Volume-7 , Issue-5 , May - 2019 Go Back

The use of social media sites and Internet is increasing at an alarming rate. Therefore data is generated in huge amounts fraction of a second. This huge amount of data which is characterized by volume, velocity and variety is termed as big data. There is need for a framework that can process this huge amount of data and also analyze it efficiently. Apache Spark is an open source cluster computing platform which can process and analyze data efficiently. In this paper an overview and a simple example of analysis of retail data using Apache Spark is given to demonstrate its functionality.

Big Data, Hadoop, MapReduce, Yarn, Spark, RDD

[1]. Mantripatjit Kaur, Gurleen Kaur Dhaliwal, “Performance Comparison of Map Reduce and Apache Spark on Hadoop for Big Data Analysis”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.66-69, 2015.
[2]. Lei Gu, Huan Li, “Memory or Time: Performance Evaluation for Iterative Operation on Hadoop and Spark”, In IEEE 10th International Conference on High Performance Computing and Communications and IEEE International Conference on Embedded and Ubiquitous Computing (IEEE 2013), pp.721-727 , 2013.
[3]. Abdul Ghaffar Shoro, Tariq Rahim Soomro, “Big Data Analysis: Ap Spark Perspective”, Global Journal of Computer Science and Technology, Vol.15, Issue.1, pp.7-14, 2015.
[4]. Abhishek Bhattacharya, Shefali Bhatnagar, “Big Data and Apache Spark: A Review”, International Journal of Engineering Research & Science (IJOER), Vol.2, Issue.5, pp.206-210, 2016.
[5]. V Srinivas Jonnalagadda, P Srikanth, Krishnamachari Thumati, Sri Hari Nallamala, ”A Review Study of Apache Spark in Big Data Processing”, International Journal of Computer Science Trends and Technology (IJCST), Vol. 4, Issue.3, pp.93-98, 2016.
[6]. Priya Dahiya, Chaitra.B, Usha Kumari, “Survey on Big Data using Apache Hadoop and Spark”, International Journal of Computer Engineering In Research Trends, Vol. 4, Issue.6, pp.195-201, 2017.
[7]. Amit Palve1, Rohini D. Sonawane, Amol D. Potgantwar, “Sentiment Analysis of Twitter Streaming Data for Recommendation using Apache Spark”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017.
[8]. Vivek Francis Pinto, Sampath Kini, Igneta Mcluren Dsouza, “A Review Document on Apache Spark for Big Data Analytics with Case Studies”, International Journal of Computer Science Trends and Technology (IJCST),Vol.5, Issue.5, pp.99-103, 2017
[9]. Kalyani K. Pathrikar, Prof. Arundhati A. Dudhgaonkar, ”Review on apache spark technology”, International Research Journal of Engineering and Technology (IRJET), Vol.4, Issue.10, pp.1386-1388, 2017.
[10]. Smita M. Deshpande, R. S. Shirsath, “Ranking of Product on Big Data using Apache Spark”, Sixth Post Graduate Conference for Computer Engineering (cPGCON 2017) Procedia International Journal on Emerging Trends in Technology (IJETT), 2017.
[11]. S.N. Patil, S.M. Deshpande , Amol D. Potgantwar, “Product Recommendation using Multiple Filtering Mechanisms on Apache Spark”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.76-83, 2017.

Himani Agnihotri, Bharti Nagpal, "Analysis of Retail Data using Apache Spark," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1162-1165, 2019.

Early diagnosis of children’s health problems helps the professionals to treat it at an earlier stage and improves their quality of life. The life skills of the young minds are the best investment to build the healthy society of the Country. Researchers across various countries are interested to predict health problems related to children with respect to parenting style, hereditary health issues, food habits and physical activities. Machine learning plays a vital role in analyzing and predicting the hidden facts in the data we collected. The main objective of this study is to present an overview of many machine learning techniques such as Support Vector Machines, Naive Bayes classifier, K-Nearest Neighbor, Decision Tree, K-means algorithm and perform a comparative analysis of their accuracy and help the researchers to choose best algorithm on prediction of Children’s various health problems such as Early Childhood Obesity, Anxiety Disorders, Attention Deficit Hyperactive Disorder, Mental Health Problems, Child Post Traumatic Stress, Autism Spectrum Disorder and Insulin Resistance in Children. This survey paper can lead to develop innovative and efficient algorithms on prediction of children’s health problems to improve their quality of life in a better way.

