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Spell checking is a very important phase of any document processing system and Natural Language Processing. Spell Checking is a process to find the incorrect spells in a text document and to correct that particular incorrect spelling. There are various spell checking systems for various languages Like Hindi, Punjabi, English, French, Germen that can detect and correct the spell from a particular document. In this paper, we proposed a hybrid algorithm to detect and correct misspelled words from a text document written in three languages Hindi, English and Punjabi. Hybrid approach is a combination of various approaches like Dictionary lookup approach, Edit Distance Approach, Rule based approach and N-Gram approach. Proposed system can detect and correct the misspelled words from three given languages. A collision detection and correction system for alternates for misspell words has been also provided. Performance of proposed system is checked on various inputs collected from various books, websites etc. Results of the proposed system are evaluated on these outputs which have accuracy values higher than that of existing system.

Spell Checking; Hybrid approach for Spell Checking; N-Gram Approach; Rule Based Approach; Edit distance approach

[1] Ritika Mishra, Navjot Kaur, Design and Implementation of Online Punjabi Spell Checker Based on Dynamic Programming, Volume 3, Issue 8, August 2013, ISSN: 2277 128X, International Journal of Advanced Research in Computer Science and Software Engineering
[2] Neha Gupta, Pratistha Mathur, Spell Checking Techniques in NLP: A Survey, Volume 2, Issue 12, December 2012 , ISSN: 2277 128X, International Journal of Advanced Research in Computer Science and Software Engineering
[3] Baljeet Kaur, Review On Error Detection and Error Correction Techniques in NLP: Volume 4, Issue 6, June 2014 ISSN: 2277 128X, International Journal of Advanced Research in Computer Science and Software Engineering.
[4] Rupinderdeep Kaur and Parteek Bhatia, “Design and Implementation of SUDHAAR-Punjabi Spell Checker,” International Journal of Information and Telecommunication Technology, Vol. 1, Issue 15 May, 2010.
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[6] Neha Gupta &PratisthaMathur,“Spell Checking Techniques in NLP: A Survey,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue 12, December 2012.
[7] Gurpreet Singh Lehal, “Design and Implementation of Punjabi Spell Checker”, International Journal of Systemics, Cybemetics and Infomatics, 2007.
[8] Amit Sharma & Pulkit Jain, “Hindi Spell Checker”, Indian Institute of Technology Kanpur, April 17, 2013.
[9] MeenuBhagat, (2007), “Spelling Error Pattern Analysis of Punjabi Typed Text”, Thesis Report, Thapar University, Patiala.
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[17] Jesus Vilares& Manuel Vilares, “Managing Misspelled Queries in IR Application,” Issue 8, October 2010.
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Shaina, Naresh Kumar, "Detection and correcting the wrong words from Hindi, English and Punjabi Text Documents," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.314-318, 2019.

Technological advancement in all the fields leads to the problem of information overload in front of internet user. It has become extremely difficult for them to reach to the information which is most relevant to their need. Information gigantic size makes the user to wander from one web page to another in order to reach to their target information. This leads to wastage of user time and also reduces the interest of user from the search engine, websites as well as internet. The problem of accessing user relevant web pages falls into the category of NP-Complete problems. Web Mining, an application of data mining is utilized to find the solution of this issue of information extraction. For retrieving the relevant data top T web links needs to be prioritized. Here we propose a memetic algorithm and simulated annealing algorithm for selecting the most relevant web document. Both the algorithms are compared on the basis of their performance experimentally and results shows the domination of one over another.

Web Mining, NP-complete, Memetic algorithm, Simulated Annealing algorithm

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[12] C. C. Lin, “Optimal Web site reorganization considering information overload and search depth,” Eur. J. Oper. Res., vol. 173, no. 3, pp. 839–848, 2006.
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[14] F. Neri and C. Cotta, “Memetic algorithms and memetic computing optimization: A literature review,” Swarm Evol. Comput., vol. 2, no. February, pp. 1–14, 2012.
[15] P. Moscato and C. Cotta, “A Modern Introduction to Memetic Algorithms,” no. January 2003, pp. 141–183, 2010.
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Kamika Chaudhary, Neena Gupta, "Comparison of Meta-heuristic Algorithms for Web Link Prioritization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.319-324, 2019.

To reduce error Software testing is important, upholding and overall software costs. To evaluate the feature or competency testing the software is an activity of system and determining that whether it meets required scenario. One way is program slicing to comfort, this method is to break down the large programs into smaller ones and further is model based slicing that split the large software architecture model into smaller models at the initial phase of SDLC (Software Development Life Cycle). To extract the sub model from a big model diagrams it is a completely new approach on the basis of slicing criteria. This planned procedure used the notion of model based slicing to segment the sequence diagram to extract the desired piece. An overview of Model based slicing is presented by this literature survey, including the different general methods and techniques used to compute slices. Our proposed test case generation technique can be used for integration and system testing accommodating the object message and condition information associated with the use case scenarios.

