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Automatic face recognition (AFR) technologies have seen substantial developments in performance over the past years, and such systems are now broadly used for security and mercantile applications. An automated system for human face recognition in a real-time background for an Institute to mark the attendance of the employees. So Smart Attendance using Real-Time Face Recognition is a real-world solution that comes with day to day activities of handling students/employees. The task is a bit challenging as the real-time background subtraction in an image is still a bit difficult. The proposed system maintains the attendance records of students automatically. Manual entering of attendance in logbooks becomes a difficult task and it also wastes time. So developing such an efficient module that comprises of face recognition to manage the attendance records of students. This module enrolls the student’s face. This enrolling is a onetime process and their face will be stored in the database. While we go through enrolling of face, we require a system since it is only needed to be performed once. You can have your own roll number as your student ID which will be unique for each student. The presence of each student will be updated in a database. The results obtained in this method showed improved performance over the regular manual attendance management system. Attendance is marked as per the student identification. This product gives much more useful solutions with accurate and precise results in a user-interactive manner rather than existing manual attendance and leave management systems. It is then transferred to the office through RF communication which is widely using in industries recently.

Image processing, Face Detection method, HAAR Cascade, Feature Extraction, Face Recognition Method

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[3] Xiao Shang Research of AS level Network Topology, AJETR- 2014.
[4] Pei, Donghui, The Design and Implementation of Serial Monitoring Software Based on Arduino, ICICEE2012.
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[11] M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces," Proc. IEEE, 1991, 586-591.
[12] Md.T. Akhtar, S.T. Razi, K.N. Jaman, A. Azimusshan, Md.A. Sohel,”Fast and Real life object detection system using simple Webcam”,ISROSET Journal (IJSRCSE)
[13] Suma S L, Sarika Raga,” Real Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm”, ISROSET Journal (IJSRCSE).
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Darshankumar C. Dalwadi, Niralee Bhatt, "Performance Evaluation of Face Recognition Based Attendance System using RF Communication," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.124-128, 2019.

Data stored in repositories are rapidly growing in terms of instances represented with multiple attributes/dimensions. To represent characteristics of an instance, mixed type attributes are used. Banking System is one of the areas which store information of bank customers in multiple dimensions. Principal Component Analysis (PCA) is a Dimensionality reduction technique in Data Mining used to transform the attributes of a dataset to a lesser dimensional space. Classification is a Supervised Machine Learning technique used to distinguish the instances of a dataset into a number of classes. In this work, we have analyzed the Bank Marketing dataset containing 1000 instances of bank clients represented with 17 attributes, with a class label as the last attribute. Principal Components (PCs) are generated from the input dataset by applying PCA on mixed attributes. A Deep Neural Network classifier is built by applying Backpropagation on the PCs. Experimental results show that our proposed PCA mixed Deep Neural Network classifier outperforms existing classifiers in terms of accuracy.

Banking System, Mixed Data, PCA, Classification, Backpropagation, Deep Neural Network

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[11] https://pbpython.com/categorical-encoding.html
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[19] Fernandes M., “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue 1, Feb. 2017, pp.19-23.
[20] Ghuse N., Pawar P. and Potgantwar A., “An Improved Approach For Fraud Detection In Health Insurance Using Data Mining Techniques”, IJSRNSC, Vol. 5, Issue 5, June 2017.

Chittem Leela Krishna, Poli Venkata Subba Reddy, "Principal Component Analysis on Mixed Data For Deep Neural Network Classifier in Banking System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.129-134, 2019.

This study presents quasi z source inverter (QZSI) which is new topology derived from the traditional Z source inverter (ZSI). Quasi z source inverter (QZSI) with built in impedance network is an advanced compared to traditional voltage source inverter and current source inverter technology. QZSI can realize buck/boost of voltage and inversion in single stage which improves reliability. The quasi Z source inverter (QZSI) can be realize through unique control methods like simple boost control(SBC) and maximum boost control(MBC), which helps to produce wide range voltage gain. These control techniques are modification of simple PWM. The QZSI with its unique impedance network and wide range of voltage gain like features makes it most suitable for PV based power system application.

