Volume-9 , Issue-2 , Feb - 2021 Go Back

The stock market is volatile and is subject to fluctuations. There are many factors like news, fundamental indicators, and heuristic technical indicators et cetera which contribute to such fluctuations. The randomness and volatility have drawn the attention of many researchers and perplexed them. Algorithm trading has been gaining popularity, as machines are able to process tons of data. The ability of an algorithm to predict the price movement gives an opportunity to gain a fortune from the stock market. In this paper, we study the historical prices, calculate the technical indicators based on them, apply feature selection to remove multicollinearity and find the most important features affecting the prices before processing it into the LSTM network to predict the price movement. The prediction of market value can help maximize the profit while keeping the risk comparatively low.

Stock prediction, Technical Indicators, Feature Selection, XGBoost, LSTM, Neural Network

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[14] A.Sahoo, J.K.Mantri, “Stock Market Prediction Using Text Mining Approaches: A Survey”, IJCSE, Vol. 7, Issue.2, pp. 443-450, 2019.
[15] V. Ingle, S. Deshmukh, “Predictive mining for stock market based on live news TF-IDF features”, International Journal of Autonomic Computing, Vol.2 No.4, pp. 341-345,2017.

Shreshtha Sarkar, Inteshab Nehal, "Stock Market Trend Prediction using Technical Indicators and Deep Learning Methods," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.1-4, 2021.

With the rapid growth in the elderly population, conventional health care system is no longer sufficient to provide personalized healthcare services for the elderly and healthcare givers are looking for a technological based solution. Ambient Assisted Living(AAL) is such a solution and at the heart of AAL is human activity recognition. Modern smartphone embedded with a lot of sensors has become an integral part of our life and is a vital option for collecting data for activity recognition. In this paper we looked at the use of smartphone accelerometer with supervised machine learning algorithm in WEKA framework for monitoring Activity of Daily Living (ADL): standing, walking, lying, walking upstairs and walking down stairs. Sitting, for the elderly in their environment of choice. We examined two common classification algorithms: Random Forest (RF), instance-based learning (KNN), RF gave us the highest accuracy of 94.4% which is considered adequate for activity recognition.

Human Activity Recognition, Ambient Assisted Living Smartphone, ReliefF, Sequential Forward Floating Selection.

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[3] M. J. Rodrigues, O. Postolache, & F. Cercas, Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review”. Sensors, 20, 2186; doi:10.3390/s20082186, 2020.
[4] M. Z. Uddin, W. Khaksar, & J. Torresen, “Ambient Sensors for Elderly Care and Independent Living: A Survey”. Sensors, 18, 2027, 2018.
[5] R. DamaševiIius, M. Vasiljevas, J. ŠalkeviIius, & M. Wofniak, “Human Activity Recognition in AAL Environments Using Random Projections”. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, 4073584, doi./10.1155/2016/4073584, 2016.
[6] S. Majumder, & M. J. Deen, ”Review Smartphone Sensors for Health Monitoring and Diagnosis”; Sensors 2019, 19, 2164; doi:10.3390/s19092164.
[7] L. Parra, S. Sendra, J. M. Jiménez, & J. Lloret, “Multimedia Sensors Embedded in Smartphones for Ambient Assisted Living and e-Health”. DOI: 10.1007/s11042-015-2745-8, 2015.
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[10] S. Eisa, A. Moreira, ”A Behaviour Monitoring System (BMS) for Ambient Assisted Living”. Sensors, 17, 1946.
[11] M. Ehatisham-ul-Haq, M. A. Azam, U. Naeem, S. R?hman, A. Khalid, “Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing”. Procedia Computer Science 113 (2017) 202–209, 2017.
[12] C. Wang, S. Lee, H. Ho, Y. Na, S. D. Min, “Detection of Optimal Activity Recognition Algorithm for Elderly Using Smartphone”. Advances in Computer Science and Ubiquitous Computing, DOI 10.1007/978-981-10-3023-9_157, 2017.
[13] A. Rasekh, C. A. Chen, Y. Lu “Human activity recognition using smartphone”. arXiv preprint arXiv:14018212, 2014.
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CG Igiri, OE Taylor, Orji Friday, "Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.5-11, 2021.