Machine Learning Algorithms, Prediction, Children’s Health Problems

[1]. Dugan, T. M., Mukhopadhyay, S., Carroll, A., & Downs, S. (2015). Machine learning techniques for prediction of early childhood obesity. Applied clinical informatics, 6(03), 506-520.
[2]. Theodore, K., Ioannis, L., Panagiotis, P., & Emmanouil, P. (2018). Reviewing machine learning techniques for predicting anxiety disorders.
[3]. Shah, A. (2015). Predicting ‘Attention Deficit Hyperactive Disorder ‘using large scale child data set.
[4]. Sumathi, M. R., & Poorna, B. (2016). Prediction of mental health problems among children using machine learning techniques. International Journal of Advanced Computer Science and Applications, 7(1), 552-557.
[5]. Usta, M. B., Karabekiroglu, K., Sahin, B., Aydin, M., Bozkurt, A., Karaosman, T., ... & Sahin, İ. (2018). Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders. Psychiatry and Clinical Psychopharmacology, 1-6.
[6]. Alarifi, H. S., & Young, G. S. (2018). Using Multiple Machine Learning Algorithms to Predict Autism in Children. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (pp. 464-467). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (World Comp).
[7]. Saxe, G. N., Ma, S., Ren, J., & Aliferis, C. (2017). Machine learning methods to predict child posttraumatic stress: a proof of concept study. BMC psychiatry, 17(1), 223.
[8]. Hall, A. J., Hussain, A., & Shaikh, M. G. (2016, November). Predicting insulin resistance in children using a machine-learning-based clinical decision support system. In International Conference on Brain Inspired Cognitive Systems (pp. 274-283). Springer, Cham.
[9]. Caicedo-Torres, W., Paternina, Á., & Pinzón, H. (2016, November). Machine learning models for early dengue severity prediction. In Ibero-American Conference on Artificial Intelligence (pp. 247-258). Springer, Cham.
[10]. Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health information science and systems, 6(1), 8.
[11]. Yildirim, P., Majnarić, L., Ekmekci, O. I., & Holzinger, A. (2013, September). On the prediction of clusters for adverse reactions and allergies on antibiotics for children to improve biomedical decision making. In International Conference on Availability, Reliability, and Security (pp. 431-445). Springer, Berlin, Heidelberg.
[12]. Oliveira, A. L. I., Baldisserotto, C., & Baldisserotto, J. (2005, November). A comparative study on machine learning techniques for prediction of success of dental implants. In Mexican International Conference on Artificial Intelligence (pp. 939-948). Springer, Berlin, Heidelberg.
[13]. Dwivedi, A. K. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 1-9.
[14]. Hassan, M. R., Al-Insaif, S., Hossain, M. I., & Kamruzzaman, J. A machine learning approach for prediction of pregnancy outcome following IVF treatment. Neural Computing and Applications, 1-15.
[15]. Jaafar, J., Atwell, E., Johnson, O., Clamp, S., & Ahmad, W. A. W. (2013). Evaluation of Machine Learning Techniques in Predicting Acute Coronary Syndrome Outcome. In Research and Development in Intelligent Systems XXX (pp. 321-333). Springer, Cham.
[16]. Dyne, M., Tsatsoulis, C., & Thorp, J. (1994, September). Using inductive machine learning, expert systems and case based reasoning to predict preterm delivery in pregnant women. In Database and Expert Systems Applications.
[17]. Husain, A., & Khan, M. H. (2018, April). Early Diabetes Prediction Using Voting Based Ensemble Learning. In International Conference on Advances in Computing and Data Sciences (pp. 95-103). Springer, Singapore.
[18]. Belciug, S. (2016). Machine learning solutions in computer-aided medical diagnosis. In Machine Learning for Health Informatics (pp. 289-302). Springer, Cham.
[19]. Fernandez-Granero, M. A., Sanchez-Morillo, D., Lopez-Gordo, M. A., & Leon, A. (2015, June). A machine learning approach to prediction of exacerbations of chronic obstructive pulmonary disease. In International Work-Conference on the Interplay Between Natural and Artificial Computation (pp. 305-311). Springer, Cham.
[20]. Martínez-Velasco, A., Zenteno, J. C., Martínez-Villaseñor, L., Miralles-Pechúan, L., Pérez-Ortiz, A., & Estrada-Mena, F. J. (2016). Machine learning method to establish the connection between age related macular degeneration and some genetic variations. In Ubiquitous Computing and Ambient Intelligence (pp. 28-39). Springer, Cham.
[21]. Shankle, W. R., Mani, S., Pazzani, M. J., & Smyth, P. (1997). Dementia screening with machine learning methods. In Intelligent Data Analysis in Medicine and Pharmacology (pp. 149-165). Springer, Boston, MA.
[22]. S. JabeenBegum., B. Swaathi April (2019). A Survey for identifying Parkinson’s disease by Binary Bat Algorithm in International Journal of Scientific Research in Computer Science and Engineering Vol.7, Issue.2, pp.17-23,