Quasi Z-source Inverter, Z-source Inverter, Simple boost control, Maximum boost control

[1] Fang Zheng Peng, "Z-source inverter," in IEEE Transactions on Industry Applications, vol. 39, no. 2, pp. 504-510, March-April 2003.
doi: 10.1109/TIA.2003.808920.
[2] Fang Zheng Peng, “Maximum Boost Control of the Z-Source Inverter,” in IEEE Transaction on Power Electronics, Vol. 20, NO. 4, JULY 2005.
[3] Shen, M., Joseph, A., Wang, J., Peng, F. Z., & Adams, D. J. (2007). Comparison of Traditional Inverters and $Z$-Source Inverter for Fuel Cell Vehicles. IEEE Transactions on Power Electronics, 22(4), 1453–1463. doi:10.1109/tpel.2007.900505
[4] Li Y, Anderson J, Peng FZ, Liu DC. “Quasi-Z-source inverter for photovoltaic power generation systems”. In Proceedings of the twenty-fourth annual IEEE applied power electronics conference and exposition, Washington (DC, USA); 2009. p. 918–24.
[5] H. Rostami and D. A. Khaburi, "Voltage gain comparison of different control methods of the Z-source inverter," 2009 International Conference on Electrical and Electronics Engineering - ELECO 2009, Bursa, 2009, pp. I-268-I-272.
[6] Hanif, M., Basu, M., & Gaughan, K. (2011). Understanding the operation of a Z-source inverter for photovoltaic application with a design example. IET Power Electronics, 4(3), 278.doi:10.1049/iet-pel.2009.0176.
[7] N. R. Sreeprathab and X. F. Joseph, "A survey on Z-source inverter," 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, 2014, pp. 1406-1410.
[8] Singh, N., & Jain, S. K. (2016). Single phase Z-source inverter for photovoltaic system. 2016 7th India International Conference on Power Electronics (IICPE).doi:10.1109/iicpe.2016.8079340.
[9] N. Kshirsagar, P. D. Debre, A. Kadu and R. Juneja, "Design of three phase Z-source inverter for solar photovoltaic application," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2017, pp. 1-6.
[10] M. A. Mawlikar and S. S. Nair, "A comparative analysis of Z source inverter and DC-DC converter fed VSI," 2017 International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, 2017, pp. 1-6.

Sukhdev. N.Joshi, R.D. Bhagiya, "Quasi Z source inverter," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.135-141, 2019.

High Performance Computing (HPC) is a highly emerging concept in the field of computer science and technology. HPC makes the use of parallel computing to solve complex computational problems at a very high speed. Data and compute intensive applications require distinct and different resources, so it becomes utmost important to manage resources and schedule jobs accordingly. HPC is a hard and complex concept to be understood so most of it remains under-utilized. To improve operational functionality and enhance utilization of HPC many systems have been developed. The system used in this research is PBS. Resource management and job scheduling is a major research area in high performance computing. Portable Batch System (PBS) is a scheduling and resource management system. It is used for job accounting and extensible batch job queueing. Three primary goals of PBS are queueing, scheduling and monitoring the jobs. Along these lines, the fundamental objective of this paper is to give novel powerful resource management and job planning and scheduling techniques that is reasonable for all the above purposes and can be coordinated with HPC frameworks.

High Performance Computing (HPC), Job Scheduling, Portable Batch System (PBS), Resource Scheduling

[1] B. Bode, D.M. Halstead, R. Kendall and L. Zhou, “The Portable Batch Scheduler and the Maui Scheduler on Linux Clusters”, In the Proceedings of 4th Annual Linux Showcase & Conference, Atlanta, 2000.
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[3] J. Cao, A. Chan, Y. Sun, S.K. Das and M. Guo, “Taxonomy of application scheduling tools for high performance cluster computing”, Cluster Computing, Vol. 9, pp. 355– 371,2006.
[4] C. Engelmann, S.L. Scott, C. Leangsuksun and X. He, “Towards high availability for high-performance computing system services: Accomplishments and limitations”, In the proceedings of High Availability and Performance Workshop, Santa Fe, NM, USA, Oct. 17, 2006.
[5] Gabriel E, Feki S, Benkert K and Resch M M, “Towards performance portability through runtime adaptation for high-performance computing applications”, Concurrency Computation: Practical Experiment, Vol. 22, pp. 2230–46,2010.

