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The web crawler is an automated program, or script, that methodically scans or “crawls” through web pages to create an index of the data. It is mainly used to crawl the web pages and targets at fetching new or updated data from any websites and store the data for an easy access. Web crawler tools are getting well known to the common, since the web crawler has simplified and automated the entire crawling process to make web data resource become easily accessible to everyone. Using a web crawler tool will set people free from repetitive typing or copy-pasting, and could expect a well-structured and complete data collection. Moreover, these web crawler tools enable users to crawl the World Wide Web in a methodical and fast manner without coding and transform the data into various formats conforming to the user requirements. This research work aims at comparison of various available open source web crawlers which are intended to search and scrape the web data. Comparison between various open source crawlers like Visual SEO Studio, Screaming frog, Wild shark SEO Spider, ParseHub and HTTrack Website. The experimental analysis shows the best crawler based on the performance factors.

Visual SEO Studio, Screaming Frog SEO Spider, Wild Shark, ParseHub, HTTrack

[1] Monika Yadav, Neha Goyal (2015), “Comparison of Open Source Crawlers- A Review”, International Journal of Scientific & Engineering Research, Volume 6, Issue 9.
[2] https://www.parsehub.com/intro
[3] http://www.kwasistudios.com/seo-spider-tools/
[4] https://www.screamingfrog.co.uk/seo-spider/
[5] https://www.crunchbase.com/organization/parsehub
[6] https://www.cabotsolutions.com/2016/11/a-detailed-overview-of-web-crawlers
[7] https://visual-seo.com/
[8] https://wildshark.co.uk/spider-tool/
[9] https://www.parsehub.com/
[10] https://www.httrack.com/

E. Suganya, Vijayarani, "Web Crawlers for Web Content Extraction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.238-247, 2019.

Network attacks persist to pose a major threat to the internet. Various techniques are suggested for its mitigation from time to time but newer procedures of performing network attacks are continuously being promulgated by the intruders. The mitigation process becomes really difficult when it comes to highly distributed attacks performed using botnets. These attacks pose a major challenge to both the legitimate users as well as the infrastructure and to protect them, early discovery of the attacks is important. In this paper, Intrusion Detection and prevention System (IDPS) Snort is presented as a solution to identify different Network Attacks. Snort has been evaluated in a high-speed real network for different DoS and Port Scan attacks to examine its behaviour and capacity in detecting them. A set of custom rules have been proposed which show promising results in detecting the attacks but it still has scope for improvement.

