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Air transport provides efficient, well organized, and time effective services. Even though flights are the fastest way to transport, its delay leads to customer dissatisfaction. Many factors effect flight delays, some of them are weather, operational imperfection, baggage loading etc. In this paper, we are developing a predictive system which predicts flight delays based on weather data. Flightdata set taken from US DEPARTMENT OF TRANSPORTATION and weather data set from HOURLY LAND-BASED WEATHER OBSERVATIONS FROM NOAA. We have implemented Ensemble method, Decision tree and Random forest on the balanced data set. For balancing the data set we are using sampling techniques. The algorithms are applied on the combined flight and weather data set to predict the flight delays.

Flights, Weather, Random Forest, Decision Tree, Ensemble

[1] Sun Choi, Young Jin Kim, Simon Briceno and Dimitri Mavris, “Prediction of Weather-induced Airline Delays Based on Machine Learning Algorithms, “978-1-5090-2523-7/16/2016 IEEE
[2] Suvojit Manna, Sanket Biswas, Riyanka Kundu Somnath Rakshit, Priti Gupta and Subhas Barman,”A Statistical Approach to Predict Flight Delay Using Gradient Boosted Decision Tree,” in International Conference on Computational Intelligence in Data Science (ICCIDS), 978-1-5090-5595-1/17/2017 IEEE
[3] Sina Khanmohammadi, Salih Tutun, Yunus Kucuk, “A New Multilevel Input Layer Artificial Neural Network for Predicting Flight Delays at JFK Airport, ” in Conference Organized by Missouri University of Science and Technology-Los Angeles, CA, Sina Khanmohammadi et al. / Procedia Computer Science 95 ( 2016 ) 237 – 244
[4] Balasubramanian Thiagarajan, Lakshminarasimhan Srinivasan, Aditya Vikram Sharma, Dinesh Sreekanthan, Vineeth Vijayaraghavan, “A Machine Learning Approach for Prediction of On-time Performance of Flights”, 978-1-5386-0365-9/17/2017 IEEE
[5] Rong Yao, Wang Jiandong, Xu Tao , “A Flight Delay Prediction Model with consideration of Cross-Flight Plan Awaiting Resources, “978-1-4244-5848-6/10/2010 IEEE
[6] Guansheng Tong, Jianli Ding “ Real time Sub time Early Warning of Airport Scheduled Flight Delay Based on Immune Algorithm, “ in Second International Symposium on Intelligent Information Technology Application, 978-0-7695-3497-8/08/2008 IEEE
[7] Ding Jianli , Yu Yuecheng , Wang Jiandong, “A Model for Predicting Flight Delay and Delay Propagation Based on Parallel Cellular Automata, ” in ISECS International Colloquium on Computing, Communication, Control, and Management , 978-1-4244-4246-1/09/2009 IEEE
[8] Jianli Ding , Xuesen Li ,Guansheng Tong , “The dynamic immune forecasting method of the airdrome flight delay under considering the stochastic factors, ” in Ninth International Conference on Hybrid Intelligent Systems, 978-0-7695-3745-0/09/2009 IEEE
[9] Yujie Liu, Song Ma,” The Multimode Estimation Modeling for Flight Delay of a Busy Hub-Airport in Flight Chain,” in IITA International Conference on Services Science, Management and Engineering, 978-0-7695-3729-0/09/2009 IEEE
[10] Yu-Jie Liu, Fan Yang , “Initial Flight Delay Modeling and Estimating Based on an Improved Bayesian Network Structure Learning Algorithm, “ in Fifth International Conference on Natural Computation, 978-0-7695-3736-8/09/2009 IEEE
[11] Yujie Liu, Song Ma, “Modeling and Estimating for Flight Delay Propagation in a Reduced Flight Chain Based on a Mixed Learning Method,” in International Symposium on Knowledge Acquisition and Modeling, 978-0-7695-3488-6/08/2008 IEEE
[12] Zhiwei Xing, Yunxiao Tang, “The model for optimizing airport flight delays allocation,” in 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, 978-1-5090-0768-4/16/2016 IEEE
[13] Rong Yao, Wang Jiandong, “Prediction Model and Algorithm of Flight Delay Propagation Based on Integrated Consideration of Critical Flight Resources,” in ISECS International Colloquium on Computing, Communication, Control, and Management, 978-1-4244-4246-1/09/2009 IEEE
[14] Lu Zonglei, Wang Jiandong, Xu Tao, “A New Method for Flight Delays Forecast Based on the Recommendation System, “in ISECS International Colloquium on Computing, Communication, Control, and Management, 978-1-4244-4246-1/09/2009 IEEE

K.Ebenezer, K.N. Brahmaji Rao, "MACHINE LEARNING APPROACH TO PREDICT FLIGHT DELAYS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.231-234, 2018.

