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Job scheduling is state of the art problem in advanced computing system. To tackle the issue of larger Make span and Flow time, several techniques are being researched over. This paper works toward secretion of job scheduling policy where Burst time is considered for arranging the jobs in clusters. Proposed system is categorised into two phases: first phase arranges the jobs by following shortest job first scheduling. The queue thus formed is presented to round robin scheduler with time quantum that varies depending upon the burst time of job. Jobs arranged are arranged in batches of 10% of total jobs in queue. SJF scheduler considered is non primitive where RRS scheduler is primitive. Second phase executes the jobs by looking at the resource clusters. Multi-source shortest path dynamic algorithm is used for selection of job that can be assigned to the resource cluster. Once job execution is complete credits are assigned which will be from 0-10. Higher the credit more proficient is the result. Optimal result is obtained by the application of proposed system in terms of Make span and flow time. Simulation is conducted in MATLAB showing improvement of 6% in overall result.

Job Scheduling, SJF, RRS, PBS, Multi source shortest path

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Jasjit Singh, Anil Kumar, "Analysis of Two Phase Scheduling within Distributed System for Enhancement of Make span and Flow time," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.278-282, 2018.

This investigation is designed to shed light on identification and translation of idiomatic expression from Hindi to English language. The main problem encountered in idiomatic translation was investigated. Identification of idiom is utmost important resource for machine translation system and research. Machine translation system has been developed for many languages such as we have google translator, Bing translator etc. But they are failed to provide the correct translation of sentences containing idioms. The idiom parallel corpus was manually created to test the generated resource. The sentences containing idioms are translated with google translate system and noticed that it does not provide figurative meaning [1] which makes the translation of idioms rather difficult than any text translation. They are the real challenge for machine translation from the preliminary stage of machine translation development. A lot of research has been done for extraction and translation of text in many languages, but no significant research has been captured in Hindi to English idiom translation. In this paper we have given a rule based approach for the identification and translation of idiom using machine translation. The aim of a proper idiom translation is achieving equivalent sense and provide figurative meaning, strategies, cultural aspects and effects. The output is evaluated manually for intelligibility and accuracy. Further This Hindi to English idiom translation system can be expanded for other language pairs to improve their translation by encapsulating correct idiom translation with their ordinary translation.

Idioms, Idiom translation, Idiom identification, Machine translation, Hindi, English, Language

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N. Thakre, V. Gupta, N. Joshi, "Identification and Translation of Idiomatic Sentence from Hindi to English," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.283-287, 2018.

The fundamental reason for submerged image handling is to enhance submerged image enhancement. The preparing of submerged image caught is essential in light of the fact that the nature of submerged images influence and these images drives some significant issues when contrasted with images from a clearer domain. Because of the presence of clean particles in the water, submerged images suffer from the backscattering impact. To overcome this drawback I propose the new method called illumination normalization and deblurring of underwater image restoration. In this paper propose, first estimate the illumination directions of underwater images and cope with the problem of illumination normalization. Secondly deblurring of the underwater image using deconvolution algorithm and finally by the fusing both the results the restored image is acquired.The quality of the enhanced image is evaluated by using the metric is called blind/reference less image spatial quality evaluator (BRISQUE).

Image Restoration, Illumination Direction, Illumination Normalization, Deblurring, Deconvolution

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I. Jeya Kumar, A. Lenin Fred, C. Seldev Christopher, "Underwater Image Restoration Based on Illumination Normalization and Deblurring," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.288-296, 2018.

Video steganography is a process of embedding data inside in a raw or compressed video sequence. Nowadays compressed videos are preferred over raw videos for data transfer, and H.264/ AVC video format is the most frequently used video standard over the internet. H.264/AVC provides more compression as compared to the previous standards and maintains better visual quality. In H.264/ AVC video steganography the message hiding can be done by conventional methods such as spatial domain and transform domain techniques. Additionally, it has more hiding options as compared to the previous standards which are also utilized by researchers for secret data embedding. In this paper, a concise overview of H.264/ AVC video standard is given, and survey of existing video steganography techniques in H.264/ AVC is also done with the implementation of a 4-0-4 LSB technique to show the impact of embedding. The pros and cons of different hiding techniques used for data embedding in H.264/AVC video are discussed towards the end. This paper aims to provide a brief introduction of video steganography techniques in H.264/ AVC video coding standard.

Video Steganography, H.264/AVC, Motion vector, Entropy Coding, DCT, Spatial domain, Transform domain

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Mukesh Dalal, Mamta Juneja, "H.264/AVC Video Steganography Techniques: An Overview," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.297-303, 2018.

