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Game engines are software toolkits which are very helpful in developing games. There are two types of game engines, commercial and free open source. Developers use these for creating and developing games. In this paper a discussion related to 3D game engines is made. Some of these engines are also helpful for creating games for gaming consoles device. Choosing a good game engine is an important decision. While designing a game it is very important to consider the platform for which these are developed. One factor which is also important is scripting language. Various resources which are utilized during the play of the games are also important. Main attention is given to CPU utilization, GPU utilization and RAM used by the games which are developed by these engines.

Rendering, Scripting, Physics, 3D Game Engine, 3D Modelling

[1] Navarro, Andres, Juan Vicente Pradilla, and Octavio Rios. "Open source 3D game engines for serious games modeling." Modeling and Simulation in Engineering. IntechOpen, 2012.
[2] Finney, Kenneth C. 3D game programming all in one. Cengage Learning, 2013.
[3] https://en.wikipedia.org/wiki/Rendering_(computer_graphics)
[4] https://docs.unity3d.com/Manual/CreatingAndUsingScripts.html
[5] https://hub.packtpub.com/best-game-engines-for-ai-game-development/
[6] https://www.g2.com/categories/physics-engine
[7] https://www.giantbomb.com/profile/michaelenger/blog/game-engines-how-do-they-work/101529/
[8] https://en.wikipedia.org/wiki/3D_modeling
[9] https://unity.com/
[10] https://www.panda3d.org/
[11] http://jmonkeyengine.org/
[12] https://www.unrealengine.com/en-US/
[13] https://www.ogre3d.org/
[14] Remondino, Fabio, and Sabry El‐Hakim. "Image‐based 3D modelling: a review." The photogrammetric record 21.115 (2006): 269-291.
[15] Xie, Jingming. "Research on key technologies base Unity3D game engine." 2012 7th international conference on computer science & education (ICCSE). IEEE, 2012.
[16] Lewis, Michael, and Jeffrey Jacobson. "Game engines." Communications of the ACM 45.1 (2002): 27.

Rajinder Singh, "3D Game Development Engines and 3D Modelling," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1047-1053, 2019.

Sensing the aroma of Jasmine and evaluating its quality is an important commercial activity. Aroma of Jasmine has unique fragrance and high economic value. Assessing the quality of such aromas has created lot of interest in researchers. Use of passive sensors in E-noses for floral aroma sensing is available in literature. For the first time, an active device such as FET is tried for Jasmine aroma sensing. A junction field effect transistor (JFET) is fabricated and tested as sensor for jasmine aroma. Sensitivity of JFET to jasmine aroma is found to be 1.5 to 4 times higher than resistive film reported in literature. This paper presents method of fabrication of JFET, experimental procedure for measuring aroma of Jasmine flower, effect of operating in different regions of JFET on aroma sensing; repeatability of the sensor. The sensor developed is useful in evaluating quality of aroma of jasmine flower and to find the time at which flowers can be harvested and taken for extraction.