The web is full of the content which is either in complete or semi unstructured form and retrieving the essential data out of this unstructured form is very difficult so the concept of the information extraction (IE) keeping in view the necessary parameters becomes highly essential. This paper presents a comparative study for how the problem of information extraction can be handled for a dataset by taking the first step towards IE of named entity recognition (NER) into consideration. Various classifiers/techniques and impact of pipeline on some of them is discussed in this paper for NER and based on the results with keeping the due response time into consideration the classifier/technique of conditional random fields for NER serves out to be the best with an average recall and precision of 0.97 each helping in predicting efficiently of whether a given word is a part of the named entity or not. The automation in the field of medical science for search of the patient for clinical trials from the clinical databases serves to be the most important area of concern at the present time & this paper provides an approach for choosing the technique according to parameters, also discussing the results of the novel algorithmic approach.

Information extraction, Natural language processing, Named entity recognition, Conditional random fields

[1] E. H. Huang, R. Socher, C. D. Manning, and A. Y. Ng. “Improving Word Representations via Global Context and Multiple Word Prototypes.” In ACL, 2012.
[2] Charles Sutton and Andrew McCallum, “An Introduction to Conditional Random Fields”, Foundations and Trends in Machine Learning, Vol. 4, No.4, 267-373, 2012.
[3] Paul Anderson, Aspen Olmsted, Gayathri Parthasarathy, “NLP Pipeline for Temporal Information Extraction & Classification from free text Eligibility Criteria”, International Conference on Information Society, IEEE, 2016.
[4] Dekai Wu, Grace Ngai, Marine Carpuat, Jeppe Larsen, and Yongsheng Yang. “Boosting for named entity recognition.” In Dan Roth and Antal van den Bosch, editors, Proc. 6th Conf. on Computational Natural Language Learning (CoNLL), 2002.
[5] Yefeng Wang and Jon Patrick. “Cascading classifiers for named entity recognition in clinical notes.”In Proc. Workshop on Biomedical Information Extraction (WBIE), pages 42-49, 2009.
[6] Rehan Khan and A.J. Singh, "NLP: A Comparative Study with Algorithmic Approach for Information Extraction", International Journal of Emerging Technologies and Innovative Research, Vol.5, Issue 9, page no.970-979, September-2018.
[7] D. Koller and N. Friedman, “Probabilistic Graphical Models: Principles and Techniques”, MIT Press, 2009.
[8] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. “Natural Language Processing (Almost) from Scratch.” Journal of Machine Learning Research, 12:2493,2537, 2011.
[9] C. Sutton and A. McCallum, “Piecewise training for structured prediction,” Machine Learning, vol. 77, no. 2–3, no. 2–3, pp. 165–194, 2009.
[10] J. Shotton, J. Winn, C. Rother, and A. Criminisi, “Textonboost: Joint appearance, shape and context modeling for mulit-class object recognition and segmentation,” in European Conference on Computer Vision (ECCV), 2006.
[11] C. Sutton, K. Rohanimanesh, and A. McCallum, “Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data,” in International Conference on Machine Learning (ICML), 2004.
[12] Yoav Freund and Robert E. Schapire. “Large margin classication using the perceptron algorithm.”Machine Learning, 37(3):277 to 296, 1999.
[13] F. C. Peng and A. McCallum, “Accurate information extraction from research papers using conditional random fields,” in HLT-NAACL 2004: Main Proceedings, pp. 329–336, Association for Computational Linguistics, Boston, Mass, USA, 2004.
[14] A. Torralba, K. P. Murphy, and W. T. Freeman, “Contextual models for object detection using boosted random fields,” in Advances in Neural Information Processing Systems, vol. 17, pp. 1401–1408, 2005.
[15] Crammer K., Dekel O., Keshet J., Shalev-Shwartz S., Singer Y., “Online Passive-Aggressive Algorithms”, Journal of Machine Learning Research 7 (2006) 551–585.
[16] V. Vineet, J. Warrell, P. Sturgess, and P. H. S. Torr, “Improved initialisation and Gaussian mixture pairwise terms for dense random fields with mean-field inference,” in Proceedings of the 23rd British Machine Vision Conference (BMVC ’12), Surrey, UK, September 2012.
[17] S. Kumar and M. Hebert, “Discriminative random fields,” International Journal of Computer Vision, vol. 68, no. 2, pp. 179–201, 2006.
[18] N. Piatkowski and K. Morik, “Parallel loopy belief propagation in conditional random fields,” in Proceedings of the KDML Workshop of the LWA, Magdeburg, Germany, 2011.
[19] M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz, “Building a large annotated corpus of English: The Penn Treebank,” Computational Linguistics, vol. 19, no. 2, no. 2, pp. 313–330, 1993.
[20] GMB (Groningen Meaning Bank) corpus, http://gmb.let.rug.nl/.
[21] Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222–1239, 2001.
[22] Confusion Matrix, http:// scikit-learn.org/ stable/ modules/ generated/sklearn.metrics.confusion_matrix.html
[23] Scikit-learn, http://scikit-learn.org/ stable/ auto_examples/ model_selection/ plot_precision_recall.html # sphx-glr-auto-examples-model-selection-plot-precision-recall-py
[24] I. Sutskever and T. Tieleman, “On the convergence properties of contrastive divergence,” in Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
[25] Erik F. Tjong Kim Sang. “Introduction to the CoNLL-2002 shared task:Language-independent named entity recognition.”In Dan Roth and Antal van den Bosch, editors, Proc. 6th Conf. on Computational Natural Language Learning (CoNLL), pages 155 to 158, 2002.

