AlexNet Model Research Articles

ANALYSIS OF GRAPE LEAF DISEASE BY USING DEEP CONVOLUTIONAL NEURAL NETWORK

Smart agriculture is a strategy for restructuring and reorganizing agricultural systems to ensure food security in the face of emerging climate change challenges. Diseases cause problems on agricultural development and yield and they're generally tough to control. It is necessary to have a precise diagnosis of the grape leaf diseases and preventative measures before time. In order to diagnose grape leaf diseases, this research suggests a novel recognition method that is based on enhanced convolutional neural networks. Firstly; addressed the grape leaf disease types into four categories such as Esca, black rot, Leaf Blight, and healthy which cause loss for grape industry every year. A large dataset of labeled images is collected and prepared for training. The images are typically pre-processed to enhance their features and remove any noise or artifacts that might interfere with the CNN's ability to recognize patterns. Data collection, data pre-processing, and image categorization are the three main phases of the study's approach. Secondly; Images are classified and mapped to their respective disease categories on the basis of three features namely, color and texture. Extensive experiments performed on MATLAB using CNN model AlexNet. The CNN training process used learning rate 0.0001 which produced better results and obtained better accuracy. Overall, an accurate diagnosis of grape leaf diseases and the implementation of effective preventative measures will help to reduce the impact of diseases on agricultural development and yield. This will help to ensure a sustainable and profitable grape production industry for farmers and communities.

A high-efficiency spaceborne processor for hybrid neural networks

Featuring with characteristics of convolutional neural network (CNN) and recurrent neural network (RNN), hybrid neural network (H-NN) has been widely applied within the field of remote sensing. In order to satisfy de mands of on-orbit processing that requires high throughput with restriction on power consumption, designing specific heterogeneous array processor therefore becomes one of the most effective ways fulfilling various tasks engaged in the above field. In this paper, a heterogeneous array architecture is proposed to support the hybrid neural network, based on the characteristics of various computation types in between different neural network module types and of dynamic computation burden among different layers. Firstly, a heterogeneous array structure consisting of different PE, PPE, RPE and LPE units is proposed, enabling strong flexibility and high throughput. Four types of operation units are used for operations of MAC, ReLU, pooling and nonlinear lookup-table. Secondly, a multi-level on-chip memory structure and access strategy supporting different access modes are proposed to reduce the bandwidth requirements of off-chip data access and to improve the computation efficiency. Thirdly, a management strategy of heterogeneous computing array is designed, which combines pipelining and parallel processing to support efficient mapping of different types of hybrid neural networks. The hybrid neural network processor based on 65 nm CMOS technique has a peak throughput of up to 1.96 TOPS. The implementation on models of AlexNet, LRCN, VGG19-LSTM and CLDNN can achieve the throughput of 1.92 TOPS, 1.89 TOPS, 1.93 TOPS and 1.84 TOPS, respectively. Compared with the similar neural network processor that is based on the same technology, the throughput of AlexNet model is increased by 76.4%. The peak power consumption of a single processor core is 824mW, to which the power consumption restriction of on-orbit AI platform is satisfied.

Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases

The immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of WBCs. The characteristics of WBCs are determined based on the chromatic, geometric, and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and differing opinions of experts and takes a long time; however, artificial intelligence techniques can help to solve all these challenges. Determining the type of WBC using automatic diagnosis helps hematologists to identify different types of blood diseases. This work aims to overcome manual diagnosis by developing automated systems for classifying microscopic blood sample datasets for the early detection of diseases in WBCs. Several proposed systems were used: first, neural network algorithms, such as artificial neural networks (ANNs) and feed-forward neural networks (FFNNs), were applied to diagnose the dataset based on the features extracted using the hybrid method between two algorithms, the local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM). All algorithms attained superior accuracy for WBC diagnosis. Second, the pre-trained convolutional neural network (CNN) models AlexNet, ResNet-50, GoogLeNet, and ResNet-18 were applied for the early detection of WBC diseases. All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN models block for extracting deep features and the SVM algorithm block for the classification of deep features with superior accuracy and efficiency. These hybrid techniques are named AlexNet with SVM, ResNet-50 with SVM, GoogLeNet with SVM, and ResNet-18 with SVM. All techniques achieved promising results when diagnosing the dataset for the early detection of WBC diseases. The ResNet-50 model achieved an accuracy of 99.3%, a precision of 99.5%, a sensitivity of 99.25%, a specificity of 99.75%, and an AUC of 99.99%.