Irrigation system is a method used to supply water to the plants as uniformly as possible. In the Internet of Things (IoT), technology devices or sensors are connected via the internet and can be remotely operated and monitored by the user. In this research paper, the implementation is done by performing the simulation for a smart irrigation system with the help of the Cisco packet tracer simulation software with new version Cisco Packet Tracer 7.3.0 (64-bit). This technology can be implemented for developing a smart irrigation system, which consists of devices like a lawn sprinkler, temperature monitor, Humidity monitor, etc., to automate the watering system and remotely monitor the environmental conditions for better growth of the plants. All the devices are connected to the home gateway and can be remotely operated and monitored using a Tablet/PC/Smartphone. Simulation results show that the smart devices such as a sprinkler system and other essential devices for monitoring environmental conditions are connected to the home portal and can be successfully monitored, which helps the farmers/homeowners to grow and maintain plants with ease.

Internet of Things, Cisco Packet Tracer, Smart Irrigation System

[1] Egemen Hopal?, Özalp Vayvay, “Internet of Things (IoT) and its Challenges for Usability in Developing Countries” International Journal of Innovation Engineering and Science Research, Vol. 2 , Issue. 1 January 2018
[2] Ghaliya Alfarsi, Ragad M Tawafak, Abir Alsidiri, Jasiya Jabbar, Sohail Iqbal Malik, Maryam Alsinani, “Using Cisco Packet Tracer to simulate Smart Home”, Vol. 8 , Issue 12, December 2019
[3] Sahana B, D. K. Sravani , Dhanyashree R Prasad “Smart Green House Monitoring based on IOT”, IJERT, Vol.8, Issue.14, August 2020.
[4] Sneha Angal, “Raspberry pi and Arduino Based Automated Irrigation System”, International Journal of Science and Research (IJSR), Vol. 5 Issue. 7, July 2016.
[5] Isa Shemsi, “Implementing smart home using cisco packet tracer”, IJERT, Vol.4, Issue.7, January 2018.
[6] R. N. Rao and B. Sridhar, "IoT based smart crop-field monitoring and automation irrigation system," 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, pp. 478-483, 2018, doi: 10.1109/ICISC.2018.8399118.
[7] Vaishnavi S. Gunge, Pratibha S. Yalagi,” Smart Home Automation: A Literature Review”, International Journal of Computer Applications
[8] Abdulrazaq, A., A. Aboaba, G. M. Yelmis, M. Peter, S. Buba and A. Jubril. “Application of smart technology in monitoring and control of home appliances.” Arid Zone Journal of Engineering, Technology and Environment 13: 523-534, 2017.

Sanskruti Raut, Sumitra N. Motade, "IOT Based Smart Irrigation System using Cisco Packet Tracer," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.12-16, 2021.

Heart is one of the major parts of human body, which maintains life line. It pumps the blood and supplies to all parts of the body. Heart disease prediction is significant work. Here we propose a Heart disease prediction model and is a hybrid intelligent system developed using classifier such as deep learning, feature extraction tools, and normalization methods. This intelligent system shows the high accuracy than the other datamining classifier. In this paper, the proposed model will help the medical field to reduces the cost and work load, and also ensures the accuracy of result. This System is very efficient and effective.

