An Efficient Person Re-Identification Method Based on Deep Transfer Learning Techniques

Shimaa Saber; Khalid Amin; Mohamed Adel Hammad

doi:10.21608/ijci.2021.207824

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Sensors ◽

10.3390/s19224850 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4850 ◽

Cited By ~ 6

Author(s):

Carlos S. Pereira ◽

Raul Morais ◽

Manuel J. C. S. Reis

Keyword(s):

Transfer Learning ◽

Climatic Conditions ◽

Fine Tuning ◽

Variety Identification ◽

Test Accuracy ◽

Accuracy Score ◽

Learning Techniques ◽

Four Corners ◽

Integrated Software ◽

Grape Varieties

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

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“A comparative study of shallow learning and deep transfer learning techniques for accurate fingerprints vitality detection”

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.07.025 ◽

2021 ◽

Author(s):

Donato Impedovo ◽

Vincenzo Dentamaro ◽

Giacomo Abbattista ◽

Vincenzo Gattulli ◽

Giuseppe Pirlo

Keyword(s):

Comparative Study ◽

Transfer Learning ◽

Learning Techniques

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StackBC: Deep learning and transfer learning techniques based stacking approach for accurate Invasive Ductal Carcinoma classification using histology images

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212240 ◽

2022 ◽

pp. 1-12

Author(s):

Amin Ul Haq ◽

Jian Ping Li ◽

Samad Wali ◽

Sultan Ahmad ◽

Zafar Ali ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Transfer Learning ◽

Invasive Ductal Carcinoma ◽

Ductal Carcinoma ◽

Early Recognition ◽

Predictive Performance ◽

Operating Characteristics ◽

Data Set ◽

Learning Techniques

Artificial intelligence (AI) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.

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Applying a Deep Q Network for OpenAIs Car Racing Game

10.14293/s2199-1006.1.sor-.ppd7fvs.v1 ◽

2020 ◽

Author(s):

Ali Fakhry

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Transfer Learning ◽

State Of The Art ◽

Learning Techniques ◽

Car Racing ◽

Custom Made ◽

Learning Technique ◽

Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

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Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i3.pp1681-1688 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1681

Author(s):

Aditya Rajbongshi ◽

Thaharim Khan ◽

Md. Mahbubur Rahman ◽

Anik Pramanik ◽

Shah Md Tanvir Siddiquee ◽

...

Keyword(s):

Transfer Learning ◽

Image Data ◽

Network Models ◽

Plant Diseases ◽

Prevention Measures ◽

Neural Network Models ◽

Data Set ◽

Learning Techniques ◽

Overall Performance ◽

Mango Leaves

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

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