Volume-11 , Issue-1 , Feb 2023, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  A Convolutional Neural Network Approach for Detecting the Distracted Drivers

  Ajay Kumar Sahu, Pankaj Kumar Gupta, Amit Kumar Singh, Kushal Singh, Ujjwal Kumar

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.1-6, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  In recent years, there has been an increase in traffic accidents caused by distracted driving. According to a 2022 report by the Federal Ministry of Road Transport and Highways, in India have 18 deaths in one hour in two road accidents. Therefore, measures to reduce traffic fatalities are essential. Tragically, the number of lives lost on the roads this year has reached a peak not seen since 2014, with 155,622 people losing their lives in preventable accidents. This is a stark reminder of the importance of safe driving practices and the need to prioritize road safety in our communities. The main cause of these accidents is driver error. This document proposes solutions for detecting driver distraction and avoiding potential accidents. In this document, we present the use of various convolution neural network (CNN) models for classifying distracted drivers. These models include small-scale CNN, VGG16, VGG19, and the Kaggle State Farm Distracted Driver Detection Challenge model. We are using the Keras library with Tensor Flow as our deep learning platform. Our highest performing model achieved a categorical cross-entropy loss of 0.899 on the validation set. These models may serve as a starting point for further research on distracted driver detection.

  Key-Words / Index Term
  Classification, CNN, Keras, Transfer learning, VGG

  References
  [1] J. M. D. Murthy and S. V. S. S. S. S. S. R. Kumar, "Distracted driver detection using CNN,"5th International Conference on Computing, Communication and Security (ICCCS), Rourkela, India, pp. 1-6, 2020.
  [2] Shao, W., Li, W., Li, D., Zhang, H., Liu, G., & Huang, HA, “Novel Method of Distracted Driver Detection Based on CNN and SVM”. IEEE Access, Vol.7, pp. 39098-39105, 2019.
  [3] Mittal, P., Bansal, M., Singh, R., & Kumar, S., “Distracted Driver Detection using CNN and SVM”, International Journal of Intelligent Systems and Applications, Vol. 12(2), pp. 19-28, 2020.
  [4] Sharma, S. K., & Gupta, T. A., “Novel Approach for Cloud Environment”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 4(12), pp. 1-5, 2014.
  [5] S. S. Sarode, S. S. Patil, and M. K. Jawandhiya, "Driver Distraction Detection using Deep Learning: A Review", International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST), Kolhapur, India, pp. 1-4, 2019.
  [6] M. S. R. Kumar, B. K. Tripathy, and P. Patra, "A CNN-Based Distracted Driver Detection System," 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, pp. 441-446, 2020.
  [7] S. Bhattacharya, N. Dutta, and M. Bandyopadhyay, "Driver Drowsiness and Distraction Detection using Convolutional Neural Network," 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp. 1-5, 2020.
  [8] A.K. Barik, A. K. Mohapatra, and S. Panda, "A Robust CNN-Based Framework for Driver Distraction Detection," 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 217-221, 2019.
  [9] S.A. E. Mohamed, M. A. Aboul Ella, and M. A. M. Abd El Aziz, "Real-Time Driver Distraction Detection System using Convolutional Neural Network, "International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Cairo, Egypt, pp. 1-6, 2020. .
  [10] N. Bansal and S. Agrawal, "A Novel Approach for Real Time Driver Distraction Detection Using Convolutional Neural Network", International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT), Chennai, India, pp. 2277-2281, 2019.
  [11] A. K. Singh, A. Kumari, and N. K. Garg, "Real-Time Detection of Driver Drowsiness and Distraction using CNN, "IEEE 2nd International Conference on Computing, Communication, and Security (ICCCS), Piscataway, NJ, USA, pp. 1-5, 2020.
  [12] Singh, T., Sahu, A. K., Dubey, S., Sharma, M. P., Verma, S., & Kumar, C., “Treatment of Thyroid Disease Through Machine Learning Predictive Model”, International Journal of Health Sciences, vol. 6(S8), pp 3176–3188, 2022.
  [13] K. Suresh Kumar, Vinay Kumar Nassa, Dipesh Uike, Ashima kalra, Ajay Kumar Sahu, Vijay Anant Athavale, V. Saravanan, "A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis", Journal of Food Quality, vol. 2022, Article ID 1689913, 6 pages, 2022.
  [14] Sahu, A. K., & Kumar, A., “A Novel Verification Protocol to Restrict Unconstitutional Access of Information From Smart Card”, International Journal of Digital Crime and Forensics (IJDCF); Vol. 13(1): pp. 65-78, 2021.
  [15] Sahu, A.K., & Kumar, A., “SPAS: An authentication scheme to prevent unauthorized access of information from smart card”, Pertanika Journal of Science and Technology, Vol. 27(1): pp. 175-192, 2019.
  [16] Sahu A. K., & Kumar, A., “An Improved Remote User Password Authentication Scheme Using Smart Card with Session Key Agreement”, International Journal of Control Theory and Applications, Vol. 9(17), pp. 8445-8454, 2016.
  [17] Sahu A. K., & Kumar, A., “A More Efficient and Secure Untraceable Remote User Password Authentication Scheme Using Smart Card with Session Key Agreement”, In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Computer Communication, Networking and Internet Security; Lecture Notes in Networks and Systems; Vol 5. Springer, Singapore.
  [18] F. Torres-Cruz, A. K. Sahu et al., "Comparative Analysis of High-Performance Computing Systems and Machine Learning in Enhancing Cyber Infrastructure: A Multiple Regression Analysis Approach," 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), pp. 69-73, IEEE Xplore, 23-25, 2022.
  [19] Anand Dohare, Megha, Ravi Pathak, “Human behaviour analysis and face detection using machine learning”, accepted in 4th IEEE International Conference on Advances in Computing, Communication Control and Networking –ICACCCN (ICAC3N-22).
  [20] Anand Dohare, Tulika, “A CNN and LSTM-based Data Prediction Model for WSN” paper accepted in 3rd IEEE International Conference on Advances in Computing, Communication Control and Networking –ICACCCN (ICAC3N-21).
  [21] Anand Dohare, TulikaB.Mallik arjuna, “A Data Prediction in Wireless Sensor Networks using Deep Learning-based RSA Algorithm”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9(9), 2020.
  [22] Anand Dohare, Tulika, B.Mallikarjuna,”, Avoid delay of packet and improve behaviour of Switch/Router under Self similar Type variable input traffic”, International Journal of Computer Trends and Technology (IJCTT) – Volume 67(6), 2019.
  [23] Anand Dohare, Tulika, “Development of Hybrid model for data prediction and improving network performance in wireless sensor networks”, Turkish Journal of Computer and Mathematics Education Vol.12(12), pp. 1257-1264, 2021.
  [24] Anand Dohare, Tulika, B.Mallikarjuna,” Data Collection in Wireless Sensor Networks using Prediction Method”, JARDCS, E-ISSN: 2278-3091, 2021.
  [25] Anand Dohare, B.Mallikarjuna, “Feed Forward Approach for Data Processing in IoT over Cloud” International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8(5), 2019.
  [26] Anoushka, Shivani Dubey, Vikas Singhal, "Student Grade Prediction by using Machine Learning Methods and Data Analytics Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, (6), pp. 22-29, 2022.
  [27] Md. Ibrahim Abdullah, Md. Atiqur Rahman, Mohammad Alamgir Hossain, Md. Shohidul Islam, Md. Shamim Hossain, "To Identify the Untrustworthy Leader of a Hierarchical Wireless Sensor Network Using Received Signal Strength," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, (6), pp.30-39, 2022.