NIDS, Snort v2.X, D-ITG, Scapy, DoS attacks, flooding, Port Scan

[1] Q. Gu and P. Liu, “Denial of Service Attacks, Technical Report,” http://s2.ist.psu.edu/paper/DDoS-Chap-Gu-June-07.pdf.
[2] S. Acharya and N. Tiwari, “Survey Of DDoS Attacks Based On TCP/IP Protocol Vulnerabilities,” IOSR Journal of Computer Engineering, vol. 18, no. 3, pp. 68-76, 2016.
[3] T. PENG, C. LECKIE and K. RAMAMOHANARAO, “Survey of Network-Based Defense Mechanisms Countering the DoS and DDoS problems,” ACM Computing Surveys, vol. 39, no. 1, April 2007.
[4] S. Sharma, Y. Verma and A. Nadda, “Information Security: Cyber Security Challenges,” International Journal of Scientific Research in Computer Science and Engineering, vol. 7, no. 1, pp. 10-15, 2019.
[5] M.Shivakumar, R.Subalakshmi, S. Shanthakumari and S. Joseph, “Architecture for Network-Intrusion Detection and Response in open Networks using Analyzer Mobile Agents,” International Journal of Scientific Reearch in Network Security and Communication, vol. 1, no. 4, pp. 1-7, 2013.
[6] P. Innella, “ An Introduction to IDS,” 5 dec 2011. [Online]. Available: https://www.symantec.com/connect/articles/introduction-ids.. [Accessed Accessed Dec 2017].
[7] S. Chakrabarti, M. Chakraborty and I. Mukhopadhyay, “Study of Snort-Based IDS,” in Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ACM, 2010.
[8] S. Bansal and N. Bansal, “Scapy–A Python Tool For Security Testing,” Journal of Computer Science & Systems Biology, vol. 8, no. 3, pp. 140-159, 2015.
[9] A. P. G. V. D. Emma, Analysis and experimentation of an open distributed platform for synthetic traffic generation, Suzhou, 2004, pp. 277-283.
[10] S. Avallone, S. Guadagno, D. Emma, A. Pescap and G. Ventre, “D-ITG Distributed Internet Traffic GeneratorS. Avallone S. Guadagno D. Emma A. Pescap `eG. Ventre,” in 1st International Conference on Quantitative Evaluation of Systems , Enschede, The Netherlands, 27-30 September 2004.
[11] C.-L. Chen, “A new Detection Method for Distributed Denial-of-Service Attack Traffic based on Statistical Test”, J,” Journal of Universal Computer Science, vol. 15, no. 2, 2009.
[12] N. Khamphakdee, N. Benjamas and S. Saiyod, “Improving Intrusion Detection System Based on Snort Rules for Network Probe Attack Detection,” in 2nd International Conference on Information and Communication Technology (ICoICT), 2014.
[13] J. François, I. Aib and R. Boutaba, “FireCol: A Collaborative Protection Network for Detection of Flooding DDoS Attack,” IEEE/ACM Transactions on Networking, vol. 20, no. 6, pp. 1828 - 1841, 2012.
[14] S. T, Application of Machine Learning Algorithms for Real Time Intrusion Detection and Classification, chennai, Tamil Nadu: Shodhganga, 2011.
[15] L. Xiaoming, V. Sejdini and H. Chowdhury, Denial‐of‐Service (DoS) Attack with UDP Flood., Windsor, Ontario: School of Computer Science, University of Windsor:, 2007.
[16] Z. Trabelsi and L. Alketbi, “Using network packet generators and snort rules for teaching denial of service attacks,” in Annual Conference on Innovation and Technology in Computer Science Education, 2013.
[17] M. Saritha and M. Chinta, “Countering Varying DoS Attacks using Snort Rules,” International Journal of Advanced Research in Computer science and Software Engineering, vol. 3, no. 10, October 2013 .
[18] M. Gandhi and S.K.Srivatsa, “Detecting and preventing attacks using network intrusion detection System,” International Journal of Computer Science and Security, vol. 2, no. 1, pp. 49-60, 2008.
[19] D. Lin, “Network Intrusion Detection and Mitigation against Denial of Service Attack,” University of Pennsylvania, Philadelphia, 2013.
[20] F. Hsu, Y. Hwang, C. Tsai, W. Cai, C. Lee and K. Chang, “TRAP: A three-way handshake server for TCP connection establishment,” Appl. Sci., vol. 6, no. 11, 2016.
[21] K. Kendall, “Intrusion Detection Attacks Database,” 1999.
[22] S. M. Aaqib, “To analuse performane, scalability and security mechanisms of apache web server vis-a-vis with contemporary web servers,” University of Jammu, 2014.
[23] M. d. Vivo, L. Ke, G. Isern and G. O. d. Vivo, “A review of port scanning techniques,” ACM SIGCOMM Computer Communication Review , vol. 29, no. 2, pp. 41-48, 1999 Computer Communication Review. .
[24] T. S. Buddy, “What is FTP Bounce Attack ?,” 7 march 2017. [Online]. Available: https://www.thesecuritybuddy.com/vulnerabilities/what-is-ftp-bounce-attack/. [Accessed 1 feb 2018].
[25] “Study of Snort based IDS,” in Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ACM, 2010.
[26] M. Roesch, “SNORT 3 User manual,” SourceFire.Inc, 2017. [Online]. Available: http://manual-snort-org.s3-website-us-east-1.amazonaws.com/. [Accessed Nov 2017].
[27] T. W. Shinder, The Best Damn Firewall Book Period, Second ed., Syngress, 2007.

Alka Gupta, Lalit Sen Sharma, "Mitigation of DoS and Port Scan Attacks Using Snort," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.248-258, 2019.

