AlexNet Model Research Articles

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results. Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling. The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard. The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset. There were four combinations of model and sampling technique. The F-measure for the AlexNet model was 0.807 while that for the DenseNet model was 0.808. Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852. Combination 3 was the AlexNet model and reservoir sampling. It had an average F-measure of 0.8545. Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017. Finally, combination 4 was the DenseNet model and reservoir sampling. It had an average F-measure of 0.8111. Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.

Analysis and Classification of Bone Fractures Using Machine Learning Techniques

Human bones are the hard organs that protect vital organs such as the heart, lungs, and other internal organs. Fractures of the bones are a prevalent issue among humans. Bone fractures may develop from an accident or another circumstance when there is great pressure on the bones. It may be difficult and time-consuming to determine the site of a fracture in a patient who is suffering discomfort. The manual examination of fractures during radiological interpretation is a time-consuming and error-prone process. This may result in erroneous detection, poor fracture healing, and an extensive procedure. So, this research proposed an effective approach to rectifying bone fractures with the inclusion of the latest technologies. The solution is proposed by employing a Deep learning model. Moreover, a novel concept of classification is also incorporated. Firstly; the MURA dataset was collected from Stanford. Secondly; The proposed model used techniques like DCNN (Deep Convolution Neural Network) and use Alex Net model. Bones are classified into fractured or non-fractured through a classification approach. The proposed model was created using Google Colab. The proposed model was trained by repeating several experiments. The performance was evaluated based on accuracy. The suggested model results were compared with baseline algorithms as well. Consequently, the findings of this work will be useful for the medical industry.

Flower image classification based on an improved lightweight neural network with multi-scale feature fusion and attention mechanism.

In order to solve the problem that deep learning-based flower image classification methods lose more feature information in the early feature extraction process, and the model takes up more storage space, a new lightweight neural network model based on multi-scale feature fusion and attention mechanism is proposed in this paper. First, the AlexNet model is chosen as the basic framework. Second, a multi-scale feature fusion module (MFFM) is used to replace the shallow single-scale convolution. MFFM, which contains three depthwise separable convolution branches with different sizes, can fuse features with different scales and reduce the feature loss caused by single-scale convolution. Third, two layers of improved Inception module are first added to enhance the extraction of deep features, and a layer of hybrid attention module is added to strengthen the focus of the model on key information at a later stage. Finally, the flower image classification is completed using a combination of global average pooling and fully connected layers. The experimental results demonstrate that our lightweight model has fewer parameters, takes up less storage space and has higher classification accuracy than the baseline model, which helps to achieve more accurate flower image recognition on mobile devices.

Lung carcinoma prediction for computed tomography using deep learning

Lung carcinoma is one of the most common diseases in the world. In India one of the most occurring disease is lung cancer and lot of people die due to the reason that it can only be cured during its initial stages. It is caused by the uncontrollable cell proliferation in lung tissues. It can be treated in Initial phase by seeking therapy is recommended. Only Computed Tomography (CT) scans and blood test results can detect this. The tumour is diagnosed by blood test after the humans have been affected for at least four years. As a result, CT scanning is utilised to determine the early stage of cancer. CT pictures are classed as normal or abnormal. Focusing on the tumour region reveals the aberrant image. The dataset is in jpg format and is made up of CT scans. The dataset is in jpg format and is made up of CT scans. This algorithm is used to process images. During training, image expansion method like zooming, linear fills, trimming, and rotation are used to sample, to improve classification success rates. Cancerous cells are discovered early by whenever the lungs provide oxygen to humans and expel carbon dioxide from the body as it plays a key role. Using various deep learning models, we can detect lung cancer. These deep learning models are employed across a wide range of datasets. We can achieve accurate findings by employing the image processing method. Because the AlexNet model was so successful, the model-derived characteristics are final used to fully connected layer, individually as feedback to the softmax layer. Both together yielded an accuracy of 99.52%.

Medical Image Classification for Monkeypox Case using Deep Learning Algorithms: A Survey

Due to the importance of maintaining public health and preventing the spread of diseases, nowadays, new diseases have spread at a lot of countries called Monkeypox after the world get rid of covid-19.it is crucial to diagnose Monkeypox and stop the spread of this disease. so that we make this review to give a point of view to Monkeypox spread nowadays. We have recently done nine research to overlay it with different artificial intelligence deep learning methods to diagnose Monkeypox from digital skin images due primarily to AI's success in COVID-19 identification. The VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models were used in our proposed method to classify patients with monkeypox symptoms with other diseases of a similar kind (chickenpox, measles, and normal)., Due to the importance of facing this disease and summarizing these researches according to: methodology and results of detection accuracy, precision.

Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes computing operations by eliminating the requirement for physical equipment for calculations. Instead, specialized companies provide cloud-based computing services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring the availability of affordable and dependable electricity is paramount for the efficient design and management of these logistics services. Cloud centers, which are power-intensive, face the challenge of reducing their energy consumption due to escalating power costs. To address this issue, efficient data placement and node management strategies are commonly employed in logistics operations. An AlexNet model has been designed to optimize storage relocation and predict power prices. The outcome of this initiative has resulted in a considerable reduction in energy consumption at data centres. The model uses Dwarf Mongoose Optimization Algorithm (DMOA) to produce an optimal solution for the AlexNet and increase its performance with a real-world dataset from IESO in Ontario, Canada. 75% of the available data was used for training to assure the model’s precision, with the remaining 25% allocated to testing purposes. The model forecasts power prices with an MAE of 2.22% and an MSE of 6.33%, resulting in an average reduction of 22.21% in electricity expenses. Our proposed method has an accuracy of 97% compared to 11 benchmark algorithms, including CNN, DenseNet, and SVM having an accuracy of 89%, 88%, and 82%, respectively.

A novel intrusion detection system for IIoT using inception convolutional neural network

The purpose of this study is to compare the accuracy of several deep-learning models for the identification of rice weed. In this study, 1500 datasets of local rice and 1000 datasets of weed were resized and applied to the input size of the network, respectively. A total of 70% of the data were used for training, and the remaining 30% were used for validation. MATLAB R2018a was used to construct the AlexNet pre-trained model using a transfer learning strategy, and by changing the AlexNet model, RiceWeedNet, a convolutional neural network, was created. Metrics such as network accuracy, recognition accuracy, precision, and recall were used to assess both models’ performances. While the test set’s identification accuracy is 97.713415%, its precision is 0.9776, and its recall value is 0.9803. The RiceWeedNet model achieved a network accuracy of 100%. A network accuracy of 90% and a recognition accuracy of 73.780488% were reported by the AlexNet model, respectively. The created model may be used instead of conventional weed detectors.

Early Detection of Autism in Children Using Transfer Learning

Autism spectrum disorder (ASD) is a challenging and complex neuro-development syndrome that affects the child’s language, speech, social skills, communication skills, and logical thinking ability. The early detection of ASD is essential for delivering effective, timely interventions. Various facial features such as a lack of eye contact, showing uncommon hand or body movements, babbling or talking in an unusual tone, and not using common gestures could be used to detect and classify ASD at an early stage. Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial features. A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet (ASDDTLA) model. Our model achieved a detection accuracy of 87.7% and performed better than other established ASD detection models. Therefore, this model could facilitate the early detection of ASD in clinical practice.

PANCODE: Multilevel Partitioning of Neural Networks for Constrained Internet-of-Things Devices

The increasing number of Internet-of-Things (IoT) devices will generate unprecedented data in the upcoming years. Fog computing may prevent the saturation of the network infrastructure by processing data at the edge or within these devices. Consequently, the machine intelligence built almost exclusively on the cloud can be scattered to the edge devices. While deep learning techniques can adequately process IoT-massive data volumes, their high resource-demanding nature poses a trade-off for execution on resource-constrained devices. This paper proposes and evaluates the performance of the PArtitioning Networks for COnstrained DEvices (PANCODE), a novel algorithm that employs a multilevel approach to partition large convolutional neural networks for distributed execution on constrained IoT devices. Experimental results with the LeNet and AlexNet models show that our algorithm can produce partitionings that achieve up to 2173.53 times more inferences per second than the Best Fit algorithm and up to 1.37 times less communication than the second-best approach. We also show that the METIS state-of-the-art framework only produces invalid partitionings in more constrained setups. The results indicate that our algorithm achieves higher inference rates and low communication costs in convolutional neural networks distributed among constrained and exceptionally very constrained devices.

Identifying rice leaf diseases using an improved AlexNet model

Identifying rice leaf diseases using an improved AlexNet model

Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning

Ferroresonance is a non-linear and dangerous resonance phenomenon that can affect power networks and damage electrical equipment. The ferroresonance phenomenon is examined by dividing it into classes, with chaotic ferroresonance being the most dangerous type that causes overvoltage’s. Detecting chaotic ferroresonance in a short period of time is of great importance in terms of taking measures and reducing equipment damage. In this study, we explored the application of deep convolutional neural networks (DCNNs) for the identification and classification of chaotic ferroresonance phenomena. Two pre-trained AlexNet models were adapted using transfer learning to perform these tasks. The first model was utilized to identify chaotic ferroresonance, while the second was employed to distinguish between different subtypes of chaotic ferroresonance by dividing voltage curve graphs into different periods and shapes. The training and testing of both DCNN models were conducted using snapshot images extracted from the voltage curves of all phase voltages. The results of the experiments showed high accuracy in both the identification and classification of chaotic ferroresonance phenomena.

