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i-manager's Journal on Computer Science (JCOM)

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Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
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Gesture Language Translator using Morse Code

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Volume 8 Issue 1 March - May 2020

Research Paper

Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection

I. O. Oyefolahan* , S. Idris, J. N. Ndunagu*

* Department of Information Technology, School of ICT, Federal University of Technology, Minna, Nigeria.

Department of Computer Science, School of ICT, Federal University of Technology, Minna, Nigeria.

* Department of Computer Science, National Open University of Nigeria, Abuja, Nigeria.

Oyefolahan, O. I., Idris, S., and Ndunagu, J. N.(2020). Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection. i-manager's Journal on Computer Science, 8(1), 1-13. https://doi.org/10.26634/jcom.8.1.17459

Abstract

Intrusion detection system (IDS) like firewall, access control and encryption mechanisms no longer provide the much needed security for systems and computer networks. Current IDS are developed on anomaly detection which helps in identifying attacks both known and unknown. Unfortunately, these anomaly-based IDS features high false rate. In a bid to reduce this false alarm rate, this paper proposed an intrusion detection model based on Support Vector Machine (SVM) optimized with Cat swarm optimization (CSO) algorithm. Attribute reduction has been carried out based on Information Gain (IG) and classification has been performed based on the optimized Support vector. The result obtained shows that our model performs well with the least false alarm rate and good accuracy value compared with other classification algorithms evaluated using the same datasets.

References

[1]. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2018). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152-160. https://doi.org/10.1016/j.jocs.2017.03.006

[2]. Al-Yaseen, W. L., Othman, Z. A., & Nazri, M. Z. A. (2017). Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Systems with Applications, 67, 296-303. https://doi.org/10.1016/j.eswa.2016.09.041

[3]. Amami, R., Ayed, D. B., & Ellouze, N. (2015). Practical selection of SVM supervised parameters with different feature representations for vowel recognition. International Journal of Digital Content Technology and its Applications, 7(9), 418-424.

[4]. Ashfaq, R. A. R., Wang, X. Z., Huang, J. Z., Abbas, H., & He, Y. L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484-497. https://doi.org/10.1016/j.ins.20 16.04.019

[5]. Bahrami, M., Bozorg-Haddad, O., & Chu, X. (2018). Cat swarm optimization (CSO) algorithm. In Advanced Optimization by Nature-Inspired Algorithms (pp. 9-18). Singapore: Springer. https://doi.org/10.1007/978-981-10- 5221-7_2

[6]. Daş, R., Karabade, A., & Tuna, G. (2015, May). Common network attack types and defense mechanisms. In 2015 23rd Signal Processing and Communications Applications Conference (SIU) (pp. 2658-2661). IEEE. https://doi.org/10.1109/SIU.2015.7130435

[7]. Desai, S. P., Hadule, P. R., & Dudhgaonkar, A. A. (2017). Denial of service attack defense techniques. International Research Journal of Engineering and Technology (IRJET), 4(10), 1532 – 1535.

[8]. Devare, A., Shelake, M., Vahadne, V., Kamble, P., & Tamboli, B. (2016). A system for denial-of-service attack detection based on multivariate correlation analysis. International Research Journal of Engineering and Technology (IRJET), 3(04). 1917 – 1923.

[9]. Dhanabal, L., & Shantharajah, S. P. (2015). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 446-452.

[10]. Enache, A. C., & Patriciu, V. V. (2014, May). Intrusions detection based on support vector machine optimized with swarm intelligence. In 2014 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) (pp. 153-158). IEEE. https://doi.org/10. 1109/SACI.2014.6840052

[11]. Hadi, I., & Sabah, M. (2015). Improvement cat swarm optimization for efficient motion estimation. International Journal of Hybrid Information Technology, 8(1), 279-294. https://doi.org/10.14257/ijhit.2015.8.1.25

[12]. Harshita, H. (2017). Detection and prevention of ICMP flood DDOS attack. International Journal of New Technology and Research, 3(3), 63-69.

[13]. Hassan, A. A., Sheta, A. F., & Wahbi, T. M. (2017). Intrusion detection system using Weka data mining tool. International Journal of Science and Research, 6(9), 337 – 342.