Resource allocation is critical to investigate the need for resources in substantially enhancing every day. To tackle this issue our proposed policy presents a new hybrid strategy known as the fittest job firefly algorithm(FJFFA) which sorts the jobs in the queue according to least cost and maximum profit. This queue is presented to firefly algorithm. Jobs are again sorted randomly and presented to firefly algorithm. The solution thus obtained from the algorithm is superior. Makespan and Flowtime obtained as a result is improved by 6%.

FJFFA, Optimal job selection, least cost and maximum cost

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Lovejoban Preet Singh, Anil Kumar , "Improved Scheduling Procedure for Intensify Resource Utilization in Heterogeneous Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.304-308, 2018.

New research uncovers how social network information can be utilized to predict users’ psychological and physical wellbeing, adding to a developing number of researchers utilizing social media to make startlingly precise forecasts from the most basic data. The same number of users think what they share on the web, the discoveries give occasion to feel qualms about customary thoughts of "safe" surfing. In spite of the fact that rates of diagnosing mental illness have enhanced in the course of recent decades, numerous cases stay undetected. Side effects related with mental illness are noticeable on Twitter, Facebook, and web forums, and automated techniques are progressively ready to identify misery and other psychological instabilities. In this paper, late investigations that expected to anticipate psychological sickness utilizing web-based social networking are explored. . Mentally ill users have been observed utilizing screening reviews, their open sharing of a determination on Twitter, or by their participation in an online discussion, and they were discernable from control users by designs in their language and online activity. Automated detection techniques may recognize discouraged or generally in danger people through the vast scale passive monitoring of social media, and in the future may complement existing screening methodology.

Social Media, Prediction, Classification, Image, Probabilities, Decisiontree

[1] Ang Li, Dongdong Jiao, Tingshao Zhu, Detecting depression stigma on social media: A linguistic analysis, Journal of Affective Disorders, May 2018.
[2] Christopher T. Barry, Chloe L. Sidoti, Shanelle M. Briggs, Shari R. Reiter, Rebecca A. Adolescent social media use and mental health from adolescent and parent perspectives, Journal of Adolescence, December 2017.
[3] Eugene Brusilovskiy, Greg Townley, Gretchen Snethen, Mark S. Salzer, Social media use, community participation and psychological well-being among individuals with serious mental illnesses, Computers in Human Behavior, December 2016.
[4] Gillian Fergie, Kate Hunt, Shona Hilton, Social media as a space for support: Young adults` perspectives on producing and consuming user-generated content about diabetes and mental health, Social Science & Medicine, December 2016
[5] Jenna Glover, Sandra L. Frits, KidsAnxiety and Social Media: A Review, Child and Adolescent Psychiatric Clinics of North America, April 2018.
[6] M. Krausz, Social media and e-mental health, European Psychiatry, April 2017.
[7] Mike Conway, Daniel O’Connor, Social media, big data, and mental health: current advances and ethical implications, Current Opinion in Psychology, June 2016
[8] Renwen Zhang, The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, socialsupport, and mental health among college students, Computers in Human Behavior, October 2017.
[9] Sharath Chandra Guntuku, David B Yaden, Margaret L Kern, Lyle H Ungar, Johannes C Eichstaed, Detecting depression and mental illness on social media: an integrative review, Current Opinion in Behavioral Sciences, December 2017.
[10] Simon M. Rice, Rosemary Purcell, Patrick D. McGorry, Adolescent and Young Adult Male Mental Health: Transforming System Failures Into Proactive Models of Engagement, Journal of Adolescent Health, March, 2018.

D. Sridhar, V. Kathiresan, "The Psychology behind Users Mental Health in Social Media," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.309-316, 2018.

A spatiotemporal database handles both the space and time information. A spatiotemporal database includes spatial (i.e., location and geometry of the object) and temporal data (i.e., timestamp or time interval of valid objects) where geometry of object changes over time. Spatio-temporal correlation analysis is used for identifying the spatial and temporal relationships of multiple events. The spatio-temporal objects contain number of features in pattern discovery process. However, the existing spatio-temporal pattern discovery and prediction techniques are failed to predict the future event in accurate manner and time consumption remained unaddressed. Our main objective of the research is the spatio-temporal correlation, spatio-temporal pattern discovery and prediction with higher accuracy. In order to increase the performance of spatio-temporal pattern discovery and prediction, machine learning technique are employed in our work.