The main objective of this research is to find the efficiency of prediction model based on support vector machine for predicting the stocks in NIFTY 50 during different trends of the market in last 10 years. The prediction model takes different features and predict the next day price as up or down as compared to previous day price. The features used in the model are difference of current day and previous day low-high, open, close, and moving average of open, close, high and low. Label is the difference between open and close prices of the current day. It is observed that the implemented support vector machine algorithm performs very well in predicting the stock price when there is drop in price , irrespective of market trend , but performance reduces significantly in predicting the stock price when there is increase in price ,when market follows upward trend.

support vector machine, stock , market trend, prediction , efficiency

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[4] Bruno Miranda Henrique, Vinicius Amorim Sobreiro*, Herbert Kimura "Stock price prediction using support vector regression on daily and up to the minute prices" . The Journal of Finance and Data Science 4 (2018) pp 183e201
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Shailja Kashyap , "Estimating Efficiency of Support Vector Machine Based Model in Prediction of The Direction of Future Stock Price During Different Trends of Market," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.259-262, 2019.

Optimization of image contrast in color images is a challenging and a typical area of study in image processing. A small change in intensity, color, texture etc. can affect the visualization of image with a big difference. In this paper performance of various color images are evaluated on an important parameter named contrast of image under different illumination conditions. Various image contrast enhancement algorithm are analyzed based on computational complexity, computational cost and visual understandings. The main focus of study image contrast enhancement methods 1) Histogram based methods 2) Retinex based method 3) Fuzzy contextual contrast enhancement techniques and 4) Wavelet domain image contrast enhancement techniques.

Contrast enhancement, Histogram Equalization, Wavelet, Retinex, Fuzzy Contextual Contrast Enhancement