Model Based Slicing, Quality Based Slicing, UML/OCL Model Verification, Model Revolution Verification Through Slicing, Dependency Graph, Model, Test Case Generation.

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[8]. H. Kagdi, J.I. Maletic, and A. Sutton, “Context- Free Slicing of UML Class Models,” Proc. 21st IEEE Int’l Conf. Software Maintenance, pp. 635-638, 2005.
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[10]. J.H. Bae, K. Lee, and H.S. Chae, “Modularization of the UML Metamodel Using Model Slicing,” Proc. Fifth Int’l Conf. Information Technology: New Generations, pp. 1253-1254, 2008.
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[13]. Jung Ho Bae and Heung Seok Chae, “UMLSlicer: A tool for modularizing the UML metamodel using slicing,” In 8th IEEE International Conference on Computer and Information Technology (CIT), pp.772–777, 2008.
[14]. Jaiprakash T. Lallchandani, R. Mall, "Static Slicing of UML Architectural Models," Journal of Object Technology, vol. 8, no. 1, pp. 159- 188, January-February 2009.
[15]. J. Lallchandani and R. Mall, “A Dynamic Slicing Technique for UML Architectural Models,” IEEE Transaction on Software Engineering, Vol. 37, No. 6, NOV/DEC 2011.
[16]. Philip Samuel, Rajib Mall, “Slicing-Based Test Case Generation from UML Activity Diagrams,” ACM SIGSOFT Software Engineering Notes, Vol. 34 No. 6, November 2013.
[17]. Philip Samuel, Rajib Mall, “A Novel Test Case Design Technique Using Dynamic Slicing of UML Sequence Diagrams,” e-Informatics Software Engineering Journal, Vol. 2, Issue 1, 2008.
[18]. Nisansala Yatapanage, KirstenWinter, and Saad Zafar, “Slicing behavior tree models for verification,” In IFIP Advances in Information and Communication Technology, Vol. 323, pp. 125–139, 2010.
[19].Philip Samuel , Rajib Mall, Pratyush Kanth, “Automatic test case generation from UML communication diagrams,” Information and Software Technology (ELSEVIER), 2007.
[20]. Asadullah Shaikh, Robert Clarisó, Uffe Kock Wiil, and Nasrullah Memon, “Verification-driven slicing of UML/OCL models,” In Proceedings of the IEEE/ACM international conference on Automated software engineering, pp. 185–194, ACM, 2010.
[21].Asadullah Shaikh, Uffe Kock Wiil, and Nasrullah Memon, "Evaluation of tools and slicing techniques for efficient verification of UML/OCL class diagrams," Advances in Software Engineering, vol.18, pp 173-192, 2011.
[22].Monalisa Sarma, Debasish Kundu, Rajib Mall, “Automatic Test Case Generation from UML Sequence Diagrams,” 15th IEEE International Conference on Advanced Computing and Communications, 2007.
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[24]. Mathieu Acher, Philippe Collet, Philippe Lahire, and Robert France, “Separation of Concerns in Feature Modeling: Support and Applications,” In Aspect- Oriented Software Development(AOSD’12), ACM Press, 2017.
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[28]. Davide Falessi, Shiva Nejati, Mehrdad Sabetzadeh, Lionel Briand, and Antonio Messina, “SafeSlice: a model slicing and design safety inspection tool for SysML,” In SIGSOFT/FSE’11 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-19) and ESEC’11: 13rd European Software Engineering Conference (ESEC-13), ACM, 2018.
[29]. Zoltán Ujhelyi, Ákos Horváth, and Dániel Varró, “Towards dynamic backward slicing of model transformations,” In 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), pp.404–407, IEEE Computer Society, 2011.
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Venus Grover, Jitender Kumar, "A Review of Model Based Slicing and Test Case Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.325-329, 2019.

Rate of heart-related diseases are growing day by day on a greater pace from the last 15 years, which is a major concern. Through the classification, we can understand the possibilities of such worst-case scenarios at an earlier stage which can help us in being cautious and moreover being prepared for it in the immediate future.

Classification, possibilities of heart diseases, comparative analysis of algorithms using R

[1] C. S. Dangare, S S. Apte Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques , International Journal of Computer Applications (0975 – 888) Volume 47– No.10, June 2012.
[2] J . Soni, U. Ansari, D. Sharma, S. Soni. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications (0975 – 8887)Volume 17– No.8, March 2011
[3] K. Srinivas, B. Kavihta Rani, Dr A. Govrdhan. Application of data mining techniques in healthcare and prediction of heart attacks .

Bindu Trikha, Dhruv Dixit, "Comparative Study of Classification Methods using Algorithms of Data Mining for Possibilities of Heart Diseases," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.330-336, 2019.

