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Archive Issue – Vol.2, Issue.1 (January-March 2022)

Archive Issue – Vol.2, Issue.1 (January-March 2022)


Real Time Driver Drowsiness Detection using Landmark Predicator in Deep Neural Network

Hemant Ahirwar, Minal Saxena

Research Paper | Journal Paper

Vol.2, Issue.1, pp.1-07, Mar-2022

Abstract References Citation Download

Abstract

In the modern era, road accident is very common and there are lots of casualties happen. The major reason is drowsiness of drivers which should be rectified at real time. One of these is drowsiness in drivers. A little tiredness could cause a catastrophic accident with many fatalities. Many lives could be saved if a device could instantly detect a driver's alertness and tiredness. Different behaviours, such as expanding the mouth wide, closing both eyelids and a combination of the two, can be used to identify drowsiness. It may be suggested not to drive while fatigued. Drowsiness can be identified in real time using a variety of techniques, but accuracy is important. The proposed system is based on Landmark Predicator algorithm in Deep Neural Network. A computer vision task called "facial landmark detection" identifies and tracks significant points on a person's face. Dlib is a library for using computer vision and machine learning techniques. Because the model will predict continuous values, ensemble regression trees are the foundation of the model. The iBUG-300 W dataset, which includes photos and their accompanying 68 face landmark points, served as the basis for training this model. The nose, the eyes, the mouth, and the edge of a face are often those landmarks.

Key-Words / Index Term: Landmark Predicator, Deep Neural Network, Machine Learning, Drowsiness Detection, Face Detection, Eye Detection, Computer Vision.

References

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    • Gupta, S., Jain, P., & Rufus, E. (2018). Drowsy Driver Alerting System. 2018 ICECA, 1665–1670. doi:10.1109/ICECA.2018.8474931
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    • Kumari, B. M. K., & Kumar, P. R. (2017). A survey on drowsy driver detection system. 2017 ICBDAC, 272–279. doi:10.1109/ICBDACI.2017.8070847
    • Pinto, A., Bhasi, M., Bhalekar, D., Hegde, P., & Koolagudi, S. G. (2019). A Deep Learning Approach to Detect Drowsy Drivers in Real Time. 2019 INDICON, 1–4. doi:10.1109/INDICON47234.2019.9030305
    • Miranda, M., Villanueva, A., Buo, M. J., Merabite, R., Perez, S. P., & Rodriguez, J. M. (2018). Portable Prevention and Monitoring of Driver’s Drowsiness Focuses to Eyelid Movement Using IoT. 2018 HNICEM, 1–5. doi:10.1109/HNICEM.2018.8666334
    • Jie, Z., Mahmoud, M., Stafford-Fraser, Q., Robinson, P., Dias, E., & Skrypchuk, L. (2018). Analysis of Yawning Behaviour in Spontaneous Expressions of Drowsy Drivers. 2018 FG, 571–576. doi:10.1109/FG.2018.00091
    • Yu, J., Park, S., Lee, S., & Jeon, M. (2019). Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework. IEEE Transactions on Intelligent Transportation Systems, 20(11), 4206–4218. doi:10.1109/TITS.2018.2883823
    • Kusuma, S., Udayan, J. D., & Sachdeva, A. (2019). Driver Distraction Detection using Deep Learning and Computer Vision. 2019 ICICICT, 289–292. doi:10.1109/ICICICT46008.2019.8993260
    • Wang, Y., Jin, L., Li, K., Guo, B., Zheng, Y., & Shi, J. (2019). Drowsy Driving Detection Based on Fused Data and Information Granulation. IEEE Access, 7, 183739–183750. doi:10.1109/ACCESS.2019.2960157
    • Yang, C., Wang, X., & Mao, S. (2020). Unsupervised Drowsy Driving Detection With RFID. IEEE Transactions on Vehicular Technology, 69(8), 8151–8163. doi:10.1109/TVT.2020.2995835
    • Islam, M. M., Kowsar, I., Zaman, M. S., Rahman Sakib, M. F., & Saquib, N. (2020). An Algorithmic Approach to Driver Drowsiness Detection for Ensuring Safety in an Autonomous Car. 2020 TENSYMP, 328–333. doi:10.1109/TENSYMP50017.2020.9230766

Citation

Hemant Ahirwar, Minal Saxena "Real Time Driver Drowsiness Detection using Landmark Predicator in Deep Neural Network" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.1, pp.1-07, 2022.