[1] Jinshan Tang, Eli Peli, and Scott Acton, Senior Member, IEEE, “Image Enhancement Using a Contrast Measure in the Compressed Domain”, IEEE SIGNAL PROCESSING LETTERS, VOL. 10, NO. 10, PP.289-292 OCTOBER 2003.
[2] Mohd Firdaus Zakaria, Haidi Ibrahim, and Shahrel Azmin Suandi, “A Review: Image Compensation Techniques”, In the proceedings of 2010 2nd International Conference on Computer Engineering and Technology, vol. 7, pp. 404-408.
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[4] Li He, Ling Luo, and Jin Shang, “An Image Enhancement Algorithm Based Retinex Theory”, International Workshop on Education Technology and Computer Science 2009, pp. 350-352.
[5] Sangjin Kim, Wonseok Kang, Eunsung Lee, and Joonki Paik, “Wavelet-Domain Color Image Enhancement using Filtered Directional Bases and Frequency-Adaptive Shrinkage”, IEEE Trans. on consumer Electronics, vol. 56, no. 2, pp. 1063-1068, May 2010.
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[8] Changjiang Zhang, Xiaodong Wang, Haoran Zhang, “Contrast Enhancement for Fruit Image by Gray transform and Wavelet Neural Network”, Zhejiang Normal Unversity Foundation, pp.1064-1069, 2006 IEEE.
[9] Zhi Yu Chen, Senior Member, IEEE, Besma R. Abidi, Senior Member, IEEE, David L. Page, Member, IEEE, and Mongi A. Abidi, Member, IEEE “Gray-Level Grouping(GLG): An Automatic Method for Optimized Image Contrast Enhancement—Part II: The Variations”, IEEE Transaction on Image Processing, Vol. 15, NO. 8, pp.2308-2314 August 2006.
[10] Madasu Hanmandlu, Member IEEE, and Devendra Jha,“An Optimal Fuzzy System for Color Image Enhancement”, IEEE Transaction on Image Processing, VOL. 15, NO. 10, pp.2956-2966 Oct 2006.
[11] Rodrigo Palma-Amestoy, Edoardo Provenzi, Marcelo Bertalmı´o, and Vicent Caselles, “A Perceptually Inspired Variational Framework for Color Enhancement”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, Issue 3, pp.458-474 March 2009.
[12] Gabriele Simone1, Marius Pedersen1, Jon Yngve Hardeberg1, Alessandro Rizzi2, “Measuring Perceptual Contrast in a Multi-level Framework”, Human Vision and Electronic Imaging XIV of SPIE-IS&T Electronic Imaging, SPIE Vol. 7240, pp. 9-18.
[13] Karen A. Panetta, Fellow, IEEE, Eric J. Wharton student member IEEE and Sos S. Agaian, Senior member IEEE “Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure”, IEEE Transaction on System, Man And Cybernetics-Part B: Cybernetics, Vol. 38, No.1 pp.174-188, Feb-2008.
[14] Tarik Arici, Salih Dikbas, Membe, IEEE, Yucel Altunbasak, Senior Member, IEEE, “A Histogram Modification Framework and Its Application for Image Contrast Enhancement”, IEEE Transaction on Image Processing, VOL. 18, NO. 9, pp.1921-1935, Sep 2009.
[15] Xiaolin Wu, Xiaolin Wu, Fello, IEEE, “A Linear Programming Approach for Optimal Contrast-Tone Mapping”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 5, pp.1262-1272, May 2011.
[16] Iyad F. Jafar1, Khalid A. Darabkh1 and Ghazi M. Al-Sukkar2, “A Rule-Based Fuzzy Inference System for Adaptive Image Contrast Enhancement”, Oxford University Press on behalf of The British Computer Society Advance Access publication, Vol. 55 No. 9 pp.1041-1057, Dec 2011.
[17] Amina Saleem1*, Azeddine Beghdadi1 and Boualem Boashash2,3, “Image fusion-based contrast enhancement”, EURASIP Journal on Image and Video Processing Vol. 10, No. 1, pp.1-17, 2012.
[18] Mohan Liu and Patrick Ndjiki-Nya, “A NEW PERCEPTUAL-BASED NO-REFERENCE CONTRAST METRIC FOR NATURAL IMAGES BASED ON HUMAN ATTENTION AND IMAGE DYNAMIC”, QoMEX 2012, pp.254-259, 2012.
[19] Shih-Chia Huang, Fan-Chieh Cheng, and Yi-Sheng Chiu, “Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution”, IEEE Transaction on Image Processing, VOL. 22, NO. 3, pp.1032-1041 MARCH 2013.
[20] V. Magudeeswaran1 and C. G. Ravichandran2, “Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement”, Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 891864, pp.1-10, Jun 2013.
[21] K. Santhi#1, RSD. Wahida Banu*2 , “Contrast Enhancement Using Brightness Preserving Histogram Plateau Limit Technique”, International Journal of Engineering and Technology (IJET), Vol. 6 No. 3 pp.1747-1753, Jul 2014.
[22] K. Singh, R. Kapoor, “Image enhancement via Median-Mean Based Sub-Image Clipped Histogram Equalization”, Optik - Int. J. Light Electron Opt., Vol. 93 Issue 4, pp.1-6, 2014.
[23] Shyam Lal1 and Mahesh Chandra2 ‘Efficient Algorithm for Contrast Enhancement of Natural Images”, The International Arab Journal of Information Technology, Vol. 11, No. 1, pp.95-102, Jan 2014.
[24] Hongteng Xu, Guangtao Zhai,Member, IEEE, Xiaolin Wu, Fellow, IEEE, and Xiaokang Yang, Senior Member, IEEE, “Generalized Equalization Model for Image Enhancement”, IEEE Transaction on Multimedia, VOL. 16, NO. 1, pp.68-82, Jan 2014.
[25] Jeyong Shin, Student Member, IEEE, and Rae-Hong Park, Senior Member, IEEE, “Histogram-Based Locality-Preserving Contrast Enhancement”, IEEE Signal Processing Letters, Vol. 22, No. 9, pp.1293-1296 Sep 2015.
[26] Se Eun Kim, Jong Ju Jeon, Il Kyu Eom, “Image contrast enhancement using entropy scaling in wavelet Domain”, Signal Processing Vol. 127 pp. 1-11 2016.
[27] Anil Singh Parihar, Member, IEEE, Om Prakash Verma, Member, IEEE, and Chintan Khanna, “Fuzzy-Contextual Contrast Enhancement”, IEEE Transaction on Image Processing, Vol. 26, No. 4, pp.1-11, April 2017.
[28] Xuewen Wang a,c,d, Lixia Chen b,c,d,*, “An effective histogram modification scheme for image contrast Enhancement”, Signal Processing: Image Communication, Vol. 58 pp.187–198, 2017.
[29] M.Shakeri, M.H.Dezfoulian*, H.Khotanlou, A.H.Barati, Y.Masoumi, “Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram- equalization”, Digital Signal Processing Vol. 62 pp. 224–237, 2017.
[30] Amandeep Kaura*, Chandan Singhb, “Contrast enhancement for cephalometric images using Wavelet based modified adaptive histogram equalization”, Applied Soft Computing Vol. 51, pp.180–191, 2017.
[31] Zohair Al-Ameen,“Contrast Enhancement for Color Images Using an Adjustable Contrast Stretching Technique”, International Journal of Computing, Vol. 17, Issue. 2, pp.74-80, 2018.
[32] LONG YU, HAONAN SU , AND CHEOLKON JUNG, (Member, IEEE), “Perceptually Optimized Enhancement of Contrast and Color in Images”, IEEE Access Digital Object Identifier, Vol. 6, pp.36132-36142, 2018.
[33] Abdullah Amer Mohammed Salih1, Khairunnsia Hasikin2 and Norashidi “Adaptive Fuzzy Exposure Local Contrast Enhancement”, Digital Object Identifier, Vol. 6, pp.58794-58806, 2018.