Heart disease, Deep learning, Classification

[1] K.Gomathi, Dr. D.Shanmuga Priyaa, “Multi Disease Prediction using Data Mining Techniques”, International Journal of System and Software Engineering, pp.12-14, 2016.
[2] Boshra Brahmi, Mirsaeid Hosseini Shirvani, “Prediction and Diagnosis of Heart Disease by Data Mining Techniques”, Journals of Multidisciplinary Engineering Science and Technology, Vol.2, pp.164168, 2015.
[3] V.V. Ramalingam, Ayantan Dandapath, M Karthik Raja “Heart disease prediction using machine learning techniques: A survey”, Int. J. of Engineering & Technology, Vol.7, No. 2, pp684-687, 2018.
[4] F. D. Kusuma, E. M. Kusumaningtyas, A. R. Barakbah and A. A. Hermawan, "Heart Abnormalities Detection Through Iris Based on Mobile," In Proceedings of Int. Electronics Symposium on Knowledge Creation and Intelligent Computing, Bali, Indonesia, pp. 152-157, 2018.
[5] Sairabi H.Mujawar, P.R.Devale, “Prediction of Heart Disease using Modified K-means and by using Naïve Bayes”, Int. J. of Innovative research in Computer and Communication Engineering, Vol.3, pp.10265-10273, 2015.
[6] Abhishek Taneja, “Heart disease Prediction System Using Data Mining Techniques”, Oriental Journal of Computer Science & Technology, ISSN: 0974-6471, Vol. 6, No. 4, pp 457-466, 2013.
[7] Purushottam, Prof. Dr. Kanak Saxena and Richa Sharma, “Efficient Heart Disease Prediction System”, Procedia Computer Science 85, pp.962-969, 2016.
[8] Jaymin Patel, Prof.Tejal Upadhyay, Dr.Samir Patel, “Heart Disease Prediction using Machine Learning and Data Mining Technique”, Int. J. of Computer Science and Communication, pp.129-137, 2016
[9] Mythili T, Dev Mukherji, Nikita Padalia, Abhiram Naidu, “A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)”, Int. J. of Computer Applications, 0975– 8887, Vol.8, No.16, 2013.
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[11] Senthilkumar Mohan, Chandrasegar Thirumalai, Gautham Srivastava, “Effective Heart Disease Prediction using Hybrid Machine Learning Techniques”, IEEE Access,Vol.7, pp 81542-81554, 2019.
[12] S. U. Amin, K. Agarwal and R. Beg, "Genetic neural network based data mining in prediction of heart disease using risk factors”, In Proceedings of IEEE Conference on Information & Communication Technologies, Thuckalay, India, pp. 1227-1231, 2013.
[13] Sujata Joshi, Mydhili K Nair, “Prediction of heart disease using classification-based data mining techniques”, Computational Intelligence in Data Mining-. Springer, New Delhi, Vol. 2, pp.503-511, 2015.
[14] Sellappan Palaniappan and Rafiah Awang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, Int. J. of Computer Science and Network Security, Vol. 8 No.8, 2008.
[15] Hlaudi Daniel Masethe, Mosima Anna Masethe, “Prediction of Heart Disease using Classification Algorithms”, In Proceedings of World Congress on Engineering and Computer Science, Vol II, San Francisco, USA, 2014.
[16] G. Parthiban, S.K. Srivatsa “Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients”, Int. J. of Applied Information Systems, Vol. 3, No.7, pp. 25-30, 2012.
[17] K. Muhammad, J. Ahmad, I. Mehmood, S. Rho and S. W. Baik, "Convolutional Neural Networks Based Fire Detection in Surveillance Videos," in IEEE Access, Vol. 6, pp. 18174-18183, 2018.

Julie M. David, Sarika S., "Performance Analysis of Convolutional Network System for Heart Disease Prediction," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.17-22, 2021.

Researchers are always on the move to innovate something new from their side. Such a work by researchers in the field of biometrics has led to identify the finger knuckle print as a biometric trait with distinct features. There are certain biometric parts such as fingerprint, iris, palm print and now knuckle print. Knuckle contains rich texture that is distinct for each fingers it selves. Knuckle has potential information that can differentiate persons uniquely. System is intended to acquire the knuckle image and process it for data acquisition and generate code map. Code map is a template that localized in database and compare with input code maps. The proposed system is able to extract information from knuckle image with high precision using different kind of filters and image enhancement techniques such as Gabor, Spatial filters and Sobel that facilitate SURF (Speeded Up Robust Feature). Proposed system possess low error rate with zero false recognition recall. If a system has false acceptance rate then the precision does not follow ideal system. System should have zero false acceptance and high false rejection rate along with true acceptance. Precision is based on high quality feature extraction that could be made by some image enhancement techniques that proposed system follows.

Knuckle Print, Sobel Edge Detection, SURF, Gabor Filter, Biometric and Binary Localization

