IOSR Journal of Computer Engineering (IOSR-JCE)

Jul - Aug 2018 Volume 20 - Issue 4

Version 1 Version 2 Version 3

Paper Type : Research Paper
Title : Overview of Biometric and Facial Recognition Techniques
Country : India
Authors : Omoyiola || Bayo Olushola
: 10.9790/0661-2004010105     logo

Abstract: Security has become a major issue globally and in order to manage the security challenges and reduce the security risks in the world, biometric systems such as face detection and recognition systems have been built. These systems are capable of providing biometric security, crime prevention and video surveillance services because of their inbuilt verification and identification capabilities(Hjelmas & Kee Low, 2001). This has become possible due to technological advancement in the fields of automated face analysis, machine learning and pattern recognition (Wojcik et al, 2016). In the paper, we review some biometric and facial recognition techniques.

 

Keywords–Biometrics, Face recognition, Face detection, Algorithms, Techniques, System, Verification, Identification, Faces and Image

[1]. Abboud B, Davoine F & Dang M (2004). Facial expression recognition and synthesis based on an appearance model. Signal Processing: Image Communication, 19(8), pp723–740.

[2]. Chunhua S., Paisitkriangkrai S., Zhang J., (2008), Face detection from few training examples. ICIP 15th IEEE International Conference on Image Processing, San Diego, CA, USA, pp. 2764 – 2767, 2008.

[3]. Down M.P, Sands R.J. (2004). Biometrics: An overview of the technology, challenges and control considerations. Information Systems Control Journal, 4 (2004), pp.53-56.

[4]. Guo J.M, Lin C.C., Wu M.F, Chang C.H, Lee H. (2011). Complexity reduced face detection using probability-based face mask prefiltering and pixel-based hierarchical-feature

[5]. Hjelmas E., Kee Low B. (2001). Face detection: A survey. Computer Vision and Image Understanding, 83(2001). pp.236 - 274..


Paper Type : Research Paper
Title : Bayesian Classification Model in Predicting Tuberculosis Infection
Country : India
Authors : Bukola Badeji – Ajisafe
: 10.9790/0661-2004010616     logo

Abstract:Predictive model for predicting Tuberculosis infection risk in individuals who came to receive treatment in Tuberculosis and leprosy centre (TBL) Ado – Ekiti was developed. The risk variables were identified and developed a predictive model based on the idenified factors. Interviewed were conducted with the staff of of TBL centre to identify risk variables, individuals that come for treatments at the TBL centres with one of the risk factors data set were generated which amounted to 699 patients data were preprocessed and 10-fold cross validation technique was used to partition the dataset into training and testing data. The model was developed using machine learning technique (Naïve Bayes' classifiers) and the result show that Naïve Bayes' classifiers was suitable in carrying out the task for predicting risk with minimum 92% accuracy of predictive model. Receiver Operating Characteristics area for the model was also 0.959 showing the level of bias was low.

[1]. Amor, N.B., Beferhat, S. and Elouedi, Z.(2004) Naïve Bayes vs Decision Trees in Intrusion
[2]. Detection Systems, ACM Symposium on Applied Computing, pp. 420 – 424
[3]. Benko, A & Wilson, B. (2003) online decision support gives plans an edge. Managed healthcare executive, Vol. 13 No. 5, p.20
[4]. Christopher, K., Darren, M., William, R.and Fredik, V. (2003) "Bayesian Event classification for intrusion detection", Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC‟03), 2003
[5]. Rupali .R. Patil (2014) Heart Disease Prediction system using Naïve Bayes‟ and Jlinck Mercer Smoothing Kumar V. et al 2007. Robbins basic pathology (8th ed.). Saunders Elsevier. pp. 516–522. ISBN 978-1-4160-2973-1..


Paper Type : Research Paper
Title : Investigation on Distribution of Nodal Multiplications on T3 Tree
Country :  
Authors : Guihong Chen || Jianhui Li
: 10.9790/0661-2004011722     logo

Abstract: The article investigates distribution law of node-multiplications of T3 tree that is an important valuated binary tree. It exhibits the multiplication of two nodes of the T3 tree merely distributes in specific range on specific levels of the tree. By intuitive figures the paper makes it easy to know what range of the multiplication is. Mathematical deductions are showed in detail ,which can enhance the theory of valuated binary tree.

