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Characters are the symbols made by man that are composed of different structure and strokes for easy communication. The intrinsic characteristics of the characters can be utilized to design the stroke and structure based models for handwritten character recognition. This paper focus to learn the part based and the stroke detector based models to recognize the characters by detecting the elastic strokes. The Tree Structured Model (TSM) and the Mixture of parts Tree Structured Model (MTSM) are the part based models that uses the trained part models on the images to recognize the characters. These models require manually labelled key points. In order to learn the discriminative stroke detectors automatically, the discriminative spatiality embedded dictionary learning-based representation (DSEDR) is used for character recognition. A comparative study is made on all the three models on the chars74k dataset to determine the model that shows the best performance.

Character recognition, stroke detector, codewords, spatially embedded dictionary, part based model.

[1] Cun-Zhao Shi, Song Gao, Meng-Tao Liu, Cheng-Zuo Qi, Chun-Heng Wang, "Stroke detector and structure based models for Character Recognition: A Comparative Study" in IEEE transactions on image processing, volume 24, no. 12, Dec 2015.
[2] C. Yao, X. Bai, B. Shi, and W. Liu, “Strokelets: A learned multi-scale representation for scene text recognition,” in the Proceedings CVPR, Jun. 2014, pp. 4042–4049.
[3] S. Gao, C. Wang, B. Xiao, C. Shi, and Z. Zhang, “Stroke bank: A high level representation for scene character recognition,” in the Proceedings on 22nd International Conerence Pattern Recognition (ICPR), Aug. 2014, pp. 2909–2913.
[4] C. Shi, C. Wang, B. Xiao, Y. Zhang, S. Gao, and Z. Zhang, “Scene text recognition using part-based tree-structured character detection,” In the Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR), Jun. 2013, pp. 2961–2968.
[5] T. E. de Campos, B. R. Babu, and M. Varma, “Character recognition in natural images,” In the Proceedings of VISAPP, 2013, pp. 273–280.
[6] S. Tian, S. Lu, B. Su, and C. L. Tan, “Scene text recognition using co-occurrence of histogram of oriented gradients,” in the Proceedings on 12th International Conference Document Analytical Recognition. (ICDAR), Aug. 2013, pp. 912–916.
[7] L. Wang, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus, “Regularization of Neural Networks using drop connect,” in Proceedings 30th International Conference Machine Learning (ICML), 2013, pp. 1058–1066.
[8] X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark
localization in the wild,” in Proceedings CVPR, Jun. 2011, pp. 2879–2886.
[9] D. L. Smith, J. Field, and E. Learned-Miller, “Enforcing similarity constraints with integer programming for better scene text recognition,” in Proceedings CVPR, Jun. 2011, pp. 73–80.
[10] L. Neumann and J. Matas, “A method for text localization and recognition in real-world images,” in Proceedings Asian Conference Computer Vision, 2011, pp. 770–783.
[11] A. Bissacco, M. Cummins, Y. Netzer, and H. Neven, “PhotoOCR: Reading text in uncontrolled conditions,” in Proceedings IEEE International Conference Computer Vision, Dec. 2011, pp. 785–792.
[ 12] Y. Yang and D. Ramanan, “Articulated pose estimation with flexible mixtures-of- parts,” in Proceedings CVPR, Jun. 2011, pp. 1385–1392.
[13] T. E. de Campos, B. R. Babu, and M. Varma, “Character recognition in natural images,” in Proceedings VISAPP, 2009, pp. 273–280.
[14] C. Yi, X. Yang, and Y. Tian, “Feature representations for scene text character recognition: A comparative study,” in Proceedings IEEE 12th International Conference Document Analytics Recognition Aug. 2011, pp. 907–911.
[15] T. Wang, D. J. Wu, A. Coates, and A. Y. Ng, “End-to-end text recognition with convolutional neural networks,” in Proceedings 21st International Conference Pattern Recognition (ICPR), Nov. 2012, pp. 3304–3308.
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[18] S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, and R. Young, “ICDAR 2003 robust reading competitions,” in Proceedings ICDAR, vol. 2. Aug. 2003, pp. 682–687.
[19] C.-L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, “Handwritten digit recognition: Benchmarking of state-of-the-art techniques,” Pattern Recognition., volume 36, no. 10, pp. 2271–2285, Oct. 2003.

Bhavana Shastry M., Pradeep N., "Comparison of Structure Based Models for Handwritten English Character Recognition," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.126-130, 2017.