Avadhesh Kumar Dixit, Rakesh Kumar Yadav, "A Review on Image Contrast Enhancement in Colored images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.263-273, 2019.

Health related issues are most common among sports persons. The persons indulging in athletic activities have to face or suffer from various injuries and diseases. Hence sports medicine is a field which is specifically meant for providing medical aid to suffering sport persons. It is such a vast field that lot of amendments can be done in this field. This study provides an overview to the previously done work by various experts. It is seeing that most of the expert systems were created by using some basic technologies which are not in use today. And lot of crucial injuries and diseases were not considered by them such as injuries of knee. The objective behind this work is to design and implement such a multi agent system which can be able to detect the knee injuries. The work is implemented by using the trending and advance technology of current generation i.e. fuzzy logics or system which is based on some rules of real world application. MATLAB is used as simulation platform to evaluate the proficiency of the present work. The result section shows that how efficiently the system detects the disease and generates medical advice to the patient.

Fuzzy logic, Clinical decision making,knee injury treatment.

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Naveen Dalal, "Clinical Decision Support System for Knee Injuries," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.274-284, 2019.

Role of Antenna in Wireless communication is really vital whether it is point to point wireless communication or Wi-Fi. The size of antenna should be compact. In this work a novel design of the metamaterial based unit cell 7X7 arrays has been used as superstrate for the gain enhancement of a microstrip patch antenna operating at 7.1 GHz frequency. This frequency lies in the C-band which is normally used in satellite communication. Due to the unusual properties, metamaterial can change the electric and magnetic property of electromagnetic wave passing through it. Hence metamaterial is used in the fabrication of antenna enhanced the required properties. The proposed design provides a high gain of 9.4 dB using a superstrate, when suspended above a microstrip patch at 7.1 GHz. Simulation and measurement results confirm that near-zero index metamaterial (NZIM) array significantly improves the gain (by more than 1.8 dBi) and reduces the half power beamwidth in both E-plane and H-plane by 20o. The S parameters are extracted using HFSS,which is a Finite-Element-Method (FEM)-based full-wave simulator.

Microstrip Patch Antenna, Frequency 7.1GHz, NZIM cell array, ANSOFT HFSS Software, Gain, Return loss