Anika Karwal, O.P. Gupta, "Performance Evaluation of High Performance Computing Resources and Job Management," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.142-146, 2019.

Text mining has played a important role in providing product recommendations to users. Online reviews have become an important factor when people make purchase and business decisions. Efficient recommendation systems help in improving business and also enhance customer satisfaction. The credibility of purchasing a product highly depends on the e-commerce online reviews. However most of people wrongly promote or demote a product by buying and selling fake reviews. Many websites have become source of such opinion spam. These fake/fraudulent reviews are deliberately written to trick potential customers in order to promote/hype them or defame their reputations. Our work is aimed at identifying whether a review is fake or truthful one. Naïve Bayes Classifier, Logistic regression and Support Vector Machines are the classifiers using in our work. This in turns leads to recommending undeserving products. This paper aims to classify online reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we find online reviews using SA methods in order to detect fake reviews. SA and text classification methods are applied to a dataset of movie reviews. More specifically, we compare five supervised machine learning algorithms: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbours (KNN-IBK) for sentiment classification of reviews using two different datasets, including movie review dataset and movie reviews dataset. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches the highest accuracy not only in text classification, but also in detecting fake reviews.

Amazon E-Commerce dataset, Active Learning, Dataset acquisition, Data pre-processing, KNN Classifier, Rough Set Classifier, Support Vector Machine

[1] Jindal, N., Liu, B.: “Opinion Spam and Analysis” in Proceedings of the International Conference on Web Search and Web Data Mining (pp. 219–230), 2008.
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[3]Jindal, N., Liu, B. and Lim, E.-P. “Finding Unusual Review Patterns Using Unexpected Rules” CIKM (2010)., 2010 92.
[4]Mukherjee, A., Liu, B., Wang, J., Glance, N. and Jindal, N. 2011. Detecting Group Review Spam 2011
[5] Mukherjee, A., Liu, B., & Glance, N. “Spotting fake reviewer groups in consumer reviews” in Proceedings of the ACM international conference on world wide web (pp. 191–200). ACM., 2012
[6] Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N.” Fake Review Detection: Classification and Analysis of Real and Pseudo Reviews”, UIC-CS-03- 2013. Technical Report.
[7] B. Liu, “Sentiment analysis and opinion mining,” Synthesis lectures onhuman language technologies, vol. 5, no. 1, 2012, pp. 1–167.
[8]RAYMOND Y. K. LAU, S. Y. LIAO, RON CHI WAI KWOK, KAIQUAN XU, YUNQING XIA, YUEFENG LI,” TextMining and Probabilistic Language Modeling for Online Review Spam Detection “ACM Trans. Manag. Inform. Syst. 2, 4, Article 25 (December 2011)
[9] Yoo and Gretzel “Comparison of deceptive and truthful reviews” (2009)
[10]Xie, S.,Wang, G., Lin, S., & Yu, P. S” Review spam detection via temporal pattern discovery”, in Proceedings of the ACM international conference on knowledge discovery and data mining (pp. 823–831). ACM., 2012.
[11] Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R.” Exploiting burstiness in reviews for review spammer detection”,in Proceedings of the ICWSM. Citeseer., 2013
[12] Miss Dipti S.Charjan , Prof. Mukesh A.Pund “ Pattern Discovery For Text Mining Using Pattern Taxonomy” (IJETT) Volume 4 Issue 10- October 2013.
[13]Lin, Y., Zhu, T., Wang, X., Zhang, J., & Zhou, A. “Towards online antiopinion spam: Spotting fake reviews from the review sequence”,in 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) (pp. 261–264). IEEE.
[14] Heydari, A., Tavakoli, M., Salim, N.”Detection of fake opinions using time series” Expert Systems with Applications, 58, 83-92, 2016
[15] Ye, J., Kumar, S., Akoglu, L”Temporal Opinion Spam Detection” Multivariate Indicative Signals, 2016.
[16] Liu, Pan, et al. "Identifying Indicators of Fake Reviews Based on Spammer`s Behavior Features." Software Quality, Reliability and Security Companion (QRS-C), 2017 IEEE International Conference on. IEEE, 2017.
[17] SP.Rajamohana, Dr.K.Umamaheswari, M.Dharani, R.Vedackshya. “Survey of review spam detection using machine learning techniques.” 2017/978-1-5090-5778-8/17.
[18] Mr.Akshat Uike, Mr Ram Deshmukh, Dr.S R.Gupta, Dr. S.W.Ahmad4 ,“Improved text mining techniques for spam review detection” (IJIIRD), Vol. 03 Issue 01 2019