In this paper we can introduce the three metrics of the spatio-temporal database which can scrutinize the data and give us the result. And large number of applications of real world is depending on the object, event and states that are changes in day to day life. The combination of the three metrics with the vent state can convert the quantity in to quality so we can use the control point selection method. The whole process of conversion is known as appropriation. Spatio-temporal data easily become massive, either because the spatial domain contains a lot of information (satellite images) or many times the steps are available (high resolution sensor data) or both. This vignette shows how data residing in a database can be read using spatial and temporal selection and the combination of these two databases can make the output more innovative and useful.

Spatio-temporal data ,time series, trajectory, coverage,RANSAC, data metrics

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[10] X.Wang,H.Zhang,S.liu, “Reliable RANSAC using a Novel Preprocessing Model” Computational and Mathematical Methods in Medicine,2013,D.O.I.10.1155/2013/672509.
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Sonia Rathee, Rahul Rishi, "Interactive selection of Multivariate Features in Spatio-temporal Data and its Change depends on the selection of Object, Event, State and its 3C’s," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.131-135, 2017.

Right and high quality image makes a huge difference, whether it can be any field of digital image processing. It is often not possible to get an image that contains every object in focus. To obtain an image that contains every object in focus a Multifocus image fusion process is required. In the process of image fusion good information from each of the given image is fused together to form a resultant image whose quality is far better than the any of the Multifocus input image. In this paper we proposed an algorithm which is the enhanced version of the existing algorithm. We utilize the concept of alpha factor and we vary the values of alpha factor according to our requirement. The proposed method is also compared with different methods. It is observed that proposed method preserves more information compared to earlier methods. It evaluates the performance of each algorithm with Matlab 2012a .We evaluate the algorithm by using performance parameter peak signal to noise ratio and the mean square error value.

Image fusion; PCA; Pixel level transform; alpha factor

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Namrata, Anamika Maurya, "An Enhanced Version of Combination of Multifocus Image Using Alpha Factor," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.136-140, 2017.

Recently, discovering outliers among large scale Facebook multimedia have attracted attention of many Facebook mining researchers. There are number of outlier multimedia exists in each category of Facebook multimedia such as- ‘Entertainment’, ‘Sports’, ‘News and Politics’, etc. The task of identifying and manipulate (to remove from the Facebook or to share with others in the Facebook, or to watch/download from the Facebook etc.) such outlier Facebook multimedia have gained significant important research aspect in the area of Facebook Mining Research. In this work, we propose a novel method to detect outliers from the Facebook multimedia based on their metadata objects. Large scale Facebook multimedia metadata objects such as- length, view counts, numbers of comments, rating information are considered for outliers’ detection process. The outlier detection method–Inter-Quartile Range (IQR) is used to find outlier Facebook multimedia of same age. The resultant outliers are analysed and compared as a step in the process of knowledge discovery.

Outliers, Inter-Quartile Range, Facebook Multimedia Outliers, Facebook, Metadata

[1] Chueh-Wei Chang, Ti-Hua Yang and Yu-Yu Tsao, “Abnormal Spatial Event Detection and Multimedia Content Searching in a Multi-Camera Surveillance System”, MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN.
[2] Fan Jiang, Ying Wu, Aggelos K. Katsaggelos, “Abnormal Event Detection from Surveillance Multimedia by Dynamic Hierarchical Clustering”, Northwestern University, USA.
[3] Tushar Sandhan et al., “Unsupervised learning approach for abnormal event detection in surveillance multimedia by revealing infrequent patterns”, IEEE 28th International Conference on Image and Vision Computing, 2013- New Zealand
[4] Thi-Lan Le and Thanh-Hai Tran, “Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization”, Advances in Intelligent Systems and Computing 341, DOI 10.1007/978-3-319-14633-1_2, Springer International Publishing-2015, Switzerland.
[5] Yang Cong et al., “Abnormal Event Detection in Crowded Scenes using Sparse Representation”, Pattern Recognition, January 30, 2013
[6] Cewu Lu et al., “Abnormal Event Detection at 150 FPS in MATLAB”, The Chinese University of Hong Kong.
[7] Y. Cong, J. Yuan, and J. Liu, “Sparse reconstruction cost for abnormal event detection,” CVPR 2011, Jun. 2011.
[8] Bin Zhao et al., “Online Detection of Unusual Events in Multimedia via Dynamic Sparse Coding”, 2011.
[9] Mahmoudi Sidi Ahmed et al., “Detection of Abnormal Motions in Multimedia”, Chania ICMI-MIAUCE’08 workshop, Crete, Greece, 2008.
[10] Du Tran et al., “Multimedia Event Detection: From Subvolume Localization to Spatiotemporal Path Search”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, 2014.
[11] Dataset for "Statistics and Social Network of YouTube Multimedia", http://netsg.cs.sfu.ca/youtubedata/.
[12] Siddu P. Algur, Prashant Bhat, “Metadata Based Classification and Analysis of Large Scale Facebook Multimedia”, International Journal of Emerging Trends and Technologies in Computer Science, May-June 2015.
[13] Siddu P. Algur, Prashant Bhat, "Abnormal Web Video Prediction Using RT and J48 Classification Techniques", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.101-107, 2016.