[1] Hemant Suthar et al.,"Gain Enhancement of Microstrip patch antennausong near-zero index Metamaterial (NZIM) Lens", IEEE Twenty First National Conference on Communications (NCC), Mumbai, India, 2015.
[2] Gijo Augustin et al., “A zero index metamaterial unit cell for Antenna Gain Enhancment”,IEEE Antennas and Propagation Society International Symposium (APSURSI), USA, 2013.
[3] Ahmed B. Numan et al.,"Extraction of Material Parameters for Metamaterials Using a Full-Wave Simulator", IEEE Antennas and Propagation Magazine, Vol. 55, No. 5, October 2013.
[4] C. Caloz et al., “Electromagnetic metamaterial Transmission line theory and microwave applications,” John Wiley & Sons, 2005.
[5] D. R. Smth et al.,“Composite medium with simultaneously negative permeability and permittivity” Phy. Review Lett., Vol. 84, No. 18, pp. 4184-4187, 2000.
[6] M. A. Antoniades et al., “Multiband Compact Printed Dipole Antennas Using NRI-TL Metamaterial Loading,” IEEE Transactions on Antennas and Propagation, Vol. 60, No. 12, pp. 5613-5626, 2012.
[7] Anwer Mekki et al., “Gain Enhancement of a Microstrip Patch Antenna Using a Reflecting Layer,” International journal of Antennas and Propagation, 2015.
[8] Arvind Kumar and Mithilesh Kumar, “Gain Enhancement in a Microstrip Patch Antenna Using metallic ring at 10 GHz ,” International journal of Computer Application, 2014.
[9] Ashmita et al., “Gain Enhancement in a Microstrip Patch Antenna Using H shaped defected ground structure ,” Advances in Intelligent System and Computing, 2017.
[10] Sandeep Kumar Singh, Himanshu Parasar, Rajendra Singh, Vepakomma Kavya "Design of Compact UWB Rectangular Patch Antenna for WiMax/WLAN Applications," International Journal of Computer Sciences and Engineering, vol. 6, issue.3, pp. 157-160, 2018.
[11] Anand Mohan, Ashok Kumar "Cylindrical Dielectric Resonator Optical Antenna (CDROA) & its Applications for Convenient Technology," International Journal of Computer Sciences and Engineering, vol. 7, issue.1, pp. 283-286, 2019.
[12] R. Luo et al., "Negative index material composed of electromagnetic resonators," Appl. Phys.Lett., vol. 90, pp. 263504-263504-3, 2007.

Ritu Goyal, Y K Jain, "Metamaterial based Microstrip Patch Antenna Using Unit Cell Array for Gain Enhancement," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.285-288, 2019.

Data mining techniques are being extensively used in almost every field to gain insights into the large data that is being generated worldwide. These techniques are also being used in analysing the student’s performance in several educational institutions. Some of the student data of a particular college was collected with information about their academic performances and individuals’ skills such as their aptitude level and communication skills. This data was gathered in order to analyse and predict the placement of the students by applying classification techniques such as decision tree algorithm (J48), K-Nearest Neighbour (IBk) and Naïve Bayes using one of the Data Mining tools known as WEKA. The rules were generated using the decision tree algorithm which helped us in visualizing the decision tree using the above dataset. The accuracy of all the three classifier models was compared and it was shown that the highest accuracy was shown by KNN classifier (IBk in Weka) followed by J48 classifier and Naïve Bayes classifier. This system is thus much reliable and can be used to predict if the student can be placed or not.

Data Mining, Classification, Decision Tree, Naive Bayes, Prediction

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[2] A. S. Sharma, S. Prince, S. Kapoor and K. Kumar, "PPS — Placement prediction system using logistic regression," 2014 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE),Patiala,pp.337-341, 2014.
[3] M. Gera and S. Goel, "A model for predicting the eligibility for placement of students using data mining technique," International Conference on Computing, Communication & Automation, Noida, pp.114-117, 2015.
[4] K. Pruthi and P. Bhatia, "Application of Data Mining in predicting placement of students," 2015 International Conference on Green Computing and Internet of Things (ICGCIoT),Noida,pp.528-533,2015.
[5] P. Guleria and M. Sood, "Predicting student placements using Bayesian classification," 2015 Third International Conference on Image Information Processing (ICIIP), Waknaghat,pp.109-112,2015.
[6] Ashok M V and Apoorva A, "Data mining approach for predicting student and institution`s placement percentage," 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, pp. 336-340, 2016.
[7] A. Giri, M. V. V. Bhagavath, B. Pruthvi and N. Dubey, "A Placement Prediction System using k-nearest neighbors classifier," 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysore, pp.1-4, 2016.
[8] S. K. Thangavel, P. D. Bkaratki and A. Sankar, "Student placement analyzer: A recommendation system using machine learning," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS),Coimbatore,pp.1-5,2017.
[9] Pal, Saurabh, “Analysis and Mining of Educational Data for Predicting the Performance of Students”, International Journal of Electronics Communication and Computer Engineering, 4, pp.1560-1565, 2013.
[10] Pal, Saurabh, “Classification Model of Prediction for Placement of Students”, I.J.Modern Education and Computer Science, 2, pp. 49-56, 2013.

