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i-manager's Journal on Computer Science (JCOM)

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Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
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Most Cited

Volume 5 Issue 1 March - May 2017

Research Paper

Test model for Cyberbullying

Anjusha*

Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India.

Pimpalshende, A. (2017). Test Model for Cyber Bullying. i-manager’s Journal on Computer Science, 5(1), 1-6. https://doi.org/10.26634/jcom.5.1.13791

Abstract

With the development of Web 2.0, online correspondence and interpersonal organizations are rising. This variation helps clients to impart their data and team up to each other effectively. These web administrations help build up new associations between people or strengthen existing ones. Notwithstanding, they can likewise prompt malicious activities or a digital criminal is dealt in a cyberbullying case. In the meantime, it can make children and young age people to utilize the advances for the expectation of hurting another person. The proposed strategy is a powerful technique in order to recognize cyberbullying exercises via web-based networking media. The recognition strategy can recognize the nearness of cyberbullying terms and group cyberbullying exercises in informal community, for example, Flaring, Provocation, and Bigotry utilizing Semantic-upgraded Minimized Stack Denoising Autoencoders (smSDA). Due to the negative impact of cyberbullying, a few procedures and strategies are proposed to conquer this issue.

References

[1]. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.

[2]. Biggs, B. K., Nelson, J. M., & Sampilo, M. L. (2010). Peer relations in the anxiety–depression link: Test of a mediation model. Anxiety, Stress, & Coping, 23(4), 431-447.

[3]. Georgiou, C., Anderson, S., & Dodd, T. (2017). Constructing informative Bayesian map priors: A multiobjective optimisation approach applied to indoor occupancy grid mapping. The International Journal of Robotics Research, 36(3), 274-291.

[4]. Gerlach, A. (2016). Improving e-commerce fraud investigations in virtual, inter-institutional teams: Towards an approach based on Semantic Web technologies (Master Thesis TH Koln - University of Applied Sciences, Institute of Informatics).

[5]. Hsu, W. N., Zhang, Y., & Glass, J. (2017). Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation. arXiv preprint arXiv:1707.06265.

[6]. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68.

[7]. Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4):1073-1137.

[8]. Silva, Y. N., Rich, C., & Hall, D. (2016, August). Bully Blocker: Towards the identification of cyberbullying in social networking sites. In Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on (pp. 1377-1379). IEEE.

[9]. Zhao, R., & Mao, K. (2017). Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder. IEEE Transactions on Affective Computing, 8(3), 328-339.

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Research Paper

A Mining Analysis over Psychiatric Database for Mental Health Classification

Shivangi Jain* , Mohit Gangwar**

* PG Scholar, Department of Computer Science and Engineering, Bhabha Engineering Research Institute, Bhopal, India.

** Department of Computer Science and Engineering, Bhabha Engineering Research Institute, Bhopal, India.

Jain, S., and Gangwar, M. (2017). A Mining Analysis over Psychiatric Database for Mental Health Classification. i-manager’s Journal on Computer Science, 5(1), 7-13. https://doi.org/10.26634/jcom.5.1.13792

Abstract

Data mining approach helps in various extraction unit from large dataset. Mental health and brain statistics is an important body part which is directly connected with the human body. There are many symptoms which can observe from the mental health care dataset, especially with psychiatric dataset. There are many health diseases associated with such symptoms, i.e. Anxiety, Mood Disorder, Depression, etc, such as mental retardation, Alzheimer, dementia, and many other related with such symptoms. A proper classification and finding its efficiency is needed while dealing with different set of data. A classification of these diseases and analysis requirement makes it working for user understanding over disease. In this paper different classification algorithms are presented and classification is performed using J48 (C4.5), Random Forest (RF), and Random Tree (RT) approaches. The classification with precision, recall, ROC curve, and F-measure is taken in as computation parameter. An analysis shows that the Random tree based approach finds efficient result while comparing with J48 and Random Forest algorithm.

References

[1]. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders.(1994). ((DSM IV)) Washington DC. American Psychiatric Association.

[2]. Cook, M., Soloveichik, D., Winfree, E., & Bruck, J. (2009). Programmability of chemical reaction networks. Algorithmic Bioprocesses, 543-584.

[3]. Gangwar, M., Mishra, R. B., & Yadav, R. S. (2012). Intelligent Computing Method for the Interpretation of Neuropsychiatric Diseases. International Journal of Computer Applications, 55(17), 23-31.

[4]. Gangwar, M., Mishra, R. B., & Yadav, R. S. (2013). Intelligent Computing Methods for The Interpretation of Neuropsychiatric Diseases Based on Rbr-Cbr-Ann Integration. International Journal of Computers & Technology, 11(5), 2490-2511.

[5]. Jackson, R. G., Patel, R., Jayatilleke, N., Kolliakou, A., Ball, M., Gorrell, G., et al. (2017). Natural language processing to extract symptoms of severe mental illness from clinical text: The Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project. BMJ Open, 7(1), e012012.