Rehan Khan, A.J. Singh, "Developing & Deploying Algorithms for Information Extraction using Classification Measures for Named Entity Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.235-248, 2018.

The world’s business environment has become volatile today. The industrial sectors have been struggling to make their mask. All manufacturing industries have been battling for their survival. In India agricultural sector too seems lying on death bed due to adverse weather conditions, less rainfalls, apathy of farmers and other numerous reasons. Sugar industry is one of the most important agro-based industries in India and ranks second amongst major agro-based industries. The sugar industry is not an exception it has been observed that sugar industries in India have been practicing old traditional methods and systems for even procuring sugarcane the essential raw material for production of sugar. There are infinite reasons for the gloomy picture of sugar industries in India. This industry faces many problems as short-margin, persistent losses in sugar recovery and losses in the sugar manufacturing due to old machinery and lack of new technology for producing of sugar. The registration system of sugarcane itself has many loopholes and needs to be upgraded. Thus the researcher has concentrated his whole study on methods, systems & technologies used by sugar industry today and the inbuilt lacuna in whole process and also want to upgrade whole process by this research, by finding out problems in registration system.

sugarcane registration system, technology, sugar industry, process

Journal papers:
[1] R. D. Kumbhar, “ERP System for Effective Management of Co-Operative Sugar Industries - A Case Study of Sahyadri SSK Ltd. Shiravade Karad (M.S.)”, International Journal of Information Technology and Knowledge Management, Volume-4, No-1, pp-33-37. June 2011.
[2] B.S. Sawant & Uma Yadav, “A Case Study: “Problems and Prospects of IT Implementation in Sugar Factory”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume-2, Issue-8, ISSN: 2277128X, August 2012
[3] D. Pisal, A. Kumar, V. Kakade and N. Chavan, “Computerized Model for Sugar Cane Harvesting for Effective Planning and Control in Co-operative Sugar Factories in Pune District”, Abhinav, National Monthly Refereed Journal of Research in Commerce & Management. Volume-1, Issue-9 ISSN: 2277-1166, 2012
[4] D. Pisal, A. Kumar, “Computerized Decision Support System for Sugar Industry: A Literature Review”, International Journal in Multidisciplinary and Academic Research (SSIJMAR). Volume-1 No-4, ISSN: 2278-5973, Nov-Dec 2012
[5] R.P. Koli, V.D. Jadhav, “Agriculture Decision Support System as Android Application”, International Journal of Science & Research (IJSR), ISSN (Online): 2319-7064. 2013
[6] S. Sivasubramain, S. Sivaskaran, S. Thiru and N. Senthil “A Proposed Android Based Mobile Application to Monitor Works at Remote Site”, International Journal of Science & Research (IJSR), ISSN (online): 2319-7064 Volume-3 Issue-2, Feb 2014.
[7] R. Dhobale, P. Khet, G. Pandhare, P. Thakare, “Smart Krushi”, International Journal of Advance Research in Computer Science and Management Studies. Volume-4, Issue-5, ISSN (Online): 2321-7782., May 2016
[8] S.K. Shirole, C. A. Talavekar, A. S. Jadhav, C M. Kamble, “Android Application for Sugar Cane Field Registration”, International Research Journal of Engineering and Technology (IRJET), Volume-03 Issue-03, ISSN (Online): 2395-0056, ISSN(Print): 2395-0072., March-2016
[9] H. Patel and D. Patel,”Survey of Android Apps for Agriculture Sector”, International Journal of Information Sciences and Techniques (IJIST), Volume-6, No.1/2, March 2016.
[10] N. Pushkar, S. Kumbhar, S. Mali, D, Vahagaonkar, and A.N. Mandale: “Sugar cane Management Using Android Application.” International Journal of Modern Trends in engineering and Research. ISSN (Online): 2349-9745. ISSN (Print): 2393-8161, (2016).
[11] A. D., Shiva K. S. Prasad, Shrivaishnavi J. K., P. Sowmya, T. Agarwal, “Agriculture Based Android Application”, International Journal of Advancement in Engineering Technology, Management & Applied Science. ISSN: 2349-3224 Volume-3, Issue-2, pp 124-131., May 2016
[12] K.J. Fexi. And K. Sabapathi, “Constraints Faced by the Registered and Non-Registered Cane Growers in Amaravathy Cooperative Sugar Mills-A Comparative Study”, International Journal of Recent Scientific Research. Volume-7, Issue-2, pp-8859-8862. ISSN: 0976-3031. 2016
[13] H.P. Thorat, V.C. Borkar, “Scope of Mathematical Programming in Sugar Industry-Harvesting & Transportation of Sugarcane”, International Journal of Applied Agricultural Research. Volume-11, Issue-1, ISSN: 0973-2683., 2016
[14] V. Patil, S.l Payer, T. Teli, S. Jayachandran, “Decision Support System for Agriculture Management”, International Journal of Emerging Trends in Science & Technology. Volume-03 Issue-02. Pages: 3505-3508 ISSN: 2348-9480. Feb 2016
[15] R.S. Deshmukh & D.G. Harkut, “Proposed Authentication Model for location based queries”, International journal of Scientific research in computer science and engineering. Vol 5, Issue 4. Pp. 66-69. E-ISSN 2320-7639. August 2017
Theses:
[16] R.D. Kumbhar, “A Study of Present Status, Problems and Prospects of Computerization in Selected Co-Operative Sugar Factories in Western Maharashtra”