A. Meharaj Begum, "Applications of Machine Learning Techniques on Prediction of Children’s various health problems: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1166-1176, 2019.

In this paper, we’ll influence the Arduino to construct hardware that can yield and produce stimulating laser designs from auditory and music signals. Python will be doing more of the substantial work. Along with supervising serial port communications, it will be performing certain calculations basing itself on real-time audio data and implement those figures to adjust the motors in a laser show hardware rig. We use Fast Fourier Transform Technique for the conversion from audio signals to frequency. In order to govern the motors using Python and Arduino programming, we will be using python’s numpy library to retrieve the Fast Fourier Transform of all the inputted audio figures. Our final end product would only start making sense when the laser and the twin motors would be arranged in an alignment that promotes projection of interesting and attractive patterns on the screen. The project also keeps in mind the voltage intricacies while operating the motors through the Arduino board.

Laser, Fast Fourier Transform, pyaudio, Arduino, motors, numpy

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[8] V. Tiwari, P. Adkar, "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, 2019.
[9] M. S. Musabbin Ahmed & Shalini, "Reconfigurable Hardware Implementation of Adaptive LMS algorithm for Noise Cancellation on Real-time Audio signals", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.2, pp.29-30, 2013.

Alind Sharma, Sagarika Srivastava, Namrata Dhanda, "Laser Display Setup Responsive to Audio Based Input," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1177-1180, 2019.

The World Wide Web has become a huge part of millions of people who use online services e.g. net banking, net shopping, social networking, e-commerce, and store and manage user sensitive information, etc. In fact, it is a popular tool for all user over the Internet. Rich Web based applications are available over the World Wide Web to provide all types of services. At the same time, the Web has become an important means for people to interact with each other and so on. This is the positive side of this technology. Unfortunately, the Web has also become a more dangerous technique. The popularity of World Wide Web has also attracted obtrudes and attackers. These obtrudes abuse the Internet and users by performing illegal activity for financial profit. The Web pages that contain such types of attacks or malicious code are called as malicious Web pages or malware. While the existing system are good sign to detecting malicious Web pages, there are still open issues in Web page features extraction and detection techniques. In this paper, we are detecting and identified malicious or benign URL classification using machine learning in real time.

URLs, Detection, Malicious Webpages, Machine learning.