[1] K.Usha, M.Ezhilarasan, “Fusion of geometric and texture features for finger knuckle surface recognition” , Science Direct, Volume 55, Issue 1, Pages 683-697, March 2016.
[2] Neerja Deogaonkar , Harshada Kahar ,Bhagyshri Parab ,Snehal Rajpure , Disha Bhosle, “Biometric Authentication Using Finger Knuckle Print” IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 6, Issue 1, Ver. I, PP 55-59. Jan. -Feb. 2016.
[3] Amine AMRAOUI*, Youssef FAKHRI and Mounir AIT KERROUM, ” Finger Knuckle Print Recognition System using Compound Local Binary Pattern”, 3rd International Conference on Electrical and Information Technologies ICEIT’2017, IEEE.
[4] Jooyoung Kim, Kangrok Oh, Andrew Beng-Jin Teoh and Kar-Ann Toh, “Finger-Knuckle-Print for Identity Verification Based on Difference Images” 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA).
[5] Arulalan. V and Dr. K.Suresh Joseph, “Score Level Fusion of Iris and Finger Knuckle Print”, 2016 10th International Conference on Intelligent Systems and Control (ISCO), IEEE.
[6] FarzamKharajiNezhadian and Saeid Rashidi, “Inner-knuckle-print for human authentication by using ring and middle fingers”, ICSPIS 2016, 14-15 Dec. 2016, Amirkabir University of Technology, Tehran, Iran, IEEE.
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[8] Wafa El-Tarhouni1, Larbi Boubchir2 and Ahmed Bouridane1, “Finger-Knuckle-Print Recognition Using Dynamic Thresholds Completed Local Binary Pattern Descriptor”, 2016 39th International Conference on Telecommunications and Signal Processing (TSP), IEEE.
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[20] D. Zhang, V. Kanhangad, N. Luo, and A. Kumar, “Robust palmprint verification using 2d and 3d features,” Pattern Recognition, vol. 43, no. 1, pp. 358–368, 2010.
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[25] S.S. Khot, V.A. Mane and K.P. Paradeshi, "Real Time Palm print Identification Technique-Effective Biometric Identification Technique", International Journal of Societal Applications of Computer Science, Vol. 1, Issue 1, November 2012.
[26] Wenxin Li, David Zhang and Zhuoqun Xu, "Image alignment based on invariant features for Palm print identification", Signal Processing: Image Communication, Vol. 18, pp. 373-379, 2003.
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[28] David Zhang, Vivek Kanhangad, Nan Luo and Ajay Kumar, "Robust Palm print Verification Using 2D and 3D Features", Pattern Recognition, Vol. 43, No. 1, pp. 358-368, January 2010.
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Sonali Patel, Arun Jhapate, "Biometric Finger Knuckleprint based Authentication System using Sobel Edge Detection & Emboss," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.23-28, 2021.

— Several methods had been investigated in the literature for rule hiding involving sensitive items. Some methods use co-operative models for mining functional association rules and some use distortion-based rule hiding technique. The present paper focuses on fast mining of rules using rank-based sensitive rule hiding framework called, Fisher’s Filtered Gravitational Search and Rank-based Gene (FFGS-RG) for hiding sensitive association rules. To start with Fisher’s Filtered is applied to filter the association rule and speeding up the mining process among the generated rule with Gravitational Search technique to select the sensitive rules from the transactional database. Once the sensitive rules are selected, the gene property of hidden and exposed items is mapped to the vector data item of sensitive rules for minimum distortion based on weighted ranking. The new gene data item population is generated using genetic algorithm operations to minimize the distortion via ranking. With distorted minimized offspring gene data item population, new sensitive rules are generated using Fisher’s test that speeds up the rule selection process and provided to the transactional users. The distorted minimized offspring generated new rules are obtained then tested for side effects. This process is continued till the final sensitive rule hiding has minimal distortion on the gene populated data item rules and higher data item utility to the transactional users using weighted rank. A benchmark dataset is used to evaluate the FFGS-RG framework and the results show more efficient in improving the rule hiding accuracy with minimal rule selection time and also optimizing the sensitive rules hiding process.

Gravitational Search, Gene Pattern, Rule Hiding, Sensitive rule, Fisher’s test.