Spatiotemporal database, correlation analysis, features, pattern discovery, prediction, machine learning technique

Xiabing Zhou, Haikun Hong, Xingxing Xing, Kaigui Bian, Kunqing Xie, Mingliang Xu, “Discovering Spatio-temporal Dependencies Based on Time-lag in Intelligent Transportation Data”, Neurocomputing, Elsevier, Vol. 259, pp. 76-84, 2017.
[2] Mete Celik and Ahmet Sakir Dokuz, “Discovering Socio-Spatio-Temporal Important Locations of Social Media Users”, Journal of Computational Science, Elsevier, Vol. 22, pp. 85-98, 2017.
[3] Chung-Hsien Yu, Wei Ding, Melissa Morabito, and Ping Chen, “Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling”, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, Issue 4, pp. 979 – 993, 2016.
[4] Banafsheh Zahraie, Mohsen Nasseri and Fariborz Nematizadeh, “Exploring spatiotemporal meteorological correlations for basin scale meteorological drought forecasting using data mining methods”, Arabian Journal of Geosciences, Springer, Vol. 10, Issue 419, pp. 1-15, 2017.
[5] Dongzhi Zhang, Kyungmi Lee and Ickjai Lee “Hierarchical Trajectory Clustering for Spatio-temporal Periodic Pattern Mining”, Expert Systems with Applications, Elsevier, Vol.92, pp.1-11, 2018.
[6] Alberto Belussi and Sara Migliorini, “A spatio-temporal framework for managing archeological data”, Annals of Mathematics and Artificial Intelligence, Springer, Vol. 80, Issue. 3-4, pp. 175-218, 2017.
[7] Aboubacar Amiri and Sophie Dabo-Niang, “Density estimation over spatio-temporal data streams”, Econometrics and Statistics, Elsevier, Vol. 5, pp.148-170, 2017.
[8] Sanchita Mal-Sarkar, Iftikhar U. Sikder and Vijay K. Konangi, “Spatio-temporal Pattern discovery in sensor data: A multivalued decision systems approach”, Knowledge-Based Systems, Elsevier, Vol. 109, pp.137–146, 2016.
[9] Siming Chen, Xiaoru Yuan, Zhenhuang Wang, Cong Guo, Jie Liang, Zuchao Wang, Xiaolong (Luke) Zhang and Jiawan Zhang, “Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data”, IEEE Transactions on Visualization and Computer Graphics, Vol. 22, Issue 1, pp. 270 – 279, 2016.
[10] Hoang Nguyen, Wei Liu and Fang Chen, “Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data”, IEEE Transactions on Big Data, Vol. 3, Issue 2, pp.169 – 180, 2017.
[11] Feng Mao, Minhe Ji and Ting Liu, “Mining spatiotemporal patterns of urban dwellers from taxi trajectory data”, Frontiers of Earth Science, Springer, Vol.10, Issue 2, pp. 205–221, 2016.
[12] Douglas E. Galarus and Rafal A. Angryk, “Spatio-temporal quality control: implications and applications for data consumers and aggregators”, Open Geospatial Data, Software and Standards, Springer, Vol.1, Issue 2, pp. 1-23, 2016.
[13] Juanjuan Zhao, Qiang Qu, Fan Zhang, Chengzhong Xu and Siyuan Liu, “Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data”, IEEE Transactions on Intelligent Transportation Systems, Vol. 18, Issue 11, pp. 3135 – 3146, 2017.
[14] Vijay Kumar Verma and Pradeep Sharma, “Data Dependencies Mining In Database by Removing Equivalent Attributes”, International Journal of Computer Science International Journal of Computer Science and Engineering, Vol.1, Issue. 1,pp. 13-16, 2013.
[15] Pradeep Chouksey, “An Apriori based Algorithm for mining interesting patterns Using Conjunctive datasets”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 4 , Issue.5, pp.31-36, 2016.
[16] Namrata Ghuse, Pranali Pawar, Amol Potgantwar, “An Improved Approch For Fraud Detection In Health Insurance Using Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol.5 , Issue.3,pp.27-33, 2017.
[17] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol. 5, Issue.6, pp.5-8, 2017.

R. Sarala, V. Saravanan, "A Comparative Study on Spatio-Temporal Data Correlation and Pattern Discovery Techniques for Prediction Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.317-324, 2018.

