Volume-7 , Issue-2 , Apr 2019, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Flower Classification using Different Color Channel

  Riddhi H.Shaparia, Narendra M.Patel, Zankhana H. Shah

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.2 , pp.1-6, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.16

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  In this paper, flowers are classified based on color and texture features where features are extracted from different color channel. Database have been used for experiments is Oxford flower 17 category. There are numbers of steps to get the final classification system. The pre-processing techniques like background elimination, noise removal and segmentation are applied on an original color images. From segmented color image R, G and B color channel is obtained if we use the RGB color model. GLCM (Gray Level Co-occurrence Matrix) method and LBP (Local Binary Pattern) methods are used for texture features extraction from the segmented color image. Color moment is used for color features extraction. A well-known classifier like SVM (Support Vector Machine) and NN (Neural Network) are used for classification. This paper contains comparison of classification results, as which classifier is more accurate for flower classification.

  Key-Words / Index Term
  Classification, GLCM, LBP, Color moment, SVM, Neural network

  References
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  [2] Basavanna M and S. S. Gornale, “Identification and Classification of Flowers Images using Colour models,” International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), pp. 1003-1007, 2015
  [3] D S Guru, Y. H. Sharath and S. Manjunath, “Texture Features and KNN in Classification of Flower Images,” Recent Trends in Image Processing and Pattern Recognition (RTIPPR), 2010.
  [4] F. Siraj, H. M. Ekhsan and A. N. Zulkifli, "Flower image classification modeling using neural network," 2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), Bandung, pp.81-86, 2014.
  [5] C. Y. Huang, Y. L. Lin and W. H. Hsu, "Augmenting flower recognition by automatically expanding training data from web," Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on, Xiamen, pp.1-6, 2015.
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  [8] S. W. Hong and L. Choi, "Automatic recognition of flowers through color and edge based contour detection," Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on, Istanbul, pp.141-146, 2012.
  [9] A. Angelova, S. Zhu and Y. Lin, "Image segmentation for large-scale subcategory flower recognition," 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, pp.39-45, 2013.
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  [11] Balvant V. Biradar and Santosh P. Shrikhande, “Flower Detection and Counting Using Morphological and Segmentation Technique,” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3), pp.2498-2501, 2015.
  [12] H. M. Zawbaa, M. Abbass, S. H. Basha, M. Hazman and A. E. Hassenian, "An automatic flower classification approach using machine learning algorithms," 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, pp.895-901, 2014.
  [13] M. E. Nilsback and A. Zisserman, "Automated Flower Classification over a Large Number of Classes," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, pp.722-729, 2008.
  [14] Y. Liu, F. Tang, D. Zhou, Y. Meng and W. Dong, "Flower classification via convolutional neural network," 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA), Qingdao, pp.110-116, 2016.
  [15] W. Qi, X. Liu and J. Zhao, "Flower classification based on local and spatial visual cues," 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, pp.670-674, 2012.
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  [17] C. Pornpanomchai, P. Sakunreraratsame, R. Wongsasirinart and N. Youngtavichavhart, "Herb flower recognition system (HFRS)," 2010 International Conference on Electronics and Information Engineering, Kyoto, pp.V1-123-V1-127, 2010.
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  Citation
  Riddhi H.Shaparia, Narendra M.Patel, Zankhana H. Shah, "Flower Classification using Different Color Channel," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.1-6, 2019

• Open Access   Article

  Smart Voting System through Facial Recognition

  Nilam Choudhary, Shikhar Agarwal, Geerija Lavania

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.2 , pp.7-10, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.710

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  Facial recognition is a category of biometric software which works by matching the facial features. We will be studying the implementation of various algorithms in the field of secure voting methodology. There are three levels of verification which were used for the voters in our proposed system. The first is UID verification, second is for the voter card number, and the third level of verification includes the use of various algorithms for facial recognition. In this paper, we will provide a comparative study between these algorithms, namely: Eigenface, FisherFace & SURF.

  Key-Words / Index Term
  Smart Voting; Facial Recognition; EigenFace ; FisherFace; SURF

  References
  [1] A. Baumberg, “Reliable feature matching across widely separated views”, in CVPR, 2000
  [2] F. Song, D. Zhang, J. Wang, H. Liu, and Q. Tao, "A parameterized direct LDA and its application to face recognition," Neurocomputing, Vol.71, 2007.
  [3] H. Bay, T. Tuytelaars, L. Van Gool, “SURF: speeded up robust features”, in ECCV, 2006.
  [4] KashifHussainMemon, Dileep Kumar and Syed Muhammad Usman, “Next Generation A Secure EVoting System Based On Biometric Fingerprint Method”, 2011.
  [5] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711--720, 1997.
  [6] P. Viola and M. J. Jones, "Robust real-time face detection," International Journal of Computer Vision, Vol. 57, pp. 137-154, 2004.
  