  Citation
  Ajay Kumar Sahu, Pankaj Kumar Gupta, Amit Kumar Singh, Kushal Singh, Ujjwal Kumar, "A Convolutional Neural Network Approach for Detecting the Distracted Drivers," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.1-6, 2023

• Open Access   Article

  RPL Protocol Load balancing Schemes in Low-Power and Lossy Networks

  Poorana Senthilkumar S., Subramani B.

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.7-13, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The Routing Protocol for Low-power and Lossy Networks (RPL) is the most crucial in the Internet of Things (IoT) for the routing process. The RPL protocol is proposed by the Internet-Engineering Task-Force (IETF)-Routing over low-power and lossy-networks (ROLL) working group for IoT environments routing service. RPL has been improved for a wide range of IoT application scenarios and environments. One of the primary reasons for RPL improvement is load balancing. In Low-Power and Lossy Networks (LLNs), the load balancing can even degrade network performance and node lifetime. In this article, contains the RPL enhanced schemes offered for the load balancing, for IoT/LLNs, and explores future research options.

  Key-Words / Index Term
  IoT, LLNs, RPL, Routing, node, Load balancing

  References
  [1] J. Santosa and G. Kurniawan, “IOT BASED LOW-COST PLATFORM FOR SMART ORNAMENTAL PLANT MONITORING SYSTEM,” J. Theor. Appl. Inf. Technol., vol. 99, no. 24, pp. 6102–6117, 2021.
  [2] P. Sethi and S. R. Sarangi, “Internet of Things: Architectures, Protocols, and Applications,” J. Electr. Comput. Eng., vol. 2017, pp. 1–25, 2017, doi: 10.1155/2017/9324035.
  [3] N. H. Motlagh, M. Mohammadrezaei, J. Hunt, and B. Zakeri, “Internet of things (IoT) and the energy sector,” Energies, vol. 13, no. 2. MDPI AG, 2020. doi: 10.3390/en13020494.
  [4] S. Dhingra, R. B. Madda, A. H. Gandomi, R. Patan, and M. Daneshmand, “Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair),” IEEE Internet Things J., vol. 6, no. 3, pp. 5577–5584, Jun. 2019, doi: 10.1109/JIOT.2019.2903821.
  [5] A. Osorio, M. Calle, J. D. Soto, and J. E. Candelo-Becerra, “Routing in LoRaWAN: Overview and Challenges,” IEEE Commun. Mag., vol. 58, no. 6, pp. 72–76, Jun. 2020, doi: 10.1109/MCOM.001.2000053.
  [6] J. G. Ko, A. Terzis, S. Dawson-Haggerty, D. Culler, J. Hui, and P. Levis, “Connecting low-power and lossy networks to the internet,” IEEE Commun. Mag., vol. 49, no. 4, pp. 96–101, 2011, doi: 10.1109/MCOM.2011.5741163.
  [7] A. Verma and V. Ranga, “Security of RPL Based 6LoWPAN Networks in the Internet of Things: A Review,” IEEE Sens. J., vol. 20, no. 11, pp. 5666–5690, Jun. 2020, doi: 10.1109/JSEN.2020.2973677.
  [8] E. K. Pister, E. C. S. S. D. S. P. Thubert, and T. Phinney, “Industrial Routing Requirements in Low-Power and Lossy Networks,” IETF Network Working Networks, Request for Comments: 5673, 2009. https://www.rfc-editor.org/rfc/rfc5673 (accessed Jan. 02, 2023).
  [9] J. Buron, A. Brandt, and G. Porcu, “Home Automation Routing Requirements in Low-Power and Lossy Networks,” Internet Engineering Task Force (IETF) Request for Comments: 5826, 2010. https://www.rfc-editor.org/rfc/rfc5826 (accessed Jan. 02, 2023).
  [10] D. B. M. Dohler, T. Watteyne, T. Winter, “Routing Requirements for Urban Low-Power and Lossy Networks,” Internet Engineering Task Force (IETF), 2009. https://www.rfc-editor.org/rfc/rfc5548 (accessed Jan. 02, 2023).
  [11] O. Iova, P. Picco, T. Istomin, and C. Kiraly, “RPL: The Routing Standard for the Internet of Things... Or Is It?,” IEEE Commun. Mag., vol. 54, no. 12, pp. 16–22, Dec. 2016, doi: 10.1109/MCOM.2016.1600397CM.
  [12] H.-S. Kim, J. Ko, D. E. Culler, and J. Paek, “Challenging the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL): A Survey,” IEEE Commun. Surv. Tutorials, vol. 19, no. 4, pp. 2502–2525, 2017, doi: 10.1109/COMST.2017.2751617.
  [13] H. Bello-Salau, A. M. Aibinu, Z. Wang, A. J. Onumanyi, E. N. Onwuka, and J. J. Dukiya, “An optimized routing algorithm for vehicle ad-hoc networks,” Eng. Sci. Technol. an Int. J., no. xxxx, 2019, doi: 10.1016/j.jestch.2019.01.016.
  [14] P. O. Kamgueu, E. Nataf, and T. D. Ndie, “Survey on RPL enhancements: A focus on topology, security and mobility,” Comput. Commun., vol. 120, no. July 2017, pp. 10–21, May 2018, doi: 10.1016/j.comcom.2018.02.011.
  [15] Poorana Senthil Kumar S Subramani B, “Evolutions of Low power and Lossy Networks Protocols in the Internet of Things: Review,” ITEE J. Inf. Technol. Electr. Eng., vol. 11, no. 3, pp. 1–12, 2022, [Online]. Available: http://www.iteejournal.org/Archive_june_2022.php
  [16] A. P. Anagha Jadhav, Sudarshan Dutonde, Archana Todarmal, “IoT Based Biometric Attendance System for Smart Classroom,” Int. J. Sci. Res. Comput. Sci. Eng., vol. 8, no. 4, pp. 58–57, 2020, [Online]. Available: https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=2007
  [17] H. F. GATETE Marcel, “Routing In Mobile Ad-Hoc Networks Coupled With IoT Technologies- A Review,” Int. J. Sci. Res. Comput. Sci. Eng., vol. 10, no. 6, pp. 49–60, 2022, [Online]. Available: https://www.isroset.org/pdf_paper_view.php?paper_id=3000&6-ISROSET-IJSRCSE-08170-63.pdf
  [18] N. Nasser, L. Karim, A. Ali, M. Anan, and N. Khelifi, “Routing in the Internet of Things,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Dec. 2017, pp. 1–6. doi: 10.1109/GLOCOM.2017.8253955.
  [19] M. M. Martín-Lopo, J. Boal, and Á. Sánchez-Miralles, “A literature review of IoT energy platforms aimed at end users,” Comput. Networks, vol. 171, 2020, doi: 10.1016/j.comnet.2020.107101.