Siddu P Algur, Suraj Jain, Prashant Bhat, "Abnormal Facebook Multimedia Detection on Facebook using IQR Method," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.141-146, 2017.

Heart problems are increasing frequently day by day and ECG reflects the activities and the attributes of the human heart. The information extracted from the signal is used for analysis and identifying various pathological conditions, but these ECG signal can be distorted with noise as Electrocardiogram (ECG) signals are the electrical recording of heart activity. These signals are very low frequency signals of about 0.5Hz -100Hz. Noise can be any interference due to motion artifacts or due to power equipment that are present where ECG had been taken. A typical computer based ECG analysis system includes a signal pre-processing, beats detection and feature extraction stages, followed by classification. Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. This paper focuses on ECG signal processing using Notch Filter for biomedical application.

ECG, Signal Pre-processing, Pattern recognition, Noise

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Chhavi Saxena, P.D. Murarka, Hemant Kumar Gupta, "Noise Reduction in ECG Signals Using Notch Filter," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.147-150, 2017.

As of late, enormous info has switched into a hot analysis theme. The expanding resolution of immense information builds the shot of breaking the safety of people. Since enormous info needs high calculation ability and intensive stockpiling, use distributed frameworks. As different gatherings are enclosed in these frameworks, the danger of security violation is expanded. Many securities safeguarding systems are created at information era, information stockpile and information handling levels of huge info life cycle. Protection conservation parts with large info and the difficulties for existing instruments are the prevailing goals. Specifically, we represent the new framework for securing immense info named ring signature. Moreover safeguarding modules in every section of the large fact life chain. In this, the file will be encrypted and stored in the cloud storage. If the attackers get the decryption key, the privacy of the file will be violated. And the integrity of the file not guaranteed. The file should be securely shared inside the group of the user without outsiders` inference. Besides, we speak the difficulties and future analysis directions determined with security in big data.

Info stockpile, data auditing, privacy, data handling, ring signature

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[12] B. Sowmya, K. Madhavi, "Secure Cloud Storage via Attribute-based Encryption", International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.96-100, 2017.

M. Prashanthi, A.P. Siva Kumar, "Privacy Preservation in Big Data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.151-154, 2017.

satisfactory patient queuing system to limit patient waits and patient congestion is a specific problem confronted by most of the hospitals. Unavoidable and irritating waits for prolonged intervals result in generous human efforts, misuse of time and also raise the dissatisfaction persisted by patients. For each individual in the line, the absolute treatment time of overall patients leading him endures the time that fellow should stay. It could be helpful and ideal if patients could have the knowledge about the treatment design and learn the foreseen time for holding up. Thus, a Patient Treatment Time Prediction (PTTP) method is used to estimate the delay time of treatment activities for an individual. We make use of patient factual records of different clinical centers to get a person’s treatment time consumption procedure for each treatment duty. Over the vast extent, and practical data set, the treatment time for an individual in the line of each operation is anticipated. Build upon the forecast delay time, a Queuing Recommendation (QR) process is produced. Queuing Recommendation framework computes and predicts the proficiency and helpful treatment schedule prescribed for the patient. To accomplish this, patient records are collected from different clinical centers and stored in the Hadoop environment. Enhanced Random Forest (RF) technique is used to educate the treatment time consumption. Thus, every individual in line can be suggested completing their treatment activities in the easiest way and with the appropriate time.

Waiting time, PTTP, Queuing Recommendation, Random Forest, Hadoop

[1] Jianguo Chen, Kenli Li, Zuho Tang, Kashif Bilal and Keqin Li, “A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing Recommendation in a Big Data Environment”, Journal of IEEE Access, Vol.4, pp.1767-1783, 2016.
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[10] E. LaxmiLydia, M. Ben Swarup ‘‘Analysis of Big Data through Hadoop Ecosystem Components like Flume, MapReduce, Pig, and Hive,’’ International Journal of Computer Sciences and Engineering, vol. 5, no. 1, pp. 21–29, Jan. 2016.