[6]. Johnson, P., Vandewater, L., Wilson, W., Maruff, P., Savage, G., Graham, P., et al. (2014). Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease. BMC Bioinformatics, 15(16), S11.

[7]. Kovalchuk, Y., Stewart, R., Broadbent, M., Hubbard, T. J., & Dobson, R. J. (2017). Analysis of diagnoses extracted from electronic health records in a large mental health case register. PloS One, 12(2), e0171526.

[8]. Pistollato, F., Ohayon, E. L., Lam, A., Langley, G. R., Novak, T. J., Pamies, D., et al. (2016). Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities. Oncotarget, 7(26), 38999.

[9]. Prinzie, A., & Van den Poel, D. (2008). Random forests for multiclass classification: Random multinomial logit. Expert Systems with Applications, 34(3), 1721-1732.

[10]. Sumathi, M. R., & Poorna, B. (2016). Prediction of Mental Health Problems Among Children Using Machine Learning Techniques. International Journal of Advanced Computer Science and Applications, 7(1), 552-557.

[11]. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.

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Research Paper

Back Propagation Network Based Brain Tumor Classification in Magnetic Resonance Image

L. Rajesh* , Kaavia**

*-** Department of Electronics Engineering, MIT Campus, Anna University, Chennai, India.

Rajesh, L., and Kaaviya. (2017). Back Propagation Network Based Brain Tumor Classification in Magnetic Resonance Image. i-manager’s Journal on Computer Science, 5(1), 14-21. https://doi.org/10.26634/jcom.5.1.13793

Abstract

Brain tumor is a major cause of death among many people. There are over one hundred and twenty types of brain and central nervous system tumors. In this paper, an automated support system has been proposed for the classification of tumor with the help of soft computing techniques. The usual detection of the brain tumor is accompanied with a lot of complexities due to the structure of the cells. Artificial Neural Network is used to classify the MRI image whether it is a tumor or benign. The constraints of manual analysis of the signal are it is time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors. The Probability of proper classification has been increased by using efficient image segmentation algorithm, such as solidity and level set technique. Back Propagation Network (BPN) with image and data processing techniques is employed to implement an automated tumor classification. The performance of BPN classifier was evaluated in terms of training performance and classification accuracies.

References

[1]. Al-Badarneh, A., Najadat, H., & Alraziqi, A. M. (2012, August). A classifier to detect tumor disease in MRI brain images. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) (pp. 784-787). IEEE Computer Society.

[2]. Deshmukh, R. J., & Khule, R. S. (2014). Brain tumor detection using artificial neural network fuzzy inference system (ANFIS). International Journal of Computer Applications Technology and Research, 3(3), 150-154.

[3]. El-Dahshan, E. S. A., Hosny, T., & Salem, A. B. M. (2010). Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing, 20(2), 433-441.

[4]. Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, (6), 610-621.

[5]. Hussain, S. J., Savithri, T. S., & Devi, P. S. (2012). Segmentation of tissues in brain MRI images using dynamic neuro-fuzzy technique. International Journal of Soft Computing and Engineering, 1(6), 2231-2307.

[6]. Joseph, R. P., Singh, C. S., & Manikandan, M. (2014). Brain tumor MRI image segmentation and detection in image processing. International Journal of Research in Engineering and Technology, 3(1), 1-5.

[7]. Kumar, S. S, and Shanmugapriya, M. M. (2016). Brain Tumor Detection Based on Probabilistic Neural Network (PNN) Training Process with Back Propagation Neural Network (BPNN) Classification. International Journal of Inventions in Computer Science and Engineering, 3(5).

[8]. Liew, A. W. C., & Yan, H. (2006). Current methods in the automatic tissue segmentation of 3D magnetic Resonance Brain Images. Current Medical Imaging Reviews, 2(1), 91-103.

[9]. Murugavalli, S., & Rajamani, V. (2006). A high speed parallel fuzzy c-mean algorithm for brain tumor segmentation. BIME Journal, 6(1), 29-33.

[10]. Zijdenbos, A. P., Forghani, R., & Evans, A. C. (2002). Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE transactions on Medical Imaging, 21(10), 1280-1291.

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Review Paper

An Exposition on Rescaling of Cache

Mayuri Chawla* , Sanjay M. Asutkar, Vijay Chourasia*

* Assistant Professor, Department of Electronics & Telecommunication Engineering, Jhulelal Institute of Technology, Nagpur, India.

Associate Professor, Department of Electronics & Telecommunication Engineering, MIET, Gondia, India.

* Assistant Professor, Department of Electronics & Telecommunication Engineering, MIET, Gondia, India.