Dayanand Mhetre, Sampada Gulavani, "A Revival Study of Existing Technology for Sugarcane Plantation Registration System with Special Reference to Solapur District," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.249-253, 2018.

Cloud computing is a technological advancement which is based on the concept of dynamic resources provisioning using internet. Web 2.0 technologies play a central role. Hybrid approach to load balancing will provides better results in providing the improvement in the load balancing. Combination of Round robin with Throttled Algorithm in a combination RTH is observed with some disadvantages such as updating the Index table leads to an overhead and sometimes it is causing delay in providing response to arrived requests. It is found a slight improvement in combination with RTH and ESCE in hybrid approach manner to called RTEH.RTEH Algorithm also shown drawback as it is still getting overhead in updating the index table. Then Artificial Bee Colony Optimization technique is combined with RTEH and obtained RTEAH Algorithm has shown betterment in case of some load balancing parameters such as response time, Execution time etc.

Cloud computing, Task scheduling, CloudSim, Round robin, Equally spread current execution, Throttled, Artificial bee colony

[1] Cloud Computing AWS for Dummies,Bernard Golden, October 2013
[2] Dr. P. Neelakantan,"A Study on E-Learning and Cloud Computing" , International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3 , Issue 1, PP 1534-1539
[3] Cloud Computing Protected: Security Assessment Handbook,John Rhoton,January 2013
[4] Cloud Computing Building the Infrastructure for Cloud Security,Raghuram Yeluri,March 2014
[5] Cloud Computing Cloud Computing,Tanmay Deshpande,April 02 2012
[6] Cloud Computing Scheduling and Isolation in Virtualization A study on scheduling and isolation for data centers and cloud computing,Gaurav Somani,September 29 2010
[7] Cloud Computing Management Strategies For The Cloud Revolution How Cloud Computing is Transforming Business and Why You Can’t Afford to Be Left Behind,Charles Babcock May 22 2010
[8] Enterprise Cloud Computing A Strategy Guide for Business and Technology Leaders,Peter Fingar , Andy Mulholland & Jon Pyke,April 23 2010
[9] Cloud Computing Above the Clouds Managing Risk in the World of Cloud Computing,Kevin T McDonald,February 23 2010
[10] Mithilesh Mittal, Pradeep Sharma and Pankaj Kumar Gehlot, “A Comparative Study of Security Issues& Challenges of Cloud Computing”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.9-15, 2013.
[1] S. Renu, OHH Parveen, “Biometric Based Approach for Data Sharing in Public Cloud”, International Journal of Advanced Research in Computer and Communication Engineering Vol.4, Issue.2 pp.1-9, 2015

G. Srinivasa Rao, T. Anuradha, "Improved Implementation of Hybrid Approach in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.254-260, 2018.

The emotion recognition from speech is used for in human computer interaction. Most of researchers doing research on emotion recognition using speech signal. This project attempts language emotion recognition using speech signal of English language. The emotional speech samples are stored in database and used for Training And Testing. The feature extraction MFCC, PSD and Pitch detection algorithms are used. For classification of different emotions like Angry, Happy/Joy and Normal state SVM classifier is used. The all steps are implemented using MATLAB software. Raspberry pi is used for detection of emotion on hardware This classified emotions can be used for various application areas like medical, security, military etc.