Due to popularity of Cloud computing environment, the cloud computing users are increasing day by day and that has become one of the important challenge for the cloud providers in terms of load balancing. Load balancing distributes the traffic evenly over multiple paths. In this research work, we have proposed the Dynamic Improved PSO Load balancing algorithm and implement it over CloudSim toolkit. This toolkit assisted the modeling and generation of virtual machines in a simulated manner such that datacenters, jobs and their mapping to VMs can be done on a same node whereas provide the desirable result. Therefore, the results are compared with the existing load balancing algorithms namely Modified Throttled, FCFS and Particle Swam Optimization based on their performance using CloudSim Simulator. Simulation outcomes are recorded in terms of the Response time and datacenter processing time of these algorithms along with its performance and cost details.

Cloud Computing, Load Balancing, Virtual Machine, Scheduling, Particle Swarm Optimization, Modified Throttled, FCFS, CloudSim, Response time, Data Center Processing Time, Cost

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[5] B. Mondal, K. Dasgupta, P. Dutta, "Load Balancing in Cloud computing using stochastic hill climbing-a soft computing approach," In Proceedings of 2nd International Conference on Computer, Communication, Control and Information Technology (C3IT), Elsevier, Procedia Technology, vol. 4, pp. 783 -789, February 2012.
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[9] Vidhi Tiwari1*, Pratibha Adkar2, Implementation of IoT in Home Automation using android application, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.11-16, April (2019), E-ISSN: 2320-7639.
[10] Amogha A.K., Load Forecasting Algorithms with Simulation & Coding, International Journal of Scientific Research in Computer Science and Engineering, Vol.7 , Issue.2 , pp.16-21, Apr-2019, E-ISSN: 2320-7639.

Sanjay G. Patel, S.D. Panchal, "Improving QoS Parameters for Cloud Data Centers Using Dynamic Particle Swarm Optimization Load Balancing Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.337-342, 2019.

Today`s world surveillance system plays a major role in the security industry. In the video monitoring system, moving object detection was frequently used. Motion estimation is also an important part of video processing monitoring, such as video filtering and compression of video frames. Video Surveillance System is a powerful tool for tracking people and their public safety operations. The reason for having a monitoring system is not only to place cameras in the human eye place, but also to allow them to automatically acknowledge activities. This paper creates a smart recognition of the system of human activity. At each stage of the suggested system, image processing techniques are used A system was built based on the Caltech database of human activity features acquired from frame sequences. Relevance Vector classifier used in the dataset to classify the model of activity. Classification results show high effectiveness throughout the training, testing and validation stages.

Human activity recognition, relevance vector classifier, histogram of gradients, Background subtraction

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[2] Htike, Kyaw Kyaw, Othman O. Khalifa, Huda Adibah Mohd Ramli, and Mohammad AM Abushariah. "Human activity recognition for video surveillance using sequences of postures." In e-Technologies and Networks for Development (ICeND), 2014 Third International Conference on, pp. 79-82. IEEE, 2014.
[3] M. Thida, Y. L. Yong, P. Climent-P´erez, H.-l. Eng, and P. Remagnino, “A literature review on video analytics of crowded scenes,” in Intelligent Multimedia Surveillance. Springer, 2013, pp. 17–36..
[4] Kaur, Rajvir, and Sonit Singh. "Background modelling, detection and tracking of human in video surveillance system." In Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), 2014 Innovative Applications of, pp. 54-58. IEEE, 2014.
[5] Meng, Binghao, Lu Zhang, Fan Jin, Lu Yang, Hong Cheng, and Qian Wang. "Abnormal Events Detection Using Deep Networks for Video Surveillance." In International Conference on Cognitive Systems and Signal Processing, pp. 197-204. Springer, Singapore, 2016.
[6] Peng, Qiwei, Gongyi Hong, Min Feng, Yuan Xia, Lei Yu, Xu Wang2and, and Yang Li. "Off-position detection based on convolutional neural network." (2016).
[7] Sapana, Miss, K Mishra, and K S Bhagat. 2015. “A Survey on Human Motion Detection and Surveillance.” International Journal of Advanced Research in Electronics and Communication Engineering 4 (4).
[8] Automated Daily Human Activity Recognition for Video Surveillance Using Neural Network Mohanad Babiker1, Othman O. khalifa1, Kyaw Kyaw Htike2, Aisha Hassan1, Muhamed Zaharadeen1 1Department of Electrical and Computer Engineering International Islamic University Malaysia 2Department of Information Technology UCSI University, Malaysia
[9] An Approach of Understanding Human Activity Recognition and Detection for Video Surveillance using HOG Descriptor and SVM Classifier Jagadesh B Assistant Professor, Department of ECE VVCE, Mysuru jagadeesh.b@vvce.ac.in Meghana M N Department of ECE VVCE, Mysuru.