[1]. Chaitrali Amrutkar, Young Seuk Kim, and Patrick Traynor, Senior Member, IEEE “Detecting Mobile Malicious Webpages in Real Time” Chaitrali Amrutkar, Young Seuk Kim, and Patrick Traynor, Senior Member, IEEE
[2]. Sujata Doshi, Niels Provos, Monica Chew, and Aviel D. Rubin. A Framework for Detection and Measurement of Phishing Attacks. Technical report, Johns Hopkins University, SPAR, December2006. http://www.cs.jhu.edu/~sdoshi/index_files/ phish_measurement.pdf. .
[3]. Charles Arthur, “Mobile internet devices ’will outnumber humans this year’.” http://www.theguardian. Com/ technology/ 2013/feb/07/ mobile-internet-outnumber-people.
[4]. S. Garera, N. Provos, M. Chew, and A. D. Rubin. A framework for detection and measurement of phishing attacks. In Proceedings of the ACM workshop on recurring malcode, 2007.
[5]. N. Provos, P. Mavrommatis, M. A. Rajab and F. Monrose, “All Your iFRAMEs Point to Us”, Proceedings of the 17th Conference on Security Symposium, SS, USENIX Association Berkeley, (2008); CA,USA.
[6]. J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Beyond blacklists: learning to detect malicious web sites from suspicious URLs” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 1245–1254.Justin Ma, Lawrence K Saul, Stefan Savage, Geoffrey M
[7]. D. Canali, M. Cova, G. Vigna, and C. Kruegel. “Prophiler: a fast filter for the large-scale detection of malicious webpages”. In Proceedings of the 20th International Conference on World Wide Web (WWW), 2011.
[8]. L. Bilge, E. Kirda, C. Kruegel, and M. Balduzzi. EXPOSURE: Finding malicious domains using passive DNS analysis. In Proceedings of the 18th Annual Network and Distributed System Security Symposium (NDSS), 2011.
[9]. A. P. Felt and D. Wagner. Phishing on mobile devices. In Web 2.0 Security and Privacy (W2SP), 2011.
[10]. A. P. Felt and D. Wagner. Phishing on mobile devices. In Web 2.0 Security and Privacy (W2SP), 2011.
[11]. Chaitrali Amrutkar, K. Singh, A.Verma, and P. Traynor. Vulnerable Me: Measuring systemic weaknesses in mobile browser security. In Proceedings of the International Conference on Information Systems Security (ICISS), 2012.
[12]. Chakradeo, S., Reaves, B., Traynor, P., and Enck, W., “MAST: Triage for Market-scale Mobile Malware Analysis,” Tech. Rep. GT-CS-12-01, College of Computing, Georgia Institute of Technology, 2012.
[13]. C. Amrutkar, K. Singh, A.Verma, and P. Traynor. Vulnerable Me: Measuring systemic weaknesses in mobile browser security. In Proceedings of the International Conference on Information Systems Security (ICISS), 2012.
[14]. C. Amrutkar, P. Traynor, and P. C. van Oorschot. Measuring SSL indicators on mobile browsers: Extended life, or end of the road? In Proceedings of the Information Security Conference (ISC), 2012.
[15]. “Cross-site Scripting (XSS) Attacks and Defense Mechanisms: classification and state-of-art” by Shashank Gupta and B.B Gupta, 14 September, 2015, Springer.
[16]. Dr. Jitendra Agrawal, Dr. Shikha Agrawal, Anurag Awathe, Dr. Sanjeev Sharma. “Malicious Web Page Detection through Classification Technique: A Survey”. In Proceeding of the IJCST March 2017

Pritee Rameshrao Waghmare, Manish B. Gudadhe, "An Approach to Design Technique Using Classification for Analysis Malicious Web Page in Real Time," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1181-1185, 2019.

IoMUT refers to the inter-networking of physical musical objects which are used in the production and/or reception of musical content. A network infrastructure that interconnects various smart musical instruments or devices and permits multidirectional communication between them, both locally and remotely is designed in IoMUT. The IoMUT system interconnects performers and audiences and enables performer-performer and audience-performers interactions. The paper presents a survey on this novel concept of IoMUT.

IoT, IoMUT, Smart Devices, Musical Things

[1] The Internet of Things: an Overview, Internet society, October 2015.
[2] Santosh S. Kulkarni, Sanjeev G. Kulkarni, Vani P. Datar, "Current Trends in Internet of Things: A Survey", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1223-1226, 2018.
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[4] G. P. Fettweis, “The Tactile Internet: applications and challenges,” IEEE Vehicular Technology Magazine, vol. 9, no. 1, pp. 64–70, 2014.
[5] H. Shokri-Ghadikolaei, C. Fischione, G. Fodor, P. Popovski, and M. Zorzi, “Millimeter wave cellular networks: A MAC layer perspective,” IEEE Transactions on Communications, vol. 63, no. 10, pp. 3437– 3458, 2015.
[6] H. Shokri-Ghadikolaei, C. Fischione, P. Popovski, and M. Zorzi, “Design aspects of short-range millimeterwave networks: A MAC layer perspective,” IEEE Network, vol. 30, no. 3, pp. 88–96, 2016.
[7] Luca Turchet, Carlo Fischione, Mathieu Barthet, “Towards The Internet Of Musical Things”, Proceedings of the 14th Sound and Music Computing Conference, July 5-8, Espoo, Finland
[8] S. Jord`a, G. Geiger, M. Alonso, and M. Kaltenbrunner, “The reactable: exploring the synergy between live music performance and tabletop tangible interfaces,” in Proceedings of the 1st international conference on Tangible and embedded interaction, 2007, pp. 139–146.
[9] L. Turchet, A. McPherson, and C. Fischione, “Smart Instruments: Towards an Ecosystem of Interoperable Devices Connecting Performers and Audiences,” in Proceedings of the Sound and Music Computing Conference, 2016, pp. 498–505.