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[3] UCI Machine Learning Repository: Abalone Data Set. http://archive.ics.uci.edu/ml/datasets/Abalone.
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[15] M. Dolores Ruiz, Daniel Sanchez, Miguel Delgado1, “Discovering Fuzzy Exception and Anomalous Rules”, IEEE Transactions on Fuzzy Systems, volume: 24, Issue: 4, Aug. 1 2016.
[16] Peng Cheng, Chun-Wei Lin, Jeng-Shyang Pan, “Use HypE to Hide Association Rules by Adding Items”, PLOS ONE | DOI: 10.1371/journal.pone.0127834 June 12, 2015.
[17] Sachin Kumar, DurgaToshniwal, “A data mining approach to characterize road accident locations”, Journal of Modern Transportation, Springer, May 2016.
[18] Akbar Telikani, AsadollahShahbahrami, “Optimizing association rule hiding using combination of border and heuristic approaches, Applied Intelligence, Apr 2017.
[19] Guangtao Wang and Qinbao Song, “A novel feature subset selection algorithm based on association rule mining”, Intelligent Data Analysis, ACM, Volume 17 Issue 5, September 2013.
[20] Gayathiri P and B Poorna,” Gravitational Search Algorithm for Effective Selection of Sensitive Association Rules”, Journal of Theoretical and Applied Information Technology, Vol.96. No 10, 31st May 2018.
[21] Gayathiri P and B Poorna,” Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database”, CYBERNETICS AND INFORMATION TECHNOLOGIES, BULGARIAN ACADEMY OF SCIENCES, Volume 17, No 3, 2017.
[22] Tran Huy Duong, Demetrovics Janos and Vu Duc Thi,” An Algorithm for Mining High Utility Sequential Patterns with Time Interval” CYBERNETICS AND INFORMATION TECHNOLOGIES, BULGARIAN ACADEMY OF SCIENCES, Volume 19, No 4, 2019.

Gayathiri P., B. Poorna, "Fishers Filtered Gravitational Rule Selection and Weighted Rank Based Genetic Algorithm for Association Rule Hiding to Preserve Privacy in Transactional Database," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.29-38, 2021.

Advanced Encryption Standard (AES) is an approved encryption algorithm that has been used so far in many applications. A strength of AES algorithm depends on substitution box (S-Box) that is the main component to provide nonlinearity operations. Although AES algorithm has been proven to be the most secure algorithm to date, the advances in computer processing speed nowadays and the attempts to break such algorithm through the linear and differential cryptanalysis made it vulnerable to obsolescence. Therefore, the development of the algorithm is still ongoing especially for modification of the static nature of its S-Box. This paper proposes a method to improve the security of AES algorithm by suggesting treatment in the Substitution Box which is used to generate nonlinear relationship. Experimental results showed that the proposed method can enhance security of AES algorithm in the same condition of efficiency.

Cryptography, symmetric key, block cipher, AES algorithm and dynamic S-Box.

[1] J. Daemen, and V. Rijmen, "AES Proposal:Rijndael", Version 2. Submission to NIST, 1999.
[2] J. Daemen, and V. Rijmen,"The block cipher Rijndael", Proceedings of the Third International Conference on smart card Reseacrh and Applications, CARDIS`98, 1820, pp.277-284, Berlin: Springer, 2000.
[3] Advanced Encryption Standard (AES). Federal Information Processing Standards Publication 197, 2001.
[4] K. Anurupam, "Dynamic S-box implementation in PRESENT Cipher", International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.426-431, 2018.
[5] K. Kazlauskas and J. Kazlauskas, "Key-Dependent S-Box Generation in AES Block Cipher System", INFORMATICA, Vol.20, No.1, pp.23-34, 2009.
[6] H.M. El-Sheikh, O.A. El-Mohsen, S.T. Elgarf and A. Zekry, "A New Approach for Designing Key-Dependent S-Box Defined over GF (24) in AES", International Journal of Computer Theory and Engineering Vol.4, No.2, pp.158-164, 2012.
[7] G.N. Krishnamurthy and V. Ramaswami, "Making AES Stronger: AES wich Key - Dependent S-Box", International Journal of Computer Science and Network Security, Vol.8, No.9, pp. 388-398, 2008.
[8] J. Juremi, R. Mahmod and S. Sulaiman, "A Proposal for Improving AES S-box with Rotation and Key-Dependent", in the proceedings of the 2012 IEEE International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), Kuala Lumpur, Malaysia, pp.38-42. 2012.
[9] W. Stallings, "Cryptography and Network Security", principles and practices,7th Edition, Pearson Prentice Hall, pp.172-189, 2017.
[10] A. Karki, "A Review on Advanced Encryption Standard", International Journal of Computer Sciences and Engineering (ICSE), Vol.6, Issue.8, pp.551-556, 2018.
[11] J. Juremi, R. Mahmod, S. Sulaiman and J. Ramli, "Enhancing Advanced Encryption Standard S-Box Generation Based on Round Key", International Journal of Cyber-Security and Digital Forensics, Vol.1, No.3, pp.183-188, 2012.
[12] R. Hosseinkhani and H.H.S Javadi, "Using Cipher Key to Generate Dynamic S-Box in AES Cipher System", International Journal of Computer Science and Security, Vol.6, No.1, pp.19-28, 2012.
[13] H. M. Azzawi, "Enhancing The Encryption Process of Advanced Encryption Standard (AES) By Using Proposed Algorithm to Generate S-Box", Journal of Engineering and Development, Vol.18, No.2, 2014.
[14] K. Kazlauskas, G. Vaicekauskas and R. Smaliukas,"An Algorithm for Key-Dependent S-Box Generation in Block Cipher System", INFORMATICA, Vol.26, No.1, pp.51-56, 2015.
[15] N. Tiwari and A. Kumar, "Security Effect on AES in Terms of Avalanche Effect by Using Alternate S-Box", in Proc. Int. Conf. Intell. Data Commun. Technol. Internet Things, pp.1-14, 2018.
[16] A. Datta, D. Bhowmik and S. Sinha, "A Novel Technique for SAC Analysis of S-Boxes for Boomerang-Style Attacks", International Journal of Computer Sciences and Engineering (ICSE), Vol.7, Issue.5,pp.7-13, 2019.