Aroma, Jasmine, quality, evaluation, FET, PEDOT: PSS

[1] George F. Fine, Leon M. Cavanagh, Ayo Afonja and Russell Binions, “Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring”, Sensors 2010, 10, pp. 5469-5502; ISSN 1424-8220 doi:10.3390/s100605469.
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[3] Haoshuang Gu, Zhao Wang and Yongming Hu, “Hydrogen Gas Sensors Based on Semiconductor Oxide Nanostructures”, Sensors 2012, 12, pp. 5517-5550; ISSN 1424-8220.
[4] Malik Abid Mahmood, Muhammad Saeed and Naveed Ahmad, “Quantitative and Qualitative Analysis of Essential Oil of Arabian Jasmine (Jasminum sambac) Flowers Harvested from Pothohar Region of Pakistan”, Journal of Ornamental Plants, Volume 7, Number 1: 17-24, March, 2017, ISSN(Print): 2251-6433, ISSN(Online): 2251-644117.
[5] Hena Ray, Nabarun Bhattacharyya, Alokesh Ghosh, Bipan Tudu, Rajib Bandyopadhyay, Arunangshu Ghosh, et.al., “Fragrance Profiling of Jasminum Sambac Ait. Flowers Using Electronic Nose”, IEEE Sensors Journal, Vol. 17, No. 1, January 1, 2017.
[6] Sajad Kiani, Saeed Minaee, Mahdi Ghasemi-Varnamkhasti, “A portable electronic nose as an expert systemfor aroma-based classification of saffron”, Chemometrics and Intelligent Laboratory Systems • May 2016.
[7] Sook-Hyun Hwang, Mi-Seon Kim, Pue-Hee Park and So-Young Park, “Scent Analysis Using an Electronic Nose and Flowering Period of Potted Diploid and Tetraploid Cymbidium”, Korean J. Hortic. Sci. Technol. 34(1):163-171, 2016.
[8] Kouki Fujioka, Mika Shirasu et. al., “Objective Display and Discrimination of Floral Odors from Amorphophallus titanum, Bloomed on Different Dates and at Different Locations, Using an Electronic Nose”, Sensors 2012, 12, 2152-2161; ISSN 1424-8220;doi:10.3390/s120202152.
[9] S. Mitachi, K. Sasaki, M. Kondoh and I. Sugimoto, “Odor Sensing in Natural Environment Using Quartz Crystal Resonators: Application to Aroma Sensing of Roses Cultivated in an Outside Garden”, School of Bionics, Tokyo University of Technology 1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan.
[10] Seiichi Fukai, Yoshie Abe, “Discrimination of Lily fragrance by use of an electronic nose”, Proc. XX EUCARPIA Symp. on New Ornamentals II, Eds. J. Van Huylenbroeck et al. Acta Hort. 572, ISHS 2002.
[11] R Paul Wali, “An electronic nose to differentiate aromatic flowers using a real-time information-rich piezoelectric resonance measurement”, 2nd International Conference on Bio-Sensing Technology, Procedia Chemistry 6 ( 2012 ) 194 – 202.
[12] S. Bindu, R. Anil Kumar and M. S. Suresh, “Development of Technique for Making Ohmic Contacts to PEDOT-PSS Films”, Lecture Notes in Electrical Engineering 258, DOI: 10.1007/978-81-322-1524-0_28, Springer India 2013.
[13] Rekha P, M S Suresh, Vrunda Kusanur, “SENSOR FOR MEASURING AROMA OF JASMINE”, International Conference IEEE SENSORS 2018, 28th October to 31st October 2018, 978-1-5386-4707-3/18/$31.00 ©2018 IEEE.

Rekha P, Bindu S, Subodh Kumar Panda, "An active device-JFET for sensing Jasmine aroma," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1054-1059, 2019.

Today technology is increasing at very rapid pace, which can be used for good as well as for bad purposes. So with this growing technology e-commerce and online transactions also grown up which mostly contain transactions through credit cards. Credit cards help People to enjoy buy now and pay later for both online and offline purchases. It provides cashless shopping at every shop in all countries. As the usage of credit cards is increasing more, the chances of credit card frauds are also increasing dramatically. Credit card system is most vulnerable for frauds. These credit card frauds costs financial companies and consumers a very huge amount of money annually, fraudsters always try to find new methods and tricks to commit these illegal and outlaw actions. Online transaction fraud detection is most challenging issue for banks and financial companies. So it is much essential for banks and financial companies to have efficient fraud detection systems to reduce their losses due to these credit card fraud transactions. Various approaches have been found by many researchers till date to detect these frauds and to reduce them. Comparison of Local Outlier Factor and Isolation Factor algorithms using python and their detailed experimental results are proposed in this paper. After the analysis of the dataset we got the accuracy of 97% by Local Outlier Factor and 76% by Isolation Forest.

Fraud Detection, Isolation Forest, Local Outlier Factor, Credit card, Dataset

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Hyder John, Sameena Naaz, "Credit Card Fraud Detection using Local Outlier Factor and Isolation Forest," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1060-1064, 2019.

Chronic kidney Disease (CKD), also known as chronic renal disease, which is continuous malfunction of kidney for months or even years. Identified based on the kidney damage or decrease in glomerular filtration rate (GFR). People with CKD are more prone to cardiovascular death than actual kidney failure. CKD is progressively predominant in patients with CVD or factors such as dyslipidemia, diabetes mellitus, hypertension and metabolic disorder. Classification models are built and are called classifiers. These classifiers will group the entered data set information to prominent classes. Chronic kidney disorder means the damage lasts and it only worsens over the period of time if not taken care of properly. This illness commonly known as kidney failure does not have any symptoms specific to the disease also sometimes the symptoms are not present and is diagnosed only by a lab test. The illness is highly diagnosed in the age of range 19-40 and higher in ages >40, here the waste starts accumulating over time as the Glomerular Filtration Rate(GFR) decreases overtime leading to increase in impurity of the blood. In this paper we are predicting the severity of kidney stage with the help of patients test report and using prediction algorithms, also we are doing a cross validation using C4.5 algorithms.