MFCC, PSD, SVM etc

[1] Moataz El Ayadi, Mohamed S. Kamel,Fakhri Karray, “Survey on speech emotio n recognition: Features, classification schemes and databases” , W aterloo, Ontario, Canada,july 2011, , Pages 572-587.
[2] http://cs229.stanford.edu/proj2007/ShahHewlett%20
[3] Surekha Reddy Bandela, T. Kishore Kumar “Stressed Speech Emotion Recognition using feature fusion of Teager Energy Operator and MFCC,”IEEE 2017.
[4] Jeet Kumar, Om Prakash Prabhakar , Navneet Kumar Sahu,” Comparative Analysis of Different Feature Extraction and Classifier Techniques for Speaker Identification Systems: A Review”, IJIRCCE 2014.
[5] Sreeram Ganji, Rohit Sinha,“ Exploring Recurrent Neural Network based Acoustic and Linguistic Modeling for Children’s Speech Recognition”, IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017.
[6] Namrata Dave, “Feature Extraction Methods LPC, PLP and MFCC In Speech Recognition,” Ieee International Journal For Advance Research In Engineering And Technology, July 2013.
[7] Dr.V.AjanthaDevi,Ms.V.Suganya,” An Analysis on Types of Speech RecognitionandAlgorithms,”IJCST,April2016.
[8] Pavol Harár1, Radim Burget1 and Malay Kishore Dutta,” Speech Emotion Recognition with Deep Learning,” 2017 4th International Conference on Signal Processing and Integrated Networks.
[9] Markus Niermann, Peter Jax, Peter Vary,” Joint Near-End Listening Enhancement And Far-End Noise Reduction,” 2017 Ieee.
[10] Amritha Vijayan, Bipil Mary Mathai, Karthik Valsalan, Riyanka Raji Johnson, Lani Rachel Mathew,” Throat Microphone Speech Recognition using MFCC,” International Conference on Networks & Advances in Computational Technologies, 2017.
[11] Pooja A, Pravena D, Govind D,” Significance of Exploring Pitch only Features for the Recognition of Spontaneous Emotions from Speech Signals,”IEEE 2017.
[12] D.S.Shete, Prof. S.B. Patil, Prof. S.B. Patil,” Zero crossing rate and Energy of the Speech Signal of Devanagari Script,” IOSR Journal of VLSI and Signal Processing, Jan 2014.
[13] Mohan Ghai, Shamit Lal, Shivam Dugga l and Shrey Manik,” Emotion Recognition On Speech Signals Using Machine Learning,”IEEE2017.

R.D. Bodke, M.P. Satone, "Recognition of Human Emotion by Speech Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.261-264, 2018.

Given the stock lengths U_j,j=1,2,…,n, this paper computes the total trim loss of One-dimensional-cutting stock problem (1D-CSP) by considering the cutting plan of at most two order lengths at a time of the required order lengths l_1,l_2,…,l_n. The Total Trim Loss (TTL) is computed by fixing a variable t as the percentage of the Pre-Defined Sustainable Trim Loss(PDSTL) on the given stock by the industry. In view of the past experience, it has been noticed that the trim loss up to 3% is viable for the smooth running of the industry. Hence, we consider 3 as the upper bound of the pre-defined sustainable trim t. Considering the variable t:0.5≤t≤3 with the stepping of 0.5 as the nodal points in the domain, we have first computed the corresponding TTL and plotted these points in the range. With this information, Lagrange Interpolation method has been applied to predict the TTL at any arbitrary point (0.5≤t≤3).

Pre-Defined Sustainable Trim Loss, Sustainable Trim Loss, Lagrange interpolation approximation, Total Trim Loss

[1]. Alem Douglas José, Munari Pedro Augusto, Arenales Marcos Nereu, Ferreira Paulo Augusto Valente, “On the cutting stock problem under stochastic demand”, Annals of Operations Research, vol. 179 (1), pp. 169-186, 2010.
[2]. Araujo Silvio Alexandre de, Poldi Kelly Cristina, Smith Jim, “A Genetic Algorithm for the One-Dimensional Cutting Stock Problem with Setups”, Pesquisa Operacional, vol. 34(2) pp. 165-187, 2014.
[3]. Arenales, M. N., Cherri, A. C., Nascimento, D. N. do, & Vianna, A., “A New Mathematical Model for the Cutting Stock/Leftover Problem”, Pesquisa Operacional, vol. 35(3), pp. 509–522, 2015.
[4]. Dikili Ahmet Cemil, Barlas Baris, “A Generalized approach to the solution of One-Dimensional Stock-Cutting Problem for Small Shipyards”, Journal of Marine Science and Technology, vol. 19, no. 4, pp. 368-376, 2011.
[5]. Erjavec, J., Miro Gradišar, Trkman Peter, “Renovation of the Cutting Stock Process”, International Journal of Production Research, vol. 47(14), pp. 3979-3996, 2009.
[6]. Gilmore, P. C. and Gomory, R. E., “A linear programming approach to the cutting-stock problem”, Operations Research, vol. 9, pp. 849-859, 1961.
[7]. Gilmore, P. C. and Gomory, R. E., “A linear programming approach to the cutting-stock problem Part II”, Operations Research, vol. 11, pp. 863-888, 1963.
[8]. Gilmore, P. C. and Gomory, R. E., “Multi-stage cutting-stock problems of two or more dimensions”, Operations Research, vol. 13, pp. 94-120, 1965.
[9]. Ibrahim Muter, Zeynep Sezer “Algorithms for the One-Dimensional Two-Stage Cutting Stock Problem”, European Journal of Operational Research, vol 271(1), pp. 20-32, 2018.
[10]. Mobasher A.,Ekici A., “Solution approaches for the cutting stock problem with setup cost”, Journal Computers and Operations Research, vol. 40(1), pp. 225-235, 2013.
[11]. Powar P.L., Jain V., Saraf M., Vishwakarma R., “One-Dimensional Cutting Stock Problem with First Order Sustainable Trim: A Practical Approach,” International Journal of Computer Science Engineering and Information Technology Research, vol. 3(3), pp. 227-240, 2013.
[12]. Rodrigo N., Shashikala S., “One-Dimensional Cutting Stock Problem with Cartesian Coordinate Points”, International Journal of Systems Science and Applied Mathematics, vol 2(5), pp: 99-104, 2017.
[13]. Suliman S. M. A., “An Algorithm for Solving Lot Sizing and Cutting Stock Problem within Aluminum Fabrication Industry”, Proceedings of the International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 –6, pp. 783-789, 2012.