  Citation
  Nilam Choudhary, Shikhar Agarwal, Geerija Lavania, "Smart Voting System through Facial Recognition," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.7-10, 2019

• Open Access   Article

  Implementation of IoT in Home Automation using android application

  Vidhi Tiwari, Pratibha Adkar

  Technical Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.2 , pp.11-16, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.1116

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  Today’s world functions on technology and machines, IOT (The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.”) provides the user with various advanced features to interact with technology and make it secure for the user. Nowadays, majority of people have access to smartphones which makes the implementation of idea easier.[4] Home automation using android application makes it easier to interact with the home appliances. It uses 8-bit microprocessor, arduino that is used to control home appliances using relay. Bluetooth is used as a reliable form of communication for short distance. Sensors have been used for efficiency and safety. This paper provides an efficient approach in the field of home automation and reducing human efforts.[2]

  Key-Words / Index Term
  IoT, Automation, Sensors, Android Application

  References
  [1] Nupur Tyagi, “A Reference Architecture For Iot”,in International Journal of Computer Engineering and Applications, Volume X, Issue I, Jan.1www.ijcea.com ISSN 2321- 3469, 2013.
  [2] SomayyaMadakam, R. Ramaswamy, SiddharthTripathi, “Internet of Things (IoT): A Literature Review”, Journal of Computer andCommunications,2015.
  [3] Tasneem M. Yousif, Aysha K. Alharam, WaelElmedany, Abeer A. Al Khalaf, ZainabFardan: "ROBODEM Remote Monitoring System Using Web/Mobile Applications" ,Fifth International Conference on e-Learning (econf), 2015
  [4] Sharon Panth, Mahesh Jivani: "Home Automation System (HAS) using Android for Mobile Phone”: International Journal of Electronics and Computer Science Engineering (IJECSE), Volume3, Number 1, 2011
  [5] Robert A. Sowah, Abdul R. Ofoli, SelaseKrakani, Seth Fiawoo: " Hardware Design and Web-based Communication Modules of a Real-time Multi-Sensor Fire Detection and Notification System using Fuzzy Logic" :IEEE Transactions on Industry Applications, Volume: PP, Issue: 99 , 2016
  [6] DhirajSunehra, M. Yeena: "Implementation of interactive home automation systems based on email and Bluetooth technologies” International Conference on Information Processing (ICIP), 2015.
  [7] Atzori, Luigi, Antonio Iera, and Giacomo Morabito. "The internet of things: A survey." Computer networks 54.15: 2787-2805, 2010
  [8] Sharon Panth and Mahesh Jivani, “Device Control in an Ad-hoc Network Environment by using MoSync for Multiple Platform Mobile Application Development”, International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 4. No. 8, pp. 1145-1152, 2013.
  [9] M. N. Jivani, Sharon Panth, “Wireless IEEE 802.11 based Device Control Ad-hoc Network using Responsive Web Design (RWD)”, International Journal of Emerging Technologies and Applications in Engineering, Technology and Sciences (IJ-ETA-ETS), ISSN: 0974-3588, pp.67-72, Vol. 1, Issue 7. 2014
  [10] Mohamed Salman and Jayavrinda Vrindavanam, “Efficient Interactive Control System based on GSM”, International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 3 No. 2, pp. 50=56, November 2011.
  [11] Khadija Tul kobra, Arnab Debnath and Proteeti Prova, “Temperature and power control of water Geyser Using Learning Technique”, 3rd International Conference for Convergence in Technology (I2CT), 2018
  [12] Andrea Zanella, Nicola Bui ,Angelo Castellani,Lorenzo Vangelistaand Michele Zorzi. “Internet Of Things For Smart Cities”, IEEE Internet of Things Journal, 2016
  

  Citation
  Vidhi Tiwari, Pratibha 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

• Open Access   Article

  A Survey for identifying Parkinson’s disease by Binary Bat Algorithm

  S. JabeenBegum, B. Swaathi

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.2 , pp.17-23, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.1723

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  Parkinson’s disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity tremors. Analyzing human gait serves to be useful in studies aiming at early recognition of the disease. In the present work, we perform a comparative analysis of various nature-inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest. Binary Bat Algorithm (BBA) has searched the feature space for optimal feature combinations with good accuracy. Bats use echolocation to detect prey and avoid obstacles in the dark by emitting ultrasound waves and listening to the echo produced through the waves reflecting from the surrounding objects. The accuracy can be made possible through various classification techniques such as SVM, NB, kNN.

  Key-Words / Index Term
  Parkinson’sDisease(PD),Echolocation,SupportVectorMachine(SVM),NaïveBayesian(NB),k-Nearest Neighbor(kNN).

