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Online Examination System is a software solution, which allows any industry or institute to arrange, conduct and manage examinations via an online environment. It can be done through the Internet/Intranet and/ Local Area Network environments. Problems arising from human hacking or technical difficulties may lead to questioning of reliability and efficiency of the online exams. The system architecture proposed in this paper provides integrated management of passwords that prevent unauthorized access by the other users who nowadays are a havoc for security of the portals. In this paper, the combination of CAPTCHA and picture password system is proposed which improved the capability of resistance to the attack by malicious programs. CAPTCHAs are used to improve the security of Internet based applications in order to ensure that a web based application which is intended to be used by a human being is not maliciously used by Artificially Intelligent programs called bots. Graphical passwords, which consist of clicking on images rather than typing alphanumeric strings, may help to overcome the problem of creating secure and memorable passwords. Our results showed that this combination is practical in the aspects of security and usability.

CATCHA, Online Exam Portal, Picture Password, Security

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William Stallings and Lawrie Brown. Computer Se-
curity: Principle and Practices. Pearson Education,
2008
William Stallings and Lawrie Brown. Computer Se-
curity: Principle and Practices. Pearson Education,
2008
William Stallings and Lawrie Brown. Computer Se-
curity: Principle and Practices. Pearson Education,
2008
William Stallings and Lawrie Brown. Computer Se-
curity: Principle and Practices. Pearson Education,
2008
William Stallings and Lawrie Brown. Computer Se-
curity: Principle and Practices. Pearson Education,
2008
William Stallings and Lawrie Brown. Computer Se-
curity: Principle and Practices. Pearson Education,
William Stalings and lawrie brown: cpmputer security:priciple and practices. Pearson education 2008.
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Irtiqa Amin, Mohd Umar John, "Evaluating and Designing a Secure Text-Based CAPTCHA and Picture Password for Online Examinations," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.60-67, 2019.

Through the proposed method it can be clearly known that a human being (specially a human baby) is suffering from sickle cell anaemia or not by a fast and efficient way. This disease is caused by mutation of the gene controlling Beta-chain of haemoglobin (Hb). It replaces Glutamic acid (GAG) present at 6th position of the Beta-chain by Valine (GTG). The mutant haemoglobin molecule undergoes polymerization under low Oxygen tension causing the change in the shape of the RBC from biconcave disc to elongated Sickle-like structure. With the help of this test, it can be known that a human is a Mutant of this disorder or not, which can save the life of new generation.

Beta chain of Haemoglobin, Glutamic acid, Valine

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Anindya Sundar De, "Detection of the Sickle Cell Anaemia Disease by Simple and Efficient Way," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.68-71, 2019.

Present time demands for high-speed 4G broadband wireless network is enabled by the practice of multiple antennas at both the transmitter and receiver ends. The multiple-input multiple-output MIMO transmission creates parallel channels over the same time and frequency i.e. spatial multiplexing to achieve high capacity and link reliability without the need for additional power of spectrum. Thus, MIMO transmission exploits the multipath fading mechanism to increase data rate and system capacity. However, in order for MIMO systems to function, there has to be a means of “slicing” the carrier signal into multiple subcarriers that modulate the low-frequency information data. These parallel low-rate subcarriers can then be transmitted and received via the multiple antenna configurations. Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique that creates these parallel sub-channels that are low-rate or narrowband in nature. By inserting cyclic prefix between the sub-channels, orthogonality is maintained and inter-symbol interference is totally eliminated. Hence, a combination of MIMO and OFDM i.e. MIMO-OFDM not only drastically improves channel capacity and data rates, it also combats frequency-selective fading thereby improving link.

MIMO, OFDM, Spatial multiplexing and frequency selective fading

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Amit Shivhare, Ravi Kumar, Manish K. Patidar, "Review of MIMO-OFDM System Using Simulink Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.72-75, 2019.

Even though they are many non-profit organizations, they are still viewed as for-profit organizations, as they are run with a corporate structure. Similarly, many charity organizations are being misappropriated without as there is no way for donors to know how their money is being spent. Due to this, the needy are not receiving the full welfare. With a Charity DApp on blockchain, charity organizations can collect funds. ID can be used to register donors as well as organizations. Spending of funds is made transparent through the power of blockchain.

blockchain, Smartcontracts, consensus, Dapp

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D. Venkata Sai, A. Yeshwanth Sai, E. Koti Reddy, K. Suresh Babu, "Funds Transfer Using Blockchain," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.76-79, 2019.