P. L. Powar, Siby Samuel, "One Dimensional Cutting Stock Problem (1D-CSP): A New approach for Sustainable Trim Loss," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.265-271, 2018.

Sophisticated world has the gifted man not only with comforts but also with many problems, one of the unavoidable and the most challenging problem is vehicle parking problem. The unregulated parking system is leading to huge traffic and accidents. Parking the vehicle in the parking space is highly unorganized and people have to manually check for the vacant places for parking their vehicles. So most of the people will park their vehicles in empty spaces or on the road which increases the problem further. In recent years, a lot of papers have been published addressing this issue. However, implementing them is highly expensive due to their usage of the costly sensor technology and other hardware requirements. But this paper proposes an intelligent parking system for vacancy detection using convolution neural networks that give accurate results under any Circumstances.

Convolution neural networks, Parking lot, Vacancy detection

[1] N. Dan, "Parking management system and method" Jan. 2002, US Patent App. 10/066,215.
[2] Q. Wu, C. Huang, S.-y.Wang, W-c.Chiu, and T. Chen, "Robust parking space detection considering inter-space correlation" in Multimedia and Expo, IEEE International Conference on. IEEE, 2007, pp. 659–662.
[3] C. G. del Postigo, J. Torres, and J. M. Menendez, "Vacant parking area estimation through background subtraction and transience map analysis" IET Intelligent Transport Systems, 2015
[4] P. R. de Almeida, L. S. Oliveira, A. S. Britto, E. J. Silva, and A. L. Koerich, "Plot–a robust dataset for parking lot classification "Expert Systems with Applications, vol. 42, no. 11, pp. 4937–4949, 2015"
[5] Toshimitsu Tanaka "Locating vehicles in the parking lot by image processing". Dec 11-13, 2002, Japan.
[6] Y. Bengio," Learning the deep architectures for AI" Foundations and trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009"
[7] Jordan Cazamias and Martina Mark "Parking space classification using convolution neural networks".
[8] Fabio Carrara and Claudio Gennaro "Car parking occupancy detection using smart cameras and deep learning"
[9] SepehrValipour, Mennatullah Siam, EleniStroulia, Martin Jagersand "parking stall vacancy indicator system"
[10] R. Yusnita, FarizaNorbaya, and NorazwinawatiBasharuddin "Intelligent parking space detection based on image processing" International Journal of Innovation, Management and Technology, Vol. 3, No. 3, June 2012
[11] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Image net classification with deep convolution neural networks," in Advances in neural information processing systems, 2012, pp. 1097–1105.
[12] K. Simonyan and A. Zisserman, "Very deep convolution networks for large-scale image recognition" Vol. 3, No. 3, 2014.
[13] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Computer Vision and Pattern Recognition, 2014.
[14] KeironTeilo O`Shea "An introduction to Convolutional neural networks". Vol. 2, Dec 2015
[15] Almeida et al. (2013)Almeida, Oliveira, Silva, BrittoJr&Koerich] Almeida, P., Oliveira, L. S., Silva, E., BrittoJr, A. S., &Koerich, A. (2013). Parking space detection using textural descriptors.In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 3603_3608). doi:10.1109/SMC.2013.614.

Shaik Brahmaiah, K N Brahmaji Rao, "Parking Occupancy Detection Using Convolutional Neural Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.272-275, 2018.

Social media has been used for expressing and sharing the thoughts of people with different events. Sentiment analysis is being used for computing and satisfying a view of a person given in a piece of a text, to identify persons thinking about any topic is positive, negative or neutral. In the present work sentiment analysis has been used to analyze people’s sentiments, opinions, and emotions towards entities. In this work, sentiment 140 tools have been used for the collection of tweets on different topics. The collected tweets have been preprocessed. Different techniques have been used to present work. Classification technique has been used for the analysis of tweets how many positive, negative and neutral tweets. Sentiment analysis algorithm has been used to analyze tweets whether tweets are positive, negative or neutral. An autocorrect option has been also used to correct the sentence. Sentiment analysis has been used parameters such as accuracy, predictive and automation.