Akshat A. Uike, Sumera W.Ahmad, Sunil R.Gupta, "Improved Text Mining Techniques for Spam Review Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.147-152, 2019.

User profiles include but are not limited to social media profile, professional online profile, e-commerce profile and search profile. Each individual user nowadays has multiple user profiles, due to the fact that these users are constantly using online and offline services. These profiles are not mutually exclusive as the search habits of a user directly showcase the user`s shopping behaviour, and so on. Due to the presence of so many profiles of a single entity, there is a wide research area which has opened up in the recent years. Companies and researchers are harnessing this gap in order to provide better user experience via integrating multiple profiles and helping them to learn from one another. In this paper, we define a framework via which the user`s social and e-commerce profiles can be combined in order to better recommend their buying patterns to companies based on the items purchased by the friends which the user`s follow closely. Mining positive and negative rules (MOPNAR), firefly, top k rules and association rule mining is used in order to mine the usage patterns, and the results shows that an accuracy of more than 70% is observed when compared with the real time buying patterns.

Profile, integrated, online, MOPNAR, firefly, e-commerce, social

[1] X. Tao, Y. Li, and N. Zhong, "A customized philosophy display for web data gathering," IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 4, pp. 496– 511, April 2011.
[2] K. L. Skillen, L. Chen, C. D. Nugent, M. P. Donnelly, W. Burns, and I. Solheim, "Ontological client demonstrating and semantic standard based thinking for personalisation of assistance on-request benefits in inescapable conditions," Future Generation Computer Systems, Vol. 34, pp. 97 – 109, 2014, Special Section: Distributed Solutions for Ubiquitous Computing and Ambient Intelligence.
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[13] D. Martn, A. Rosete, J. Alcala-Fdez,and F. Herrera, “A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules”, IEEE Trans. Evol. Computing, Vol. 18, NO. 1, Feb 2014.
[14] A. K. Shingarwade, P. N. Mulkalwar, “Development of Top K Rules For Association Rule Mining on E-Commerce Dataset”, Proceeding of Global Journal of Engineering Science & Researches, pp 304-309, Feb 2019.
[15] X. S. Yang, “Nature-Inspired Metaheuristic Algorithms”, Luniver Press, 2008.
[16] X. S. Yang, “Firefly algorithms for multimodal optimisation”, Proc. 5th Symposium on Stochastic Algorithms, Foundations and Applications, (Eds. O. Watanabe and T. Zeugmann), Lecture Notes in Computer Science, 5792: 169-178, 2009.
[17] A. K. Shingarwade, P. N. Mulkalwar, “Study of Text Content Mining for E-Commerce Web Sites” International Journal of Advanced Research in Computer Science, Vol 8, No. 4, May – June 2017.
[18] A. K. Shingarwade, P. N. Mulkalwar, “Comparative Analysis of Clustering Techniques Based on Validity Measures”, Proceedings of the International Conference on Recent Trends in Science & Technology ICRTST Special Issue No. 25, pp 139-144, 2018.

A. K. Shingarwade, P. N. Mulkalwar, "Integrated User Profiles for Effective Mining in Complex Online Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.153-159, 2019.