A. Haripriya, A.P. Siva Kumar, "An Effective Patient Treatment Plan Recommendation with Predicted Treatment Time Using Hadoop," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.155-158, 2017.

Steganography is the creative method of hiding any important information or data like passcode, data file, image; spreadsheets behind the original cover file. In this paper we proposed the text-audio-video cryptstego which is the combination audio steganography and video steganography using algorithm implemented using C#.Net tool and Libraries. The main goal of our research paper is to hide the important data file or any spreadsheet behind the audio, video, or image file and also one more method we have used is to store it as cipher text. Steganography can be used for hidden communication. We have explored the limits of steganography theory and practice. The implemented algorithm is 6LSB is used for image steganography. We made out the enhancement of the image steganography system using LSB approach to provide a means of secure communication. A proposed cryptstego-key has been applied to the system during embedment of the message into the cover image, it also provide the technique to hide plain text file or any other data behind bitmap image any audio file. So the proposed system secures the data transmission using proposed stego tool. This paper mainly focuses the idea of computer forensic technique and its use of audio-video steganography technique for providing better security in concern.

Steganography, Data Extraction, Cipher, RSA Algorithm, LSB

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[2]. Sakshi and A. Kaur , "Secure Data Hiding Using Neural Network and Genetic Algorithm in Image Steganography", International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.95-99, 2017.
[3]. Mandeep Kaur Gill and Rupinder Kaur Randhawa , "Comparative Study of Multibit LSB Steganography with Cryptography", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.120-123, 2015.
[4]. Sghaier Guizani,Nidal Nasser, “An Audio/Video Crypto Adaptive Optical Steganography Technique “IEEE 2012,pp, 1057-1062.

Hitendra Donga, Kishor Atkotiya, "STEGO: A Tool for Implementing Text-Audio-Video Steganography," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.159-162, 2017.

There are number of resources available for the users but user can’t afford these resources as these resources are costly. But users can use these resources on a rental basis. Internet access is readily available everywhere and it can be used by user to access the resources. So the best option available is to use the cloud service. With the growing popularity of cloud services, there come certain challenges. The problem of load balancing is its biggest challenge. Load balancing and task scheduling helps in optimizing various resource related parameters. The idea is to reduce the cost from the customer point of view and then improve the resource utilization from service provider point of view. A number of optimization algorithms have already been proposed for load balancing. This paper proposed an enhanced ACO algorithm for cloud task scheduling that helps to improve the imbalance factor of VM;s and also improve the overall Makespan time.