  [20] S. A. Fatemifar and R. Javidan, “A new load balancing clustering method for the RPL protocol,” Telecommun. Syst., vol. 77, no. 2, pp. 297–315, Jun. 2021, doi: 10.1007/s11235-021-00760-7.
  [21] B. Djamaa, M. R. Senouci, H. Bessas, B. Dahmane, and A. Mellouk, “Efficient and Stateless P2P Routing Mechanisms for the Internet of Things,” IEEE Internet Things J., 2021, doi: 10.1109/JIOT.2021.3053339.
  [22] T. Winter et al., “RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks,” Internet Engineering Task Force (IETF). Internet Engineering Task Force (IETF), pp. 1–157, 2012. [Online]. Available: https://tools.ietf.org/pdf/rfc6550.pdf
  [23] E. C. S. P. Thubert, “Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL),” Internet Eng. Task Force, pp. 1–14, [Online]. Available: http://www.rfc-editor.org/info/rfc6552
  [24] P. L. Gnawali, O, “The Minimum Rank with Hysteresis Objective Function,” Internet Eng. Task Force, vol. 1, pp. 1–13, 2012, [Online]. Available: https://www.rfc-editor.org/rfc/pdfrfc/rfc6719.txt.pdf
  [25] R. Masadeh, B. AlSaaidah, E. Masadeh, M. R. Al-Hadidi, and O. Almomani, “Elastic Hop Count Trickle Timer Algorithm in Internet of Things,” Sustain., vol. 14, no. 19, pp. 1–19, 2022, doi: 10.3390/su141912417.
  [26] P. Levis, T. H. Clausen, J. W. Hui, O. Gnawali, and J. Ko, “The Trickle Algorithm,” Internet Engineering Task Force (IETF) Request for Comments: 6206, 2011. https://www.rfc-editor.org/info/rfc6206 (accessed Jan. 02, 2023).
  [27] A. S. Kassab, K. G. Seddik, A. Elezabi, and A. Soltan, “Realistic Wireless Smart-Meter Network Optimization Using Composite RPL Metric,” 8th IEEE Int. Conf. Smart Grid, pp. 109–114, 2020.
  [28] Z. Magubane, P. Tarwireyi, A. Abu-Mafouz, and M. Adigun, “Extended Context-Aware and Load Balancing Routing Protocol for Low power and Lossy Networks in IoT networks (ECLRPL),” in 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Nov. 2021, no. February 2022, pp. 1–9. doi: 10.1109/IMITEC52926.2021.9714710.
  [29] J. V. V. Sobral, J. J. P. C. Rodrigues, R. A. L. Rabêlo, J. Al-Muhtadi, and V. Korotaev, “Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications,” Sensors, vol. 19, no. 9, p. 2144, May 2019, doi: 10.3390/s19092144.
  [30] M. N. Moghadam and H. Taheri, “High throughput load balanced multipath routing in homogeneous wireless sensor networks,” in 2014 22nd Iranian Conference on Electrical Engineering (ICEE), May 2014, no. Icee, pp. 1516–1521. doi: 10.1109/IranianCEE.2014.6999775.
  [31] O. Iova, F. Theoleyre, and T. Noel, “Exploiting multiple parents in RPL to improve both the network lifetime and its stability,” in 2015 IEEE International Conference on Communications (ICC), Jun. 2015, vol. 2015-Septe, pp. 610–616. doi: 10.1109/ICC.2015.7248389.
  [32] H.-S. Kim, J. Paek, and S. Bahk, “QU-RPL: Queue utilization based RPL for load balancing in large scale industrial applications,” in 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Jun. 2015, pp. 265–273. doi: 10.1109/SAHCN.2015.7338325.
  [33] Xinxin Liu, Jianlin Guo, G. Bhatti, P. Orlik, and K. Parsons, “Load balanced routing for low power and lossy networks,” in 2013 IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2013, pp. 2238–2243. doi: 10.1109/WCNC.2013.6554908.
  [34] H.-S. Kim, J. Paek, D. E. Culler, and S. Bahk, “Do Not Lose Bandwidth: Adaptive Transmission Power and Multihop Topology Control,” in 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS), Jun. 2017, vol.2018-Janua, pp.99–108. doi: 10.1109/DCOSS.2017.23.
  [35] P. Di Marco, G. Athanasiou, P.-V. Mekikis, and C. Fischione, “MAC-aware routing metrics for the internet of things,” Comput. Commun., vol.74, pp.77–86, Jan.2016, doi: 10.1016/j.comcom.2015.05.010.

  Citation
  Poorana Senthilkumar S., Subramani B., "RPL Protocol Load balancing Schemes in Low-Power and Lossy Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.7-13, 2023

• Open Access   Article

  Modelling and Assessment of the Star Network Topology Using OPNET Simulation Techniques

  Adaramola Ojo Jayeola, Paul Timilehin Ayomide

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.14-22, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The behaviour of many topological types is explained by a variety of advantages and disadvantages. When constructing a local area network for an organization, the types of data distribution topologies chosen significantly impact the network. The OPNET simulator was used in this study to evaluate a star network architecture for various network sizes and analyze network performance. As the characteristics evaluated differ depending on how many nodes were attached to a particular switch, four scenarios were created for this design with node counts of 10, 20, 30, and 40, respectively. This was accomplished by increasing the nodes and then altering a few parameters. This study investigated network delay (seconds), load (bits/second), traffic delivered (packets/second), received (packets/second), and response times. The two applications examined were FTP (File Transfer Protocol) and a database. Using this topology, performance across various network sizes is compared. However, since the central switch can accommodate more nodes, an increase in response time may be expected as network capacity increases and both traffic transmitted and traffic received (packets/sec) increase. Given that this switch is an Ethernet 128 Switch Advanced, it is clear that the network`s performance can be impacted by the central switch that is employed.