[14]. Hoque, N., Kashyap, H., & Bhattacharyya, D. K. (2017). Real-time DDoS attack detection using FPGA. Computer Communications, 110, 48-58. https://doi.org /10.1016/j.comcom.2017.05.015

[15]. Kevric, J., Jukic, S., & Subasi, A. (2017). An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Computing and Applications, 28(1), 1051-1058. https://doi.org/10. 1007/s00521-016-2418-1

[16]. Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid KPCA and SVM with GA model for intrusion detection. Applied Soft Computing, 18, 178-184. https://doi.org/10. 1016/j.asoc.2014.01.028

[17]. Kumar, A., Maurya, H. C., & Misra, R. (2013). A research paper on hybrid intrusion detection system. International Journal of Engineering and Advanced Technology (IJEAT), 2(4), 294-297.

[18]. Kumar, M., Mishra, S. K., & Sahu, S. S. (2016). Cat swarm optimization based functional link artificial neural network filter for Gaussian noise removal from computed tomography images. Applied Computational Intelligence and Soft Computing, 2016, 6. https://doi.org/10.1155/201 6/6304915

[19]. Lin, K. C., Zhang, K. Y., Huang, Y. H., Hung, J. C., & Yen, N. (2016). Feature selection based on an improved cat swarm optimization algorithm for big data classification. The Journal of Supercomputing, 72(8), 3210-3221. https://doi.org/10.1007/s11227-016-1631-0

[20]. Lin, W. C., Ke, S. W., & Tsai, C. F. (2015). CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-based Systems, 78, 13-21. https://doi.org/10.1016/j.knosys.2015 .01.009

[21]. Mahjabin, T., Xiao, Y., Sun, G., & Jiang, W. (2017). A survey of distributed denial-of-service attack, prevention, and mitigation techniques. International Journal of Distributed Sensor Networks, 13(12), 1-33. https://doi.org/ 10.1177%2F1550147717741463

[22]. Nidhi, M. V., & Prasad, K. M. (2016). Detection of anomaly based application layer DDoS attacks using machine learning approaches. i-manager's Journal on Computer Science, 4(2), 6-13.

[23]. Raiyn, J. (2014). A survey of cyber attack detection strategies. International Journal of Security and its Applications, 8(1), 247-256. https://doi.org/10.14257/ijsia.2 014.8.1.23

[24]. Saied, A., Overill, R. E., & Radzik, T. (2016). Detection of known and unknown DDoS attacks using Artificial Neural Networks. Neurocomputing, 172, 385-393. https://doi.org/ 10.1016/j.neucom.2015.04.101

[25]. Singh, K. J., & De, T. (2017). MLP-GA based algorithm to detect application layer DDoS attack. Journal of Information Security and Applications, 36, 145-153. https://doi.org/10.1016/j.jisa.2017.09.004

[26]. Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2013). A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Transactions on Parallel and Distributed Systems, 25(2), 447-456. https://doi.org/10.1109/TPDS.2013.146

[27]. Yadav, V. K., Trivedi, M. C., & Mehtre, B. M. (2016). DDA: an approach to handle DDoS (Ping flood) attack. In Proceedings of International Conference on ICT for Sustainable Development (pp. 11-23). Singapore: Springer. https://doi.org/10.1007/978-981-10-0129-1_2

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Research Paper

Face Recognition for Criminal Analysis using Haar Classifier

Chethana H. T* , Trisila Devi C. Nagavi **

* Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, Karnataka, India.

** Department of Computer Science and Engineering, JSS Science & Technology University, Mysore, Karnataka, India.

Chethana, H. T., and Nagavi, T. D. C. (2020). Face Recognition for Criminal Analysis using Haar Classifier. i-manager's Journal on Computer Science, 8(1), 14-20. https://doi.org/10.26634/jcom.8.1.17390

Abstract

Face recognition plays a significant role in forensics. At present many face recognition methods has been implemented which extracts the face image of a suspect from moving or still image, then process the extracted face image by comparing it with the faces of criminals stored in a database and finally matching is done. Forensic world is challenging because every day there is increase in the number of crimes. In a crime scene, only partial information about the suspects is available and sometimes facial image of the suspect is not available. In this paper an attempt is made to speed up the process of criminal identification using Haar Classifier. Our proposed work focuses on preprocessing, feature extraction and classification using Haar Classifier. The proposed system can successfully detect and recognize most of the faces which helps to identify the criminals quickly.