Naïve Bayes, C4.5, Chronic Kidney Disease, Cross-Validation, Pre-processing

[1] Kunwar Singh Vaisla and Sithu D Sudarsan, “Role of attributes selection in classification of Chronic Kidney Disease patients”, International Conference on Computing, Communication and Security (ICCCS), 4-5 Dec, 2015, pp 1-6.
[2] M.M. Rahman, D.N. Davis, "Addressing the class imbalance problem in medical datasets", International Journal of Machine Learning and Computing, vol. 3, no. 2, 2013.
[3] S. Vijayarani S. Dhayanand "KIDNEY DISEASE PREDICTION USING SVM AND ANN ALGORITHMS" International Journal of Computing and Business Research (IJCBR) vol. 6 no. 2 2015.
[4] G. Kaur Er. N. Oberai "A REVIEW ARTICLE ON NAIVE BAYES CLASSIFIER WITH VARIOUS SMOOTHING TECHNIQUES" International Journal of Computer Science and Mobile Computing vol. 3 no. 10 pp. 864-868 october 2014.
[5] T. R. Patil S.S. Sherekar "Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification" International Journal of Computer Science and Applications vol. 6 no. 2 pp. 256-261 April 2013.
[6] "Country Statistics and Global Health Estimates. 2015" in World Health Organization (WHO)
[7] "Chronic Kidney Disease" World Kidney Day 2015 [online] Available: http://www.worldkidneyday.org/fags/chronic-kidney-disease/.
[8] “Symptoms, causes and treatment of Chronic Kidney Disease” news article 2017 [online] Available: https://www.medicalnewstoday.com/articles/172179.php
[9] Sahana B.J, “Prediction of Chronic Kidney Disease using Data Mining Classification Techniques and ANN”. International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, NCETEIT - 2017 Conference Proceedings.

Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V, "Chronic Kidney Disease Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1065-1069, 2019.

The healthcare industry generates huge data that cannot be handled manually. Using data mining methods, valuable information is extracted from this data to create a relationship between attributes. Machine learning algorithms and data mining techniques are used from data sets to predict the disease. Data mining techniques are used to study disease occurrence. One of the most frequently encountered problems in medical centres is that not all specialists are equally qualified and can give their own conclusion, which can cause the patient to die. Data mining techniques and machine learning algorithms play a dynamic role in the automatic diagnosis of diseases in health care centres to overcome such glitches prediction of diseases. The purpose of this survey paper is to analyse the prior health care research work and advanced disease analysis technologies. Support Vector Machine, Decision Tree, Naïve Bayes, K-Nearest Neighbour, and Artificial Neural Network are some machine algorithms used to predict the occurrence of diseases. Our study concludes that Support Vector Machine shows approximately 85% accuracy and has the potential to be considered as one of the disease prediction capable algorithms.

Data mining , Machine Learning, Support Vector Machine, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network

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Ovias Tajdar, Bhavya Alankar, "Disease Prediction Using Data Mining Techniques – A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1070-1075, 2019.

Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. Finally, a brief overview of the WebSIFT system as an example of a prototypical Web usage mining system is given.

Data Preprocessing, Pattern Analysis, Pattern Discovery, Web Usage Mining

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G. Ragupathy, M.K. Prakash, "A Perspective Study on Pattern Discovery of Web Usage Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1076-1081, 2019.

The World Wide Web, also called the Web, behaves as an information space where documents, web pages, graphics, audio, video files and other widespread web resources are identified and accessible at real time. Due to vast and varied information on the web, the web users cannot access the relevant information very effectively and easily. A web user spends a lot of time over the Internet. For understating web users’ interest area, it is necessary to analyse the surfing pattern of user’s internet access. Web usage mining is a tool to discover and perform analysis of interesting web usage patterns from web log data. The methodology requires to identify the usage from the web proxy log files. It also includes techniques for Noise Removal from log files; determine the Client, determine the Client Session, Access Path Enhancement, determine the Transaction, Path investigation, and association rule investigation, Consecutive Pattern, Fuzzy Clustering and Fuzzy Classification. For imprecise, vague and uncertainty in data items we must use fuzzy approach. Fuzzy C-Means (FCM) is an unsupervised clustering algorithm based on fuzzy approach that permits an element to belong to more than one cluster. Here fuzzy means “unclear” or “not defined” and C denotes “clustering”. In this paper, we have reviewed and discussed latest Web Usage Mining Fuzzy Cluster techniques, issues and challenges.