  Key-Words / Index Term
  FTP, Network Delay, Network Load, OPNET Simulator, Response Time, Star Network, Traffic Received

  References
  [1]. S. S. Rai1, A.T. Gaikwad, R.V. Kulkarni, “A Research Paper on Simulation Model for Teaching and Learning Process in Higher Education.”International Journal of Advanced Computer Research Vol.4, No.2, pp. 582-587, 2014.
  [2]. A.A.Atayero, A.S.Alatishe, J.O.Iruemi, “Modeling and Simulation of a University LAN in OPNET Modeller Environment”, Department of Electrical and Information Engineering, Covenant University, Nigeria, pp1-4, 2012.
  [3]. P.L.Francis, “A Review-Analysis of Network Topologies for Microenterprises,” Advanced Science and Technology Letters, Vol.135, pp.175-180, 2016.
  [4]. M. Sparsh, “OPNET: An Integrated Design Paradigm for Simulations. Software Engineering,” An International Journal, Vol.2, No.2, pp.57-67, 2012.
  [5]. A. Nurul, J.A. Mohammad, A. Tasnuva, “Performance Study of Star Topology in Small Internetworks,” International Journal of Computer Applications (0975 – 8887) Vol.107, No.2, pp.45-53, 2014.
  [6]. J. Garima, N. Nasreen, K. Nisha, S. Sourabh, “Designing & Simulation of Topology Network using Packet Tracer,” International Research Journal of Engineering and Technology (IRJET), Vol.2, Issue 2, pp.793-795, 2015.
  [7]. S. Pankaj, V. Shubham, “Network Topologies”, IJRDO - Journal of Computer Science Engineering, Maharishi Dayanand University Gurgaon,Haryana, India, Vol.1, ISSN: 2456-1843, pp.1-9, 2015.
  [8]. P.L.Francis,“A Review-Analysis of Network Topologies for Microenterprises,” Advanced Science and Technology Letters, Vol.135, pp.175-180 2016.
  [9]. R.S. Ranjeet, “Simulation and Implementation of Network Topology Using NS2” http: // www.ijesrt.com© International Journal of Engineering Sciences & Research Technology, ISSN: 2277-9655, pp.291-295, 2016.
  [10]. N.H. Waleed, A. M. Ali, A.T. Duaa, H.S. Raed, “A Simulation of Wireless Sensor Network Using ZigBee,” Computer Engineering and Intelligent Systems, Vol.8, No.3, pp. 23-27, 2017.
  [11]. C. Gürcan, G. Hüseyin, O.D. Mehmet. “Performance Analysis of ZigBee Protocol in the LargeScale Network Topologies”, International Journal of Computer Applications (0975 – 8887), Vol.181, pp. 11-15, 2018.
  [12]. N.Q. Noor, R. M. Z. Subhi, K.H. Bzar, “Opnet Based Performance Analysis and Comparison among Different Physical Network Topologies,” Academic Journal of Nawroz University (AJNU), pp. 48-54, 2018.
  [13]. Q.A.A. Ruhimat, G.W.Fajariyanto, D.M.Firmansyah, Slamin, “Optimal computer network based on graph topology model, IOP Conf. Series,” Journal of Physics: Conf. Series 1211, pp. 1-8, 2019.
  [14]. O.J. Adaramola, J.R. Olasina, “Network Model Analysis in Opnet Simulation,” International Journal of Engineering Applied Sciences and Technology, 2020 Vol.5, Issue 1, ISSN No. 2455-2143, pp.47-51, 2020.

  Citation
  Adaramola Ojo Jayeola, Paul Timilehin Ayomide, "Modelling and Assessment of the Star Network Topology Using OPNET Simulation Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.14-22, 2023

• Open Access   Article

  Correlating Propensity Between Code Smells and Vulnerabilities in Java Applications

  Kritika

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.23-28, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The ever-advancing world in terms of technology from web 1.0 to web 3.0, the need for designing and developing software applications has increased many folds. The digitalization of everything at a quick pace from including banking applications, mobile gaming etc. has led to the negligence of the part of the software developers which has led to increment in maintainability as well as security issue of the application, namely, code smells and vulnerability respectively. Code smells are the niggardly practices followed while developing a software by the developers or the software engineers, thwacking the rudimentary delineation principles and cynically thwacking delineation idiosyncrasy. Vulnerability is the snag, glitch or blemishes existing in software or operating system allowing the attackers to derelict the security measures. The paper focusses on finding the relationship between the code smells and vulnerability detected using an Eclipse plugin, PMD and correlating them using software metrics and rule-based machine learning approach.

  Key-Words / Index Term
  Code smell, Vulnerability, software metrics, machine learning, K means clustering, data mining

  References
  [1] Fontana, F. A., Zanoni, M., Marino, A., & Mäntylä, M. V, “Code smell detection: Towards a machine learning-based approach”, IEEE international conference on software maintenance, pp. 396-399, 2013.
  [2] Alhazmi, O., Malaiya, Y., & Ray, I, “Security vulnerabilities in software systems: A quantitative perspective”, In IFIP Annual Conference on Data and Applications Security and Privacy, pp. 281-294, 2005.
  [3] KS, V. K, “A method for predicting software reliability using object oriented design metrics”, “International Conference on Intelligent Computing and Control Systems (ICCS), pp. 679-682, 2019.
  [4] Elia, I. A., Antunes, N., Laranjeiro, N., & Vieira, M, “An analysis of openstack vulnerabilities”, “13th European Dependable Computing Conference (EDCC)”, pp. 129-134, 2017.
  [5] Reutemann, G. H. B. P. P., Hall, I. H. W. M., Frank, E., & Witten, I. H, “The weka data mining software: An update”, SIGKDD Explorations, Vol. 11, Issue. 1, pp. 10-18, 2009.
  [6] Kirkby, R., & Frank, E, “WEKA Explorer User Guide for Version 3-4”, University of Weikato, pp.3-4, 2002.
  [7] Di Nucci, D., Palomba, F., Tamburri, D. A., Serebrenik, A., & De Lucia, A, “Detecting code smells using machine learning techniques: are we there yet?”, Ieee 25th international conference on software analysis, evolution and reengineering (saner), pp. 612-621, 2018.
  [8] Kim, D.K., “Finding bad code smells with neural network models” International Journal of Electrical and Computer Engineering, Vol. 7, Issue. 6, p.3613, 2017.
  [9] Pessoa, T., Monteiro, M.P. and Bryton, S, “ An eclipse plugin to support code smells detection” arXiv preprint arXiv:1204.6492, 2012.
  [10] Sharma, S., & Rathore, M, “Comparison Study of Classification Techniques for Predicting Performance of Students Using Weka Environment”, “Rising Threats in Expert Applications and Solutions, (pp. 673-681), 2022.
  [11] Rezaei, E., Ghoreyshi, K., Dimitrov, Y., Sadique, K. M., & Campos, J, “Data Mining with WEKA”, 2021.
  [12] Medeiros, N., Ivaki, N., Costa, P., & Vieira, M, “Vulnerable code detection using software metrics and machine learning”, IEEE Access, 8, 2020.
  [13] Pereira dos Reis, J., Brito e Abreu, F., de Figueiredo Carneiro, G., & Anslow, C, “Code smells detection and visualization: a systematic literature review”, Archives of Computational Methods in Engineering, Vol. 29, Issue.1, pp. 47-94, 2022.
  [14] Rattan, D., Bhatia, R., & Singh, M, “Software clone detection: A systematic review”, Information and Software Technology, Vol. 55, Issue.7, pp. 1165-1199, 2013.
  [15] Kaur, A, “A systematic literature review on empirical analysis of the relationship between code smells and software quality attributes”, Archives of Computational Methods in Engineering, Vol. 27, Issue. 4, pp. 1267-1296, 2020.
  [16] Santos, J. A. M., Rocha-Junior, J. B., Prates, L. C. L., do Nascimento, R. S., Freitas, M. F., & de Mendonça, M. G, “A systematic review on the code smell effect”, Journal of Systems and Software, Vol.144, pp. 450-477, 2018.
  [17] Elkhail, A. A., & Cerny, T, “On relating code smells to security vulnerabilities”, IEEE 5th intl conference on big data security on cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE intl conference on intelligent data and security (IDS) pp. 7-12, 2019.
  [18] Aggarwal, K. K., Singh, Y., Kaur, A., & Malhotra, R, “Software Design Metrics for Object-Oriented Software, J. Object Technol., Vol.6, Issue.1, pp. 121-138, 2007.
  [19] Dewangan, S., Rao, R.S., Mishra, A. and Gupta, M., 2022. Code Smell Detection Using Ensemble Machine Learning Algorithms. Applied Sciences, 12(20), p.10321 2022.
  [20] Sehgal, R., Mehrotra, D. and Nagpal, R, “Is refactoring a solution to resolve code smell?”, International Journal of System of Systems Engineering, Vol.12, Issue.4, pp.371-385. 2022.
  [21] Madeyski, L. and Lewowski, T., “Detecting code smells using industry-relevant data”, Information and Software Technology, p.107112. 2023.
  [22] S. D. Raut and S. A. Thorat, "Deep Learning Techniques: A Review," International Journal of Scientific Research in Computer Science and Engineering, vol.8, Issue.1, pp. 105-109, 2020.
  [23] Anoushka, Shivani Dubey, Vikas Singhal, "Student Grade Prediction by using Machine Learning Methods and Data Analytics Techniques," International Journal of Scientific Research in Computer Science and Engineering, vol.10, no. 6, pp. 22-29, 2022.