Cloud Computing, ACO, VM, Data Center, CI, IAAS, PAAS, SAAS

[1] M. Tawfeek, A. El-Sisi, A. Keshk, F. Torkey, “ Cloud Task Scheduling Based on Ant Colony Optimization” ,The International Arab Journal of Information Technology, Vol. 12, No. 2, March 2015.
[2] L. André, “The case for energy-proportional computing”, IEEE Computer society (2007), 33-37.
[3] R. Raja, J. Amudhavel, N. Kannan, M. Monisha, “ A bio inspired Energy-Aware Multi objective Chiropteran Algorithm (EAMOCA) for hybrid cloud computing environment”, International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, 2014, pp. 1-5.
[4] Shruti, Meenakshi Sharma, "Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.227-231, 2017.
[5] Mandeep Kaur, Manoj Agnihotri, "A Hybrid Technique Using Genetic Algorithm and ANT Colony Optimization for Improving in Cloud Datacenter", International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.100-105, 2016.
[6] S.L.Mewada, U.K. Singh, P. Sharma, "Security Enhancement in Cloud Computing (CC)", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.31-37, 2013.
[7] Rajesh Verma , "Comparative Based Study of Scheduling Algorithms for Resource Management in Cloud Computing Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.4, pp.17-23, 2013.
[8] N. Siddique, H. Adeli, “Nature Inspired Computing: An Overview and Some Future Directions”, Cong Comput, Vol 7, 706-714, 2015.
[9] Vivek Raich, Pradeep Sharma, Shivlal Mewada and Makhan Kumbhkar, "Performance Improvement of Software as a Service and Platform as a Service in Cloud Computing Solution", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.6, pp.13-16, 2013.
[10] E. Hallaj, “Study and Analysis of Task Scheduling Algorithms in Clouds Based on Artificial Bee Colony Second International Congress on Technology”, Second International Congress on Technology Communication and Knowledge (ICTCK 2015) November, 11-12, 2015.
[11] A. Singh, D. Juneja, M. Malhotra, “Autonomous Agent Based Load Balancing Algorithm in Cloud Computing”, International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 832-841, 2015.
[12] M.A. Vasile, F. Pop, V. Cristea, J. Kołodziej, “ Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing”, Future Generation Computer systems , Vol 51, pp 61-71, 2015.
[13] F.F. Moghaddam, M. Ahmadi, S. Sarvari, M. Eslami ,A. Golkar, “Cloud Computing Challenges and Opportunities: A Survey”, 1st International Conference on Telematics and Future generation Networks (TAGGEN), 2015.
[14] A.R. Seifi, T. Niknam, “A modified teaching-learning based optimization for multi-objective optimal power flow problem”, Energy Conversion and Management, Volume77,January 2014.
[15] Rakesh. S.Shirsath, Vaibhav A.Desale, Amol. D.Potgantwar, "Big Data Analytical Architecture for Real-Time Applications", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.4, pp.1-8, 2017.
[16] L. Yibin, K. Gai, “Intelligent cryptography approach for secure distributed big data storage in cloud computing”, Information Sciences, Vol 387, pp 103-115, May 2017.
[17] Karger D, Stein C, Wein J, “ Algorithms and Theory of Computation Handbook: special topics and techniques”, Chapman & Hall/CRC, 2010.
[18] M. Kalra, S. Singh, “ A review of metaheuristic scheduling technique in cloud computing”, Cairo University, Egyptian Informatics Journal, Vol. 16, issue 3, pp 275-295, 2015.
[19] C. Ghribi, M. Hadji, D. Zeghlache, “ Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms”, International Symposium on Cluster, Cloud, and Grid Computing, 2013.
[20] P.E. SAN, “Classification of Web Pages using TF-IDF and Ant Colony Optimization”, International journal of Scientific Engineering and Technology Research(ISSAN 2319-8885) , Vol. 03, issue 46, pp 9450-9454, 2014.

N. Kaur, C. Kathuria, D. Gupta, "Cloud Task Scheduling Based on Enhanced Meta Heuristic Optimization Technique," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.163-168, 2017.

Since the rise of the internet, one of the most important factors of information technology and communication has been the security of information. Now a day, mostly business organizations face the problem in sharing their confidential documents. This project is developed for hiding file (secret message, document) in any image by using Steganography. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the internet. In this paper, we are proposing a method of encrypting the text files in an image in order to text the accuracy and efficiency of encryption. This process helps to send the information to the authorised party without any potential risk. The proposed method will help to secure the content within the image and will help to make the document much securer because even though if an unauthorised person succeeds in being able to hack the image, the person will not able to read the message.

Steganographic Technique, DOM, Proposed System Application Method

[1] A. Nag, S. Biswas, D. Sarkar, P.P. Sarkar, “A novel technique for image steganography based on Block-DCT and Huffman Encoding” International Journal of Computer Science and Information Technology, Vol.2, Nn.3, June 2010.
[2] Y. K. Jain and R. R. Ahirwal, “A Novel Image Steganography Method With Adaptive Number of Least Significant Bits Modification Based onPrivate Stego-Keys”, International Journal of Computer Science and Security, Vol. 4, March, 2010.
[3] Ketan Shah, Swati Kaul, Manoj S.Dhande,“Steganography Using DWT And DES”, International Journal of Science and Research (IJSR), Volume 3, Issue 5, May 2014.
[4] K. Arora, G. Gandhi, "A Review of Approaches for Steganography", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.118-122, 2014.
[5] KenCabeen and Peter Gent,Image Compression and Discrete Cosine Transform, College of Redwoods.http://online.redwoods.cc.ca.us/instruct/darnold/LAPROJ/Fall98/PKen/dct.pdf
[6] Dr. Ekta Walia, Payal Jainb and Navdeepc,“An Analysis of LSB &DCT based Steganography “ ,Global Journal of Computer Science and TechnologyVol.10 Issue.1,April 2010.

Divyank Kumar, Arun Kumar, Ankita Barnawl, Shivani Dubey, "Data Hiding in Digital Images by using DOM Steganographic Technique," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.169-172, 2017.