Sumaira Asif, Tabrez Nafis, "Students’ Placement Prediction Using Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.289-293, 2019.

To accommodate the frequently changing requirements and demands of market there is continuous research and advances going on In the field of wireless communication. In the last few years, the industry has seen drastic qualitative and quantitative growth in terms of both technical and economic aspects, which played a crucial role in the rapid growth of this industry. Researchers have developed an extraordinary range of antennas to serve today`s demands, each with its own advantages and limitations. With further developments in mobile communication technology, the technology for antennas also saw drastic advancements. There is an intensive development in the study of microstrip antennas for wireless applications demonstrates broadband characteristics in which antenna size increases which can potentially eliminate their use for mobile communication. Due to which electronic components have been minimized. Thus microstrip patch antennas emerged with attractive features like simple in structure, smaller in dimension, low profile, low cost and are extremely attractive to be used in emerging UWB applications [1]. In this paper, three different types of slotted microstrip patch antennas i.e. Half I-U slot, Half I-F slot, and Half I-B slot have been proposed and designed for various wireless applications like Wi-MAX, Wi-Fi, WLAN, Bluetooth, ISM, etc. Slots on the proposed patch can be used to optimize antenna parameters. The numerical simulation and analysis for the antennas parameters like Radiation Pattern, VSWR, Return Loss, and Directivity were performed using CST (Computer Software Technology).

Computer Software Technology, Directivity, ISM, Microstrip Patch Antenna, Return Loss, Radiation Pattern, UWB, Voltage Standing Wave Ratio, Wi-Fi, Wi-Max, WLAN

[1] Govardhani Immadi, K. Swetha, M.Venkata Narayana, M.Sowmya, R.Ranjana,“Design of microstrip patch antenna for WLAN applications using Back to Back connection of Two E-Shapes,” International Journal of Engineering Research and Applications,Vol. 2, Issue. 3, pp. 319-323, May-Jun 2012.
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[11] Neha Gupta, “Effects of slots on Microstrip Patch Antenna”, International Research Journal of Engineering and Technology (IRJET) , Vol. 04, Issue. 02, pp. 1132-1135, Feb 2017.
[12] U. Shameem, M. Ur-rehman, Q. H. Abbasi, and K. Qaraqe, “A Low Profile Penta-band Antenna For Portable Devices”, In the Proceedings of the 2016 International Workshop on Antenna Technology (iWAT), pp. 174–177, 2016.
[13] L. Peng, C. L. Ruan, and J. Xiong, “Compact EBG for multi-band applications,” IEEE Trans. Antennas Propag., Vol. 60, No. 9, pp. 4440–4444, 2012.
[14] M. Borhani, P. Rezaei, and A. Valizade, “Design of A Reconfigurable Miniaturized Microstrip Antenna for Switchable Multiband Systems”, IEEE Antennas Wirel. Propag. Lett. , Vol. 1225, No. c, pp. 1–1, 2015.
[15] M. Chang and W. Weng, “A Printed Multi-band Slot Antenna for LTE / WLAN Applications,” In the Proceedings of the 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp. 1144–1145, 2015.
[16] L. X. Truong and Tran Minh Tuan, “Design A Microstrip Antenna with Defected Ground Structure”, In the Proceedings of the 2015 International Conference on Advanced Technologies for Communications (ATC) Design, pp. 160–163, 2015.
[17] J.S. Sun and S.Y. Huang,” A small 3-D multi-band antenna of F shape for portable phones’ applications”,Progress In Electromagnetics Research letters, Vol. 9, pp. 183-192, 2009.
[18] ShivamTyagi and Mohit Gharwar, “Design of Multiband S-shape Microstrip Patch Antenna for 4G Devices”, International Journal of Creative Research Thoughts, Vol. 6, Issue. 2, April 2018.
[19] Prachi, Brajlata Chauhan, and Manish Jaiswal,” Performance Analysis of Different Slotted U, Half U, π and Half πMicrostrip Patch Antennas for LTE Application,” International Journal of Computer Applications,Vol. 86,No.18,pp. 0975 – 8887, January 2014.
[20] Denidni, D.A. ,N.Hassaine, and Q. Rao,” Broadband high –gain e-shaped Microstrip antennas for high speed wireless networks,” Progress in Electromagnetic Research C, Vol. 1,pp. 105-111, 2008.
[21] G. F. Khodae, J. Nourinia, and C. Ghobadi, "A Practical Miniaturized U-Slot Patch Antenna with Enhanced Bandwidth," Progress In Electromagnetics Research B, Vol. 3, pp. 47-62, 2008.
[22] Wu, G.-L., W. Mu, G.Zhao and Y.-C. Jiao, “A novel design of dual circularly polarized antenna feed by L-strip,” Progress In Electromagnetics Research, PIER 79, 39-46, 2008.
[23] Ansari, J.A., P. Singh, S. K. Dubey, R.U. Khan, and B.R. Vishvakarma,” H-shaped stacked patch antenna for dual band operation,” Progress In Electromagnetics Research B, Vol. 5, pp. 291-302, 2008.
[24] M.T.Islam, M.N.Shakib and N.Misran,” Broadband E-H shaped Microstrip Patch Antenna For Wireless Systems,”Progress In Electromagnetics Research,PIER 98,163-173,2009.
[25] Pavel HAZDRA, Miloš MAZÁNEK, Jiří ČERMÁK,” Wideband Rectangular Microstrip Patch Antenna Using L-Probe Feeding System,” Radio engineering, Vol. 16, No. 3, pp. 37-41,September 2007.