Color texture classification is a vital step for describing objects in natural scenes. A novel method is proposed to construct a histogram based on intensity and color channel neighborhood for the color texture classification. The goal of this paper is to explore the suitability of the histogram constructed using the intensity and color channel neighborhood relationship method in automatic classification of granite textures as an industrial application. Experimental tests are conducted on the images from VisTex database. Texture classification is performed using K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classification methods. The average classification accuracy 97.93% is obtained for K-NN classification method, where as 100% average classification accuracy is achieved for SVM classification method. Further, experimentations are performed on MondialMarmi database of granite tiles to prove the potential of the proposed method in an industrial application. The classification results demonstrate that proposed method has improved classification accuracy as compared to other color texture classification methods. The results prove that proposed method using SVM is a powerful classification method for classifying granite textures.

Color texture classification, Granite classification, Industrial application, Histogram features, Classification methods

[1] A. Agarwal, S. S. Bhadouria, “An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, No. 2, pp.56-62, 2017.
[2] S. K. Badugu, R. K. Kontham, V. K. Vakulabharanam, B. Prajna, “Calculation of Texture Features for Polluted Leaves”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, No. 1, pp.11-21, 2018.
[3] M. Turtinen, M. Pietikainen, O. Silvén, T. Maenpaa, M. Niskanen, “Paper Characterisation by Texture using Visualisation-based Training”, International Journal of Advanced Manufacturing Technology, Vol. 22, No.11-12, pp.890-898, 2003.
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[8] S. Banerji, A.Sinha, C. Liu, “New Image Descriptors Based on Color, Texture, Shape, and Wavelets for Object and Scene Image Classification”, Neurocomputing, Vol. 117, pp.173-185, 2013.
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[19] VisTex, “Vision Texture Database”, Vision and Modeling Group, MIT Media Laboratory, http://wwwwhite.media.mit.edu/vismod/imagery/VisionTexture/vistex.html, 1995.
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[21] F. Bianconi, E. González, A. Fernández, S. A. Saetta, “Automatic Classification of Granite Tiles Through Colour and Texture Features”, Expert Systems with Applications, Vol. 39, No. 12, pp.11212-11218, 2012.
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S. Shivashankar, M.R. Kagale, "Granite Classification: An Industrial Application to Color Texture Classification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.325-330, 2018.

In this paper, we have proposed a Si-Ge HBT Using Silvaco TCAD. In recent years, the band gap engineered devices have received considerable attention due to their inherent advantages such as high speed and high driving capability as compared to homo junction devices. The Si-Ge Hetero junction Bipolar Transistor (HBT) is the first band gap engineered device to be developed on Si. The recent advancement in material growth technology and device design has resulted in Si-Ge HBT’s operating at more than 250GHz cutoff frequency. The present paper deals with the simulation of Si-Ge HBT structures using Silvaco TCAD. HBT structure has been generated through DevEdit and device simulation was carried out using Atlas. Base width and Ge profile are very important parameter for HBT. In view of this, the effect of variation of base width and Ge profile on the parameters like Collector Current (Ic), Current Gain (β), fT and fmax has been studied.

