In this paper, we are introducing a fast hybrid fuzzy classification algorithm with feature reduction for medical images. We incorporated the quantum-based grasshopper computing algorithm (QGH) with feature extraction using fuzzy clustering technique ( C -means). QGH integrates quantum computing into machine learning and intelligence applications. The objective of our technique is to the integrate QGH method, specifically into cervical cancer detection that is based on image processing. Many features such as color, geometry, and texture found in the cells imaged in Pap smear lab test are very crucial in cancer diagnosis. Our proposed technique is based on the extraction of the best features using a more than 2600 public Pap smear images and further applies feature reduction technique to reduce the feature space. Performance evaluation of our approach evaluates the influence of the extracted feature on the classification precision by performing two experimental setups. First setup is using all the extracted features which leads to classification without feature bias. The second setup is a fusion technique which utilized QGH with the fuzzy C-means algorithm to choose the best features. In the setups, we allocate the assessment to accuracy based on the selection of best features and of different categories of the cancer. In the last setup, we utilized a fusion technique engaged with statistical techniques to launch a qualitative agreement with the feature selection in several experimental setups.
Localization of suspicious moving objects in dynamic environments requires high accuracy mapping. A deep learning model is proposed to track crossing moving objects in the opposite direction. Moving objects locus measurements are computed from the space included in the boundaries of the images in the intersecting cameras. Object appearance is designated by the color and textural histograms in the intersecting camera views. The incorrect mapping of moving objects in a dynamic environment through synchronized localization can be considerably increased in complex areas. This is done due to the presence of unfit points that are triggered by moving targets. To face this problem, a robust model using the dynamic province rejection technique (DPR) is presented. We are proposing a novel model that incorporates a combination of the deep learning method and a tracking system that rejects dynamic areas which are not within the environment boundary of interest. The technique detects the dynamic points from sequential video images and partitions the current video image into super blocks and tags the border differences. In the last stage, dynamic areas are computed from dynamic points and superblock boundaries. Static regions are utilized to compute the positions to enhance the path computation precision of the model. Simulation results show that the introduced model has better performance than the state-of-the-art similar models in both the VID and MOVSD4 datasets and is higher than the state-of-the-art tracking systems with better speed performance. The experiments prove that the computed path error in the dynamic setting can be decreased by 81%.
Moving object tracking techniques using machine and deep learning require large datasets for neural model training. New strategies need to be invented that utilize smaller data training sizes to realize the impact of large-sized datasets. However, current research does not balance the training data size and neural parameters, which creates the problem of inadequacy of the information provided by the low visual data content for parameter optimization. To enhance the performance of moving object tracking that appears in only a few frames, this research proposes a deep learning model using an abundant encoder–decoder (a high-resolution transformer (HRT) encoder–decoder). An HRT encoder–decoder employs feature map extraction that focuses on high resolution feature maps that are more representative of the moving object. In addition, we employ the proposed HRT encoder–decoder for feature map extraction and fusion to reimburse the few frames that have the visual information. Our extensive experiments on the Pascal DOC19 and MS-DS17 datasets have implied that the HRT encoder–decoder abundant model outperforms those of previous studies involving few frames that include moving objects.
Actual diffusion activity function is an important metric utilized to describe the diffusion activities of a vacancy defect substance. In this paper, we propose a deep learning three-dimensional convolutional CNN model (D3-CNN). A 3D convolution has its kernel slides in three dimensions as opposed to two dimensions with 2D convolutions. 3D convolution is more suitable for three-dimensional data. We also propose an amplification learning technique to predict the actual diffusion activity of a vacancy defect substance, which is impacted by the geometrical parameters of the defect substance and the vacancy distribution function. In this model, the geometric parameters of a three-dimensional constructed vacancy defect substance are generated. The 3D dataset is obtained by the atoms diffusion defect (ADD) simulation model. The geometric parameters of the 3D vacancy defect substance are computed by the proposed amplification technique. The 3D geometric parameters and the diffusion activity values are applied to a deep learning model for training. The actual diffusion activity values of a substance with a vacancy size ranging from size 0.52 mm to 0.61 mm are used for training, and the actual diffusion activity values of substance vacancy of size between 0.41 and 1.01 are classified by the three-dimensional network. The model can realize high speed and accuracy for the actual diffusion activity value. The mean relative absolute errors between the D3-CNN and the ADD models are 0.028–7.85% with a vacancy size of 0.41 to 0.81. For a usual sample with a vacancy of size equal to 0.6, the CPU computation load required by our model is 14.2 × 10−2 h, while the time required is 15.16 h for the ADD model. These results indicate that our proposed deep learning model has a strong learning capability and can function as an influential model to classify the diffusion activity of compound vacancy defect substances.
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