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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.

Traditional methods for plant disease detection involve expert inspection, which is both subjective and time-consuming. Convolutional neural networks (CNNs), Extreme Gradient Boosting are combined in this model to increase accuracy in plant disease classification. CNN can extract and learn features from leaf images, while EGB optimise accuracy by extracting patterns. XGBoost stands out with its efficient boosting techniques that combine weak learners into stronger classifiers for enhanced performance. Finally, an ensemble technique combining predictions from both classifiers leverages their respective strengths for optimal plant disease detection, achieving an overall accuracy rate of more than 97.2%.

Convolutional neural networks (CNN) have been extensively employed for image classification due to their high accuracy. However, inference is a computationally-intensive process that often requires hardware acceleration to operate in real time. For mobile devices, the power consumption of graphics processors (GPUs) is frequently prohibitive, and field-programmable gate arrays (FPGA) become a solution to perform inference at high speed. Although previous works have implemented CNN inference on FPGAs, their high utilization of on-chip memory and arithmetic resources complicate their application on resource-constrained edge devices. In this paper, we present a scalable, low power, low resource-utilization accelerator architecture for inference on the MobileNet V2 CNN. The architecture uses a heterogeneous system with an embedded processor as the main controller, external memory to store network data, and dedicated hardware implemented on reconfigurable logic with a scalable number of processing elements (PE). Implemented on a XCZU7EV FPGA running at 200 MHz and using four PEs, the accelerator infers with 87% top-5 accuracy and processes an image of 224×224 pixels in 220 ms. It consumes 7.35 W of power and uses less than 30% of the logic and arithmetic resources used by other MobileNet FPGA accelerators.

Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.

Plant growth is inevitably affected by diseases, and one effective method of disease detection is through the observation of leaf changes. To solve the problem of disease detection in complex backgrounds, where the distinction between plant diseases is hindered by large intra-class differences and small inter-class differences, a complete plant-disease recognition process is proposed. The process was tested through experiments and research into traditional and deep features. In the face of difficulties related to plant-disease classification in complex backgrounds, the advantages of strong interpretability of traditional features and great robustness of deep features are fully utilized, and include the following components: (1) The OSTU algorithm based on the naive Bayes model is proposed to focus on where leaves are located and remove interference from complex backgrounds. (2) A multi-dimensional feature model is introduced in an interpretable manner from the perspective of traditional features to obtain leaf characteristics. (3) A MobileNet V2 network with a dual attention mechanism is proposed to establish a model that operates in both spatial and channel dimensions at the network level to facilitate plant-disease recognition. In the Plant Village open database test, the results demonstrated an average SEN of 94%, greater than other algorithms by 12.6%.

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the XGBoost, Random Forest, and Decision Tree algorithms. In this work, the average accuracy was over 90%, reaching 97.4%. This work proves that using hybrid artificial intelligence systems for tasks with a low computational complexity of the processing units demonstrates a high classification accuracy. The methods used in this study, confirmed by the promising results, can be an effective tool in diagnosing other blood diseases, facilitating the work of a network of medical institutions to carry out the correct treatment schedule.

The novel coronavirus outbreak has spread worldwide, causing respiratory infections in humans, leading to a huge global pandemic COVID-19. According to World Health Organization, the only way to curb this spread is by increasing the testing and isolating the infected. Meanwhile, the clinical testing currently being followed is not easily accessible and requires much time to give the results. In this scenario, remote diagnostic systems could become a handy solution. Some existing studies leverage the deep learning approach to provide an effective alternative to clinical diagnostic techniques. However, it is difficult to use such complex networks in resource constraint environments. To address this problem, we developed a fine-tuned deep learning model inspired by the architecture of the MobileNet V2 model. Moreover, the developed model is further optimized in terms of its size and complexity to make it compatible with mobile and edge devices. The results of extensive experimentation performed on a real-world dataset consisting of 2482 chest Computerized Tomography scan images strongly suggest the superiority of the developed fine-tuned deep learning model in terms of high accuracy and faster diagnosis time. The proposed model achieved a classification accuracy of 96.40%, with approximately ten times shorter response time than prevailing deep learning models. Further, McNemar’s statistical test results also prove the efficacy of the proposed model.

Most of the wheat produced and consumed worldwide is generally bread wheat and is used for bread making. Bread wheat varieties can affect the quality of bread. When comparing bread wheat to other varieties, there may be differences in taste, cost, and impact on human health. This study aims to classify bread wheat varieties using deep learning methods. Wheat cultivars used in this research (‘Ayten Abla’, ‘Bayraktar 2000’, ‘Hamitbey’, ‘Şanlı’, and ‘Tosunbey’) were obtained from the Central Field Crop Research Institute, Ministry of Agriculture and Forestry, Republic of Türkiye. First, a dataset of 8354 images of these wheat varieties was created. Then, the images in this dataset were trained with tree different Convolutional Neural Networks (CNNs) using the transfer learning method. The CNN models used are Inception-V3, Mobilenet-V2, and Resnet18, and the classification accuracies obtained are 97.37%, 97.07%, and 97.67%, respectively. Finally, the images not used for training and validation of the CNN models were segmented using image processing techniques. The segmented images were classified as bread wheat and unidentified seeds in the Resnet18 CNN model.

This research proposes a study on two-way communication between deaf/mute and normal people using an Android application. Despite advancements in technology, there is still a lack of mobile applications that facilitate two-way communication between deaf/mute and normal people, especially by using Bahasa Isyarat Malaysia (BIM). This project consists of three parts: First, we use BIM letters, which enables the recognition of BIM letters and BIM combined letters to form a word. In this part, a MobileNet pre-trained model is implemented to train the model with a total of 87,000 images for 29 classes, with a 10% test size and a 90% training size. The second part is BIM word hand gestures, which consists of five classes that are trained with the SSD-MobileNet-V2 FPNLite 320 × 320 pre-trained model with a speed of 22 s/frame rate and COCO mAP of 22.2, with a total of 500 images for all five classes and first-time training set to 2000 steps, while the second- and third-time training are set to 2500 steps. The third part is Android application development using Android Studio, which contains the features of the BIM letters and BIM word hand gestures, with the trained models converted into TensorFlow Lite. This feature also includes the conversion of speech to text, whereby this feature allows converting speech to text through the Android application. Thus, BIM letters obtain 99.75% accuracy after training the models, while BIM word hand gestures obtain 61.60% accuracy. The suggested system is validated as a result of these simulations and tests.