Web Usage Mining, Cluster, Fuzzy Set, Cluster, K-Means, FCM, FPCM, MFPCM, EMFPCM, KFCM, YKFCM, KFCM

[1] G.Kiran Kumar, T. Bala Chary and P.Premchand, “A New and Efficient K-Means Clustering Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 3, Issue 11, November 2013.
[2] Zahid Ansari, Mohammad Fazle Azeem, A. Vinaya Babu and Waseem Ahmed, “A Fuzzy Clustering Based Approach for Mining Usage Profiles from Web Log Data”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011.
[3] R. Khanchana and M. Punithavalli, “Web Usage Mining for Predicting Users’ Browsing Behaviors by using FPCM Clustering”, IACSIT International Journal of Engineering and Technology, Vol. 3, No. 5, October 2011.
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[7] Samarjit Das and Hemanta K. Baruah (2014), “A New Kernelized Fuzzy C-Means Clustering Algorithm with Enhanced Performance”, International Journal of Research in Advent Technology, E-ISSN: 2321-9637, Vol. 2, No.6, June 2014.
[8] Neha Singla and Er. Navroz Kahlon (2016), “Extracting Knowledge from Web Server Log Files and Future Prediction of Web Pages Using Kernel Based Fuzzy Clustering Method”, Imperial Journal of Interdisciplinary Research (IJIR) , ISSN: 2454-1362, Vol.2, Issue.8, 2016.
[9] Rehna Kalam, Dr Ciza Thomas and Dr M Abdul Rahiman, “GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATION” 2016.
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[12] D.A. Adeniyi, Z. Wei, Y. Yongquan, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method”, Applied Computing and Informatics, Received 3 August 2014; revised 29 September 2014; accepted 17 October 2014.
[13] Srivastava, Mitali & Srivastava, Atul & Rakhi Garg, Dr & K. Mishra, “ANALYSIS OF USER IDENTIFICATION METHODS IN WEB USAGE MINING”, International Journal of Computer Engineering and Applications, ISSN 2321-3469, Volume 9, Issue 8, Sep. 2015.
[14] G.D.Praveenkumar,R.Gayathri, “A Process of Web Usage Mining and Its Tools”, International Journal of Advanced Research in Science, Engineering and Technology, ISSN: 2350-0328, Vol. 2, Issue.11, November 2015.
[15] Vinod Kumar and Ramjeevan Singh Thakur, “Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database”, International Journal of Intelligent Engineering and System, 2017.
[16] Ashish Gupta, Anil Khandekar, “Development of Web Log Mining Based On Improved Fuzzy C-Means Clustering Algorithm Hermitition Distance”, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 5, Issue 3, March 2016.
[17] D. Uma Maheswari A and Dr. A. Marimuthu (2016), “An Ensemble Fuzzy Rough Set Jaccard Similarity measure-based Approach on User Session Clustering”, International Journal of Computer Systems (ISSN: 2394-1065), Volume 03– Issue 04, April 2016.
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[19] K. Gayathri, D. Vasanthi, "Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2 Issue 2, pp. 704-707, March-April 2017.
[20] U. S. Patki, Dr. S. B. Kishor, Dr. P. G. Khot, "Fuzzy Document Clustering based on Frequent Features and Feature Length", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3 Issue 1, pp. 1418-1422, January-February 2018.
[21] Shafalika Vijayal, Mohit Mittal, "Intrusion Detection in IoT based on Neuro-Fuzzy Approach", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2 Issue 7, pp. 113-120, September 2017.
[22] Hemina Bhavsar, Dr. Jeegar Trivedi, "Image Based Sign Language Recognition using Neuro - Fuzzy Approach", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3 Issue 1, pp. 487-491, January-February 2018.

Hardik A. Gangadwala, Ravi M. Gulati, "Web Usage Mining Using Fuzzy Approach – A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1082-1087, 2019.