Santosh S. Kulkarni, Sanjeev G. Kulkarni, "Internet of Musical Things: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1186-1189, 2019.

Articulation of features of fashion into keywords is an arduous task. Description of fashion seen on other people is insufficient, not to mention, inaccurate for a conventional search engine that takes keywords as queries. To overcome this shortcoming, this paper outlines a model for a search engine that takes a fashion image as a query and returns five most similar images from its database. The model consists of CNN classification model that classifies the query image into one of the five classes and a Convolutional Autoencoder that returns five images with most similar features from that class of images. Similarity between images is found by calculating the similarity of the encoded vector of the query image with the encoded vectors of the images in the class predicted by the classifier. Since the encoding and decoding is done by the Autoencoder based on the nature of the images given for training, the model returns images based on features that prove important enough to be encoded based on the training images. In other words, the features that are necessary to ensure as little loss as possible when decoding the encoded vector. This forms the basis for using similarity between encoded vectors to find similar images to the given query image. The model is trained using fashion images to find similar fashion to query image.

Classification, Similar images, CNN, Autoencoder, Clothes

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[3] A. Y. Ivanov, G. I. Borzunov and K. Kogos, "Recognition and identification of the clothes in the photo or video using neural networks", 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, 2018, pp. 1513-1516.
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[17] M. H. Kiapour, X. Han, S. Lazebnik, A. C. Berg, and T. L. Berg, “Where to buy it: Matching street clothing photos in online shops”, In ICCV, (2015)
[18] A. Y. Ivanov, G. I. Borzunov and K. Kogos, "Recognition and identification of the clothes in the photo or video using neural networks," 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, 2018, pp. 1513-1516.
[19] Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang. “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations”, In CVPR, pages 1096–1104, 2016.
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S.R. Katasani, T. Rachepalli, N. Kamat, M. Jadhav, "Similar Fashion Finder using Reverse Image Search," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1190-1195, 2019.

In recent years, the IEEE 802.11 based wireless LANs (WLANs) have gained great popularity. The WLAN uses the two mechanisms to access the medium in 802.11 distributed coordination function (DCF) and point coordination function (PCF). To improve the PCF large work is performed by different researchers and new methods were proposed. In this work the Improved BDPCF algorithm is proposed to enhance the WLAN. The scheme is designed and implemented. The results are obtained by performing different experiments. The obtained results are analysed.