Automatic Recognition of Rice Leaf Diseases Using Transfer Learning

Rice, the world’s most extensively cultivated cereal crop, serves as a staple food and energy source for over half of the global population. A variety of abiotic and biotic factors such as weather conditions, soil quality, temperature, insects, pathogens, and viruses can greatly impact the quantity and quality of rice grains. Studies have established that plant infections have a significant impact on rice crops, resulting in substantial financial losses in agriculture. To accurately diagnose and manage the diseases affecting rice plants, plant pathologists are seeking efficient and reliable methods. Traditional disease detection techniques, employed by farmers, involve time-consuming visual inspections and result in inadequate farming practices. With advancements in agricultural technology, the identification of pathogenic organisms in rice plants has become significantly more manageable through techniques such as machine learning and deep learning, which are receiving significant attention in crop disease research. In this paper, we used the transfer learning approach on 15 pre-trained CNN models for the automatic identification of Rice leave diseases. Results showed that the InceptionV3 model is outperforming with an average accuracy of 99.64% with Precision, Recall, F1-Score, and Specificity as 98.23, 98.21, 98.20, and 99.80, and the AlexNet model resulted in poor performance with average accuracy of 97.35% among others.

A time domain 2D OaA-based convolutional neural networks accelerator

Convolutional neural networks (CNNs) are widely implemented in modern facial recognition systems for image recognition applications. Runtime speed is a critical parameter for real-time systems. Traditional FPGA-based accelerations require either large on-chip memory or high bandwidth and high memory access time that slow down the network. The proposed work uses an algorithm and its subsequent hardware design for a quick CNN computation using an overlap-and-add-based technique in the time domain. In the algorithm, the input images are broken into tiles that can be processed independently without computing overhead in the frequency domain. This also allows for efficient concurrency of the convolution process, resulting in higher throughput and lower power consumption. At the same time, we maintain low on-chip memory requirements necessary for faster and cheaper processor designs. We implemented CNN VGG-16 and AlexNet models with our design on Xilinx Virtex-7 and Zynq boards. The performance analysis of our design provides 48% better throughput than the state-of-the-art AlexNet and uses 68.85% lesser multipliers and other resources than the state-of-the-art VGG-16.

Tomato leaf diseases classification using alexnet

The economy is largely reliant on agricultural productivity. Plant diseases and pests are also a serious issue in agriculture. It is necessary to recognise them early on in order to eliminate all infections as rapidly as possible and avoid crop devastation. To protect the plants against illness, a variety of insecticides have been utilised. Despite these precautions, the illness continues to spread throughout the field. Because we don't always know what kind of sickness we're dealing with, a bad pesticide may have been applied instead. As a result, it's all futile.Tomato leaf infections, on the other hand, are a severe problem for many farmers, thus mastering the severity of diseases in a fast and precise manner is critical to assisting staff in taking additional intervention steps to prevent plants from becoming more afflicted. An Alexnet model for detecting tomato leaf disease is proposed in this study, which may minimise the number of training processes while enhancing computation accuracy and gradient flow. This indicates that categorization of diseases is equally important. An improved classification model is presented in this paper for identifying and categorising tomato leaf disease. Before CNN is used to identify the pictures, a training dataset containing a large number of images is utilised, and visual characteristics are extracted using several methods. The Alexnet model achieves the highest classification accuracy when compared to other models.

IOT and Deep Learning Assisted Smart Agriculture System for Plant Disease Identification

Agriculture employed more than 50% of labours and contributed nearly 18% to country's GDP. India is the leading producers of Many crops, such as wheat, rice, lentils, cotton, peanuts and perishable products but in recent years, the percentage of people getting into agriculture has been reduced. Crop disease and incorrect maintenance of humidity and moisture is the major reason that makes new farmers frustrated. Developing a deep learning model, that can identify the crop disease with high accuracy, and using this model one can easily identify the crop disease which makes easier for them treat the crop with pesticide, so that we cant wait for the experts. Using the IOT architecture to find the humidity and soil moisture of the field so that the farmer can maintain the correct level of humidity and soil moisture through irrigation, sometimes extreme high and very low moisture and humidity leads to diseases. An enhanced classification model was suggested in this paper to classify leaf diseases. Dataset with more than 10000 images is used for image classification. Classification accuracy is achieved using AlexNet model and it came out to be the highest in comparison to other models, and IOT part using the Raspberry pi 3 kit with soil moisture and humidity and soil moisture sensor.