Alzheimer's Disease Detection using Support Vector Machine

Vikash Kumar, Devendra Rewadikar

Research Paper | Journal Paper

Vol.2, Issue.1, pp.08-13, Mar-2022

Abstract References Citation Download

Abstract

A neurological disease that progresses over time, Alzheimer's disease (AD) is characterized by behavioral abnormalities, memory loss, and cognitive impairment. For management and intervention to be successful, early detection is essential. In order to diagnose Alzheimer's disease early, this research investigates the use of Support Vector Machines (SVM), a supervised machine learning approach. We evaluate the effectiveness of SVM classifiers, examine several characteristics taken from neuroimaging data and clinical evaluations, and talk about the clinical implications of our results. Encouragement For tasks involving regression and classification, supervised learning models called vector machines are employed. Their method involves locating the best hyperplane in a high-dimensional space to divide data points belonging to several classifications. SVMs work especially well with high-dimensional data, which makes them appropriate for use in neuroimaging applications.

Key-Words / Index Term: Alzheimer's Disease, Machine Learning, Neuroimaging, Early Diagnosis, Deep Learning, Biomarkers, Supervised Learning.

References

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  12. Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016). Alzheimer's disease diagnostics by adaptation of 3D convolutional network. arXiv preprint arXiv:1607.06583.
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Citation

Vikash Kumar, Devendra Rewadikar, "Alzheimer's Disease Detection using Support Vector Machine" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.1, pp.08-13, 2022.

Automatic Diabetic Retinopathy Detection By Using Deep Learning

Akrity Kumari, Aditi Pathak, Amit Saxena, Arun Pratap Singh

Research Paper | Journal Paper

Vol.2, Issue.1, pp.14-19, Mar-2022

Abstract References Citation Download

Abstract

Diabetes mellitus frequently results in diabetic retinopathy (DR), a condition that causes irreversible retinal lesions and increases the risk of blindness if left untreated. Ophthalmologists' manual diagnosis is labor-intensive and error-prone, necessitating computer-aided methods. In the area of DR detection using color fundus images, deep learning—more specifically, convolutional neural networks (CNNs)—has demonstrated encouraging outcomes. Current research has examined and studied cutting-edge techniques for DR classification that make use of deep learning. These methods provide enhanced medical image analysis capabilities, supporting early diagnosis and therapy. But issues like robust algorithms and the availability of datasets still need to be addressed in order to improve the accuracy and dependability of DR diagnosis.

Key-Words / Index Term: Diabetic Retinopathy; Deep Learning; Convolutional Neural Network (CNN); Fundus images; Stages of DR; Ophthalmology.