Data Mining, Sentiment Analysis, Twitter, Classification

[1] R. Mehta, D.S. Jain, “Sentiment Mining and Related Classifiers: A Review”, IOSR Journal of Computer Engineering, Vol.18, Issue.1, pp.50-54, 2016.
[2] Chandni, N. Chndra, S. Gupta, R. Pahade, “Sentiment Analysis and its Challenges”, International Journal of Engineering Research & Technology, Vol.4, Issue.3, pp.968-970, 2015.
[3] A.P. Rajan, S.P. Victor, “Web Sentiment Analysis for Scoring Positive or Negative Words Using Tweeter Data”, International Journal of Computer Applications, Vol.96, Issue.6, pp.33-37, 2014.
[4] A. Gupta, J. Pruthi, N. Sahu, “Sentiment Analysis of Tweets Using Machine Learning Approach”, International Journal of Computer Science and Mobile Computing, Vol.6, Issue.4, pp.444-458, 2017.
[5] B. Alvares, N. Thakur, S. Patil, D. Fernandes, K. Jain, “Sentiment Analysis Using Opinion Mining”, International Journal of Engineering Research & Technology, Vol.5, Issue.4, pp.88-91, 2016.
[6] B.M. Bandgar, D.S. Sheeja, “Analysis of Real Time Social Tweets for Opinion Mining”, International Journal of Applied Engineering Research, Vol.11, Issue.2, pp.1404-1407, 2016.
[7] B.S. Dattu, P. Deipali, V.Gore, “A Survey on Sentiment Analysis on Twitter Data Using Different Techniques”, International Journal of Computer Science and Technologies, Vol.6, Issue.6, pp.5358-5362, 2015.
[8] D.E. Oleary, “Twitter Mining for Discovery, Prediction and Causality: Applications and Methodologies”, International Journal of Intelligent Systems in Accounting and Finance Management, Vol.22, Issue.3, pp.222-247, 2015.
[9] G. Sabarmathi, D.R. Chinnaiyan, “Reliable Data Mining Tasks and Techniques for Industrial Applications”, IAETSD Journal for Advanced Research in Applied Sciences, Vol.4, Issue.7, pp.138-142, 2017.
[10] H.P. Rahmath, “Opinion Mining and Sentiment Analysis- Challenges and Applications”, International Journal of Application or Innovation in Engineering & Management, Vol.3, Issue.5, pp.401-403, 2014.
[11] I. Smeureanu, C. Bucur, “Applying Supervised Opinion Mining Techniques on Online User Reviews”, Informatica Economică, Vol.16, Issue.2, pp.81-91, 2012.
[12] K.I. Umar, F. Chiroma, “Data Mining for Social Media Analysis: Using Twitter to Predict the 2016 US Presidential Election”, International Journal of Scientific & Engineering Research, Vol.7, Issue.10, pp.1972-1980, 2016.
[13] K. Sutar, S. Kasab, S. Kindare, P. Dhule, “Sentiment Analysis: Opinion Mining of Positive, Negative or Neutral Twitter Data Using Hadoop”, International Journal of Computer Science and Network, Vol.5, Issue.1, pp. 177-180, 2016.
[14] L.J. Sheela, “A Review of Sentiment Analysis in Twitter Data Using Hadoop”, International Journal of Database Theory and Application, Vol.9, Issue.1, pp.77-86, 2016.
[15] S.A.A. Hridoy, M.T. Ekram, M.S. Islam, F. Ahemed, R.M. Rahman, “Localized Twitter Opinion Mining Using Sentiment Analysis”, Decision Analytics, Vol.2, Issue.1, pp.1-19, 2015.

Amritpal Kaur, Seema Baghla, "Sentiment Analysis of English Tweets Using Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.276-284, 2018.

Cloud computing is the set of IT services that are provided by cloud service provider to the users over the Internet. It is Pay-per-use for On-demand service. The main concern of the cloud computing is Security. In this paper, studies the basics of cloud computing and two main encryption/decryption algorithms that are AES (Advanced Encryption Standard) and DES (Data Encryption Standard). The paper shows the implementation of AES and DES algorithms for the conversion of Plaintext to Cipher text.

AES, DES, cloud computing, issues, cryptography, plaintext, cipher text

[1]. Kevin Hamlen et al.; (April-June 2010) International Journal of Information Security and Privacy, 4 (2), 39-51, April-June 2010 39,”Security issues for Cloud Computing”.
[2]. Rabi Prasad Padhy et al. (December 2011) IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS) Vol. 1, No. 2, “Cloud Computing: Security issues and Challenges”.
[3]. Manpreet Kaur et al. (June 2015), International Journal of Advances in Engineering & Technology, June 2015, © IJAET ISSN: 22311963,” A Review of Cloud Computing Security Issues”.
[4]. Leena Khanna, Anant Jaiswal, “Cloud Computing: Security Issues and Description of Encryption Based Algorithms to Overcome Them”, IJARCSSE 2013

Shaffy Bansal, Vijay Bhardwaj, "IMPLEMENTATION OF CRYPTOGRAPHY TECHNIQUES IN CLOUD COMPUTING," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.285-289, 2018.

The mobile ad hoc networks (MANETs) has a dynamic topology and open wireless medium, may leads to MANET suffering from many security liabilities. In this paper, using recent progresses in uncertain reasoning initiated from artificial intelligence community, an unified trust management scheme has been implemented that improves the security in MANETs. In the proposed trust management pattern, the trust model has two components: trust from direct observation and trust from indirect observation. In direct observation from an observer node, the trust value is derived using Bayesian inference, which is a form of uncertain reasoning. In Indirect observation, also called secondhand evidence that is obtained from neighbor nodes of the observer node, here the trust value is derived using the Dempster-Shafer theory, which is another form of uncertain reasoning. Merging these two components in the trust model can achieve more accurate trust values of the observed nodes in MANETs. Then evaluate this pattern under the situation of MANET routing protocol (OLSRv2). The simulation result shows the effectiveness of the proposed scheme. Exactly, throughput and packet delivery ratio can be improved considerably.