Research on Speech Emotion Recognition Analysis Based on Deep Learning

This paper combines two aspects of feature selection and building deep neural networks to carry out targeted research to improve recognition accuracy. Firstly, speech preprocessing techniques are introduced to extract the speech spectrogram and lay the foundation for building the speech emotion recognition network model study. The focus is on building a speech emotion recognition network model based on residual network improvement and comparing experiments with AlexNet model network and ResNet-18 network model.

Analyzing Gas Data Using Deep Learning and 2-D Gramian Angular Fields

The notion of employing deep learning (DL) for gas classification has kindled a revolution that has improved both data collection measures and classification performance. Yet, the current literature, with its vast contributions, has the potential in enhancing the current state of the art by employing both DL and novel visualization methods to boost classification performance and speed. Therefore, this article presents a dual classification system for high-performance gas classification: on 1-D time series data and 2-D Gramian Angular Field (GAF) data. For the GAF case study, 1-D data are converted into 2-D counterparts by means of normalization, segmentation, averaging, and color coding. The gas sensor array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2-D GAF data and an improved version of GasNet for 1-D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state of the art. Evaluation results of the modified GasNet model on time series data signify the state-of-the-art accuracy of 96.5%, while AlexNet achieved 81.0% test accuracy of GAF classification with near real-time performance on edge computing platforms.

Research on sheep face recognition algorithm based on improved AlexNet model

Research on sheep face recognition algorithm based on improved AlexNet model

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

Pineapple Sweetness Classification Using Deep Learning Based on Pineapple Images

In Thailand, the pineapple is a valuable crop whose price is determined by its sweetness. An optical refractometer or another technique that requires expert judgment can be used to determine a fruit's sweetness. Furthermore, determining the sweetness of each fruit takes time and effort. This study employed the Alexnet deep learning model to categorize pineapple sweetness levels based on physical attributes shown in images. The dataset was classified into four classes, i.e., M1 to M4, and sorted in ascending order by sweetness level. The dataset was divided into two parts: training and testing datasets. Training accounted for 80% of the dataset while testing accounted for 20%. This study's experiments were repeated five times, each with a distinct epoch and working with data that had been prepared. According to the experiment, the Alexnet model produced the greatest results when trained with balancing data across 10 epochs and 120 figures per class. The model's accuracy and F1 score were 91.78% and 92.31%, respectively.

A deep learning content-based image retrieval approach using cloud computing

Due to the rapid growth in multimedia content and its visual complexity, contentbased image retrieval (CBIR) has become a very challenging task. Existing works achieve high precision values at first retrieval levels such as top 10 and top 20 images, but low precision values at subsequent levels such as top 40, 50, and 70, so the goal of this paper is to propose a new CBIR approach that achieves high precision values at all retrieval levels. The proposed method combines features extracted from the pre-trained AlexNet model and discrete cosine transform (DCT). Then principal components analysis (PCA) is performed on AlexNet’s features and feeding these combination to multiclass support vector machine (SVM). The euclidean distance is used to measure the similarity between query and stored images features within the predicted class by SVM. Finally top similar images are ranked and retrieved. All above techniques require huge computational power which may not be available on client machine thus, the processing of these tasks is processed on cloud. Experimental results on the benchmark Corel-1k show that the proposed method achieves high precision value 97% along all retrieval levels top 10, 20 and 70 images and requiring less memory compared to other methods.

Classifying urban areas from learned acoustic and seismic data

In this work, a deep transfer learning algorithm is used to classify different urban areas from acoustic and seismic data. The deep transfer learning model combines Google’s deep learning model AlexNet. Measurements of acoustic and seismic ambient noise were conducted in an urban environment. The urban acoustic and seismic measurements are heavily influenced by traffic noise but exhibit more variation with respect to urban location due to the influence of subsurface conditions. A K-means clustering analysis is employed on the acoustic and seismic spectrogram to classify the urban areas. The results demonstrate that seismic and acoustic data can have similar cluster centroid locations in the frequency band of 4 Hz–10 Hz. The transfer learning results will be presented and discussed.