References

[1]. Abuzneid, M. A., & Mahmood, A. (2018). Enhanced human face recognition using LBPH descriptor, multi-KNN, and back-propagation neural network. IEEE Access, 6, 20641-20651. https://doi.org/10.1109/ACCESS.2018.28 25310

[2]. Ali, T., Veldhuis, R. N. J., & Spreeuwers, L. J. (2010). Forensic Face Recognition: A Survey (CTIT Technical Report Series; No. TR-CTIT-10-40). Centre for Telematics and Information Technology (CTIT).

[3]. Ayyavoo, T., & Jayasudha, J. S. (2013, December). Face recognition using enhanced energy of Discrete Wavelet Transform. In 2013 International Conference on Control Communication and Computing (ICCC) (pp. 415- 419). IEEE. https://doi.org/10.1109/ICCC.2013.6731690

[4]. Bakshi, N., & Prabhu, V. (2017, December). Face recognition system for access control using principal component analysis. In 2017 International Conference on Intelligent Communication and Computational Techniques(ICCT) (pp.145-150). IEEE. https://doi.org/10.1109/INTEL CCT.2017.8324035

[5]. Bhat, F. A., & Wani, M. A. (2014, December). Performance comparison of major classical face recognition techniques. In 2014 13th International Conference on Machine Learning and Applications (pp. 521-528). IEEE. https://doi.org/10.1109/ICMLA.2014.91

[6]. Hassaballah, M., & Aly, S. (2015). Face recognition: challenges, achievements and future directions. IET Computer Vision, 9(4), 614-626. https://doi.org/10.1049/ iet-cvi.2014.0084

[7] Fan, C. L., & Chen, X. T. (2012, May). Research of face recognition based on wavelet transform and principal component analysis. In 2012 8th International Conference on Natural Computation (pp. 575-578) IEEE. https://doi.o rg/10.1109/ICNC.2012.6234703

[8]. Deng, W., Hu, J., & Guo, J. (2017). Face recognition via collaborative representation: Its discriminant nature and superposed representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2513-2521. https://doi.org/10.1109/TPAMI.2017.2757923

[9]. Hickman, D., Goode, A., & Gandolfi, P. (2005, June). Forensic image comparison techniques. In the IEE International Symposium on Imaging for Crime Detection and Prevention ICDP 2005 (pp. 99-103). https://doi.org/ 10. 1049/ic:20050078

[10]. Jain, A. K., Klare, B., & Park, U. (2011, March). Face recognition: Some challenges in forensics. In Face and Gesture 2011 (pp. 726-733). IEEE. https://doi.org/10.1109/F G.2011.5771338

[11]. Kumar, M.P., Sravan, R.K., & Aishwarya, K. (2015). Artificial neural networks for face recognition using PCA and BPNN. In TENCON 2015-2015 IEEE Region 10 Conference (pp. 1-6). IEEE. https://doi.org/10.1109/TENCON.2015.737 3165

[12]. Lohiya, R., & Shah, P. (2015). Face recognition techniques: A survey for forensic applications. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(4).

[13]. Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., ... & Chen, J. C. (2018). Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. In Proceedings of the National Academy of Sciences, 115(24), 6171-6176. https ://doi.org/ 10.1073/pnas.1721355115

[14]. Salici, A., & Ciampini, C. (2017, September). Automatic face recognition and identification tools in the forensic science domain. In International Tyrrhenian Workshop on Digital Communication (pp. 8-17). Cham: Springer. https://doi.org/10.1007/978-3-319-67639-5_2

[15]. Serajeh, R., Mohammadzadeh, Z., and Ghavitandarjazi, H. (2017). Face recognition in uncontrolled conditions. In IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI) (pp. 0902-0906). https://doi.org/10.1109/K BEI.2017.83249 26

[16]. Shnain, N. A., Lu, S. F., & Hussain, Z. M. (2017, December). HOS image similarity measure for human face recognition. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC) (pp. 1621-1625). IEEE. https://doi.org/10.1109/CompComm.2017.8322814

[17]. Spaun, N. A. (2011). Face recognition in forensic science. In Handbook of Face Recognition (pp. 655-670). London: Springer. https://doi.org/10.1007/978-0-85729-93 2-1_26

[18]. Turan, C. (2017, November). Robust face recognition via sparse reconstruction vector. In 2017 13th International Conference on Electronics, Computer and Computation (ICECCO) (pp. 1-4). IEEE. https://doi.org/10.1109/ICECC O.2017.8333342

[19]. Turk, M. A., & Pentland, A. P. (1991, January). Face recognition using eigenfaces. In Proceedings of1991 IEEE computer society conference on computer vision and pattern recognition (pp. 586-587). IEEE Computer Society.