Pooja Japra, Divyanshu Gupta, "Design and Analysis of Half I-U, Half I-F and Half I-B Microstrip Patch Antennas for Wireless Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.294-300, 2019.

Social Networking applications are become best place for the people to convey their views on the growth of the society. Studying these views has benefited for more research interest in understand the people and for appropriate decision for the growth. To study the views of the sentiment analysis is the most used technique, which uses NLP, ML to realize the input in terms of positive, negative and neutral opinions. It is more tedious job to analyze the input text shared by the user in social networking applications. Here, proposed a novel bounded logistic regression and inquired with Random Forest techniques with Stand-Up India scheme dataset. From the obtained results, approached technique gives the good accuracy against to other existing approaches.

Tweet cleansing, Sentiment Analysis, Machine Leaning

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Kumar P K, S Nandagopalan, "Location based Sentiment Analysis on Stand-Up India scheme," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.301-305, 2019.

Agricultural productivity are some things on that economy extremely depends. This can be the one among the explanations that disease detection in plants plays a crucial role in agriculture field, as having diseases in plants are quite natural. If correct care isn`t taken during this space then it causes serious effects on plants and because of that several product quality, amount or productivity is affected. Crop diseases function a significant threat to food provide. As a results of the growing of smartphone technology throughout the globe, it`s currently become technical possible to leverage image process techniques to identity variety of disease from a straightforward photograph. Employing a public dataset of 54,306 pictures of pathological and healthy plant leaves collected beneath controlled conditions, we have a tendency to train a deep convolutional neural network to spot fourteen crop species and twenty six diseases.This paper discuss about the crop diseases diagnosing using Deep Learning which becomes the foremost correct and precise paradigms for the detection of disease. Leaves of Infected crops are collected and labelled in line with the illness, process of image is performed together with pixel-wise operations to boost the image info. It`s followed with feature extraction, segmentation and therefore the classification of patterns of captured leaves so as to spot plant leaf diseases. Four classifier labels are used as microorganism Spot, Yellow Leaf Curl Virus, blight and Healthy Leaf. The options extracted are match into the neural network with twenty epochs. Many artificial neural network architectures are enforced with the most effective performance of ninety 98.59% accuracy in determinative the disease. This was a good success, demonstrating the feasibleness of this approach within the field of disease diagnosis and high crop yielding.

Deep Learning, Image-based Crops, CNN diagnosing

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Pragya Lariya, Mukul Shrivastava, "“Review Paper on Crops Disease Diagnosing Using Image-Based Deep Learning Mechanism”," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.306-312, 2019.