  Citation
  Kritika, "Correlating Propensity Between Code Smells and Vulnerabilities in Java Applications," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.23-28, 2023

• Open Access   Article

  Design of Wireless Sensor Network of Monitoring and Controlling Fish Farm

  Ehab K., Badraldeen A., Alaa A., Osman A., Mortada M. Abdulwahab

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.29-33, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Recently fish farming has been played an important role in developing economics and providing health food. The uses of modern technology in the fish farms assist to increase both the amount of production and the quality. The objective of this paper was designing an electronic system based on using wireless sensor network to improve the operating system of traditional fish farm by establishing a real time monitoring of the water quality. The proposed design consists of Arduino controller, pumps and a group of sensors. The water quality has been measured by monitoring specific factors such as the pH factor of water, temperature of water, the dissolved oxygen in water, and the quantity of Ammonia. The system provided with monitoring page which was designed with visual basic platform and it has been connected wirelessly to the field using XBee technology. The system provides an automatic control mechanism of water recycling through mechanical filter the automotive system based on the received information from the field which determined the water quality. The system was simulated using electronic workbench software which also used in testing the expected operating of the system in different scenarios. The overall results were reliable and were successful in all possible cases as planned for the required goals.

  Key-Words / Index Term
  XBee; Visual basic; Sensors; Arduino; electronic design

  References
  [1]. Q.Ubaid, Mushtaq, Zahoor ,Shakir & Gul, Shahid. "Health benefits of eating fish Smart Agripost" Fisheries, PP:38-42 2020.
  [2]. E.Dujmi?, G.Kreši?, D.Lon?ari?, "Anamarija Buneta Nutritional Value And Health Benefits Of Fish Consumption," University Of Rijeka, Faculty of Tourism and Hospitality Management, ISBN 978-953-7842-49-9,2021
  [3]. D.Gwaska," Fish Farming Business Plan", M.Sc., A.C.A. Business Consultant, Majesty of Justice Farms Ltd business 2022.
  [4]. Martin F ,Kevin F,Tomas N,EIrik S,Jo, Tim D,Harkaita E,Win W,Annette S,Leif M,Christian S,Mortin O A,Daniel B," A Precision fish farming: A new framework to improve production in aquaculture, Biosystems Engineering, Special Issue: Engineering Advances in Precision Livestock Farming. Vol. 173, PP:176-193,2018 .
  [5]. Environmental Monitoring of Fish Farming", Guidance document, environmental agency, 2022. http://doi.org/10.1016/J.biosystemseng.2017.10.O14.
  [6]. W. Dargie, C. Poellabauer " Fundamentals of wireless sensor networks : theory and practice", Wiley; 1st edition , 2010 .
  [7]. K Sohraby, D Minoli and T Znati“ Wireless Sensor Networks, Wiley; 1st edition , pp:12-15,2007.
  [8]. Mortada M.Abdulwahab, Abdalrhman A. Mohammed, Abdalrhman H. Mohammed, Mohammed M. Hamza, " Design of Wireless Sensor System of Monitoring and Controlling Parking Cars ", World Academic Journal of Engineering Science, ,Vol. 9,No 1,,pp:18-23,2022.
  [9]. A. Taher, Tamim, H. Begum, S A Shachcho, M Monirujjaman, K. Bright Y.Akowuah, M. Masud , J F. Al-Amri, " Development of IoT Based Fish Monitoring System for Aquaculture", Intelligent Automation & Soft Computing IASC, , Vol.32, No.1, pp.20-26, 2022.
  [10]. A. K. Poddar, A. A. Bukhari, S. Islam, S. Mia, M. A. Mohammed et al., “IoT based smart agrotech system for verification of urban farming parameters. Microprocessors and microsystems,” Microprocessors and Microsystems, Vol. 82, No. 104025, pp. 1–10, 2021.
  [11]. S. Saha, R. H. Rajib and S. Kabir, “IoT based automated fish farm aquaculture monitoring system,” in Proc. Int.Conf. on Innovations in Science, Engineering and Technology, Chittagong, Bangladesh, pp. 201–206, 2018.
  [12]. N. Rosaline and S. Sathyalakshimi, “IoT based aquaculture monitoring and control system,” Journal of Physics: Conf. Series, Vol. 1362, pp. 1–7, 2019.
  [13]. Mohammad Fahmi Suhaimi 1, Nurul Huda Mat Tahir 1,*, Safuan Naim Mohamad, Suzanna Ridzuan Aw," IoT Based Automatic Aquarium Monitoring System for Freshwater Fish," International Journal of Synergy in Engineering and Technology Vol. 2 No. 1, pp: 125-133, (2021).
  [14]. Aman Shakirin Shamshulbahrin, Dr. Izanoordina Ahmad," Aquarium Monitoring System via IOT", Journal of Engineering Technology, Vol. 10, pp. 13-16, ISSN 2231-8798,2022.
  [15]. Wen Ding, Yinchi Ma, Jinjing Zhang. Remote Monitoring and Control System for Aquarium Based on Mobile Communication Platform. 11th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Aug 2017, Jilin, China. pp.462-467, 10.1007/978-3-030-06137- 1_42. hal-0212422.
  [16]. Raj, Albert & Arumugam, Selvan & Saravanaraj, Rakesh. (2020). Arduino based Fish Monitoring System", international Journal of Scientific & Engineering Research Vol 11, No 7, pp.1622-1632 , 2020, ,ISSN 2229-5518
  [17]. Moiseenko, Tatiana. (2005). Effects of acidification on aquatic ecosystems. Russian Journal of Ecology. Vol.36. pp. 93-102,2005.