MANETs, Security, Trust Management, Uncertain Reasoning

[1]. Zhexiong Wei, Helen Tang, F. Richard Yu, Maoyu Wang, and Peter Mason “Security Enhancements for Mobile Ad Hoc Networks with Trust Management using Uncertain Reasoning” IEEE Transaction Vehicular technology, VOL. 13, NO. 3, 2014.
[2]. Bhavyesh Divecha, Ajith Abraham, Crina Grosanand Sugata Sanyal “Impact of Node Mobility on MANET Routing Protocols Models”, Vol.3, No.1, july 2014.
[3]. Boun padith Kann havong, Hideshisa Nakayama, Yoshiaki Nemoto, Nei Kato – Tohoku university, Abbas Jamalipour – university of Sydney “A Survey Of Routing Attacks In Mobile Adhoc Networks”, Vol.2, No.2, august 2001.
[4]. C´edricAdjih, Daniele Raffo, Paul M¨uhlethaler INRIA, Domaine de Voluceau, France1 “Attacks Against OLSR: Distributed Key Management for Security”, Vol.2, No.2, july 2000.
[5]. Cedric Adjih, Thomas Clausen, Philippe Jacquet, Anis Laouiti, Paul M¨uhlethaler, Daniele Raffo “Securing the OLSR protocol”, Vol.2, No.2, july 2000.
[6]. Huanyu Zhao, Xin Yang, and Xiaolin Li, Member, IEEE ” cTrust: Trust Management in Cyclic Mobile Ad Hoc Networks” IEEE Transaction on Vehicular Technology, VOL. 62, NO. 6, July 2013
[7]. Philip England, Dr Qi Shi, Dr Bob Askwith, Dr.Faycal Bouhafs “A Survey of Trust Management in Mobile Ad-Hoc Networks”, Vol.1, No.2, January 2013
[8]. 7.Quansheng Guan, Member, IEEE, F. Richard Yu, Senior Member, IEEE, Shengming Jiang, Senior Member, IEEE, and Victor C. M. Leung, Fellow, IEEE “Joint Topology Control and Authentication Design in Mobile Ad Hoc Networks With Cooperative Communications” IEEE Transaction on Vehicular Technology, VOL. 61, NO. 6, July 2012.
[9]. F.Richard Yu, Helen Tang, Peter C. Mason, and Fei Wang “A Hierarchical Identity Based Key Management Scheme in Tactical Mobile Ad Hoc Networks” IEEE Transaction on Network and service management, VOL. 7, NO. 4, December 2010.
[10]. Rui Zhang • Yanchao Zhang • Yuguang Fang “AOS: an anonymous overlay system for mobile ad hoc networks”, Vol.3, No.1, july 2005.
[11]. Shengrong Bu, Student Member, IEEE, F. Richard Yu, Senior Member, IEEE, Xiaoping P. Liu, Senior Member, IEEE, Peter Mason, and Helen Tang, Member, IEEE “Distributed Combined Authentication and Intrusion Detection With Data Fusion in High-Security Mobile Ad Hoc Networks” IEEE Transaction on Vehicular Technology, VOL. 60, NO. 3, March 2011.
[12]. Shengrong Bu, Student Member, IEEE, F. Richard Yu, Senior Member, IEEE, Xiaoping P. Liu, Senior Member, IEEE, Helen Tang, Member, IEEE “Structural Results for Combined Continuous User Authentication and Intrusion Detection in High Security Mobile Ad-Hoc Network” IEEE Transaction on Wireless Communications, VOL. 10, NO. 9, September 2011.
[13]. Shohreh Honarbakhsh, Liza Binti Abdul Latif, Azizahbt Abdul Manaf, and BabakEmami “Enhancing Security for Mobile Ad hoc Networks by Using Identity Based Cryptography” International Journal of Computer and Communication Engineering, Vol. 3, No. 1, January 2014.
[14]. Thomas M. Chen and Varadharajan Venkataramanan, Southern Methodist University “Dempster-Shafer Theory for Intrusion Detection in Ad Hoc Networks”,Vol.1, No.3, september 2000.
[15]. Thomas Clausen, Ulrich Herberg “Vulnerability Analysis of the Optimized Link State Routing Protocol version 2 (OLSRv2)” ,Vol.3, No.1, march 2005.
[16]. Yanwei Wang, F. Richard Yu, Senior Member, IEEE, Helen Tang, Senior Member, IEEE, Minyi Huang, Member, IEEE “A Mean Field Game Theoretic Approach for Security Enhancements in Mobile Ad hoc Networks” IEEE Transaction on Wireless Communications, VOL. 13, NO. 3, March 2014.

R. Esther Raja Pushpa, "Security Enhancements for Mobile Ad Hoc Networks with Trust Management Using Uncertain Reasoning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.290-295, 2018.