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Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

Shih-Yu Chen, Chinsu Lin, Guan-Jie Li, Yu-Chun Hsu, Keng-Hao Liu
2021 Sensors  
This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE).  ...  It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change  ...  We would also like to appreciate ISUZU OPTICS CORP. for the financial and technical support. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21062077 pmid:33809537 fatcat:ad6n4d4jyzdjply47oxjwdtgve

Editorial for Special Issue "Hyperspectral Imaging and Applications"

Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu
2019 Remote Sensing  
The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore  ...  This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification  ...  Applications (Forestry: Detection of newly grown tree leaves) 10-00096 Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves Shih-Yu Chen, Chinsu Lin, Chia-Hui  ... 
doi:10.3390/rs11172012 fatcat:c23u3rahgjhctowk5xwllt2qea

Robust Intelligent Malware Detection Using Deep Learning

Vinayakumar R, Mamoun Alazab, Soman KP, Prabaharan Poornachandran, Sitalakshmi Venkatraman
2019 IEEE Access  
Third, our major contribution is in proposing a novel image processing technique with optimal parameters for MLAs and deep learning architectures to arrive at an effective zero-day malware detection model  ...  Overall, this paper paves way for an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments.  ...  ACKNOWLEDGEMENT The authors would like to thank NVIDIA India, for the GPU hardware support to research grant.  ... 
doi:10.1109/access.2019.2906934 fatcat:hr4vctlh55cbhamkvh5fq2hubu

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

Mamunur Rashid, Bifta Sama Bari, Yusri Yusup, Mohamad Anuar Kamaruddin, Nuzhat Khan
2021 IEEE Access  
This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development of an extremely effective model for the predicti [...]  ...  Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil  ...  Historically, traditional machine learning techniques, classical image processing methods and deep learning methods can be applied to tree crown detection.  ... 
doi:10.1109/access.2021.3075159 doaj:b22dca1ce3294b0fa7916d35217bbb9d fatcat:iha4be2c5beplhi52ajj3b5si4

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems.  ...  of ML-based RSP techniques.  ...  In this way, the representation capability of the sparse model was enhanced.  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Melissa R. Allen-Dumas, Haowen Xu, Kuldeep R. Kurte, Deeksha Rastogi
2021 Frontiers in Water  
In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges  ...  However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary  ...  Random forests are extensions of decision tree analysis that start with classification trees-types of decision trees that can be grown together as a "forest" in a computational system.  ... 
doi:10.3389/frwa.2020.562304 fatcat:4g4x5qsljva63fzfibqyjhsdsi

An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques

Nabeel Albishry, Rayed AlGhamdi, Abdulmohsen Almalawi, Asif Irshad Khan, Pravin R. Kshirsagar, BaruDebtera, Arpit Bhardwaj
2022 Computational Intelligence and Neuroscience  
This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection.  ...  Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware.  ...  Acknowledgments is project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no.  ... 
doi:10.1155/2022/5061059 pmid:35510059 pmcid:PMC9061036 fatcat:jcly4o4tznhtrjkkriaytj53w4

Machine learning for biochemical engineering: A review

Max Mowbray, Thomas Savage, Chufan Wu, Ziqi Song, Bovinille Anye Cho, Ehecatl A. Del Rio-Chanona, Dongda Zhang
2021 Biochemical engineering journal  
Finally, core challenges into the application of machine learning in biochemical engineering are thoroughly discussed, and further insight into adoption of innovative hybrid modelling and transfer learning  ...  In doing so we provide insights into the true benefits of each technique, and obstacles for their wider deployment.  ...  leaves per tree) and learning rate.  ... 
doi:10.1016/j.bej.2021.108054 fatcat:jvbkblcoevghxm4swnormswt64

Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity [article]

Arnaud Joly
2017 arXiv   pre-print
, (ii) learning with large sample datasets and stringent memory constraints at prediction time and (iii) learning over high dimensional sparse input spaces.  ...  Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior.  ...  For these two reasons, linear methods are often preferred to decision tree algorithms to learn with sparse datasets.  ... 
arXiv:1704.08067v1 fatcat:avmezfswrra6xbm6ed7ryrihwa

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Lei Feng, Baohua Wu, Susu Zhu, Yong He, Chu Zhang
2021 Frontiers in Nutrition  
In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning  ...  Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.  ...  Model transfer, transfer learning, reinforcement learning, and other methods will enhance the universality and stability of models.  ... 
doi:10.3389/fnut.2021.680357 fatcat:rbpztnvsi5gbtpi75rddkiig7a

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation  ...  The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are  ...  However, deep learning algorithms are well suited for fusing diverse data streams. Thus, this approach promises to enhance the potential of radiomics techniques in all aspects of radiology.  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

Survey on deep learning with class imbalance

Justin M. Johnson, Taghi M. Khoshgoftaar
2019 Journal of Big Data  
deep learning techniques for addressing class imbalanced data.  ...  Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their constructive evaluation of this paper, and the various members of the Data Mining and Machine Learning Laboratory, Florida  ... 
doi:10.1186/s40537-019-0192-5 fatcat:dor65fgn7ffhxmqqv3mkold6wq

Improving Pharmacological Research of HIV-1 Integrase Inhibition Using Differential Evolution - Binary Particle Swarm Optimization and Nonlinear Adaptive Boosting Random Forest Regression

Richard Adrian Galvan, Ahmad Reza Hadaegh, Matinehalsadat Kashani Moghaddam
2015 2015 IEEE International Conference on Information Reuse and Integration  
This report extends an initial investigative study of Aryl β-Diketo Acids for HIV-1 Integrase inhibition that used linear QSAR models implemented using a Multiple Linear Regression (MLR) machine learning  ...  This comparative study uses a non-linear Random Forest Regression (RFR) strategy with Adaptive Boosting (AdaBoost) to generate QSAR models with greater predictive accuracy in identifying optimal Aryl β-Diketo  ...  Decision trees that are grown very deep, for example, tend to learn highly irregular patterns making them more prone to over-fitting a training set by having low bias and high variance [24] .  ... 
doi:10.1109/iri.2015.80 dblp:conf/iri/GalvanHM15 fatcat:mhh7yyiewfhgtg6dyblw7l3v7i

Pixel-based reverse engineering of graphical interfaces

Morgan Dixon
2013 Proceedings of the adjunct publication of the 26th annual ACM symposium on User interface software and technology - UIST '13 Adjunct  
and serve a wide variety of people with complex needs.  ...  For example, we use our pixel-based methods to implement many different modifications including accessibility enhancements, improved input on mobile devices, interface translation for improved localization  ...  We ignore elements in dense layouts, and leave widgets in sparse layouts with a default left or horizontal direction.  ... 
doi:10.1145/2508468.2508469 dblp:conf/uist/Dixon13 fatcat:4ixpcqz64fcwfaoahgfu2a5efu

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Simon Nam Thanh Vu, Mads Stege, Peter Issam El-Habr, Jesper Bang, Nicola Dragoni
2021 Future Internet  
Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions.  ...  The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security.  ...  Detection models were developed for each device using numerous machine learning modes, including deep learning models.  ... 
doi:10.3390/fi13080198 fatcat:5umqenw47ncdxggi4kiotkeag4
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