A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties

Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.

Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric

Using a pre-trained Convolutional Neural Network (CNN) model for a practical biometric authentication system requires specific procedures for training and performance evaluation. There are two criteria for a practical biometric system studied in this paper. First, the system’s ability to handle identity theft or impersonation attacks. Second, the ability of the system to generate high authentication performance with minimal enrollment period. We propose the use of the Multiple Clip Contrast Limited Adaptive Histogram Equalization (MC-CLAHE) technique to process finger images before being trained by CNN. A pre-trained CNN model called AlexNet is used to extract features as well as classify the MC-CLAHE images. The authentication performance of the pre-trained AlexNet model has increased by a maximum of 30% when using this technique. To ensure that the pre-trained AlexNet model is evaluated based on its ability to prevent impersonation attacks, a procedure to generate the Receiver Operating Characteristics (ROC) curve is proposed. An offline procedure for training the pre-trained AlexNet model is also proposed in this paper. The purpose is to minimize the user enrollment period without compromising the authentication performance. In this paper, this procedure successfully reduces the enrollment time by up to 95% compared to using on-line training.

Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique

Plant pests and diseases play a significant role in reducing the quality of agricultural products. As one of the most important plant pathogens, pests like Mediterranean fruit fly cause significant damage to crops and thus annually farmers face a lot of loss in their products. Therefore, the use of modern and non-destructive methods such as machine vision systems and deep learning for early detection of pests in agricultural products is of particular importance. In this study, citrus fruit images were taken in three stages: 1) before pest infestation, 2) beginning of fruit infestation, and 3) eight days after the second stage, in natural light conditions (7000–11,000 lux). A total of 1519 images were prepared for all classes. To classify the images, 70% of the images were used for the network training stage, 10% and 20% of the images were used for the validation and testing stages. Four pre-trained CNN models, namely ResNet-50, GoogleNet, VGG-16 and AlexNet as well as the SGDm, RMSProp and Adam optimization algorithms were used to identify and classify healthy fruit and fruit infected with the Mediterranean fly. The results of evaluating the models in the pest outbreak stage showed that the VGG-16 model with the help of SGDm algorithm had the best efficiency with the highest detection accuracy and F1 of 98.33% and 98.36%, respectively. The evaluation of the third stage showed that the AlexNet model with the help of SGDm algorithm had the best result with the highest detection accuracy and F1 of 99.33% and 99.34%, respectively. AlexNet model using SGDm optimization algorithm had the shortest network training time (323 s). The results of this study showed that convolutional neural network method and machine vision system can be effective in controlling and managing pests in orchards and other agricultural products.

RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion

Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.

Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet–Quantum Learning

The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson’s disease (PD) and 96% for Alzheimer’s disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain.

A precise model for accurate rice disease diagnosis: a transfer learning approach

Rice being a principal cereal, is consumed by a large portion of the world's population as a staple food. Like other plants, it is susceptible to a variety of diseases associated with themselves which can affect greatly the quantity and quality of yield. It is essential to detect the disease in its early stage and take appropriate treatment measures to ensure proper growth of the plant as well as maximum quality yield. Detection of the diseased plants through the naked eye seems to be an impossible task taking into consideration the large range of leaf diseases and the vast areas of farmlands. Technological advancement in the areas of machine learning, computer vision, and IoT has led to the development of automated systems for the detection of diagnosis of plant diseases from the image of diseased leaves. Though machine learning (ML) based solutions are available for this task, still there is potential for further improvement in the automated systems build on transfer learning approaches based on different CNN architectures. This paper proposes a transfer learning-based AlexNet model for automatic detection and diagnosis task. Three of the most common rice plant disease namely bacterial leaf blight, brown spot, and leaf smut which are very difficult to distinguish by the naked eye have been considered for investigation. The dataset is obtained from the 'Kaggle' website. The proposed AlexNet model performs significantly well for the detection and classification task on 'Kaggle' dataset in terms of accuracy, precision, recall, F1 score, and kappa coefficient in comparison with other ML and transfer learning-based approaches reported in the literature for the same task on same or different datasets.