Rawia Alkhamery, Yasser Alahmadi, Mokhtar Alsorori, Saleh Alassali, "Enhance Security of AES Algorithm Based on S-Box," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.39-45, 2021.

While capturing underwater image there are lot of imposed due to low light, light variation, poor visibility. Photography is about light, but since water has an a lot more prominent density than air — around 800 times more noteworthy — not all orientation of light travel similarly well inside it. This implies as we go down into deep water, we lose the shades of the range one by one. This is the reason submerged photographs lose all the red and orange hues even at a genuinely shallow profundity and appear to be increasingly more blue as we go deep in water, henceforth captured image need enhancement. It’s a vital research area. We proposed an effective technique so that we can improve the images which are captured underwater and degraded because of the medium scattering and absorption. Our technique is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It is build on the blending of two images that are directly derived from a color compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are de?ned to promote the transfer of edges and color contrast to the output image. To avoid that sharp weight map transitions builds artifacts in the low frequency components of the reconstructed image, we also conform a multi scale fusion strategy. Our extensive qualitative and quantitative analysis reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, enhanced global contrast, and edges sharpness. Our validation also shows that our algorithm is reasonably independent of the camera settings, and enhance the accuracy of several image processing applications, such as image processing and key-point matching.

Image Enhancement, De-blurring, Color Correction, Histogram Stretching, Gamma correction.

[1]. Md. J. Islam, Y. Xia and J. Sattar, “Fast Underwater Image Enhancement for Improved Visual Perception,” arXiv:1903.09766v1 [cs.CV] 23 Mar 2019.
[2]. C. O. Ancuti , C. Ancuti, C. De Vleeschouwer , and P. Bekaert, “Color Balance and Fusion for Underwater Image Enhancement,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 27, NO. 1, JANUARY 2018
[3]. L. Neumann, R. Garcia, J. Jánosik, N. Gracias, “Fast Underwater Color Correction Using Integral Images,” arXiv 2017.
[4]. M. D. Kocak, F. R. Dalgleish, M. F. Caimi, Y. Y. Schechner, “A Focus on Recent Developments and Trends in Underwater Imaging,” Marine Technol. Soc. J., vol. 42, no. 1, pp. 52-67, 2008.
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[6]. A. Olmos and E. Trucco, “Detecting man-made objects in unconstrained subsea videos,” in Proc. BMVC, Sep. 2002.
[7]. M. Mangeruga, F. Bruno, M. Cozza, P. Agrafiotis and D. Skarlatos, “Guidelines for Underwater Image Enhancement Based on Benchmarking of Different Methods,” 2018, 10, 1652; doi:10.3390/rs10101652.
[8]. R. Protasiuk, A. Bibi, “Local Color Mapping Combined with Color Transfer for Underwater Image Enhancement,” 978-1-7281-1975-5/19/$31.00 ©2019 IEEE DOI 10.1109/WACV.2019.00157.
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[10]. J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag., vol. 4, no. 2, pp. 6–20, Aug. 2012.
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[12]. Y. Y. Schechner and Y. Averbuch, “Regularized image recovery in scattering media,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 9, pp. 1655–1660, Sep. 2007.
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[14]. P. Drews, Jr., E. Nascimento, F. Moraes, S. Botelho, M. Campos and R. Grande-Brazil, “Transmission estimation in underwater single images,” in Proc. IEEE ICCV, pp. 825–830. Dec. 2013.
[15]. A. Galdran, D. Pardo, A. Picón, and A. Alvarez-Gila, “Automatic red-channel underwater image restoration,” J. Vis. Commun. Image Represent., vol. 26, pp. 132–145, Jan. 2015.
[16]. S. Emberton, L. Chittka, and A. Cavallaro, “Hierarchical rank-based veiling light estimation for underwater dehazing,” in Proc. BMVC, pp. 125.1–125.12, 2015.
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[24]. P. L. J. Drews, Jr., E. R. Nascimento, S. S. C. Botelho, and M. F. M. Campos, “Underwater depth estimation and image restoration based on single images,” IEEE Comput. Graph. Appl., vol. 36, no. 2, pp. 24–35, Mar./Apr. 2016.
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Arpit Wany, Yogesh Rathore, "Underwater Image Enhancement using Image Processing Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.46-52, 2021.