  References
  [1] Francisco Cardoso “Botulinum toxin in parkinsonism: The when, how, and which for botulinum toxin injections,” IEEE Transactions on Neurology Service,2017.
  [2] Roland Dominic G. Jamoraa, Leah L. ShiongShua, Alvin Rae F. Ceninaa,Carissa Paz C. Dioquinoa, Patricia Ann D. Cantoa“Parkinsonism among retired Filipino boxers,”IEEE Transactions on Neuroscience,2017.
  [3] Srishti Grover, SaloniBhartia, Akshama, Abhilasha Yadav, Seeja K. R. “Predicting Severity of Parkinson`s disease Using Deep Learning,” IEEE Transactions on Learning paradigms,2018.
  [4] Haiping Ma, Shigen Shen , Mei Yu , Zhile Yang , MinruiFei , Huiyu Zhou “Multi-population techniques in nature inspired optimization algorithms,”Science direct,2018.
  [5] Alberto Barontinia*, Maria-Giovanna Masciottaa, Luís F. Ramosa, Paulo Amado- Mendesb, Paulo B. Lourençoa “An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings,”IEEE Transactions on Health Monitoring,2018.
  [6] Prashant Shrivastava , Anupam Shukla , PraneethVepakomma ,NeeraBhansali , KshitijVerma“A survey of nature-inspired algorithms for feature selection to identify Parkinson’s disease,” 2016.
  [7]. DamianJaniga, Robert Czarnota, Jerzy Stopa, PawełWojnarowski, Piotr Kosowski“Performance of nature inspired optimization algorithms for polymerEnhanced Oil Recovery process,”2017.
  [8 ]. GebrailBekdaşa, Sinan MelihNigdelia,, Xin-She Yang“ A novel bat algorithm based optimum tuning of mass dampers for improving the seismic safety of structures,”pp 89-98,2018.
  [9] Yasmine Aboubi, Habiba Drias, Nadjet Kamel ”BAT-CLARA: BAT- inspired algorithm for Clustering Large Applications,” pp243–248,2016.
  [10] MessaoudiImane. _ KamelNadjet.”Hybrid Bat algorithm for overlapping community detection,” pp 1454–1459,2016.
  [11] Mohamed A. Tawhid , Kevin B. Dsouza “Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm for solving feature selection problems,”2018.
  [12] Yongbo Chen, ZhenyueJia, Xiaolin Ai, Di Yang, Jianqiao Yu“ A modified two-part wolf pack search algorithm for the multiple traveling salesmen problem,”Applied Soft Computing pp 714–725,2017.
  [13] E.EmaryHossamM.Zawbaa, AboulEllaHassanien,“ Binary grey wolf optimization approaches for feature selection,”pp 371-381,2016
  [14] Wenhua Han , Xiaohui Shen , Edwin Hou , Jun Xu “Precision time synchronization control method for smart grid based on wolf colony algorithm,”IEEE Transactions on Electrical Power and Energy Systems,pp 816–822,2016.
  

  Citation
  S. JabeenBegum, B. Swaathi, "A Survey for identifying Parkinson’s disease by Binary Bat Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.17-23, 2019

• Open Access   Article

  Comparative Study on Various Techniques Used in Examination System: A Survey

  Prerna Rai, Anurodhan Pradhan, Mohit Pradhan, Aryaman Chettri, Beneta Limboo

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.2 , pp.24-28, Apr-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.2428

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  Abstract
  In the field of academics one of the most popular and important assessment processes is the process of Examination. This system of Education started long way back. Even today, after multiple changes in the traditional method of assessment, there exists little effect in the method of teaching, learning and specially assessment which is the evaluation technique. This paper presents a comparative study on various techniques and tool that exists and is being used widely. It also studies the effectiveness of one technique over another and identifies required changes one need to bring about in the current method of our Examination system. The comparative study made between various database systems based on varied matrices also highlights the usage of one technique over another in varied environment.

  Key-Words / Index Term
  Examination system, VIVA, Server, .net

  References
  [1] Vamsi Krishna Yepuri, Gopi Chand Pamu, Naveen Kodali, Pradyumna L, Examination Management Automation System.
  [2] Prof.Sejal Dmello, Ashraf Pinjari, Harishchandra Damre, Aishwarya Sankhe, Vrushali Ambre, Exam Cell Automation System.
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  [9] G.Sunitha, Dr.A.Jaya, A Knowledge Based Approach for Automatic Database Normalization.
  [10] Angitha Saju, An integrated Solution for Examination Management.
  

  Citation
  Prerna Rai, Anurodhan Pradhan, Mohit Pradhan, Aryaman Chettri, Beneta Limboo, "Comparative Study on Various Techniques Used in Examination System: A Survey," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.24-28, 2019