In this paper authors describes the automatic music generation system and automatic music evaluation system. The system composes short pieces of music by choosing some factors in music, such as timbre, pitch interval, rhythm, tempo etc. The most important features of the system the music is generated according to the mood and sentiments of person. In the implemented work mode control the pitch interval and density control the rhythm of music. Neural Network Algorithm for automatic evaluation system of music. Music composition is an art, even the task of playing composed music takes considerable effort by humans. Given this level of complexity and abstractness, designing an algorithm to perform both the tasks at once is not obvious and would be a fruitless effort. In this paper authors describe new music composition by using trained music data set to extract useful music pattern and generate the music in the form of chord.In this paper also discussed about method or platform use for automatic music generation.

Music Generator, Generative Model, The Restricted Boltzman Machine, MIDI file, Tensorflow

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Lawakesh Patel, Nidhi Singh, Rizwan Khan, "Automatic Music Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.80-82, 2019.

Throughout the 20th century, views about breast cancer have drastically changed. Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012. This type of cancer is the second most common cancer overall. Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine (SVM), Decision Tree (DT), K Nearest Neighbour (KNN), and Naïve Bayes (NB). This paper mostly focuses on detailed analysis and comparing the performance of above-mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, Precision, Misclassification Rate, False Positive Rate, True Positive Rate and Specificity. The main part of the project is creating a useful tool for predicting breast cancer with high accuracy before getting ill or in the initial stage of the disease. In other words, we can anticipate the future for women diseases.

Machine Learning, Breast Cancer, CART, Naive Bayes, K nearest neighbors, Support Vector Machine

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Pragati Prakash, Nidhi ekka, Manjit Jaiswal, "A Reckoning Analysis and Assessment of Different Supervised Machine Learning Algorithm for Breast Cancer Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.83-88, 2019.

Google Cloud Vision is readily used for major purposes such as label detection face recognition mood analysis, object detection content filtering and that is to a certain extent. The efficiency of any system is based on the fact that how well the system is performing in suboptimal conditions in case of Google Cloud Vision the suboptimal working condition include the use of noisy images instead of perfect ones. This paper deals with how this Google Cloud Vision works under noisy images and how robust the system stays under these conditions. This API generates different outputs by adding different noises with different intensity in noise. It is clearly observed that with the mean value of 20% impulse noise and 0.1 variance Gaussian noise, the API can be easily misguided in predicting the actual label and text for the images. A better and accurate outcome can be obtained by pre-processing and validating the image for any noise and denoising an image up to some extent for a better and accurate outcome which could be more beneficial than updating the currently working algorithm.

Google Cloud Vision, Robustness, Noisy Images, Gaussian Noise, Impulse Noise

[1] F. R¨ohrbein, P. Goddard, M. Schneider, G. James, and K. Guo, “How does image noise affect actual and predicted human gaze allocation in assessing image quality?” Vision research, vol. 112, pp. 11–25, 2015.
[2] K. Simonyan and A. Zisserman. “Very deep convolutional networks for large-scale image recognition”. In Proceedings of conference paper at ICL, 2015
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[6] Ratnesh Kumar Shukla, Ajay Agarwal, Anil Kumar Malviya, "An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43, 2018
[7] Dougherty G. “Digital Image Processing for Medical Applications,” second ed., Cambridge university press, 2010.
[8] Boyat, A. and Joshi, B. K. “Image Denoising using Wavelet Transform and Median Filtering”, 2013 Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, pp. 1-6, 2013.doi: 10.1109/NUiCONE.2013.6780128
[9] Mandeep Kaur, Balkrishan Jindal, "Improved Sparse matrix Denoising Techniques using affinity matrix for Geographical Images", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.51-56, 2017
[10] Priyanka Kamboj, Versha Rani,” Brief study of various noise model and filtering techniques”, Journal of Global Research in Computer Science, vol.4, No.4, pp.166-171, April 2013.
[11] Monika Raghav, and Sahil Raheja,” Image Denoising Techniques: Literature Review”, International Journal of Engineering and Computer Science, vol.3, pp. 5637-5641, Issue 5, May 2014.
[12] Joshi, A., Boyat, A. and Joshi, B. K. “Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper noise in Digital Images,” IEEE International Conference on Issues and Challenges in Intelligant Computing Teachniques, Gaziabad, India, 2014

Akshat Pathak, Aviral Ruhela, Anshul K. Saroha, Anant Bhardwaj, "Examining Robustness of Google Vision API Based on the Performance on Noisy Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.89-93, 2019.

Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing field of deep learning. In this paper, deep learning techniques and their applications to medical image analysis are surveyed. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The comparisons between MLs before and after deep learning revealed that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is learning image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The survey of deep learning also revealed that there is a long history of deep-learning techniques in the class of ML with image input, except a new term, “deep learning”. “Deep learning” even before the term existed, namely, the class of ML with image input was applied to various problems in medical image analysis including classification between lesions and non-lesions, classification between lesion types, segmentation of lesions or organs, and detection of lesions. ML with image input including deep learning is a very powerful, versatile technology with higher performance, which can bring the current state-of-the-art performance level of medical image analysis to the next level, and it is expected that deep learning will be the mainstream technology in medical image analysis in the next few decades. “Deep learning”, or ML with image input, in medical image analysis is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical image analysis in the next few decades.

Deep learning, Convolutional neural network, Massive-training artificial neural network, Computer-aided diagnosis, Medical image analysis, Classification (key words)

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[3]. Suzuki K, Wang F, Shen D, et al.: Machine Learning in Medical Imaging (MLMI). Lecture Notes in Computer Science Vol. 7009 , Springer-Verlag, Berlin, 2011
[4]. Suzuki K: Machine Learning for Medical Imaging 2012. A special issue of Algorithms, 2012
[5]. Suzuki K, Yan P, Wang F, et al.: Machine learning in medical imaging. Int J Biomed Imaging 2012: 123727, 2015
[6]. Suzuki K: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. IGI Global, Hershey, PA, 2016
[7]. Wang F, Shen D, Yan P, et al.: Machine Learning in Medical Imaging (MLMI). Lecture Notes in Computer Science Vol. 7588 , Springer-Verlag, Berlin, 2012
[8]. Suzuki K: Machine learning in computer-aided diagnosis of the thorax and colon in CT: A survey. IEICE Trans InfSyst E96-D: 772-783 , 2013
[9]. Wu G, Zhang D, Shen D, et al.: Machine Learning in Medical Imaging (MLMI). Lecture Notes in Computer Science Vol. 8184 , Springer-Verlag, Berlin, 262, 2013
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[16]. Doi K: Diagnostic imaging over the last 50 years: Research and development in medical imaging science and technology. Phys Med Biol 51: R5-R27, 2016
[17]. Doi K: Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31: 198-211 , 2018
[18]. Lostumbo A, Wanamaker C, Tsai J, et al.: Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography. AcadRadiol 17: 39-47 , 2019
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T. Vigneswari, K. Kiruthika, M. Salmabee, "Survey of Deep Learning Applications to Annotation Image Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.94-103, 2019.

To validate the management or organization`s creditworthiness, bankruptcy prediction model (BPM) is significantly important for financial firms. The socio-economic effects can be devastated if there exist a lack in predicting bankruptcy precisely. To anticipate this, it is important to provide financial decision makers with bankruptcy prediction in a efficient manner. To deal with this bankruptcy prediction problems, this paper projects a inclusive review depending on different machine learning and statistical methods. The methods of machine learning includes decision trees, artificial neural networks (ANN) and support vector machines (SVM) whereas the statistical methods such as logistic regression (LR), multivariate discriminant analysis (MDA) and linear discriminant analysis (LDA) are used. To manage huge data sets without degrading performance by means of prediction, conventional statistical methods were employed. For small data sets, the machine learning methods offers better accuracy in terms of predictions when compared with the conventional statistical methods. Depending on the respective methods advantages and drawbacks, this paper examines a comparative study of different methods. To enhance the accuracy for massive data sets, particle swarm optimization (PSO) and genetic algorithm (GA) are the optimization methods that were combined for bankruptcy prediction

Bankruptcy, ANN; SVM; Machine learning

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[7] P. R. Kumar and V. Ravi, “Bankruptcy prediction in banks and firms via statistical and intelligent techniques — A review,” European Journal of Operational Research, vol. 180, no. 1, pp. 1-28, July 2007.
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[10] S. Lee and W. S. Choi, “A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis,” Expert Systems with Applications, vol. 40, no. 8, pp. 2941-2946, June 2013.