With the huge amount of data in web, web mining is the process to extract useful data from web. Web usage mining is the type of web mining to retrieve data from web in form of logs, and it is also called web log mining. Web log mining extract useful pattern or information from log files and it help to determine user behaviour. In this paper we proposed an algorithm for data pre-processing and personalization in web usage mining. Firstly collect the data from server and merge these log files into single log file. After collection of data separate each field using field separate algorithm, then cleaning the data to remove noise and unwanted data and after personalize these data for further used.

Data Cleaning, Data field Extraction, Cluster, Session Identification, User Identification, Pre-processing, personalization.

[1]. Bhupendra Kumar Malviya, Jitendra Agrawal, ”A Study on Web Usage Mining: Theory and Applications”, Fifth International Conference on Communication Systems and Network Technologies, IEEE, Page: 935-939, April 2015, ISBN (Print) 978-1-4799-1797-6/15
[2]. Dr. Girish S. Katkar, Amit Dipchandji Kasliwal,” Use of Log Data for Predictive Analytics through Data Mining”, Current Trends in Technology and Science, page-217-222, ISSN: 2279-0535. Volume: 3, Issue: 3 (Apr-May. 2014).
International Journal of Computer Applications (0975 – 8887) Volume 103 – No.6, October 2014
[3]. M.Praveen Kumar,” An Effective Analysis of Weblog Files to improve Website Performance”, International Journal of Computer Science & Communication Networks, Vol. 2(1), Page: 55-60, 2011, ISSN: 2249-5789.
[4]. Mr. Jitendra B. Upadhyay, Dr. S. V. Patel,” A Review Analysis of Preprocessing Techniques in Web usage Mining”, International Journal of Engineering Research & Technology (IJERT), Vol. 4 Issue 04, April-2015, page -1160-1166,ISSN: 2278-0181
[5]. Nehal G. Karelia, Prof. Shweta Shukla,” Data Preprocessing: A Pre requisite for Web Log Files”, International Journal of Engineering Research & Technology (IJERT), page-1571-1574, Vol. 3 Issue 4, April – 2014, ISSN: 2278-0181
[6]. Oren Etzioni,” The World-Wide Web: Quagmire or Gold Mine?” ACM, Vol. 39, No. 11, November 1996, Page: 66-68.
[7]. Sameer Dixit, Navjot Gwal,” An Implementation of Data Pre-Processing for Small Dataset”,
[8]. Saurabh Choudhry, Prof A. K Solanki “ Errors in Internet Log files for Website Improvement and Interaction”, International Journal of Advanced Research in Computer Science and Software Engineering, Page-365-371, Volume 4, Issue 10, October 2014, ISSN- 2277 128X
[9]. Shakti Kundu, “An Intelligent approach of web data mining”, International Journal on Computer Science and Engineering, page-919-928, Vol. 4 No. 05 May 2012, ISSN: 0975-3397.
[10]. Sheetal A. Raiyani, Rakesh Pandey, Shivkumar Singh Tomar, ”Performance Enhancement of Web Server log for Distinct User Identification through different Factors”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 6, June 2014, Page: 7262-7267, ISSN (Online) : 2278-1021, ISSN (Print) : 2319-5940.
[11]. Shivaprasad G., N.V. Subba Reddy, U. Dinesh Acharya,” Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 111 – No 13, February 2015
[12]. Surbhi Anand , Rinkle Rani Aggarwal “An Efficient Algorithm for Data Cleaning of Log File using File Extensions “, International Journal of Computer Applications (0975 – 888)Volume 48– No.8, June 2012
[13]. V.Chitraa, Dr.Antony Selvadoss Thanamani ,” A Novel Technique for Sessions Identification in Web Usage Mining Preprocessing”, International Journal of Computer Applications (0975 – 8887) Volume 34– No.9, November 2011
[14]. Jiang Chang-bin, “Web Log Data Preprocessing Based on Collaborative Filtering”, 2010 Second International Workshop on Education Technology and Computer Science.
[15]. K. S. R. Pawan Kumar,”A Critique on Web Usage Mining”, International Journal of Computer Science and Information Technologies, Vol. 3 (5) , 2012,5276-5279.
[16]. Gajendra Singh, “A New Algorithm for Web Log Mining”, International Journal of Computer Applications (0975 – 8887) Volume 90 – No 17, March 2014 20
[17]. Gajendra Singh Chandel, “A Result Evolution Approach for Web usage mining using Fuzzy C-Mean Clustering Algorithm”, IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.1, January 2016
[18]. Doddegowda B J,” A Novel Algorithm for Web Personalization through Integration of Web User Profiles and Behavioural Patterns”, IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555, Vol.7, No.2, Mar-April 2017