References

  1. Dutta, Suvajit, Manideep, Bonthala Basha, Syed Muzamil Caytiles, Ronnie Iyenger, N Ch Sriman Narayana. (2018). Classification of Diabetic Retinopathy Images by Using Deep Learning Models. International Journal of Grid and Distributed Computing, 11(1), 89–106. doi:10.14257/ijgdc.2018.11.1.09
  2. H Manoj S., & Ary A. Bosale. (2024). Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction. arXiv, abs/2401.02759. Link
  3. V. S., & V. R. (2021). A Survey on Diabetic Retinopathy Disease Detection and Classification using Deep Learning Techniques. Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, pp. 1–4. doi:10.1109/ICBSII51839.2021.9445163
  4. Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., & Tamura, T. (2009). Computer-based detection of diabetic retinopathy stages using digital fundus images. Journal of Engineering in Medicine, 223(5), 545–553.
  5. Gazala Mushtaq, & Farheen Siddiqui. (2021). IOP Materials Science and Engineering, 1070, 012049.
  6. Sudha, V., Priyanka, K., Kannathal, T., & Monisha, S. (2020). Diabetic Retinopathy Detection. International Journal of Engineering and Advanced Technology, 9(4), 2249–8958. doi:10.35940/ijeat.D7786.049420
  7. Diabetic Eye Exams. (truevisionoptometry.com)
  8. Zhiguang Wang, & Jianbo Yang. (2019). Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation. arXiv, 1703.10757.
  9. S. Mishra, S. Hanchate, & Z. Saquib. (2020). Diabetic Retinopathy Detection using Deep Learning. International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, pp. 515–520. doi:10.1109/ICSTCEE49637.2020.9277506
  10. Bajwa, A., Nosheen, N., Talpur, K. I., & Akram, S. (2023). A Prospective Study on Diabetic Retinopathy Detection Based on Modified CNN Using Fundus Images at Sindh Institute of Ophthalmology. Diagnostics, 13(3), 393. doi:10.3390/diagnostics13030393
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  18. Sisodia, D. S., Nair, S., & Khobragade, P. (2017). Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction for Early Detection of Diabetic Retinopathy. Biomedical and Pharmacology Journal.
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Citation

Akrity Kumari, Aditi Pathak, Amit Saxena, Arun Pratap Singh "Automatic Diabetic Retinopathy Detection By Using Deep Learning" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.1, pp.14-19, 2022.

Leaf Disease Detection using Polynomial SVM and Euclidean Distance Metric

Ritika Chouksey, Preety D. Swami

Research Paper | Journal Paper

Vol.2, Issue.1, pp.20-25, Mar-2022

Abstract References Citation Download

Abstract

Agriculture productivity is highly important in the world to survive. There are lots of involvements of artificial intelligence in agriculture to help the productivity. Automatic leaf disease detection is one of them. It is hard to diagnose the leaf disease by normal vision because it looks quite natural. If care is not handled properly then it directly affect the quality of the production. So, it is important to detect the disease at early stage through which production can be improved and proper care can be taken place. There are so many researches have been done in this field but there are certain flaws present in resulting the system. Proposed system is based on Polynomial SVM (Support Vector Machine) and Euclidean Distance Metric. Polynomial SVM is a classifier that can handle the non-linear data in a very effective manner. Euclidean Distance Metric calculates distance between two different clusters or points; through which decision can be made easily. Dataset has been taken from kaggle for four different categories; such as Alternaria Alternata, Bacterial Blight, Cercospora Leaf Spot and Healthy Leaves. System pertained 97.30 % of accuracy which is bit higher than previous one.

Key-Words / Index Term: Leaf Disease, Polynomial SVM, Euclidean Distance Metric, Alternaria Alternata, Bacterial Blight, Cercospora Leaf Spot .

References

  1. Vijai Singh, & A.K. Misra. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49.
  2. Savita N. Ghaiwat, & Parul Arora. (2014). Detection and classification of plant leaf diseases using image processing techniques: a review. International Journal of Recent Advances in Engineering and Technology, 2(3), 2347–2812.
  3. Sanjay B. Dhaygude, & Nitin P. Kumbhar. (2013). Agricultural plant leaf disease detection using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(1).
  4. Singh, Jaskaran, & Kaur, Harpreet. (2019). Plant Disease Detection Based on Region-Based Segmentation and KNN Classifier. In Advances in Intelligent Systems and Computing (pp. 154). doi:10.1007/978-3-030-00665-5_154
  5. E. Hossain, M. F. Hossain, & M. A. Rahaman. (2019). A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier. International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6. doi:10.1109/ECACE.2019.8679247
  6. Yousuf, Aamir, & Khan, Ufaq. (2021). Ensemble Classifier for Plant Disease Detection. International Journal of Computer Science and Mobile Computing, 10, 14–22. doi:10.47760/ijcsmc.2021.v10i01.003
  7. C. U. Kumari, S. Jeevan Prasad, & G. Mounika. (2019). Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN. 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1095–1098. doi:10.1109/ICCMC.2019.8819750
  8. A. Devaraj, K. Rathan, S. Jaahnavi, & K. Indira. (2019). Identification of Plant Disease using Image Processing Technique. International Conference on Communication and Signal Processing (ICCSP), pp. 749–753. doi:10.1109/ICCSP.2019.8698056

