Deep Learning Algorithm Design for Discovery and Dysfunction of Landmines
DOI:
https://doi.org/10.22399/ijcesen.686Keywords:
Deep learning, Landmine, Dysfunction, Machine learningAbstract
Deep Learning is a cutting-edge technology which has a noteworthy impact in the real-world applications. The multi-layer neural nets involved in the blueprint of deep learning enables it to deliver a comprehensive decision-making system with quality of “think alike human cerebrum”. Deep Learning assumes an essential part in various fields like horticulture, medication, substantial business and so forth. Deep Learning can be well prompted in the remote sensing applications especially in perilous military applications. The location of land mines can be detected using a deep learning algorithm design technique aided with distinctive machine learning tools and techniques. The intelligent system designed by the deep learning process involves a massive dataset including the assorted features of the landmines like size, sort, dampness, ground profundity and so on. Incorporation of Geographical Information System can give a prevalent statistical analysis of the varied landmines. The multiple layers present in the deep learning neural schema may increase the feature extraction and the knowledge representation through increase in the complexities of landmines’ input sets. The likelihood of brokenness of landmines can be increased by the utilization of deep learning prediction model which enormously helps the survival of militaries, creating a social effect.
References
Bordes, A., Chopra, S. & Weston, J. (2014). Question answering with subgraph embeddings. In Proc Empirical Methods in Natural Language Processing http://arxiv.org/abs/1406.3676v3.
Bordes, A., Chopra, S. & Weston, J. (2014). Question answering with subgraph embeddings. In Proc. Empirical Methods in Natural Language Processing http://arxiv.org/abs/1406.3676v3.
Bottou, L. & Bousquet, O. (2007). The tradeoffs of large scale learning. In Proc. Advances in Neural Information Processing Systems, 20, :161–168.
Ciresan, D., Meier, U. Masci, J. & Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification. Neural Networks 32;333–338.
Collobert, R., et al. (2011). Natural language processing (almost) from scratch. J. Mach. Learn. Res.12;2493–2537. https://doi.org/10.48550/arXiv.1103.0398
Duda, R. O. & Hart, P. E.Pattern. (1973). Classification and Scene Analysis (Wiley). DOI:10.2307/1573081
Garcia, C. & Delakis, M. (2004). Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Machine Intell.26, “1408–1423.
Graves, A., Mohamed, A.-R. & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In Proc. International Conference on Acoustics, Speech and Signal Processing, :6645–6649.
Graves, A., Wayne, G. & Danihelka, I. (2014). Neural Turing machines. http://arxiv.org/abs/1410.5401.
Helmstaedter, M. etal. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500;168–174. https://doi.org/10.1038/nature12346
Hinton, G. E., Dayan, P., Frey, B. J. & Neal, R. M. (1995). The wake-sleep algorithm for unsupervised neural networks. Science 268;1558–1161.
Jean, S., Cho, K., Memisevic, R. & Bengio, Y. (2015). On using very large target vocabulary for neural machine translation. In Proc. ACL-IJCNLP http://arxiv.org/abs/1412.2007.
Kaggle. (2014). Higgs boson machine learning challenge. Kaggle https://www.kaggle.com/c/higgs-boson.
Lawrence, S., Giles, C. L., Tsoi, A. C. & Back, A. D. (1997). Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Networks 8;98–113. doi: 10.1109/72.554195.
Leung, M. K., Xiong, H. Y., Lee, L. J. & Frey, B. J. (2014). Deep learning of the tissue- regulated splicing code. Bioinformatics 30;1121–1129.
Nowlan, S. Platt, J. (1995). Neural Information Processing Systems, 901–908.
Osadchy, M., LeCun, Y. & Miller, M. (2007). Synergistic face detection and pose estimation with energy-based models. J. Mach. Learn. Res.8;1197–1215.
Sainath, T., Mohamed, A.-R., Kingsbury, B. & Ramabhadran, B. ( 2013). Deep convolutional neural networks for LVCSR. In Proc. Acoustics, Speech and Signal Processing 8614–8618.
Simard, D., Steinkraus, P. Y. & Platt, J. C. (2003). Best practices for convolutional neural networks. In Proc Document Analysis and Recognition, 958–963.
Socher, R., Lin, C. C-Y., Manning, C. & Ng, A. Y. (2011). Parsing natural scenes and natural language with recursive neural networks. In Proc. International Conference on Machine Learning, 129–136.
Sutskever, I. Vinyals, O. & Le. Q. V. (2014). Sequence to sequence learning with neural networks. In Proc. Advances in Neural Information Processing Systems 273104–3112.