[1]. WANG, X. B. Valuated Binary Tree: A New Approach in Study of Integers. International Journal of Scientific and Innovative Mathematical Research, 2016,4(3), 63-67
[2]. WANG, X. B. Amusing Properties of Odd Numbers Derived From Valuated Binary Tree, IOSR Journal of Mathematics, 2016, 12 (6), 53-57
[3]. WANG, X. B. Genetic Traits of Odd Numbers With Applications in Factorization of Integers, Global Journal of Pure and Applied Mathematics, 2017, 13 (2), 493-517
[4]. WANG, X. B. Two More Symmetric Properties of Odd Numbers, IOSR Journal of Mathematics, 2017, 13(3, ver. II), 37-40
[5]. WANG, X. B. T3 Tree and Its Traits in Understanding Integers, Advances in Pure Mathematics, 2018, 8(5),494-507..


Paper Type : Research Paper
Title : Comparative and Analysis Study of normal and epileptic seizure EEG signals by using various classification Algorithms
Country : Turkey
Authors : Seferkurnaz || Ahmed Ayad Saleh
: 10.9790/0661-2004012333     logo

Abstract: Epilepsy is defined as a brain activity disorder which is characterized by epileptic seizures. Electro-encephalogram (EEG) signs are one of the greatest preferable and basic methods of diagnosing Epileptic Seizures due to their practicality and simplicity. On the other hand, interpreting those signals is not an easy task, because of the non-linear and variable signal properties. In this work we offers a Data Mining classification approach by applying machine learning algorithms to detect standard and epileptic seizure from EEG brain signs, we are using t-SNE algorithm for preprocessing as adimensionality reduction algorithm onthe dataset then we applied three algorithms on the original dataset and on the preprocessed dataset to classify normal and epileptic seizure, and evaluate the performance of these three different classifiers (SVM, KNN and Random Forest), so that, the classifier with the.......

 

Keywords: -Epilepsy Seizure, EEG time series, t-SNEalgorithm, Classification, Machine Learning Algorithm

[1]. E.Bou Assi, M.Sawan, D. K.Nguyen and S.Rihana, "A Hybrid mRMR-Genetic Based Selection Method For The Prediction Of Epileptic Seizures," in Biomedical Circuits and Systems Conference, Atlanta, GA, USA, 2015.
[2]. M. Marvin Goldenberg, "Overview of Drugs Used For Epilepsy and Seizures Etiology, Diagnosis, and Treatment," National Center for Biotechnology Information, p. 392–415, 2010.
[3]. Ralph G. Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E. Elger, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state," PHYSICAL REVIEW, vol. 64, 2001.
[4]. T.Alexandros Tzallas, G.Markos Tsipouras, and I.Dimitrios Fotiadis, "Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis," Transactions on Information Technology in Biomedicine, vol. 13, no. 5, pp. 703-710, March 2009.
[5]. J. GOTMAN, "AUTOMATIC RECOGNITION OF EPILEPTIC SEIZURES IN THE EEG |," Electroencephalography and clinical N europhysiology', vol. 54, no. 5, pp. 530-540, 1982...


Paper Type : Research Paper
Title : Computerized Testing of Simulation and Embedded Software
Country : Libya
Authors : Dr. Amamer Khalil Masoud Ahmidat || Muhamad Abdulla Muhamad Abdussalam
: 10.9790/0661-2004013439     logo

Abstract: This paper deals a system for computerized testing of simulation and embedded software. This test makes thorough, frequent testing easy, which means errors will be caught more quickly. Computerized testing also means that more of the engineer's time can be spent on development, and that development can continue closer to field tests. In the proposed system, source code check-in triggers a chain of tests. These tests would include static checks, compilation, and test execution. For embedded software, it proposes extending the computerized testing to execution on digital hardware set aside for testing purposes.

[1]. Microsoft. Microsoft Visual Studio. http://www.visualstudio.com/.
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Paper Type : Research Paper
Title : Decision Support System Using Weighted Random Forest For Astronomical Data
Country : India
Authors : Ms. Shilpa Gedam || Dr. Mrs. Ranjana Ingolikar
: 10.9790/0661-2004014044     logo

Abstract: Decision Support System plays an important role in making decisions. Decision support system may use data mining techniques for solving problem. Astronomy is an area where Data Mining has been playing a major role. As the astronomical data is very huge, the classification of celestial bodies is the main issue of concern. To improve the classification accuracy a new improved weighted random Forest algorithm is suggested. A decision support system is designed using Weighted Random forest algorithm. The algorithm is implemented in Java. It is observed that weighted random forest performs better than random forest and other tree based data mining classification techniques..