[20]. Tvakolian, N., Nazemi, A., & Azimifar, Z. (2017, October). Authentication based on face recognition under uncontrolled conditions. In 2017 Artificial Intelligence and Signal Processing Conference (AISP) (pp. 113-117). IEEE. https://doi.org/10.1109/AISP.2017.8324120

[21]. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2008). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210-227. https://doi.org/10.1109/TPA MI.2008.79

[22]. Wu, M., Li, S., & Hu, J. (2017, December). Extended class-wise sparse representation for face recognition. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC) (pp. 1611-1615). IEEE. https://doi.org/10.1109/CompComm.2017.8322812

[23]. Xiao-Qiang, Y., & Xiao-Bing, Z. (2017, December). Face recognition research based on the fusion of layered LBP feature. In 2017 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) (pp. 75-78). IEEE. https://doi.org/10.1109/ICIICII.2017.13

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Research Paper

Generalized Shear Force and Bending Diagrams Via Singularity Functions by using Matlab and GNU Octave

A. Vinod Kumar* , Sivananda Reddy Y. **

*-** Department of Civil Engineering, G. Pullaiah College of Engineering and Technology, Pudur, Andhra Pradesh, India.

Kumar, A. V., and Reddy, Y. S. (2020). Generalized Shear Force and Bending Diagrams Via Singularity Functions by using Matlab and GNU Octave. i-manager's Journal on Computer Science, 8(1), 21-27. https://doi.org/10.26634/jcom.8.1.17693

Abstract

In 21^st century, computers have been playing vital role in engineering and science. Many engineers and scientists utilize computers to solve the complex engineering problems through programming languages, such as C, C++, Java, Fortran, Python, Scilab, MATLAB, etc. Even though these languages are easy to implement in civil engineering discipline such as finite element analysis, structural dynamics, complex soil mechanics problems, design of reinforced concrete structures, steel structures etc., the students are not comfortable with these languages. Generally in Civil Engineering, analysis, interpretation of analysis and design are very important. To solve these problems best suitable languages are MATLAB, Scilab, Python, GNU OCTAVE and FORTRAN. Therefore it is necessary to introduce the programming languages in civil engineering discipline to aid students to solve complex problems by their tutors. In this paper by using singularity functions, the generalized shear force and bending moment diagrams of the beams with different loading conditions via medium MATLAB and GNU OCTAVE have been drawn.

References

[1]. Bhogade, V. M., & Bolli, V. B. (2015). Developing Graphical User Interface for analysis of beam by using MATLAB software. International Journal of Research in Engineering and Technology, 04(04), 676-679.

[2]. Honson, J. S. (2011). GNU Octave beginners guide. Raigarh Publishing House.

[3]. Hossain, S. T., & Al-Faruk, A. (2017). Analysis of simple and cantilever beams using Matlab graphical user interfaces. In Proceedings of the International Conference on Mechanical Engineering and Renewable Energy 2017 (ICMER E2017).

[4]. Linge, S., & Langtangen, H. P. (2016). Programming for computations-MATLAB/Octave - A gentle introduction to numerical simulations with MATLAB/Octave. Springer

[5]. Matworks. (n.d.). MATLAB (Computer Software). Retrieved from http://www.mathworks.com

[6]. Sobaszek, Ł. (2013). Computer programming as a tool for the engineering problems analysis. Applied Computer Science, 9(2), 51-71.

[7]. Suh, Y. S. (2014). Development of educational software for beam loading analysis using pen-based user interfaces. Journal of Computational Design and Engineering, 1(1), 67-77. https://doi.org/10.7315/JCDE.20 14.007

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Research Paper

Recommender Systems Role in Deep Learning - A Survey

D. Raghava* , S. Anusha **

* Sri Venkateswara Institute of Technology, Anantapur, Andhra Pradesh, India.

** Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India.

Raghava, D., and Anusha, S. (2020). Recommender Systems Role in Deep Learning - A Survey. i-manager's Journal on Computer Science, 8(1), 28-32. https://doi.org/10.26634/jcom.8.1.17490

Abstract

Recommender systems have gained its importance because of the availability of enormous online information with the increasing use of internet, online marketing and media outlets. Currently, deep learning has gained appreciable attention in many researches such as natural language processing, artificial intelligence due to high performance and great learning feature representations. The effect of deep learning is also persistent, lately showing its usefulness in retrieval of information and recommenders work which eventually have resulted in the growth of deep learning approaches in recommender system. Hybrid approaches for designing recommender models is gaining popularity in recent years. This paper aims in giving a comprehensive insight of recent research works on recommender systems.