WLAN, DCF, PCF, BDPCF, Advanced BDPCF, Improved Bi - Directional PCF Algorithm

[1] D. Sarddar, U. Ghosh, Rajat Pandit, “A Survey on Reducing Handoff Latency in WLAN”, International Journal of Computer Sciences and Engineering, Vol.-7, Issue-2, Feb 2019.
[2] Sutar Kunal, Nikam Mohan, Gajare Kavita , Khapare Monali and Deore Pooja, “Improving throughput in Wireless LAN using Load Balancing Approach”, International Journal of Computer Science International Journal of Computer Sciences and Engineering, Volume-2, Issue-4, April 2014.
[3] Wan Hafiza Wan Hassan, Horace King, Shabbir Ahmed and Mike Faulkner, “Enhancement Techniques of IEEE 802.11 Wireless Local Area Network Distributed Coordination Function :A Review”, ARPN Journal of Engineering and Applied Sciences, VOL. 13, NO. 3, FEBRUARY 2018.
[4] Vibhavarsha Prakaulya, Prof. Neelu Pareek, Upendra Singh, “Wireless LAN Technologies IEEE 802.11”, International Journal of Scientific Development and Research (IJSDR), IJSDR, Volume 2, Issue 7, July 2017.
[5] Chen D., Garg S., Kappes M. and Trivedi K, “Supporting VBR VoIP Traffic in IEEE 802.11 WLAN in PCF Mode”, OPNETWORKS 2002 Symposium and Conference, Washington, 409, 2002.
[6] Han R., “IEEE 802.11b Wireless LANs-Wireless Freedom at Ethernet Speeds”, 3Com Corporation technical paper, 1-12, 2001.
[7] Kopsel A. and Wolisz A., “Voice Transmission in an IEEE 802.11 WLAN based access Network”, 4th ACM International Workshop on Wireless Mobile Multimedia, Rome, 23, 2001.
[8] Andrew S. Tanenbaum, “Computer Networks”, Pearson Education Asia, ISBN 81-7808-785-5, Fourth Edition, 2003.
[9] Kazemitabar H., Ahmed S., Nisar K., Said A.B. and Hasbullah H.B., “A Survey on Voice over IP over Wireless LAN,” World Academy of Science, 2(2), 352-358, 2010.
[10] Bhagat Abhimanyu, Prof. Nighot Jyoti, “Maximize Energy Efficiency with QoS Performance of Contention Window Adaptation Algorithm for IEEE 802.11WLAN”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 4, April 2017.
[11] Wang W., Liew S. and Li V., “Solutions to Performance Problems in VoIP Over a 802.11 Wireless LAN”, IEEE Transactions on Vehicular Technology, 54(1), 366-384, 2005.
[12] Zhao L. and Fan C., “M-PCF modified IEEE 802.11 PCF protocol implementing QoS”, Electronic Letters, 38(24), 1611-1613, 2002.
[13] Kanjanavapastit A. and Landfeldt B. “An Analysis of a Modified Point Coordination Function in IEEE 802.11”, 14th IEEE on Personal, Indoor and Mobile Radio Communications, Beijing, 1732, 2003.
[14] Kawata T., Shin S. and Forte A., “Using Dynamic PCF to Improve the Capacity for VoIP Traffic in IEEE 802.11 Networks”, IEEE Wireless Communications and Networking Conference, Doha, 1589, 2005.
[15] Lam R., Leung V. and Chan H., “Polling Based Protocols for Packet Voice Transport Over IEEE 802.11 Wireless local Area Networks”, IEEE Wireless Communications,13(1), 22-29, 2006.
[16] Gupta S., Sahu V., and Lall B., “Modified Isochronous Coordination Function for Enhancement of VoIP Call Capacity over IEEE 802.11 WLAN”, EURASIP Journal on Wireless Communication and Networking, 2008.
[17] Ting K.C., Kuo F.C., Hwang B.-J., Wang H., and Tseng C-C., “A power-saving and robust point coordination function for the transmission of VoIP over 802.11”, IEEE International Symposium on Parallel and Distributed Processing with Applications, Taipei, 283, 2010.
[18] Hsieh J.-R., Lee J.-R. and Kuo J.-R., “Energy-efficient multi-polling scheme for wireless LANs”, IEEE Transactions on Wireless Communications, 8(3), 1532–1541, 2009.
[19] Alonso-Z´arate J., Crespo C., Skianis C., Alonso L., and Verikoukis C., “Distributed point coordination function for IEEE 802.11 wireless ad-hoc networks”, Ad Hoc Networks, 10(3), 536–551, 2011.
[20] Palacios R., Granelli F., Gajic D., and Foglar A., “An energy-efficient MAC protocol for infrastructure WLAN based on modified PCF/DCF access schemes using a bidirectional data packet exchange”, IEEE Workshop on Computer-Aided Modeling Analysis and Design of Communication Links and Networks ’12, Barcelona, 216, 2012.
[21] Palacios R., Granelli F., Gajic D., Lib C. and Kliazovich D., “An Energy-efficient Point Coordination Function Using Bidirectional Transmissions of Fixed Duration for Infrastructure IEEE 802.11 WLANs”, IEEE International Conference on Communications, Budapest, 2443, 2013.
[22] Himanshu Yadav, Dr. Deepak Dembla, “A Novel Technique of implementing Bidirectional Point Coordination Function for Voice Traffic in WLAN”, Proceeding AICTC `16 Proceedings of the International Conference on Advances in Information Communication Technology & Computing, Article No. 85, Bikaner, India — August 12 13, 2016.
[23] Jianjun Lei, Jianhua Jiang, and Fengjun Shang, “Channel Assignment Mechanism for Multiple APs Co channel Deployment in High Density WLANs”, Wireless Communications and Mobile Computing, Volume 2018, Article ID 6931765, 2018.
[24] Vikash Kaler, Amit Kumar Bairwa, Kapil Dev Sharma, “Performance Analysis of Traffic Oriented PCF in Wireless Local Area Network”, International Conference on Communication & Computational Technologies by RIET, Jaipur & IJCRT.ORG 2017, Dec 2017.

Akshay Kumar Sharma, Ankur Goyal, "Delay Analysis of Improved Bi - Directional PCF Algorithm in Wireless Local Area Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1196-1199, 2019.