C. Punitha Devi, T. Vigneswari, C. Nancy, E. Priyanka, R. Yamuna, "A Survey on Machine Learning and Statistical Methods for Bankruptcy Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.104-111, 2019.

With the huge expansion of internet based services and important information on networks, network protection and security is a very significant task. Intrusion Detection system (IDS) is the standard component in network security framework and is essential to protect computer systems and network from different attacks. IDSs is designed to detect both known and unknown attacks in computer systems and networks. This paper presents different Machine Learning techniques of IDS for protecting computers and networks. This study analyzes different machine learning methods in IDS. It reviews related studies focusing on single, hybrid and ensemble classifiers with relevant datasets.

Machine Learning, intrusion detection, Single Classifiers, Hybrid Classifiers, Ensemble Classifiers.

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[5]. S. Soni1 , P. Sharma, “Review of Hybrid Intrusion Detection System”, International Journal of Computer Sciences and Engineering, Vol.-6, Issue-6, pp 1100-1104, 2018.
[6]. Uzair Bashir and Manzoor Chachoo, “Intrusion Detection and Prevention System: Challenges & Opportunities” IEEE ,pp. 806-809, 2014.
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[15]. C. Amali Pushpam , J. Gnana Jayanthi,” A Review on effect of SVM in Intrusion Detection System”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue.12,pp.471-474, 2018.
[16]. Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set”, Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications (CISDA`09), IEEE Press, Piscataway, NJ, USA, p. 53-58, 2009.
[17]. L.Dhanabal, Dr. S.P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, June, pp 446-452, 2015.
[18]. Hossein Shirazi, “Anomaly intrusion detection system using information theory, K-NN and KMC algorithms”, Aus tralian Journal of Bas ic and Applied Sciences, Vol. 3, pp- 2581-2597, 2009.
[19]. Ahmad, I., Abdullah, A., Alghamdi, A., & Hussain, M, “Optimized intrusion detection mechanism using soft computing techniques”. Telecommunication Systems, Vol. 52(4), 2187–2195, 2013.
[20]. W.L. Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri, “Hybrid Modified-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems”, The Scientific World Journal, 2015.
[21]. G. Wang, J. Hao, J. Ma, and L. Huang, “A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering,” Expert systems with applications, vol. 37, no. 9, pp. 6225-6232, 2010.
[22]. S.S. Sindhu, S.G. Sivatha, and A. Kannan, “Decision tree based light weight intrusion detection using a wrapper approach,” Expert Systems with applications, vol. 39, no. 1, pp. 129-141, 2012.
[23]. Reda M. Elbasiony , Elsayed A. Sallam , Tarek E. Eltobely , And Mahmoud M. Fahmy, “A hybrid network intrusion detection framework based on random forests and weighted k-means” Ain Shams Engineering Journal , 753–762, 2013.
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[25]. M.S.M. Pozi, M.N. Sulaiman, N. Mustapha, T. Perumal, “Improving anomalous rare attack detection rate for intrusion detection system using support vector machine and genetic programming,” Neural Processing Letters, vol. 44, no. 2, pp. 279-290, 2016.
[26]. Ahmed I. Saleh , FatmaM. Talaat, LabibM. Labib, “A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers”, Artif Intell Rev, 2017.
[27]. Oladeji Patrick Akomolafe and Adeleke Ifeoluwa Adegboyega, “An Improved KNN Classifier for Anomaly Intrusion Detection System Using Cluster Optimization”, International Journal of Computer Science and Telecommunications (IJCST) Vol. 8, Issue 2, 2017.
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[29]. Yassine Maleh, Abdellah Ezzati, Youssef Qasmaoui, Mohamed Mbida, 2015 ,“A Global Hybrid Intrusion Detection System for Wireless Sensor Networks”, Procedia Computer Science vol. 52, pp 1047 – 1052.
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P. Anitha, D. Rajesh, K. Venkata Ratnam, "Machine Learning in Intrusion Detection – A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.112-119, 2019.