Bug Report is one of the most consulted software artifacts during the software evolution and maintenance process. Summarization is one of the approaches which is generally performed over them to perform Bug Report Analysis tasks like Duplicate Bug Report Analysis for Bug Triagers, Quick understanding of Bug Reports, Classification of Bug Reports into priorities, etc. Information Retrieval Techniques, Natural Language Processing Techniques, Machine Learning Techniques and Deep Learning Based Techniques have been successfully implemented for doing the task. Machine Learning is one of the very popular techniques which has been used by almost 70 percent of the researchers for performing the Bug Report Summarization task. Machine Learning is a very common technique which is used in context of Bug Report Summarization due to the fact that the Bug Reports are very domain-specific in nature .In this paper we have systematically analyzed the Machine Learning works used for Bug Report Summarization. We have chosen all the popular papers available through Springer, IEEE, ACM, ACL Anthology and Google Scholar.

Bug Report, Machine Learning, Supervised Learning, Unsupervised Learning, Classifiers

[1] Barzilay, R., & McKeown, K. R. “Sentence fusion for multidocument news summarization.” Computational Linguistics, vol 31, pp. 297–327, 2005.
[2] Gupta, S., & S.K, G. “Deep learning in automatic text summa- rization.” International Journal of Computer Science and Information Security (IJCSIS), vol. 16, pp. 150–155, 2018.
[3] Gupta, S., & Gupta, S. K. “Abstractive summarization: An overview of the state of the art.” Expert Syst. Appl., vol. 121, pp. 49–65. URL: https://doi.org/10.1016/j.eswa.2018.12.011. doi:10.1016/j.eswa.2018.12.011, 2019.
[4].Kumarasamy Mani, S. K., Catherine, R., Sinha, V., & Dubey, A. “Ausum: Approach for unsupervised bug report summarization.” (p. 11). doi:10.1145/2393596.2393607, 2012.
[5]. Lotufo, R., Malik, Z., & Czarnecki, K. “Modelling the ‘hurried’ bug report reading process to summarize bug reports”. Empirical Software Engineering Journal, vol. 20, pp. 516– 548. doi:10.1007/s10664-014-9311-2, 2012.
[6] Rastkar, S., Murphy, G. C., & Murray, G. “Summarizing soft- ware artifacts:a case study of bug reports.” In Proceedings of the 26th Conference on Program Comprehension ICSE 2010.
[7] Rastkar, S., Murphy, G. C., & Murray, G. “Automatic summa- rization of bug reports.” IEEE Transactions on Software Engineering, vol. 40, pp. 366–380, 2014.
[8] YANG, C.-Z., Cheng-Min, & CHUNG, Y.-H.. “Towards an improvement of bug report summarization using two-layer semantic information.” IEICE TRANS. INF. and SYST., vol. 101, pp. 1743– 1750, 2018.
[9]. Limsettho, Nachai & Hata, Hideaki & Monden, Akito & Matsumoto, Kenichi. “Automatic Unsupervised Bug Report Categorization,” 2014.
[10]. Beibei Huai, Wenbo Li, Qiansheng Wu, Meiling Wang “:Mining Intentions to Improve Bug Report Summarization.” SEKE 2018: pp. 320-319, 2018.