The security and management of network has become a major issue in the arena of Internet. The attacker can access different types of data i.e. personal data, bank account data, and unauthorized use of system resources in campus network. Various policies and procedures have been developed to secure the network communication over the internet by employing firewalls, encryption, and virtual private networks. On the bases of security requirements, the firewall rules are created to monitor the incoming traffic. Packet filtering technique has become a regular and inexpensive approach to secure the transfer of data over the internet and is used as a first line of defense against attacks. IPTABLES is used to create, maintain and monitor packet filter rules in the Linux operating system. Strong filtering techniques in IPTABLES can be used to make a network robust in nature for securing data transfer or prevent it from attacks. In this paper, study is done, not only to safe guard the network Distributed Denial of Service (DDOS) attacks but also management the network bandwidth. The proposed policy script based on the size and count of packet, blocks the attacker for a period of time. With the use of this policy, it observed that 33.8% bandwidth is always available to genuine users of the IT services.

Network Security, Packets, IPTABLES, Policy Script, Packet Count

[1] S. Taluja, P. K. Verma, R. L. Dua, “Network Security Using IP Firewalls”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 8, pp. 348-354, 2012.
[2] K. Joshi, T. Kashiparekh, “Implementing Firewall using IP Tables in Linux”, International Journal of Emerging Trends in Science and Technology, Vol. 3, No. 3, pp. 3634-3637, 2016.
[3] S. Kadam, P. S. Tambabde, A. J. Jayant, “Adaptive Packet Filtering Techniques for Linux Firewall”, International Journal of Advance Research, Ideas and Innovations in Technology, Vol. 3, No. 1, pp. 171-174, 2017.
[4] B. Q. M. AL Musawi, “Mitigating DoS/DDoS Attacks using IP Tables”, International Journal of Engineering and Technology, Vol. 12, No. 3, pp. 101-111, 2012.
[5] B. Sharma, K. Bajaj, “Packet filtering using IP Tables in Linux”. International Journal of Computer Science Issues, Vol. 8, No. 4, pp. 320-325, 2011.
[6] N. D. Lal, B. Ghorbani, S. Vaghri, “A Survey on the use of GNS3 for Virtualizing Computer Networks”, International Journal of Computer Science and Engineering, Vol. 5, No. 1, pp. 49-58, 2016.
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[8] A. M, M. Montero, D. R, Manzano, “Design and Development of Hands-on Network Lab Experiments for Computer Science Engineers”, International Journal of Engineering Education, Vol. 33, No. 2, pp. 855-864, 2017.
[9] A. Jalaparthi, A.S. Kumar, “Monitoring and Analysis of Real time detection of traffic from twitter stream analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 4, No. 3, pp. 34-37, 2016.
[10] P. M, A. S. Thanamani, “Real-time Packet Performances under Socket Application”, International Journal of Scientific Research in Network Security and Communication, Vol. 5, No. 3, pp. 84-89, 2017.

Ishwar Dayal Singh, O. P. Gupta, "Network Resource Management using Policy based Inward Traffic," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1200-1203, 2019.

Now days parking are the critical issues in smart city. Due to parking problem traffic problems are increased, the proposed smart parking system implemented using the Android Application that’s provides to user an easy way of booking the parking slots through an application. Given system avoid the problem of traffic conjunction in commercial areas that unnecessarily consumes time, this paper provides the easy reservation system for parking. In this application the user can view various parking slots and check for the availability of slots. Whenever a user books a particular slot it will be marked red and all the available slots will be green. Booking can be done through credit card/net banking. This application also provides an additional feature of canceling the booked slot within 20 minutes from the time of booking. If the user fails to reach the destination on time then the reservation will be cancel and the payment is refunded. On successful payment a parking number is sent to user’s email or to his mobile number for further enquiry. Hence this application reduces the user’s effort and time of searching the parking slot and also avoids conjunction of traffic using the internet of things.