  Citation
  Ehab K., Badraldeen A., Alaa A., Osman A., Mortada M. Abdulwahab, "Design of Wireless Sensor Network of Monitoring and Controlling Fish Farm," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.29-33, 2023

• Open Access   Article

  EEG Signals Independent Component Analysis and Time/Frequency Analysis by EEG Lab Tools

  Manini Monalisa Pradhan

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.34-39, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  EEG machines supplies output graph records i.e. are analyses by expert knowledge of doctors. In this case we cannot found out the accurate component analysis of signal. So there may be some error. So we have two objectives. a) To propose time domain and time-space domain statistical based features that can be used to classify emotion from electroencephalogram signals (EEG). b) To implement suitable classification model to classify emotion from electroencephalogram signals (EEG) which gives efficient accuracy. We have also used EEG Lab tools of MATLAB programing language which is used for dealing out uninterrupted and event-related EEG, MEG and other electrophysiological data using neutral component analysis, time frequency analysis and artifacts elimination. This tools offers an interactive graphic user interface which permitting users to flexibly and interactively procedure their high quality density EEG and other dynamic brain data using independent component analysis (ICA) and time/frequency analysis (TFA).This paper presented using EEG machine signal implementation by EEG lab software.

  Key-Words / Index Term
  EEG, ICA, MEG, TFA, ITC, MEG, MAP

  References
  [1] W. Rosalind Picard, Elias Vyzas and Jennifer Healey, “Toward machine emotional intelligence: analysis of affective physiological state,” IEEE Transaction on pattern analysis and machine intelligence, vol. 23, Issue. 10, pp. 1175-1191.
  [2] M. Murugappan, R. Nagarajan, S.Yaacob ,”Combining spatial filtering and wavelet transform for classifying human emotions using EEG Signals”, Journal of Medical and Biological Engineering, Vol.31, Issue.1, pp.45-51, 2011.
  [3] Babiloni, Fabio, Luigi Bianchi, J. Semeraro Francesco, R. Millan del, Mourino Josep, Angela Cattini, Serenella Salinari, Maria Grazia Marciani, and Febo Cincotti, “Mahalanobis distance based classifiers are able to recognize EEG patterns by using few EEG electrodes,” Proceedings of the 23rd Annual International Conference of the IEEE, vol. 1, pp. 651-654, 2001.
  [4] K. H. Kim, S. W. Bang, S. R. Kim,” Emotion recognition system using short-term monitoring of physiological signals”, Medical and biological engineering, 2004.
  [5] Jatupaiboon, Noppadon, Pan-ngum Setha, and Pasin Israsena, “Emotion Classification using Minimal EEG Channels and Frequency Bands,” International Joint Conference on Computer Science and Software Engineering, pp. 21-24, May 2013.
  [6] Sohaib, Ahmad Tauseef, et al. ”Evaluating classifiers for emotion recognition using EEG.” Foundations of Augmented Cognition. Springer, Berlin Heidelberg, (2013). pp.492-501.
  [7] S. A. Hosseini, M. A. Khalilzadeh, M. B. Naghibi Sistani, V. Niazmand, “Higher order spectra analysis of EEG signals in emotional stress states”, 2nd International Conference on Information Technology and Computer Science, 2010.
  [8] M.R.Nazari Kousarrizi, A. Asadi Ghanbari, M.Teshnehlab, M.Aliyari, A.Gharaviri, "Feature Extraction and Classification of EEG Signals using Wavelet Transform, SVM and Artificial Neural Networks for Brain-Computer Interfaces," International Joint Conference on Bioinformatics, System Biology and Intelligent Computing(IEEE), 2009, pp. 352-355
  [9] F.Lotte and C.Guan, "Learning from other subject help reducing brain-computer interface calibration time," Acoustic Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on IEEE, pp.614-617, 2010.

  Citation
  Manini Monalisa Pradhan, "EEG Signals Independent Component Analysis and Time/Frequency Analysis by EEG Lab Tools," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.34-39, 2023

• Open Access   Article

  An Evolutionary Approach to Reinforcement Learning of Neural Network Controllers for Pendulum Tasks Using Genetic Algorithms

  Hidehiko Okada

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.40-46, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Reinforcement learning of neural networks requires gradient-free algorithms because of the absence of labeled training data. Evolutionary algorithms, which do not rely on gradients, are a viable option for this purpose. To successfully train neural networks by evolutionary algorithms, we need to carefully choose appropriate algorithms because many algorithm variations are available. This paper experimentally evaluates the effectiveness of the Genetic Algorithm, a type of evolutionary algorithm, in training neural networks for reinforcement learning. The task selected for this evaluation is pendulum control. The results show that GA is capable of efficiently training a multilayer perceptron to maintain the pendulum in an upright position. The number of hidden units (8, 16 and 32) in the MLP was found to have no significant effect on the performance. Thus, GA could train the MLP to perform the task appropriately even with a small number of hidden units. Furthermore, the results of this study were compared with those obtained from Evolution Strategy, and it was observed that GA performed better than ES when the number of generations was given priority, while ES performed better when the population size was given priority.

