEYWORDS
Human-robot Interaction; Intentions Prediction; Convolutional Neural Networks;

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A SURVEY ON DIFFERENT MACHINE LEARNING
ALGORITHMS AND WEAK CLASSIFIERS BASED ON KDD
AND NSL-KDD DATASETS
Rama Devi Ravipati1
and Munther Abualkibash2
1
Graduate student in the Department of Computer Science, Eastern Michigan University,
Ypsilanti, Michigan
2
School of Information Security and Applied Computing, Eastern Michigan University,
Ypsilanti, Michigan
ABSTRACT
Network intrusion detection often finds a difficulty in creating classifiers that could handle
unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and
User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are
only 2% of overall datasets. So, these result in model not able to efficiently learn the
characteristics of rare categories and this will result in poor detection rates of rare attack
categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-
KDD datasets using different classifiers in WEKA.
KEYWORDS
KDD, NSL-KDD, WEKA, AdaBoost, KNN, Detection rate, False alarm rate.

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ADAPTIVE LEARNING EXPERT SYSTEM FOR DIAGNOSIS
AND MANAGEMENT OF VIRAL HEPATITIS
Henok Yared Agizew
Department of Management Information System, Mettu University, Mettu, Ethiopia.
ABSTRACT
Viral hepatitis is the regularly found health problem throughout the world among other easily
transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all
hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C
infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using
both structured and semi-structured interviews from internists of St.Paul Hospital. Once the
knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both
forward and backward chaining is used to infer the rules and provide appropriate advices in the
developed expert system. For the purpose of developing the prototype expert system SWI-prolog
editor also used. The proposed system has the ability to adapt with dynamic knowledge by
generalizing rules and discover new rules through learning the newly arrived knowledge from domain
experts adaptively without any help from the knowledge engineer.
KEYWORDS
Expert System, Diagnosis and Management of Viral Hepatitis, Adaptive Learning, Discovery and
Generalization Mechanism.

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EFFICIENT POWER THEFT DETECTION FOR RESIDENTIAL
CONSUMERS USING MEAN SHIFT DATA MINING
KNOWLEDGE DISCOVERY PROCESS
Blazakis Konstantinos1
and Stavrakakis Georgios2
1
School of Electrical and Computer Engineering, Technical University of Crete, Greece
2
School of Electrical and Computer Engineering, Technical University of Crete, Greece
ABSTRACT
Energy theft constitutes an issue of great importance for electricity operators. The attempt to detect
and reduce non-technical losses is a challenging task due to insufficient inspection methods. With the
evolution of advanced metering infrastructure (AMI) in smart grids, a more complicated status quo in
energy theft has emerged and many new technologies are being adopted to solve the problem. In order
to identify illegal residential consumers, a computational method of analyzing and identifying
electricity consumption patterns of consumers based on data mining techniques has been presented.
Combining principal component analysis (PCA) with mean shift algorithm for different power theft
scenarios, we can now cope with the power theft detection problem sufficiently. The overall research
has shown encouraging results in residential consumers power theft detection that will help utilities to
improve the reliability, security and operation of power network.
KEYWORDS
Data mining, Mean Shift clustering algorithm, Principal Component Analysis (PCA), Density-Based
Spatial Clustering of Applications with Noise (DBSCAN), Non-Technical Losses (NTLs), power
theft, smart grid, smart electricity metering

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AUTHORS
Blazakis Konstantinos: Received his BSc degree in Applied Mathematical and Physical
Sciences from N.T.U.A. (National Technical University of Athens) in 2010 and his MSc
degree in Electrical and Computer Engineering from Technical University of Crete, Chania,
Greece in 2015. Currently, he is a PhD candidate at Technical University of Crete. His areas
of research include data mining, machine learning, smart grids, distributed electricity
networks, renewable energy
Stavrakakis Georgios: Received his first degree Diploma in Electrical Engineering from the
N.T.U.A. (National Technical University of Athens), Athens, in 1980. His D.E.A. in
Automatic Control and Systems Engineering was obtained from I.N.S.A., Toulouse, in 1981
and his Ph.D. in the same area was obtained from “Paul Sabatier-Toulouse III” University,
Toulouse-France, in 1984. He has worked as a Research Fellow in the Robotics Laboratory of
N.T.U.A. (1985-1988), and as a Visiting Scientist at the Institute for Systems Engineering
and Informatics/Components Diagnostics & Reliability Sector of the Joint Research Center-
EEC at Ispra, Italy (1989-1990). He was Vice President of the Hellenic Center for Renewable
Energy Sources (CRES), Pikermi, Athens-Greece (2000-2002). He is currently a Full
Professor at the Technical University of Crete, Chania, Greece.

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