Car Parking System (CPS), Android application, Parking Control Unit, IoT, ESP8266, Reader, Tag, IR Sensor

[1] Faiz Ibrahim Shaikh, Pratik Nirnay Jadhav, Saideep Pradeep Bandarkar, Omkar Pradip Kulkarni, Nikhilkumar B. Shardoor “Smart Parking System Based on Embedded System and Sensor Network”, International Journal of Computer Applications (0975 – 8887) Volume 140 – No.12, April 2016 International Journal of Pure and Applied Mathematics Special Issue 171.
[2] Thanh Nam Pham1, Ming-Fong Tsai1, Duc Binh Nguyen1, Chyi-Ren Dow1, And Der-Jiunn Deng2 “A Cloud-Based Smart-Parking System Based on Internet-of-Things Technologies”,IEEE Access, Received July 24, 2015, accepted August 16, 2015, date of publication September 9, 2015, date of current version September 23, 2015.
[3] El Mouatezbillah Karbab, Djamel Djenouri, Sahar Boulkaboul, Antoine Bagula, CERIST Research Center, Algiers, Algeria University of the Western Cape, Cape town, South Africa,”Car Park Management with Networked Wireless Sensors and Active RFID”„,978-1-4799-8802-0/15 ©2015 IEEE
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[9] Harmeet Singh, Chetan Anand, Vinay Kumar, Ankit Sharma, “Automated Parking System With Bluetooth Access”, International Journal Of Engineering And Computer Science ISSN:2319-7242,Volume 3 Issue 5, May 2014, Page No. 5773-5775
[10] C. Shiyao, W. Ming, L. Chen, and R. Na, ``The research and implementof the intelligent parking reservation management system based on ZigBee technology,`` in Proc. 6th Int. Conf. Meas. Technol. Mechatronics Autom. (ICMTMA), 2014, pp. 741_744.

Amol Pomaji, Suraj Boinwad, Shrikant Wankhede, Pushpendra Singh, Bhagyashree Dhakulkar, "Smart Parking Management System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1204-1208, 2019.

Image enhancement and thinning are very important pre-processing steps of biometric fingerprint recognition system. This reduction is accomplished by two preprocessing steps. The overall performance of the fingerprint recognition system is highly depended on image enhancement phase of recognition process. The image enhancement is a very important phase in fingerprint recognition for improving the image quality by removing the noise, connecting broken ridges and making smooth image. Then after obtaining the skeleton of the image using skeletonization is known as thinning. The enhanced image will be thinned and all ridges will be coming 1 pixel breadth. The performance of the fingerprint minutiae extraction is highly depending on the thinning process of the enhanced image. Thus, the overall performance of the fingerprint recognition system is highly affected by the image enhancement and the image thinning phase of recognition process. It is the precondition of minutiae extraction. In this paper, Image enhancement of fingerprint image is done using Gaussian Mask and Sobel Convolution and then after we propose to apply a Zhang - Suen Thinning algorithm on fingerprint image for better performance. This will give efficient results in terms of image quality and thinning speed. The implementation of research work is done in .Net platform using custom fingerprint database of 100 images of 25 users.

Fingerprint Recognition, Fingerprint Image Enhancement, Fingerprint Image Thinning, Skeletonization

[1] S.Suri, “Biometric based on fingerprint” International Journal of Computer Sciences and Engineering (ISSN: 2320-7639), Vol-6 Issue-5 June 2018.
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[6] M. B. Patel, R. B. Patel, S. M. Parikh and A. R. Patel, "An improved O`Gorman filter for fingerprint image enhancement," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017, pp. 200-209.
[7] Abu-Ain W, et al. “Skeletonization Algorithm for Binary Images” The 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013) pp.704-709.
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[14] Shrivakshan, G. T., &Chandrasekar, C. (2012). Comparative Study Among Sobel, Prewitt And Canny Edge Detection Operators Used In Image Processing, Journal of Theoretical and Applied Information Technology, Vol.96. No 19, Oct-2018.
[15] T.Y. Zhang and C.Y. Suen, A Fast Parallel Algorithm for Thinning Digital Patterns, Communication of the ACM, Vol.27 No.3. pp 236, Mar 1984.
[16] T.Y. Zhang and C.Y. Suen, A Fast Parallel Algorithm for Thinning Digital Patterns, Communication of the ACM, Vol.27 No.3. pp 237, Mar 1984.
[17] T.Y. Zhang and C.Y. Suen, A Fast Parallel Algorithm for Thinning Digital Patterns, Communication of the ACM, Vol.27 No.3. pp 238, Mar 1984.

Ronak B Patel, Dilendra Hiran, Jayesh M Patel, "Fingerprint Image Thinning by applying Zhang – Suen Algorithm on Enhanced Fingerprint Image," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1209-1214, 2019.