References

[1]. Aciar, S., Zhang, D., Simoff, S., & Debenham, J. (2007). Informed recommender: Basing recommendations on consumer product reviews. IEEE Intelligent Systems, 22(3), 39-47. https://doi.org/10.1109/MIS.2007.55

[2]. Chen, G., & Chen, L. (2014, July). Recommendation based on contextual opinions. In International Conference on User Modeling, Adaptation, and Personalization (pp. 61-73). Cham: Springer. https://doi.org/10.1007/978-3- 319-08786-3_6

[3]. Chen, G., & Chen, L. (2015). Augmenting service recommender systems by incorporating contextual opinions from user reviews. User Modeling and User- Adapted Interaction, 25(3), 295-329. https://doi.org/10. 1007/s11257-015-9157-3

[4]. Coelho, F., Devezas, J., & Ribeiro, C. (2013, May). Large-scale crossmedia retrieval for playlist generation and song discovery. In Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (pp. 61-64).

[5]. Da'u, A., & Salim, N. (2019). Sentiment-aware deep recommender system with neural attention networks. IEEE Access, 7, 45472-45484. https://doi.org/10.1109/ACCE SS.2019.2907729

[6]. Desrosiers, C., & Karypis, G. (2011). A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook (pp. 107- 144). Boston, MA: Springer. https://doi.org/10.1007/978-0- 387-85820-3_4

[7]. Khan, Z. A., Zubair, S., Imran, K., Ahmad, R., Butt, S. A., & Chaudhary, N. I. (2019). A new users rating-trend based collaborative denoising auto-encoder for top-n recommender systems. IEEE Access, 7, 141287-141310. https://doi.org/ 10.1109/ACCESS.2019.2940603

[8]. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26. https://do i.org/10.1016/j.neucom.2016.12.038

[9]. Musto, C., de Gemmis, M., Semeraro, G., & Lops, P. (2017, August). A multi-criteria recommender system exploiting aspect-based sentiment analysis of users' reviews. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 321-325). https://doi.org /10.1145/3109859.3109905

[10]. Ribeiro, D., Machado, J., Ribeiro, J., Vasconcelos, M. J. M., Vieira, E. F., & de Barros, A. C. (2017, April). SousChef: Mobile meal recommender system for older adults. In 3rd International Conference on Information and Communication Technologies for Ageing Well and e- Health (ICT4AWE 2017) (pp. 36-45).

[11]. Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). Ecommerce recommendation applications. Data Mining and Knowledge Discovery, 5(1-2), 115-153. https://doi.org /10.1023/A:1009804230409

[12]. Shoja, B. M., & Tabrizi, N. (2019). Customer reviews analysis with deep neural networks for E-commerce recommender systems. IEEE Access, 7, 119121-119130. https://doi.org/ 10.1109/ACCESS.2019.2937518

[13]. Susan, M. M., & David, S. (2010). What makes a helpful online review? A study of customer reviews on amazon. com. MIS Quarterly, 34(1), 185-200.

[14]. Tay, Y., Luu, A. T., & Hui, S. C. (2018, July). Multi-pointer co-attention networks for recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2309-2318). https://doi.org/10.1145/3219819.3220086

[15]. Toledo, R. Y., Alzahrani, A. A., & Martínez, L. (2019). A food recommender system considering nutritional information and user preferences. IEEE Access, 7, 96695- 96711. https://doi.org/10.1109/ACCESS.2019.2929413

[16]. Tran, T. N. T., Atas, M., Felfernig, A., & Stettinger, M. (2018). An overview of recommender systems in the healthy food domain. Journal of Intelligent Information Systems, 50(3), 501-526. https://doi.org/10.1007/s10844- 017-0469-0

[17]. Wang, Q., Peng, B., Shi, X., Shang, T., & Shang, M. (2019). DCCR: Deep collaborative conjunctive recommender for rating prediction. IEEE Access, 7, 60186- 60198. https://doi.org/10.1109/ACCESS.2019.2915531

[18]. Wang, Y., Wang, M., & Xu, W. (2018). A sentimentenhanced hybrid recommender system for movie recommendation: A big data analytics framework. Wireless Communications and Mobile Computing, 1-9. https://doi.org/10.1155/2018/8263704

[19]. Zheng, L., Noroozi, V., & Yu, P. S. (2017, February). Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 425-434).

