Volume-12 , Issue-4 , Apr - 2024 Go Back

Objects are located by drawing a bounding box around the detected object. One of computer vision`s specialties is object detection, which finds things in an image or video. Techniques for object detection are the foundation of the area of artificial intelligence. Typically, object detection uses deep learning and machine learning to yield accurate and significant findings. It is essentially made up of localization and classification. The state-of-the-art techniques utilized for real-time object detection have advanced recently. This study paper compares state-of-the-art techniques, such as faster region convolutional neural networks (Faster R-CNN) and you only look once V8 (YOLOV8). These algorithms are deep neural network representations, or neural networks with numerous hidden layers. Although each of these algorithms is notable for its own distinctiveness, they are compared to see which is superior. This study focuses on determining which of these algorithms is more practical to employ despite sharing a common core, namely CNNs.

YOLOV8(You only look once), Faster region convolutional neural network (faster R-CNN), object detection, deep learning, deep neural networks, and convolutional neural networks.

[1] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” arXiv, Jan. 06, 2016. Accessed: Jun. 17, 2023. [Online]. Available: http://arxiv.org/abs/1506.01497.
[2] S. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement.” arXiv, apr. 08, 2018. Accessed: Sep. 25, 2022. [Online]. Available: http://arxiv.org/abs/1804.0276.
[3] A.M.A.ghani Abdulghani and G.G. Menekse Dalveren, “Moving Object Detection in Video with Algorithms YOLO and Faster R-CNN in Different Conditions,” European Journal of Science and Technology, Jan. 2022, DOI: 10.31590/ejosat.1013049.
[4] H. Jiang and E. Learned-Miller, Face Detection with the Faster R-CNN.” arXiv, Jun. 10, 2016. Accessed: Sep. 25, 2022. [Online].Available: http://arxiv.org/abs/1606.03473.
[5] Chandana, R. K., & Ramachandra, A. C. Real time object detection system with YOLO and CNN 740 models: A review. arXiv preprint arXiv:2208.00773, 2022.
[6] JiayiFan; JangHyeon, Lee; InSuJung; YongKeunLee, “Improvement of Object Detection Based on Faster R-CNN and YOLO”, International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) June, pp.27-30 2021. DOI: 10.1109/ITC-CSCC52171.2021.9501480.
[7] J. Kim, J.-Y. Sung, and S. Park, “Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition,” in 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), Seoul, Korea (South): IEEE, Nov. pp.1–4, 2020.
DOI: 10.1109/ICCE-Asia49877.2020.9277040.
[8] F. Miao, Y. Tian, and L. Jin, “Vehicle Direction Detection Based on YOLOv3,” in 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China: IEEE, Aug., pp.268–271, 2019.
DOI: 10.1109/IHMSC.2019.10157.
[9] Rohan, A., Rabah, M., & Kim, S. H. Convolutional neural network-based real-time object detection and tracking for parrot AR drone 2. IEEE access, 7, pp.69575- 69584, 2019.
[10] Younis, A., Shixin, L., Jn, S., & Hai, Z. (January). Real-time object detection using pre- trained deep learning models MobileNet-SSD. In Proceedings of 2020 6th International Conference on Computing and Data Engineering, pp.44-48, 2020.
[11] Nguyen, N. D., Do, T., Ngo, T. D., & Le, D. D. An evaluation of deep learning methods for small object detection. Journal of electrical and computer engineering, 2020, pp.1-18, 2020.
[12] Hossain, S., & Lee, D. J. Deep learning-based real- time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors, Vol.19, Issue.15, pp.33-71, 2019.
[13] Pal, S. K., Pramanik, A., Maiti, J., & Mitra, P. Deep learning in multi-object detection and tracking: state of the art. Applied Intelligence, 51, pp.6400-6429, 2021.
[14] J. Du, Understanding of Object Detection Based on CNN Family and YOLO,” J. Phys.: Conf. Ser., Apr., Vol.1004, pp.12-29, 2018. DOI: 10.1088/1742-6596/1004/1/012029.
[15] S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., Jun., Vol.39, No.6, pp.1137–1149, 2017, DOI: 10.1109/TPAMI.2016.2577031.

B. Sai Jyothi, Chavali Saathvika Durga Abhinaya, Bellamkonda Lahari, Chinta Devika Priya, Devarapalli Anjali, Bathula Sri Navya, "Performance Evaluation on Real-time object detection using DL techniques," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.75-80, 2024.

Optic Disc (OD) is the most critical component of the human retina where blood vessels originate. In the normal retinal image OD appears as a round, bright yellowish region. An efficient localization of OD in color retinal fundus images is the most vital phase for the retinal image analysis and this information helps in finding severity of retinal diseases. Identification of OD accurately is a very difficult and challenging task because of various reasons, including the presence of lesions around the OD and variation in size, shape and color of the optic disc. In this review paper, a brief introduction about OD and some important properties of it are described. A literature survey on OD detection and the complications involved in OD detection are also discussed in this paper.

Optic Disc (OD), Optic Nerve Head (ONH), Optic Cup (OC), Age Related Macular Degeneration (AMD), Diabetic Retinopathy (DR), Blood Vessels (BVs).

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[5] Martinez-Perez ME, Witt N, Parker KH, Hughes AD, Thom SAM., “Automatic optic disc detection in colour fundus images by means of multispectral analysis and information content”, PeerJ 7: e7119 https://doi.org/10.7717/peerj.7119
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[7] Niu, Di, PeiyuanXu, Cheng Wan, Jun Cheng, and Jiang Liu. "Automatic localization of optic disc based on deep learning in fundus images." In Signal and Image Processing (ICSIP), IEEE 2nd International Conference on, IEEE, pp.208-212, 2017.
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[12] Popescu, Dan, and Loretta Ichim. "Computer—Aided localization of the optic disc based on textural features."In Advanced Topics in Electrical Engineering (ATEE), 9th International Symposium, IEEE, pp.307-312, 2015.
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Rajesh I.S., Bharathi Malakreddy A., Maithri C., Manjunath Sargur Krishnamurthy, Shashidhara M.S., "Automatic Detection of Optic Disc and Its Center in Color Retinal Images: A Review," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.81-85, 2024.

