| 1. |
- Ahmed, Faisal, et al.
(författare)
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Machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features
- 2023
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Ingår i: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022. - : Springer Science and Business Media Deutschland GmbH. - 9789811994821 - 9789811994838 ; , s. 25-35
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Konferensbidrag (refereegranskat)abstract
- Tomato leaves can be infected with various infectious viruses and fungal diseases that drastically reduce tomato production and incur a great economic loss. Therefore, tomato leaf disease detection and identification are crucial for maintaining the global demand for tomatoes for a large population. This paper proposes a machine learning-based technique to identify diseases on tomato leaves and classify them into three diseases (Septoria, Yellow Curl Leaf, and Late Blight) and one healthy class. The proposed method extracts radiomics-based features from tomato leaf images and identifies the disease with a gradient boosting classifier. The dataset used in this study consists of 4000 tomato leaf disease images collected from the Plant Village dataset. The experimental results demonstrate the effectiveness and applicability of our proposed method for tomato leaf disease detection and classification.
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| 2. |
- Ahmed, Tawsin Uddin, et al.
(författare)
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A Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time
- 2022
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Ingår i: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. - Singapore : Springer Nature. ; , s. 487-500
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Konferensbidrag (refereegranskat)abstract
- The enormous use of facial expression recognition in various sectors of computer science elevates the interest of researchers to research this topic. Computer vision coupled with deep learning approach formulates a way to solve several real-world problems. For instance, in robotics, to carry out as well as to strengthen the communication between expert systems and human or even between expert agents, it is one of the requirements to analyze information from visual content. Facial expression recognition is one of the trending topics in the area of computer vision. In our previous work, a facial expression recognition system is delivered which can classify an image into seven universal facial expressions—angry, disgust, fear, happy, neutral, sad, and surprise. This is the extension of our previous research in which a real-time facial expression recognition system is proposed that can recognize a total of ten facial expressions including the previous seven facial expressions and additional three facial expressions—mockery, think, and wink from video streaming data. After model training, the proposed model has been able to gain high validation accuracy on a combined facial expression dataset. Moreover, the real-time validation of the proposed model is also promising.
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| 3. |
- Ahmed, Tawsin Uddin, et al.
(författare)
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An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty
- 2022
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 14:2, s. 660-676
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Tidskriftsartikel (refereegranskat)abstract
- The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.
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| 4. |
- Raihan Uddin, M., et al.
(författare)
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Energy analysis of a solar driven vaccine refrigerator using environment-friendly refrigerants for off-grid locations
- 2021
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Ingår i: Energy Conversion and Management. - : Elsevier Ltd. - 2590-1745. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- In many remote localities, one of the underlying reasons for not receiving life-saving vaccines is the lack of electricity to store the vaccines in the required refrigerated conditions. Solar Photovoltaic (PV) refrigerators have been considered as a viable and green solution to store the vaccines in remote localities having no access to electricity. In this paper, a detailed methodology has been presented for the performance evaluation of a solar PV powered vaccine refrigerator for remote locations. Thermal modelling with hourly cooling load calculations and refrigeration cycle simulations were carried out. The performance parameters for three environment-friendly refrigerants: R152a, R1234yf, and R1234ze(E) has been compared against the commonly used R134a for two remote, off-grid locations in Bangladesh and South Sudan. The energy systems comprising of solar PV panels and batteries to run the refrigerator were modelled in HOMER software for techno-economic optimizations. For both the locations, R152a was found to be the best performing refrigerant exhibiting higher COP (2%−5.29%) as compared to the other refrigerants throughout the year, while R1234ze(E) exhibited COPs on par with R134a, and R1234yf had the least performance. Techno-economic analysis showed an energy system providing electricity to the refrigerator with R152a also had lower levelized cost of electricity (0.48%−2.54%) than the systems having other refrigerants in these locations.
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| 5. |
- Uddin Ahmed, Tawsin, et al.
(författare)
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Facial Expression Recognition using Convolutional Neural Network with Data Augmentation
- 2019
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Ingår i: Joint 2019 8th International Conference on Informatics, Electronics and Vision (ICIEV) & 3rd International Conference on Imaging, Vision & Pattern Recognition (IVPR) with International Conference on Activity and Behavior Computing (ABC). - : IEEE. ; , s. 336-341
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Konferensbidrag (refereegranskat)abstract
- Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.
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