iosr-JCE   Volume-20 ~ Issue-4 ~ Jul-Aug 2018

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

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[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.

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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.

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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.

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[2]. WANG, X. B. Amusing Properties of Odd Numbers Derived From Valuated Binary Tree, IOSR Journal of Mathematics, 2016, 12 (6), 53-57
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[5]. WANG, X. B. T3 Tree and Its Traits in Understanding Integers, Advances in Pure Mathematics, 2018, 8(5),494-507..

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

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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.

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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

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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..

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.

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Abstract: Computer Network remarkably affects working on the capability of the communication system and application requirements for the duration of our life. To set up transmission way with advancement ascribes and work on the capability of information transmission in Computer Network, the improved clustering routing protocol reliant upon node position using the base division routing competition instrument was proposed in this paper. This clustering routing protocol gives full idea to the circumstance of substitution nodes and the transmission course in regards to clustering and routing way decision. The current paper highlights the data transmission in networking.

Keywords: Data, Transmission, Network

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Abstract:The investigation of the feelings of the general population can provide us with information that is of
benefit to us. It has developed into a reliable tool for gaining insight into the ideas held by users of social
networking sites such as Twitter and Facebook, and it may be applied in a broad range of contexts. The analysis
of people's feelings expressed on social networks is one of these purposes. On the other hand, the challenges
that are presented by natural language processing make it challenging to do sentiment analysis in a manner that
is accurate while still being time and resource efficient (NLP). Deep learning models have emerged as a
potential solution to.....

Keywords: Deep Learning, datasets , recommender systems

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Analysis Using Deep Learning Techniques: A Review ― International Journal of Advanced Computer Science and Applications,
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Abstract: Big data and cloud computing are mixed. Big data enables clients to use thing computing for taking thought of disseminated demands in different datasets in a supportive plan. A class of passed data management frameworks is given on through the cloud computing. Gigantic cloud and Web data sources are taken thought of in a streamed disillusionment responsive database and took care of through a programming model with a similar algorithm conveyed in a gathering for goliath datasets. The unpredictability and gathering of data types can be used to outline monstrous plans of data. Cloud computing can give a productive stage to keeping an eye out for the data storage expected for immense development data analysis. Another perspective for offering a PC framework and a Big Data managing framework for a large number of resources open in the cloud through data analysis is connected to cloud computing..

Keywords: SVM, Virtual, Big Data

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Abstract: The advent of computer networks has revolutionized communication, commerce, and information sharing. However, this interconnected world has also opened up new avenues for malicious activities. Security issues in computer networking pose a significant threat to individuals, organizations, and even nations. One of the most prevalent security concerns is data breaches. Sensitive information, such as personal data, financial records, and intellectual property, is stored on networks. Cybercriminals employ various tactics, including phishing, malware, and social engineering.........

Keywords: Security, Computer, Networking

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