The Mobile Ad-Hoc Network presents a remarkable infrastructure-free approach for information exchange between source and destination utilizing intermediate nodes. It offers robust security features and tackles time complexity through its routing protocols. In the author`s prior research, solutions were proposed for approximately eight qualitative and quantitative Quality of Service (QoS) metrics, along with a reduction in solution algorithm execution time. These solutions were automated using Machine Learning (ML) algorithms, leveraging a dataset named Infra-Less KMS and an optimal algorithm, Support Vector Machine (SVM) & Gaussian mixture model (GMM) identified in previous works. In this study, the IDS-ATiC AODV Solution algorithm will be implemented using SVM, with a focus on evaluating prediction accuracy.

Security and Time Complexity, QoS, SVM, GMM, Prediction Accuracy

[1] N. Kanimozhi, S. Hari Ganesh 2, B. Karthikeyan, "An Analysis of Machine Learning Solution for QoS and QoE in Network (Infrastructure Oriented and Less)", International Journal of Computer Sciences and Engineering, May, Vol.11, Issue.5, pp.41-59, 2023. ISSN: 2347-2693 (Online)
[2] N. Kanimozhi, S. Hari Ganesh 2, B. Karthikeyan, “Performance Analysis of MANET Routing Protocols”, International Journal of Computer Applications (0975 – 8887) December, Vol.185, No. 50, pp-44-50, 2023.
[3] N. Kanimozhi, S. Hari Ganesh 2, B. Karthikeyan, "Irregularity Behaviour Detection - Ad-hoc On-Demand Distance Vector Routing Protocol (IBD - AODV): A Novel Method for Determining Unusual Behaviour in Mobile Ad-hoc Networks (MANET)", International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 September, Vol.11 Issue: 9, pp.1098-1010, 2023.
[4] N. Kanimozhi, S. Hari Ganesh 2, B. Karthikeyan, "Minimizing End-To-End Time Delay in Mobile Ad-Hoc Network using Improved Grey Wolf Optimization Based Ad-Hoc On-demand Distance Vector Protocol (IGWO-AODV)", International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, September, Vol.11, Issue:9, pp.1111-1115, 2023.
[5] B.Karthikeyan, N. Kanimozhi and Dr.S.Hari Ganesh, “Analysis of Reactive AODV Routing Protocol for MANET”, IEEE Xplore (978-1-4799-2876-7), Oct, pp. 264-267, 2014.
[6] B.Karthikeyan, N. Kanimozhi and Dr.S.Hari Ganesh, “Security and Time Complexity in AODV Routing Protocol”, International Journal of Applied Engineering Research (ISSN:0973-4562), Vol. 10, No.20,June 2015, pp.15542- 155546. – Scopus Indexed.
[7] B. Karthikeyan, Dr.S.Hari Ganesh and N. Kanimozhi, “Encrypt - Security Improved Ad Hoc On Demand Distance Vector Routing Protocol (En-SIm AODV)”, ARPN Journal of Engineering and Applied Sciences (ISSN: 1819-6608), Vol. 11, No. 2, January, pp.1092-1096, 2016.
[8] B. Karthikeyan,Dr.S.Hari Ganesh and Dr. JG.R. Sathiaseelan, “ Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (OpTiB-AODV)”, International Journal of Computer Applications (ISSN:0975 – 8887), April, Vol.140, No.6, pp 7-11, 2016.
[9] B. Karthikeyan, Dr.S.Hari Ganesh, Dr. JG.R. Sathiaseelan and N. Kanimozhi , “High Level Security with Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (HiLeSec-OpTiB AODV)”,International Journal of Computer Science Engineering(E-ISSN: 2347-2693), April, Vol.4, No.4, pp.156-164, 2016.
[10] Jhansi Rani Kaka and K. Satya Prasad, “Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification”, Hindawi, Security and Communication Networks, Vol.2022, Article ID 7275433, 13 pages.
[11] Khalid Abualsaud, Elias Yaacoub, Maazen Alsabaan and Mohsen Guizani, “Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation” Sensors, 23, 9033, 2023. https:// doi.org/10.3390/s23229033.
[12] Ceren Atik, Recep Alp Kut, Reyat Yilmaz, and Derya Birant, "Support Vector Machine Chains with a Novel Tournament Voting", Electronics, 12, 2485, 2023. https://doi.org/10.3390/electronics12112485.
[13] Zhi Quan and Luoxi Pu, "An improved accurate classification method for online education resources based on support vector machine(SVM): Algorithm and experiment" Education and Information Technologies, 28: pp.8097–8111, 2023.
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[15] Abdullahi Abubakar Mas’ud, Arunachalam Sundaram, Jorge Alfredo Ardila-Rey, Roger Schurch, Firdaus Muhammad-Sukki and Nurul Aini Bani, "Application of the Gaussian Mixture Model to Classify Stages of Electrical Tree Growth in Epoxy Resin", Sensors 2021, 21, 2562. https://doi.org/10.3390/s21072562

N. Kanimozhi, S. Hari Ganesh, B. Karthikeyan, "Integration of Machine Learning Algorithm into IDS-ATiC-AODV (Improved Data Security - Avoid Time Complexity Ad-Hoc On-Demand Distance Vector) Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.86-100, 2024.