Arvind Malge, Mallikarjuna Shastry P.M, "Human Activity Classification for Surveillance using Machine Learning and Image Processing Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.343-346, 2019.

India is a nation with more than half of its citizens dependent on agriculture for its survival, but only uses 14 percent of its GDP contribution. The nation has divided portions of land, resulting in a significant number of individual farmers with a nearly stagnant productivity. Despite government actions at both the center and the state level, a gap between land and lab our continues. With over 80% of the entire land holdings of tiny and marginal landowners, Karnataka is no exception. The search engine researchers have focused their efforts for years and years on having search engines that are more accurate and faster. In the past, this was more than enough, But the concept of getting everything intelligent became with smart phone appearance. In this paper we attempted to implement a proposed model of a voice-based farmer information system called ("RaitaSnehi") that provides data on the various schemes that farmers can get from various websites. Based on choices that farmers need to know, the user is prompted to give voice input. The word recognition algorithm is then applied using the Python environment and the recognized word is searched from the website in the parsed data and the details of the required data are read out on demand to the farmer. The word recognition algorithm is implemented, which is the template-based comparative algorithm based on hidden markov model, and the results are checked for accuracy. The words are given in the language of Kannada and the results are obtained in the language of Kannada to make the farmers comfortable. Python translation tool is used to convert English to Kannada and when reading from web sources, these words are converted to text to voice in Kannada Language. Through the document we will explain each portion of the proposed model in detail.

Speech Recognition, Hidden Markov Model, Natural Language Processing, Kannada Voice Output

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Gourish Malage, Kiran Kumari Patil, "RaitaSnehi - A Voice Based Farmer Information System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.347-352, 2019.

The accuracy of data-driven teaching methods is often unsatisfactory when training data are insufficient either in amount or quality. Usually incorporate privileged information (PI), tags, properties or attributes manually labeled to improve the learning of classification. The manual labeling process, however, takes time and works intensively. In addition, manually labeled privileged information may not be rich Sufficient due to personal knowledge limitations. In this approach, classifier learning is enhanced by exploring untagged corporate privileged information (PI), which can effectively eliminate reliance on manually labeled data and enhance privileged information. We treat each selected privileged information as a subcategory in detail and for each subcategory we learn one classifier independently. Classifiers are integrated for all sub-categories to form a more powerful category classifier. In this CNN classifier approach, in particular, to learn the optimum output based on the pictures chosen. The superiority of this proposed approach is demonstrated by extensive experiments on two benchmark data sets.

Untagged corpora, Transfer learning, privileged Information, Neural network

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Hardikkumar M. Dhaduk, Mallikarjuna Shastry P.M, "Convolution Neural Network Based Enhanced Learning Classification Using Privileged Information," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.353-357, 2019.

The main aim of this study is to analyse the effectiveness of Knowledge Management in Software Industries. The Knowledge Management dimensions which could influence the organizational effectiveness have been identified. Metric to measure the relationship between these Knowledge Management dimensions has been developed. To test the hypotheses, a sample size of 254 software company employees has been collected and Partial Least Square technique of Structural Equation Modelling (SEM) has been used to investigate the empirical relationships between the different identified dimensions. Questionnaires were distributed to 300 employees of different software companies through Google Form. Response rate of 72% (254 employees responded) was achieved in the survey. The testing of hypotheses justified that in terms of organizational effectiveness, the identified dimensions of Knowledge Management are the critical success factors. Implications of the study would enable the Human Resource managers to make their Knowledge Management process more powerful for the enhancement of effectiveness of the organization. It may not be possible to generalize the results for the full extent, as the study limits itself to the number of software industries. All the limitations of statistical testing and modelling and simulation are applicable to this research.

Knowledge Management, Software Industries, Employees, Questionnaire, Organizational Effectiveness, Structural Equation Modelling

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Rekha M, Lewlyn L R. Rodrigues, "Effectiveness of Knowledge Management in Software Industries – An Empirical Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.358-363, 2019.

With the growing complexities in Object Oriented (OO) software, the number of bugs present in the software module is increased. In this paper, a technique has been presented for minimization of test cases for the OO systems. The Camel 1.6.1 open source software was used the evaluation of proposed technique. The mathematical model used in the proposed methodology was generated using the open source software WEKA by selecting effective Object Oriented (OO) metrics. Ineffective and effective Object Oriented metrics were recognized by using the techniques based on feature selection to generate test cases that cover fault proneness classes of the software. The defined methodology used only effective metrics for assigning weights to test paths for minimization. The results show the significant improvements.

Camel 1.6.1, Test Case Minimization, WEKA

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Divya Taneja, Rajvir Singh, Ajmer Singh, "Package Level Test Case Minimization for Bug Prediction using Linear Regression Machine Learning Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.364-370, 2019.