Preeti Rathi, Nipur Singh, "An Efficient Algorithm for Data Pre-Processing and Personalization in Web Usage Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.160-164, 2019.

The disease in rice crop reduced the quality and quantity of production and mostly affect in leaf and panicle. The disease affect to the panicle is more severe than the other part of the paddy crop as it directly hampers the production. Detection and grading of rice panicle blast is required as prior condition for rice disease controlling. In this study, a novel detection and grading method for panicle blast based on imaging processing is proposed. The methodology contain some morphological operation like binary indexing, color conversion, channel extraction, Binarization and area calculation.

panicle blast; detection; grading; image processing

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[10] Huang, S., Qi, L., Ma, X., Xue, K., Wang, W., & Zhu, X. (2015). Hyperspectral image analysis based on BoSW model for rice panicle blast grading. Computers and Electronics in Agriculture, 118, 167–178. doi:10.1016/j.compag.2015.08.031.
[11] Liu, Z.-Y., Wu, H.-F., & Huang, J.-F. (2010). Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture, 72(2), 99–106. doi:10.1016/j.compag.2010.03.003.

Prabira Kumar Sethy, Swaraj Kumar Sahu, Nalini Kanta Barpanda, Amiya Kumar Rath, "Rice Panicle Blast Detection and Grading Based on Image Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.165-168, 2019.

In this paper, we have worked on digital fingerprint for secure and true verification of any human. The fingerprint fractionalization presents the extracted feature in characteristic polygon. It is accurate and secure method with the onion algorithms of computational geometry to detect the verification which are based on fingerprint over the cloud. This method is an alternative method, which used to minutiae extraction algorithm. We can compare proposed algorithm (Onion Algorithm of computational geometry) to commercial verification algorithm which works simultaneously with Ratha`s algorithm. During the execution, the experiment result comes in positive verification of the digital Fingerprint in our proposed work. Low cost and super automated technique is the best advantage of the Biometric fingerprint recognition to verify the best match among multiple human fingerprints. We have also used texture feature in this paper.

Computational Geometry, Encryption, Fingerprint, Onion layers, Verification of fingerprints

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Processing Image Algorithm For Fingerprint Recognition And Its Implementation on Dsp Tms320c6416”, International Journal of Software Engineering & Applications (IJSEA), Vol.9, pp. 65-79, 2018.
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Jaishree Jain, Heena Arora, "Image Processing Based on Verification for Secure Fingerprint," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.169-174, 2019.

Neural Networks has become one of the most demanded areas of Information Technology and it has been successfully applied to solving many issues of Artificial Intelligence, for example, speech recognition, computer vision, natural language processing, and data visualization. This thesis describes the developing the neural network model for object detection and tracking. With the progress of science and technology, information technology was advancing rapidly. The understanding of moving object based on vision has also developed rapidly. Its related technologies have been widely used in public transportation, square, government, bank and other scenes. At present, there are commonly used algorithms in moving object detection, including the difference method (background difference method and time difference method) and optical flow method and neural network. The difference method was based on the current video and the reference image subtraction to complete the detection. Some practical details for creating the Neural Network and image recognition in the Caffe Framework are given as well.

Detection of moving objects; tracking of moving objects; behavior understanding, Neural Network, Caffe model, CNN

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Anjali Nema, Anshul Khurana, "Real Time Object Identification Using Neural Network with Caffe Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.175-182, 2019.