Images have always been an attraction and our existence depends on them. Without images world would be an empty canvass. Our eyes capture thousands of images each day and our brain processes them. Interestingly we human can identify images in a micro second, a wink of an eye and we know what the object is, be it moving or static. Artificial intelligence is designed to let the computers think and behave like human beings, fuzzy logic is one of its important technique which has been used in the proposed thesis to segment and image. The proposed algorithm reads the image, pre-process it, then fuzzy rules are applied over it and finally de- fuzzification is carried over it to get the segmented image. The algorithm is compared with existing K-Mean and Modified K-Mean to access the viability of the proposed algorithm. The algorithm is tested for number of segments, segmented area, and time taken, it is observed that the proposed algorithm improves K-Mean, by 60%, 1.6%, 94% respectively and Modified K-Mean by 43%, 1.2%, 13.5% respectively. The results indicate that the proposed algorithm works better than the previous two algorithms. There is a marked improvement in number of segments maintaining the time taken. In this proposed worked has been overcome in MATLAB features.

Segmenting pixels images, Trim function, di-fuzzification, centroids methods, modified k means ,fuzzyfication

[1] Gurbinder Kaur , Balwinder Singh" Intensity Based image segmentation using wavelet analysis and clustering techniques" published in ijcse indian journol of computer science and engineering vol 2, no 3,2011.
[2] Navneet Kaur, Gagan Jindal, “A Survey Of K Means
Clustering With Modified Gradient Magnitude Region Growing Technique For Lesion Segmentation”, International Journal Of Innovations In Engineering And Technology, 2013.
[3] X. Cui, G. Yang, Y. Deng and S. Wu, “An Improved Image Segmentation Algorithm Based on the Watershed
Transform”, IEEE, pp. 428—431, 2014.
[4]Savita Agrawal et al “Survey of image segmentation techniques and color models” vol 5(3),2014, / (IJCSIT) International Journal of Computer Science and Information Technologies.
[5]Chenhang Zhou, Liwei Tian*,Hongwei Zhao, Kai Zhao,"A Method of Two-Dimensional Otsu Image Threshold Segmentation Based on Improved Firefly Algorithm"2015
[6] P.M.K. Prasad, D.Y.V. Prasad, G. Sasibhushana Rao Prof.,“Performance analysis of orthogonal and biorthogonal
wavelets for edge detection of xray images”, Procedia Computer Science, International Conference on Recent Trends in Computer Science & Engineering, Vol. 87, pp
116-121, 2016.
[6] Divya, Mr Pawan Kumar Mishra "Frequency Domain Digital Image Segmentation based on a Modified k Means" (ijircst) issn:2347-5552,vol 5,issue 4 ,2017.

Anju Bhatt, Pawan Kumar Mishra, "Segmenting RGB Image Using Fuzzified Pixel," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1088-1091, 2019.

Mining user-intents has been a core platform in semantic web search and intelligent test mining. Prior art on these arena lacks infeasibility in materialization of theoretical foundations on factual viewpoints. Literatures and artifacts are needed to twitch conceptualization of formalism and methodologies on distinct domains under user-intent mining. This chapter provides a basis formulation of automata, theories and algorithm design approach for user-intent mining on social networks. The aim and the scope of this chapter is introspection of user’s aspiration in online search mechanics symbolizing business intelligence. A concrete approach to retrieve named entities from live social networks has been modelled in this chapter. The source for retrieval are public logs, blog, social channels and web-o-media has been constructed as an activity model which is modelled as transient in nature. Formulation of automata to recognize intent-keywords and the algorithm to reason the context of dialogue on live –talks have been described. This chapter describes principles and mathematical approach to design ontologies for intelligent mining for reasoning in live-talks overcoming the problem of out-of-vocabulary (OOV).

opinion mining, social networks, ontology, live-talk reasoning, out-of-vocabulary (OOV)

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Deepali N. Pande, Kaushik R. Roy, Satyajit S. Uparkar, "Intention Mining for Introspective Behavior Modelling in Business Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1092-1106, 2019.

With the expansion of wireless technologies, the ad-hoc networking between aircraft has become possible. This kind of networks is known as Airborne Ad hoc Networks (AANETs). These networks have emerged as one of the highly challenging areas of research due to the unique flight dynamics of the aircraft being used as nodes. The cruising speed of an aircraft (around 400-500 mph) is the major reason behind the intermittent link quality between two communicating aircraft. As a result, the routing between aircraft has emerged as a major threat for research in this field. This paper presents a study of different routing solutions provided recently by the research community for AANETs. A simulation analysis of the two well-known routing protocols i.e. OLSR and GPSR has also been provided here to identify their suitability in the airborne environment.

AANET, Cruising Speed, OLSR, GPSR

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Pardeep Kumar, Seema Verma, "Routing Protocols in Airborne Ad-hoc Networks (AANETs): A Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1107-1113, 2019.