  Key-Words / Index Term
  Evolutionary algorithm, Genetic algorithm, Neural network, Neuroevolution, Reinforcement learning

  References
  [1] T. Bäck, H.P. Schwefel, “An Overview of Evolutionary Algorithms for Parameter Optimization,” Evolutionary Computation, Vol.1, No.1, pp.1-23, 1993.
  [2] D.B. Fogel, “An Introduction to Simulated Evolutionary Optimization,” IEEE Transactions on Neural Networks, Vol.5, No.1, pp.3-14, 1994.
  [3] T. Bäck, “Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms,” Oxford University Press, 1996.
  [4] A.E. Eiben, R. Hinterding, Z. Michalewicz, “Parameter Control in Evolutionary Algorithms,” IEEE Transactions on Evolutionary Computation, Vol.3, No.2, pp.124-141, 1999.
  [5] A.E. Eiben, J.E. Smith, “Introduction to Evolutionary Computing (2nd ed.),” Springer, 2015.
  [6] C.J.C.H. Watkins, “Learning from Delayed Rewards,” PhD Thesis, Cambridge University, 1989.
  [7] C.J.C.H. Watkins, P. Dayan, “Q-Learning,” Machine Learning, Vol.8, No.3, pp.279-292, 1992.
  [8] R.S. Sutton, A.G. Barto, “Reinforcement Learning: An Introduction (2nd ed.),” MIT Press, 2018.
  [9] H.P. Schwefel, “Evolution Strategies: A Family of Non-Linear Optimization Techniques based on Imitating Some Principles of Organic Evolution,” Annals of Operations Research, Vol.1, pp.165-167, 1984.
  [10] H.G. Beyer, H.P. Schwefel, “Evolution Strategies: A Compre-hensive Introduction,” Journal Natural Computing, Vol.1, No.1, pp.3-52, 2002.
  [11] D.E. Goldberg, J.H. Holland, “Genetic Algorithms and Machine Learning,” Machine Learning, Vol.3, No.2, pp.95-99, 1988.
  [12] J.H. Holland, “Genetic Algorithms,” Scientific American, Vol.267, No.1, pp.66-73, 1992.
  [13] M. Mitchell, “An Introduction to Genetic Algorithms,” MIT Press, 1998.
  [14] K. Sastry, D. Goldberg, G. Kendall, “Genetic Algorithms,” Search Methodologies, Springer, pp.97-125, 2005.
  [15] R. Storn, K. Price, “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, Vol.11, pp.341-359, 1997.
  [16] K. Price, R.M. Storn, J.A. Lampinen, “Differential Evolution: A Practical Approach to Global Optimization,” Springer Science & Business Media, 2006.
  [17] S. Das, P.N. Suganthan, “Differential Evolution: A Survey of the State-of-the-art,” IEEE transactions on evolutionary computation, Vol.15, No.1, pp.4-31, 2010.
  [18] H. Okada, “Evolutionary Reinforcement Learning of Neural Network Controller for Pendulum Task by Evolution Strategy,” International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue 3, pp.13-18, 2022.
  [19] D.E. Rumelhart, G.E. Hinton, R.J. Williams. “Learning Internal Representations by Error Propagation,” in D.E. Rumelhart, J.L. McClelland, and the PDP research group (editors), “Parallel Distributed Processing: Explorations in the Microstructure of Cognition,” Vol.1: Foundation. MIT Press, 1986.
  [20] R. Collobert, S. Bengio, “Links Between Perceptrons, MLPs and SVMs,” Proc. of the Twenty-First International Conference on Machine Learning (ICML’04), ACM, 2004.
  [21] X. Yao, Y. Liu, “A New Evolutionary System for Evolving Arti?cial Neural Networks,” IEEE Transactions on Neural Networks, Vol.8, No.3, pp.694-713, 1997.
  [22] N.T. Siebel, G. Sommer, “Evolutionary Reinforcement Learning of Artificial Neural Networks,” Internatinal Journal of Hybrid Intelligent Systems, Vol.4, No.3, pp.171-183. 2007.
  [23] K. Chellapilla, D.B. Fogel, “Evolving Neural Networks to Play Checkers Without Relying on Expert Knowledge,” IEEE Transactions on Neural Networks, Vol.10, No.6, pp.1382-1391, 1999.
  [24] L. Cardamone, D. Loiacono and P. L. Lanzi, “Evolving Competitive Car Controllers for Racing Games with Neuro-evolution,” Proc. of 11th Annual Conference on Genetic and Evolutinary Computation, pp.1179-1186, 2009.
  [25] S. Risi, J. Togelius, “Neuroevolution in Games: State of the Art and Open Challenges”, IEEE Transactions on Computational Intelligence and AI in Games, Vol.9, No.1, pp.25-41, 2017.
  [26] J. Togelius, S.M. Lucas, “Evolving Controllers for Simulated Car Racing,” Proc. of 2005 IEEE Congress on Evolutionary Computation, Vol.2, pp.1906-1913, 2005.
  [27] L.J. Eshelman, J.D. Schaffer, “Real-coded Genetic Algorithms and Interval-Schemata,” Foundations of Genetic Algorithms, Vol.2, pp.187-202, 1993.

  Citation
  Hidehiko Okada , "An Evolutionary Approach to Reinforcement Learning of Neural Network Controllers for Pendulum Tasks Using Genetic Algorithms," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.40-46, 2023

• Open Access   Article

  Comparing Belief KNN, Adaptive KNN, Fuzzy KNN and System classification hybrid Algorithms: A comprehensive guide to understanding K-Nearest Neighbors

  Ghada Rahal, Islem Dridi

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.47-50, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This research is about to present and compare four of K-Nearest Neighbors (KNN) versions (Belief KNN, Adaptive KNN, Fuzzy KNN, System classification hybrid), that have recently been studied. Furthermore, indicate their limits. Computers may now ingest data without even being entirely programmed thanks to machine learning. Unsupervised learning and supervised are both types for machine learning. Computers adopt an objective in supervised learning which translates an input to an output based on training input-output pairs. K-Nearest Neighbors is among the most successful and frequently used in supervised learning methods. This study is the first step in trying to clarify the difference in algorithm between KNN versions. Results will show benefits and drawbacks of these four KNN varieties, otherwise, representing the critics for each KNN type. This work can be beneficial for researchers learning to improve their studies in supervised machines, by showing how KNN versions whether explain the research and subsequent comparison measures for distance regulation.

  Key-Words / Index Term
  Machine learning, Supervised learning, KNN, Fuzzy KNN, System classification hybrid, Belief KNN, Adaptive KNN