 

Keywords: -Decision Support System, Ensemble learning, Random Forest, Weighted Random Forest

[1]. Thomas L Satty and Daji Erdu, "When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods", International Journal of Information Technology and Decision Making , Vol 14, Issue 6, Nov 2015

[2]. Jijun Zhang, Desheng Wu and D. L. Olson, " The method of grey related analysis to multiple attribute decision making problems with interval numbers", Mathematical and Computer Modelling , Vol 42, Issue 9-10, 991-998, Nov 2005.
[3]. Fiji Ren, Yanqiu Li and Min Hu, "Multi-Classifier ensemble based on Dynamic Weights", Multimed Tools Appl, Springer, 2017.
[4]. Franco-Arcega, L.G. Flores-Flores,Ruslan F. Gabbasov, "Application of decision trees for classifying astronomical objects",12th Mexican International Conference on Artificial intelligence,IEEE,181-186,2013.
[5]. Honghai Wang,"Pattern classification with random decision forest" International Conference on Industrial Control and Electronics Engineering,IEEE,128-130,2012...


Paper Type : Research Paper
Title : Comparative and Analysis Study for Malicious Executable by Using Various Classification Algorithms
Country : Turkey
Authors : Sefer Kurnaz || Mokhalad Eesee Khudhur
: 10.9790/0661-2004014552     logo

Abstract: There are a lot of applications regarding the data mining methods in detecting malwares. One of the most widely utilized data mining methods is the Classification method. In our research, we are presenting a data mining classification procedure through applying machine learning algorithms to detect malicious executable files, and this study will investigate the approach of classification in some algorithms such as (Support Vector Machine, Random Forest, KNN (k-Nearest Neighbors Classifier), and The Hoeffding Tree). In our classification process, we used some of well-known machine-learning algorithms by WEKA libraries, and then we train our dataset to detect malware. We made a comparative analysis between algorithms used and how they deal with the selected features based on the size of the data, to illustrate the performance efficiency. Where we got a high accuracy up to 98% with Random Forest. Moreover, this study is considered as a base for future studies regarding malware analysis through machine learning algorithms.

 

Keywords: -Machine Learning Algorithms, Computer Malicious Executable Files, Decision Tree, Classification, Active Learning.

[1]. Zhuojun Ren and Guang Chen, "EntropyVis: Malware Classification," Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017 10th International Congress on,IEEE, pp. 1-6, 2017.
[2]. Hassan Najadat, Assem Alhawari and Huthifh Al_Rushdan, "Data Mining Classification Approaches for Malicious Executable File Detection," The Fourth International Conference on Computer Science, Computer Engineering, and Education Technologi (IEEE), 2017.
[3]. Mozammel Chowdhury , Azizur Rahman and Rafiqul Islam, "Protecting Data from Malware Threats using Machine Learning Technique," Industrial Electronics and Applications (ICIEA), 2017 12th IEEE, pp. 1691 - 1694, 2017.
[4]. Muazzam Siddiqui, Morgan C. Wang and Joohan Lee, "A Survey of Data Mining Techniques for Malware Detection using File Features," Proceedings of the 46th Annual Southeast Regional, pp. 509-510 , 2008.
[5]. Moustafa Saleh, Tao Li and Shouhuai Xu, "Multi-context features for detecting malicious programs," Journal of Computer Virology and Hacking Techniques, Springer, vol. 14, no. 2, p. 181–193, 2015..


Paper Type : Research Paper
Title : Malware Analysis and Mitigation in Information Preservation
Country : Nigeria
Authors : Aru Okereke Eze || Chiaghana Chukwunonso E.
: 10.9790/0661-2004015362     logo

Abstract: Malware, also known as malicious software affects the user's computer system or mobile devices by exploiting the system's vulnerabilities. It is the major threat to the security of information in the computer systems. Some of the types of malware that are most commonly used are viruses, worms, Trojans, etc. Nowadays, there is a widespread use of malware which allows malware author to get sensitive information like bank details, contact information, which is a serious threat in the world. Most of the malwares are spread through internet because of its frequent use which can destroy large information in any system. Malwares from their early designs which were just for propagation have now developed into more advanced form, stealing sensitive and private information..............

 

Keywords: - Malware Analysis, Mitigation, Malware Analysis Methods and Techniques, Malware Softwares, and tools etc.

[1]. Ari H, N. et al. (2014). Penerapan Analisa Malware Pada Biscuit apt1 Menggunakan Teknik Reverse Engineering. Journal of KNSI.
[2]. Anderson, B., Quist, D., Neil, J., and Lane, T. (2011). Graph BasedMalware Detection Using Dynamic Analysis. Journal in Computer Virology, 7, 247-258,http://dx.doi.org/10.1007/s11416-011-0152-x.
[3]. Andreas, M. Christopher Kruegel, and Engin Kirda. (2007). Exploring Multiple Execution Paths for
[4]. Malware Analysis. In Proceeding of the IEEESymposium on Security and Privacy, Oakland, California, USA, pages 231.
[5]. Anderson, B., Storlie, C. and Lane, T. (2012). Improving Malware Classification: Bridging the Static/Dynamic Gap.


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