Citation

Ritika Chouksey, Preety D. Swami "Leaf Disease Detection using Polynomial SVM and Euclidean Distance Metric" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.1, pp.20-25, 2022.

A Review on various CAPTCHA Techniques in the field of Turing Test

Arun Pratap Singh, Amit Saxena

Research Paper | Journal Paper

Vol.2, Issue.1, pp.26-29, Dec-2021

Abstract References Citation Download

Abstract

Human and automated intervention can be measured using the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) security test. It is a test in which the intended intervention can be identified based on the subjects behavior or the way the problem is resolved. Numerous CAPTCHA challenges are available for you to select from such as distorted strings photo identification audio math and gaming CAPTCHA. In contrast to other CAPTCHAs game-based problems are both entertaining and incredibly secure. The player must use either a click-based or drag-and-drop method to solve an AI problem depending on the game. The study aims to analyze various CAPTCHA implementations contrast their shortcomings and talk about security-related concerns. The user must identify the images by their appearance and click the appropriate button in order to complete many CAPTCHAs which use click-based techniques. Nevertheless this kind of CAPTCHA can be gotten around using image processing techniques like object classifiers. Dragging an object to the desired location is an effective method but it necessitates action or the resolution of an intellectual problem. If an item is automatically dragged to the target location relay attacks could compromise the system.

Key-Words / Index Term: Web Security, Picture Recognition, Mathematics CATPCHA, Image Processing, Relay Attack.

References

  1. Adapt. (2018). CAPTCHA. [Online]. Available: https://www.adaptworldwide.com/insights/2018/the-evolution-of-captcha [Accessed: 29-Jan-2021].
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  3. Cao Lei. (2015). Image CAPTCHA technology research based on the mechanism of finger-guessing game. Third International Conference on Cyberspace Technology (CCT), pp. 1–4. doi:10.1049/cp.2015.0843
  4. Ibrahim Furkan Ince, Yucel Batu Salman, Mustafa Eren Yildirim, & Tae-Cheon Yang. (2009). Execution Time Prediction For 3D Interactive CAPTCHA By Keystroke Level Model. Fourth International Conference on Computer Sciences and Convergence Information Technology, IEEE.
  5. Aadhirai R, Sathish Kumar P J, & Vishnupriya S. (2012). Image CAPTCHA: Based on Human Understanding of Real World Distances. Proceedings of 4th International Conference on Intelligent Human Computer Interaction, IEEE.
  6. N. Payal, & R. K. Challa. (2016). AJIGJAX: A hybrid image based model for Captcha/CaRP. IEEE UPCON, pp. 38–43. doi:10.1109/UPCON.2016.7894621
  7. Ahmet Cakmak, & Muhammet Balcilar. (2019). Audio Captcha Recognition Using RastaPLP Features by SVM.
  8. Monther Aldwairi, Suaad Mohammed, & Megana Padmanabhan. (2020). Efficient and Secure Flash-based Gaming CAPTCHA. Z. Li, & Q. Liao. (2018). CAPTCHA: Machine or Human Solvers? A Game-Theoretical Analysis. 5th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) / 4th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 18–23. doi:10.1109/CSCloud/EdgeCom.2018.00013
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Citation

Arun Pratap Singh, Amit Saxena "A Review on various CAPTCHA Techniques in the field of Turing Test" International Journal of Scientific Research in Technology & Management, Vol.2, Issue.1, pp.26-29, 2021.