Tompson, J., Goroshin, R. R., Jain, A., LeCun, Y. Y. & Bregler, C. C. (2014). Efficient object localization using convolutional networks. In Proc. Conference on Computer Vision and Pattern Recognition http://arxiv.org/abs/1411.4280.
Vaillant, R., Monrocq, C. & LeCun, Y. (1994). Original approach for the localisation of objects in images. In Proc. Vision, Image, and Signal Processing, 141, :245–250.
Weston, J. Chopra, S. & Bordes, A. (2015). Memory networks. http://arxiv.org/abs/1410.3916.
Weston, J., Bordes, A., Chopra, S. & Mikolov, T. (2015). Towards AI-complete question answering: a set of prerequisite toy asks. http://arxiv.org/abs/1502.05698.
Xiong HY, Alipanahi B, Lee LJ, et al. (2015). The human splicing code reveals new insights into the genetic determinants of disease. Science. 347(6218): 1254806. doi:10.1126/science.1254806.
U. S. Pavitha, S. Nikhila, & Mohan, M. (2024). Hybrid Deep Learning Based Model for Removing Grid-Line Artifacts from Radiographical Images. International Journal of Computational and Experimental Science and Engineering, 10(4);763-774. https://doi.org/10.22399/ijcesen.514
P. Padma, & G. Siva Nageswara Rao. (2024). CBDC-Net: Recurrent Bidirectional LSTM Neural Networks Based Cyberbullying Detection with Synonym-Level N-Gram and TSR-SCSOFeatures. International Journal of Computational and Experimental Science and Engineering, 10(4);1486-1500. https://doi.org/10.22399/ijcesen.623
P. Jagdish Kumar, & S. Neduncheliyan. (2024). A novel optimized deep learning based intrusion detection framework for an IoT networks. International Journal of Computational and Experimental Science and Engineering, 10(4);1169-1180. https://doi.org/10.22399/ijcesen.597
Rama Lakshmi BOYAPATI, & Radhika YALAVARTHI. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);879-889. https://doi.org/10.22399/ijcesen.519
M, V., V, J., K, A., Kalakoti, G., & Nithila, E. (2024). Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support. International Journal of Computational and Experimental Science and Engineering, 10(4);575-584. https://doi.org/10.22399/ijcesen.479
Venkatraman Umbalacheri Ramasamy. (2024). Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review. International Journal of Computational and Experimental Science and Engineering, 10(4);898-910. https://doi.org/10.22399/ijcesen.522
Agnihotri, A., & Kohli, N. (2024). A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images. International Journal of Computational and Experimental Science and Engineering, 10(4);592-613. https://doi.org/10.22399/ijcesen.425
Naresh Babu KOSURI, & Suneetha MANNE. (2024). Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios. International Journal of Computational and Experimental Science and Engineering, 10(4);1015-1031. https://doi.org/10.22399/ijcesen.560
M. Venkateswarlu, K. Thilagam, R. Pushpavalli, B. Buvaneswari, Sachin Harne, & Tatiraju.V.Rajani Kanth. (2024). Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments. International Journal of Computational and Experimental Science and Engineering, 10(4);1140-1148. https://doi.org/10.22399/ijcesen.676
Jha, K., Sumit Srivastava, & Aruna Jain. (2024). A Novel Texture based Approach for Facial Liveness Detection and Authentication using Deep Learning Classifier. International Journal of Computational and Experimental Science and Engineering, 10(3);323-331. https://doi.org/10.22399/ijcesen.369
LAVUDIYA, N. S., & C.V.P.R Prasad. (2024). Enhancing Ophthalmological Diagnoses: An Adaptive Ensemble Learning Approach Using Fundus and OCT Imaging. International Journal of Computational and Experimental Science and Engineering, 10(4);1541-1550. https://doi.org/10.22399/ijcesen.678
Achuthankutty, S., M, P., K, D., P, K., & R, prathipa. (2024). Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4);731-743. https://doi.org/10.22399/ijcesen.512
Bolleddu Devananda Rao, & K. Madhavi. (2024). BCDNet: A Deep Learning Model with Improved Convolutional Neural Network for Efficient Detection of Bone Cancer Using Histology Images. International Journal of Computational and Experimental Science and Engineering, 10(4);988-998. https://doi.org/10.22399/ijcesen.430
Boddupally JANAIAH, & Suresh PABBOJU. (2024). HARGAN: Generative Adversarial Network BasedDeep Learning Framework for Efficient Recognition of Human Actions from Surveillance Videos. International Journal of Computational and Experimental Science and Engineering, 10(4);1379-1393. https://doi.org/10.22399/ijcesen.587
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