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Research Paper

Business Model Framework and Cost Effectiveness of IoT Solutions

N. Prasanthi Rashmi * , Ch. Radhakumari , N. Sai Pranav *

- Department of Management and Commerce, Sri Sathya Sai Institute of Higher Learning, Anantapur, Andhra Pradesh, India.

Aditya Birla Sun Life Mutual Fund, Hyderabad, Telangana, India.

Rashmi, N. P., Radhakumari , C., and Pranav, N. S. (2020). Business Model Framework and Cost Effectiveness of IoT Solutions. i-manager's Journal on Computer Science, 8(1), 33-41. https://doi.org/10.26634/jcom.8.1.17166

Abstract

IoT is the ever growing concept of connecting physical objects through internet. Internet of Things has the capability to connect everything, everyone and everywhere. It is important to accept the reality that the resources are limited and an optimum utilization of these resource have to be done for sustainability. The wastage is usually highest in the fields of water and electricity consumption. The biggest challenge for businesses is that the processes today are competitive and have to meet the customer demands without slowing down. IoT solutions such as smart irrigation system, automatic street light system and voice controlled home automation are one of the key solutions to the raising challenges. In this study prototypes of the above mentioned solutions have been built and tested to understand the potential of IoT. India predicts about 5 billion IoT connections by 2022, and an economy of $1 trillion by 2025. And the reality is that, it's already 2019 and IoT's growth is slower than predicted because of some of the barriers to implementation. Some of the barrier to the growth of IoT is unawareness of consumers and cost concerns. So it is necessary for businesses to adapt business models which are efficient and suitable for IoT businesses to reach the target segment with a proper channel and a competitive value proposition. In this study a business model framework has been suggested for the prototypes built. As cost is another barrier to the IoT adoption, the cost effectiveness of IoT solutions such as smart lighting system and smart irrigation system has been compared with respect to the traditional solutions.

References

[1]. Chowdary, T. A., Chakravarthy, D., Rupesh, R. S., & Ashish, T. S. (2019). Effective implementation of low-cost smart irrigation system. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6).

[2]. Husin, R., Al Junid, S. A. M., Abd Majid, Z., Othman, Z., Shariff, K. K. M., Hashim, H., & Saari, M. F. (2012). Automatic street lighting system for energy efficiency based on low cost microcontroller. International Journal of Simulation: Systems, Science and Technology, 13(1), 29-34.

[3]. Joyce, A., & Paquin, R. L. (2016). The triple layered business model canvas: A tool to design more sustainable business models. Journal of Cleaner Production, 135, 1474-1486. https://doi.org/10.1016/j.jclepro.2016.06.067

[4]. Ju, J., Kim, M. S., & Ahn, J. H. (2016). Prototyping business models for IoT service. Procedia Computer Science, 91, 882-890. https://doi.org/10.1016/j.procs.2016.07.106

[5]. Md, A. A., Harshasri, K., & Shanmukharao, K. (2017). Cost effective voice controlled home automation using IoT. International Journal of Engineering Research in Computer Science and Engineering, 4(3), 63-67.

[6]. Nagy, S., Mansour, H., & Presser, M. (2018, February). Case study of IoT as a driver for business model innovation in the wind industry. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) (pp. 74-79). IEEE. https://doi.org/10.1109/ WF-IoT.2018.8355222

[7]. Radadiya, B. L., Thakkar, R. G., Thumar, V. M., & Chaudhari, B. D. (2016). Cloud computing and agriculture. International Journal of Agriculture Sciences, 8(22), 1429-1431.

[8]. Sahu, C. K., & Behera, P. (2015, February). A low cost smart irrigation control system. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1146-1152). IEEE. https://doi.org/10.1109/ECS.2015.7124763

[9]. Talon, C. (2016). IoT bridging the gap for intelligent small and medium buildings. Intel- Sponsored White Paper, Navigant Consulting, Inc., USA.