  References
  [1]. John McCarthy, “What is artificial intelligence?”, 2004.
  [2]. Daniel Belanche, Luis V. Casaló Bach Xuan Tran et al., “Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study”, Journal of Clinical Medicine, 8, 360, 2019.
  [3]. Daniel Belanche, Luis Vicente Casaló Ariño, Carlos Flavian, “Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers”, Industrial Management & Data Systems, 119(7), 1411-1430, 2019.
  [4]. Flavián Sumit Das, Aritra dey, Akash Pal, “Applications of Artificial Intelligence in Machine Learning: Review and Prospect,” International Journal of Computer Applications (0975 – 8887), Volume 115 – No. 9, April 2015.
  [5]. Chashif Syadzali, Suryono Suryono2, and Jatmiko Endro Suseno2, “Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems,” E3S Web of Conferences 202, 16005, 2020.
  [6]. Zhiwei Huang et al., “Research on KNN Algorithm of Charging Device Based on Attack Recognition Method,” Journal of Physics: Conference Series 1646 (2020) 012047, 2020.
  [7]. Zhiwei Huang et al., “Research on KNN Algorithm of Charging Device Based on Attack Recognition Method,” Journal of Physics: Conference Series 1646 012047, 2020.
  [8]. D Kurniadi et al., “The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm,” IOP Conf. Series: Materials Science and Engineering 434 012039. Conference, 2018.
  [9]. James M. Keller, Michel R. Gray, James A. Givens, JR, “A Fuzzy K-Nearest Neighbor Algorithm,” IEEE transcations on systems, man, and cybernetics, vol. smc-15, no. 4, 1985.
  [10]. Zhun-ga Liu, Quan Pan, Jean Dezert, “A new belief-based K-nearest neighbor classification method,” Pattern Recognition 46 834–844, 2013.
  [11]. Puning Zhao, Lifeng Lai, “Efficient Classification with Adaptive KNN,” The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).
  [12]. Mehmet Aci, Cigdem _ Inan, Mutlu Avci, “A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm”, Expert Systems with Applications, 2010.
  [13]. Emmanuel Ramasso, Michèle Rombaut, Noureddine Zerhouni, “Joint Prediction of Continuous and Discrete States in Time-Series Based on Belief Functions,” IEEE Transactions on Cybernetics, IEEE, 43 (1), pp.37-50, 2013.

  Citation
  Ghada Rahal, Islem Dridi, "Comparing Belief KNN, Adaptive KNN, Fuzzy KNN and System classification hybrid Algorithms: A comprehensive guide to understanding K-Nearest Neighbors," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.47-50, 2023

• Open Access   Article

  Wireless Sensor Network of Monitoring Water Distribution Network Service using IoT

  Mortada M. Abdulwahab, Hadeel A.Mohamed, Mutseuim A. Alameen, Mohamed A. Mosalam, Faris M.Elsadig

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.11 , Issue.1 , pp.51-55, Feb-2023

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The demand of water is increasing every day due to the rapid growth in populations around the world. Therefore, it must be available every time and any lack of water supply service in any section of the water distribution network will affect widely in the daily life. Detecting of water flow status over the pipeline is important for providing high quality service. This paper aimed to design an electronic circuit for monitoring the flow of water based on internet of things technology (IoT) and wireless communication. The system assists to improve the quality of the service by giving real tine monitoring of water flow in the major pipelines from anywhere. The design structure consists of Node MCU ESP 32, group of sensors and GPS module. The results were displayed via internet of things and all received data were stored in a database system. The flow rating sensor used to measure the speed of water in the pipeline which is essential parameter to identify the quality of the service. Any change of flow rate in the pipeline directly displayed in the website and database; the design also included with water level sensor to measure the quality of service. The system was examined experimentally in different possible scenarios, the design was tested experimentally and all the received results were corrected and achieved the required goals of the system.

  Key-Words / Index Term
  Electronics design; Sensors; IoT; Microcontroller; Wireless communications

  References
  [1] AL-Zahrani and Baig , " A framework of monitoring water pipeline techniques based on sensors technologies", Jornal of king saud university –Computer and information Sciences Vol..34 ,No.2, pp. 47-57, 2022 .
  [2] Richard Thorn," Flow Measuments" CRC Press LLC,pp.323-330. 2000. [onlne -0ctober 2022],
  [3] E.L. Upp and Paul J. LaNasa," Fluid Flow Measurement "(2nd Edition, Gulf Professional Publishing, 2002; ISBN 9780884157588
  [4] Roger c Backer |Flow Measurement Handbook industrial design operating principles ,performance and applications", ?Cambridge University Press; 2nd edition ,pp.12-16, 2016
  [5] M.A Iqbal, S.Hussain, H. Xing, M.A Imran,“ Enabling the Internet of Things: Fundamentals, Design, and Applications,”, First Edition; John Wiley & Sons Ltd. Published 2021.
  [6] W Dargie, C Poellabauer " Fundamentals of wireless sensor networks : theory and practice", Wiley; 1st edition ,pp.16-19, 2010.
  [7] K Sohraby, D Minoli and T Znati“ Wireless Sensor Networks", 1st edition, Wiley;pp.9-15, 2007.
  [8] Mortada M. Abdulwahab, Khalid H. Ahmed, Hamza M. Al-mehrabi, Mohammed A. Al-kateb, Yazid A. Al-mezgagi, Jwher Y. Al-rashdi, " Design of Wireless Monitoring System of Body Health Using Electronic Face Mask",World Academic Journal of Engineering Science, Vol .9,No2,,pp.46-51,2021.
  [9] A..Amazyad ,YM .Seddig, A M. Alotaibei , Ahmed Y . Alnasheri ,mohammed s .Saleh ,Abdulfattah M.obeid and syed Manzoor Qasim" A Review on Water Leakage Detection Pipes Using Sensors", International journal of research in engineering science and management ,Vol. 2,No.5, 2019.
  [10] R Ramat ,I satria ,Bsiregar and R Budiar" Water Pipeline Monitoring and Leak Detection Using Flow Liquid Meter Sensor" IOP Conference series :Materials Science and engineering, pp. 012036 , Vol 190, No 1, 2017.
  [11] Abelhafidh et al ., 2017 Abdelhafidh M ., Fourati l.c ,fourati M., Abidi A.," Hybrid mechanism for remote water pipeline monitoring system", In IEEE :wireless communications and mobile computing conference (IWCMC), 13th international ,ieee ,2017 ,pp .2140-2145, 2017.
  [12] M F Abdul Jalil, Z Muhammad*, N A Mat Leh, S A Hamid, Z M Yusoff," Water Pipeline Monitoring System (WPMS) via IoT, Journal of Mechanical Engineering, Vol. 10 No.1, 293-306, 2021
  [13] N N che , k NF omar KUAzir and MF Kamarudzaman “ Water Pipeline Leakage Monitoring System Based on Internet of Things" The 1st international conference on Engineering and technology (Ico Eng tech) 2021 , March 2021 , Malaysia.
  [14] Turki Al Qahtani, Mohd Saffuan Yaakob, K. A. Ahmad," A Review On Water Leakage Detection Method In The Water Distribution Network", Engineering Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, Vol. 68,No.2, pp:152-163,2020.
  [15] Marek Babiuch, Pet r Folt ýnek, Pavel Smutný," Using the ESP32 Microcontroller for Data Processing", 20th IEEE International Carpathian Control Conference (ICCC), pp:1-6, 2019, Poland. DOI:10.1109/CarpathianCC.2019.8765944

  Citation
  Mortada M. Abdulwahab, Hadeel A.Mohamed, Mutseuim A. Alameen, Mohamed A. Mosalam, Faris M.Elsadig, "Wireless Sensor Network of Monitoring Water Distribution Network Service using IoT," International Journal of Scientific Research in Computer Science and Engineering, Vol.11, Issue.1, pp.51-55, 2023