Chawla, M., Asutkar, S., and Chourasia, V. (2017). An Exposition on Rescaling of Cache. i-manager’s Journal on Computer Science, 5(1), 22-37. https://doi.org/10.26634/jcom.5.1.13794

Abstract

Over the years, as the dependence on the computer based system has been on the rise and the researchers have made inroads in the ways in which the performance of the system gets improved with the time. The authors have reviewed and analyzed the different schemes or methodologies which have been proposed by different researchers concerned with improving the performance of the hardware with intent of developing a new caching technique that will present the cache dynamically as needed and shuts the processor cache to save the power. The review has been carried to track the development that has been carried out over the years in the domain of cache utilization and its improvisations. The review includes research papers, publications, web sources, and other available literature with an eye towards providing a comprehensive comparative analysis. Thus they put forward the insights gained from the review.

References

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[4]. Bhattacharjee, A., Contreras, G., & Martonosi, M. (2008, August). Full-system chip multiprocessor power evaluations using FPGA-based emulation. In Low Power Electronics and Design (ISLPED), 2008 ACM/IEEE International Symposium on (pp. 335-340). IEEE.

[5]. Calagos, M., & Chu, Y. (2016, July). Buffer-controlled c a c h e f o r l o w- p o w e r m u l t i c o r e s y s t e m s. I n Communications and Electronics (ICCE), 2016 IEEE Sixth International Conference on (pp. 147-152). IEEE.

[6]. Chauhan, R., Vyavahare, P., & Kothamasu, S. (2015, March). Fail-safe I/O to control RESET# pin of DDR3 SDRAM and achieve ultra-low system power. In Quality Electronic th Design (ISQED), 2015 16 International Symposium on (pp. 357-360). IEEE.

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[12]. Ito, M., & Ohara, M. (2016, April). A power-efficient FPGA accelerator: Systolic array with cache-coherent interface for pair-HMM algorithm. In Low-Power and High- Speed Chips (COOL CHIPS XIX), 2016 IEEE Symposium in (pp. 1-3). IEEE.

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[14]. Jeong, H., Park, J., Oh, T. W., Rim, W., Song, T., Kim, G., et al. (2016). Bitline Precharging and Preamplifying Switching pMOS for High-Speed Low-Power SRAM. IEEE Transactions on Circuits and Systems II: Express Briefs, 63(11), 1059-1063.

[15]. Kamoun, N., Bossuet, L., & Ghazel, A. (2008, December). SRAM-FPGA implementation of masked SBox based DPA countermeasure for AES. In Design and Test rd Workshop, 2008. IDT 2008. 3 International (pp. 74-77). IEEE.

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Review Paper

A Review on using Artificial Neural Network in Diagnosis of Autism Spectrum Disorder

Lakhwinder Kaur* , Vikas Khullar**

* Postgraduate, Department of Computer Science and Engineering, CT Institute of Engineering, Management and Technology, Shahpur.

** Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering, Management and Technology, Shahpur.

Kaur, L., and Khullar, V. (2017). A Review on using Artificial Neural Network in Diagnosis of Autism Spectrum Disorder. i-manager’s Journal on Computer Science, 5(1), 38-45. https://doi.org/10.26634/jcom.5.1.13795

Abstract

This paper discusses the concept of autism, its signs and symptoms, diagnosis and various technologies being used for diagnosis and treatment of autistic children. Autism is qualitative impairments in social interaction, communication and repetitive patterns of behavior. Autistic children can be treated as non communicative, non interactive and non responsive. The term autism was firstly used by Bleuler in 1908 to describe any patient and firstly described by Leo kanner in 1943. Autistic children suffer from mild, moderate, and severe levels of autistic behaviour. Mild level autistic children have few abnormal actitivites. Moderate level autistic children have moderate abnormal activities. Severe level autistic children have high abnormal functional activities. Autistic children symptoms can be easily seen within age of 18 to 30 months. Various methods of artificial intelligence are used for the diagnosis of activities or levels of autistic children by using various CHATS like CARS, ADI, ADOS, and DSMs. Various artificial neural network and fuzzy based system are used for detecting the severity levels of autism. By using the artificial neural networks and fuzzy system we can diagnose a child whether he is autistic or not.

References

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[14]. Figure 3. Fuzzy Expert System with Fuzzification and Defuzzification. Adapted from “Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation,” by Saritas, I., Ozkan, I. A., Allahverdi, N., & Argindogan, M., 2009. Journal of Intelligent Manufacturing, 20. 169-176. Retrieved from https://www.researchgate.net/figure/225336346_fig1_Fi g - 1 - Fu z z y- e x p e r t- s y s t em-w i t h - f u z z i f i c a t i o n - a n d - defuzzification-Allahverdi-2007.

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Volume 5 Issue 1 March - May 2017

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* PG Scholar, Department of Computer Science and Engineering, Bhabha Engineering Research Institute, Bhopal, India.

** Department of Computer Science and Engineering, Bhabha Engineering Research Institute, Bhopal, India.

Mayuri Chawla* , Sanjay M. Asutkar, Vijay Chourasia*

* Postgraduate, Department of Computer Science and Engineering, CT Institute of Engineering, Management and Technology, Shahpur.

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