[10]. Thomas A. (2017, December 28). Smart Farming: This startup has a new irrigation method - one that uses just an app. Economic Times. Retrieved from https://economic times.indiatimes.com/small-biz/startups/features/smartfarming- this-startup-has-a-new-irrigation-method-onethat- uses-just-an-app-avanijal-agritech/articleshow/6224 8946.cms?from=mdr

[11]. Vermesan, O., & Friess, P. (2016). Digitising the industry - Internet of things connecting the physical, digital and virtual worlds. Denmark: River Publishers

[12]. Wnuk, K., & Murari, B. T. (2016, June). The impact of Internet of things on software business models. In International Conference of Software Business (pp. 94- 108). Cham: Springer. https://doi.org/10.1007/978-3-319- 40515-5_7

i-manager's Journal on Computer Science (JCOM)

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Volume 8 Issue 1 March - May 2020

Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection

I. O. Oyefolahan* , S. Idris**, J. N. Ndunagu***

* Department of Information Technology, School of ICT, Federal University of Technology, Minna, Nigeria. ** Department of Computer Science, School of ICT, Federal University of Technology, Minna, Nigeria. *** Department of Computer Science, National Open University of Nigeria, Abuja, Nigeria.

Oyefolahan, O. I., Idris, S., and Ndunagu, J. N.(2020). Hybridization of Support Vector Machine with Cat Swarm Algorithm for Intrusion Detection. i-manager's Journal on Computer Science, 8(1), 1-13. https://doi.org/10.26634/jcom.8.1.17459

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Face Recognition for Criminal Analysis using Haar Classifier

Chethana H. T* , Trisila Devi C. Nagavi **

* Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, Karnataka, India. ** Department of Computer Science and Engineering, JSS Science & Technology University, Mysore, Karnataka, India.

Chethana, H. T., and Nagavi, T. D. C. (2020). Face Recognition for Criminal Analysis using Haar Classifier. i-manager's Journal on Computer Science, 8(1), 14-20. https://doi.org/10.26634/jcom.8.1.17390

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Generalized Shear Force and Bending Diagrams Via Singularity Functions by using Matlab and GNU Octave

A. Vinod Kumar* , Sivananda Reddy Y. **

*-** Department of Civil Engineering, G. Pullaiah College of Engineering and Technology, Pudur, Andhra Pradesh, India.

Kumar, A. V., and Reddy, Y. S. (2020). Generalized Shear Force and Bending Diagrams Via Singularity Functions by using Matlab and GNU Octave. i-manager's Journal on Computer Science, 8(1), 21-27. https://doi.org/10.26634/jcom.8.1.17693

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Recommender Systems Role in Deep Learning - A Survey

D. Raghava* , S. Anusha **

* Sri Venkateswara Institute of Technology, Anantapur, Andhra Pradesh, India. ** Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India.

Raghava, D., and Anusha, S. (2020). Recommender Systems Role in Deep Learning - A Survey. i-manager's Journal on Computer Science, 8(1), 28-32. https://doi.org/10.26634/jcom.8.1.17490

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Business Model Framework and Cost Effectiveness of IoT Solutions

N. Prasanthi Rashmi * , Ch. Radhakumari **, N. Sai Pranav ***

*-** Department of Management and Commerce, Sri Sathya Sai Institute of Higher Learning, Anantapur, Andhra Pradesh, India. *** Aditya Birla Sun Life Mutual Fund, Hyderabad, Telangana, India.

Rashmi, N. P., Radhakumari , C., and Pranav, N. S. (2020). Business Model Framework and Cost Effectiveness of IoT Solutions. i-manager's Journal on Computer Science, 8(1), 33-41. https://doi.org/10.26634/jcom.8.1.17166

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I. O. Oyefolahan* , S. Idris, J. N. Ndunagu*

* Department of Information Technology, School of ICT, Federal University of Technology, Minna, Nigeria.

Department of Computer Science, School of ICT, Federal University of Technology, Minna, Nigeria.

* Department of Computer Science, National Open University of Nigeria, Abuja, Nigeria.

* Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, Karnataka, India.

** Department of Computer Science and Engineering, JSS Science & Technology University, Mysore, Karnataka, India.

* Sri Venkateswara Institute of Technology, Anantapur, Andhra Pradesh, India.

** Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India.

N. Prasanthi Rashmi * , Ch. Radhakumari , N. Sai Pranav *

- Department of Management and Commerce, Sri Sathya Sai Institute of Higher Learning, Anantapur, Andhra Pradesh, India.

Aditya Birla Sun Life Mutual Fund, Hyderabad, Telangana, India.