Som Gupta, S.K. Gupta, "An Overview of the State of Machine Learning in Bug Report Summarization," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.53-56, 2021.

In modern days, Cloud storage service is being increased tremendously for storing the data. Cloud computing is the new development in the IT industry now a days. The main advantage of cloud storage service is that the data can be downloaded from any location and at any time without any limitation. But, data security is considered as main issue in the cloud computing. Mainly sharing of resources will have major issue in the data security. Cloud deployment models, delivery models are High level cloud architecture is being explained with its four levels. Various challenges and security issues in this cloud computing has been stated clearly in this paper.

Secuirty, Storage, Cloud Services, Data Center, Deployment Models.

[1] https://en.wikipedia.org/wiki/cloudstorage
[2] https://www.researchgate.net/
[3] https://www.sciencedirect.org/
[4] Lizhe Wang, Jie Tao, Kunze M., Castellanos A.C., Kramer D., Karl W., “Scientific Cloud Computing: Early Definition and Experience,” 10th IEEE Int. Conference on High Performance Computing and Communications, Dalian, China, pp. 825-830, Sep. 2008, ISBN: 978-0-7695-3352-0.
[5] Ronald L. Krutz, Russell Dean Vines “Cloud Security A Comprehensive Guide to Secure Cloud Computing”, Wiley Publishing, Inc., 2010.
[6] Aderemi A Atayero, Oluwaseyi Feyisetan, “Security Issues in Cloud Computing: The Potentials of Homomorphic Encryption”, Journal of Emerging Trends in Computing and Information Sciences, Volume 2, Issue 10, pp 546-552, 1st Oct 2011, ISSN: 2079-8407.
[7] Kuyoro S. O., Ibikunle F. & Awodele O., “Cloud Computing Security Issues and Challenges”, pp 344-349, 24-28 May 2010, print ISBN: 978-1-4244-7763-0.
[8] Traian Andrei, “Cloud Computing Challenges and Related Security Issues”, May 2012.
[9] Suba Surianarayanan, T.Santhanam, “Security Issues and Control Mechanisms in Cloud”, International Conference, pp 74-76, 2012, ISBN: 97 8-1-4673-4416-6 /12.
[10] Eystein Mathisen, “Security Challenges and Solutions in Cloud Computing”, International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011), Daejeon, Korea, pp 208-212, 31 May -3 June 2011, ISBN: 978-1-4577-0872-5.
[11] Anas BOUA Y AD, Asmae BLILA T, Nour el houda MEJHED, Mohammed EL GHAZI, “Cloud computing : security challenges”, Information Science and Technology(CIST) 2012 Colloquim, Fez, pp 26 - 31, 22-24 Oct. 2012, print ISBN: 978-1-4673-2726-8, DOI: 10.1109/CIST.2012.6388058.
[12] Padhy, R.P., Patra, M.R. and Satapathy, S.C., 2011. Cloud computing: security issues and research challenges. International Journal of Computer Science and Information Technology & Security (IJCSITS), 1(2), pp.136-146, 2011.
[13] Rachana, S.C. and Guruprasad, H.S., 2014. Emerging Security Issues and challenges in cloud computing. International Journal of Engineering Science and Innovative Technology, 3(2), pp.485-490, 2014.
[14] Alvi, F.A., Choudary, B.S., Jaferry, N. and Pathan, E., 2012. A review on cloud computing security issues & challenges. iaesjournal. com, 2.
[15] Popovi?, K. and Hocenski, Ž., 2010, May. Cloud computing security issues and challenges. In The 33rd international convention mipro, pp. 344-349, 2010. IEEE.
[16] Kuyoro, S.O., Ibikunle, F. and Awodele, O., 2011. Cloud computing security issues and challenges. International Journal of Computer Networks (IJCN), 3(5), pp.247-255, 2011.

Komala R., Pranavi B.V., "Cloud Computing: Models, Issues and Challenges," International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.57-59, 2021.