HBT, Bipolar Junction Transistor, DEVEDIT 2D, Silvaco TCAD

[1] Joseph, A.; Lanzerotti, L.; Sheridan D.; Johnson, J.; Liu, Q.; Dunn, J.; Rieh, J.-S.; Harame, D., “ Advances in Si-Ge HBT BiCMOS Technology,” Silicon Monolithic Integrated Circuits in RF Systems, PP. 1-4, 2004.
[2] Persson, S.; Fjer, M.; Escobedo-Cousin, E.; Olsen, S.H.; Malm, B.G.; Yong-Bin Wang; Hellstro; Stling, M.; O,Neill, A.G., “ Strained-Silicon Heterojunction Bipolar Transistor,” IEEE Transaction on Electronics Devices Volume 57, PP. 1243-1250, June 2010.
[3] Peter Ashburn, “Si-Ge Heterojunction Bipolar Transistor”s, John Wiley & Sons Ltd., 2003.
[4] Silvaco ATLAS manual, Dec.7, 2006.
[5] Cressler J.D., Niu, G., “Silicon Germanium Heterojunction Bipolar Transistor”, Artech House Inc., 2003.
[6] Armstrong, G.A. and Maiti,C.K., “Technology for Computer Aided Design for Si, Si-Ge and GaAs Integrated Circuits”, The Institution of Engineering and Technology, PP. 208-209, 2007.
[7] Hamed, Ghodsi; Hassan, Kaatuzian, “Physical Characteristics Modification of a Si- Ge Semiconductor Device for Performance Improvement In A Terahertz Detecting System” Journal of Semiconductors, Vol. 36, No.-5, May 2015.
[8] C. K. Maiti and G.A. Armstrong, “Applications of Silicon Germanium Hetero structure Devices,” Inst. of Physics Lab., 2001.
[9] Adel S. Sendra and Kenneth C. Smith, “Microelectronics Circuits,” 5th Edition, Oxford University Press, , PP. 382-383. 2004.
[10] Silvaco Dev Edit Mannual, March 1, 2006.
[11] Mathur, N.; Todorova, D.; Roenker, K.P., “Parasitic Barrier Effects in Si-Ge HBT’s Due to p-n Junction Displacement,” Accepted toTtopicalMmeeting on Silicon Monolithic Integrated Circuits in RF Systems, PP. 177-186, Sept. 2001.
[12] Reonker, K.P.; Alterovitz, S.A.; Mueller, C.H., “ Device Physics Analysis of Parasitic Conduction Band Barrier Formation in Si-Ge HBTs” Silicon Monolithic Integrated Circuits in RF systems, PP.182-186, April 2000.
[13] Jagannathan, B.; Khater, M.; Pagette, F.; Rieh, J.-S.; Angell, D.; Chen, H.; Florkey, J.; Golan, F.; Greenberg, D.R.; Groves, R.; Jeng, S.J.; Johnson, J.; Mengistu, E.; Schonenberg, K.T.; Schnabel, C.M.; Smith, P.; Stricker, A.; Ahlgren, D.; Freeman, G.; Stein, K.; Subbanna, S., “ Self Aligned Si-Ge NPN Transistors With 285 GHz fMAX And 207 GHz fT in a Manufacturable Technology,” IEEE Electron Device Letters, Volume 23, PP. 258-260, May 2002.

Kamal Prakash Pandey, "Device Simulation of Si-Ge HBT Using SILVACO TCAD," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.331-335, 2018.

The data which are to be stored and analyzed for various purposes have gone beyond the storage limit of the traditional relational database system. This has led in emerge of various big data technologies to store and process this huge collection of varieties of data. Vertica is an HP enterprise product, which is used in data warehouses to store and perform data analysis that are stored for decades. Vertica is not only used in data warehouses but also it can be integrated with Hadoop ecosystem for big data analysis. This paper basically describes the architecture, features, storage, various operations in Vertica analytics database that has made Vertica to be used for managing and analysis of large volumes of fast-growing data for achieving higher performance in query intensive applications and data warehouses.

Column Orientation, Hybrid Store, Projections, Partitions, Tuple Mover, High Availability, Automatic Database Designer

[1] D.J. Abadi, P.A. Boncz, S.Harizopoulos, “Column-oriented database systems”, In The Proceedings of the VLDB Endowment, pp. 1664–1665, 2009.
[2] T. Siivola, “A Short Introduction To Vertica”, RedHat Software Developer Meetup, 2014.
[3] “Vertica Analytics Platform”, Vertica Documentation, Version: 8.1.x, 2018.
[4] C. Bear, A. Lamb, N. Tran, “The Vertica Database: SQL RDBMS For Managing Big Data”, In The Proceedings of the workshop on Management of big data systems, 2012.
[5] M. Stonebraker, “One size fits all: an idea whose time has come and gone”, In The Proceedings of 21st International Conference on Data Engineering, pp. 2-11, 2005.
[6] A. Lamb, et al., “The Vertica Analytic Database : C-Store 7 Years Later”, In The Proceedings of the VLDB Endowment vol.5, No.12, pp 1790–1801, 2012.
[7] D. Abadi, D. Myers, D. DeWitt, S. Madden, “Materialization Strategies in a Column-Oriented DBMS”, In The Proceedings IEEE 23rd International Conference on Data Engineering, pp 466—475, 2007.
[8] S.Chakraborty , J. Doshi, “Data Retrieval from Data Warehouse Using Materialized Query Database”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp-280-284, 2018.
[9] Ramakrishna Varadarajan, V. Bharathan, A. Cary, J. Dave, S. Bodagala, “DBDesigner: A Customizable Physical Design Tool for Vertica Analytic Database”, In The Proceedings of IEEE 30th International Conference, pp. 1084-1095, 2014.
[10] “A DBMS Architecture Optimized for Next-Generation Data Warehousing” , The Vertica Analytic Database Technical Overview White Paper, Vertica System, 2010.

Jisha Mariam Jose, "A Detailed Study On Features of Data Warehousing Database-Vertica," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.336-348, 2018.