Evaluation of breast tumor morphologies from African American and Caucasian patients

The primary aim of this research was to investigate potential differences of breast tumor morphologies across African American and Caucasian racial groups by utilizing machine learning (ML) and artificial intelligence (AI) methods. While breast cancer disparities can partially be attributed to social determinants of health, tumor biology also contributes to survival outcomes. The rate of breast tumor growth is largely dependent on the extracellular matrix (ECM). Current research suggests the cellular components of the ECM may vary among racial and ethnic populations, and this may contribute to the incidence of cancer in African Americans. We utilized a supervised AI method to evaluate morphological differences between African American and Caucasian breast cancer tumors. Images used for analysis were downloaded from the Cancer Genome Atlas (TCGA) biorepository stored in the NIH Genomic Data Commons (GDC) data portal. We designed an ML classifier using the AlexNet model provided in PyTorch’s torchvision package. The model was pre-trained and adapted via transfer learning resulting in a classification accuracy of 92.1% when using our breast cancer tumor image database split into 80% of training set and 20% of testing set. We interpreted the results of the AlexNet and ResNet50 models using LIME and saliency mapping as the explainers. Based on the images from our bi-racial testing set, this study confirmed significant variations of tumor and ECM regions in the different racial groups evaluated. Based on these findings, further analysis and characterization may provide new insight into disparities associated with the incidence of breast cancer.

Identifying rice leaf diseases using an improved AlexNet model

Identifying rice leaf diseases using an improved AlexNet model

Deep learning framework for cancer type classification using genome sequencing with AlexaNet

Tumors and cancers are the major diseases in humans and early prediction of cancers can save human lives. Cancer genome sequencing involves sequencing the whole genome of a group of cancer cells. It is a chemical laboratory approach for describing and identifying cancerous RNA or DNA cells.Therefore, this work develops adeep learning algorithm for classifying the cancer diseases from genomic sequences. Initially, Ada-boost descriptor is used to extract the deep features from the dataset, which identifies the inter and intra relationship between multiple correlated cancers. Then, deep learning based AlexNet model is used to classify the different types of cancers. Further, the simulation results shows that the proposed method resulted in superior performance as compared to state of art approaches

Anomaly detection towards zero defect manufacturing using generative adversarial networks

Defects in manufacturing not only reduce the quality of a produced part but also cause a waste of resources. Conventionally, quality control relies on manual inspection or the use of sensor technology to gather data to train machine learning models to identify defects. Although quite common, defects and anomalies in manufacturing do not occur often enough to gather a dataset with balanced representation of defective and normal samples. Using supervised classification models in such scenarios can be challenging. In this work, we propose a generative-adversarial-network-based approach to anomaly detection, where the models are trained only on normal samples. During testing, samples are classified as normal or anomalous based on the ability of the model to create samples as similar as possible to the test sample. We compare and evaluate the performance of the proposed model against the pretrained classification model AlexNet as well as an autoencoder model. Furthermore, we show that the proposed model performs on par with the pretrained classification model and allows localization of the anomalies in the data.

Intrusion Detection and Network Information Security Based on Deep Learning Algorithm in Urban Rail Transit Management System

The exploration of the intrusion detection effect of urban rail transit management system aims to further improve the safety performance of the traffic field in urban construction. Thus, the deep convolution neural network model AlexNet with more network layers and stronger learning ability is adopted and improved, to ensure the safe operation of urban rail transit. Meanwhile, the GRU (Gate Recurrent Unit) neural network is introduced into the improved AlexNet to build an intrusion detection model of urban rail transit management system. Finally, the model performance is verified through the collected data and simulation experiments. Through the comparative analysis of the model and other scholars’ models in related fields, the recognition accuracy of intrusion detection of the intrusion detection model reaches 96.00%, which is at least 1.55% higher than that of other neural network models. Besides, its training time is stable at about 55.05 seconds, and the test time is stable at about 22.17 seconds. Moreover, the analysis result of data transmission security performance indicates that the data message delivery rate of this model is more than 80%, the data message leakage rate and packet loss rate are less than 10%, and the average delay is basically stable at about 350 milliseconds. Therefore, the constructed model can achieve high data transmission security performance under the premise of ensuring prediction accuracy, which can provide experimental basis for improving the safety performance of rail transit systems in smart cities.

Attention‐enhanced Alexnet for improved radar micro‐Doppler signature classification

AbstractThis work introduces an attention mechanism that can be integrated into any standard convolution neural network to improve model sensitivity and prediction accuracy with minimal computational overhead. The attention mechanism is introduced in a lightweight network – Alexnet and its classification performance for human micro‐Doppler signatures is evaluated. The Alexnet model trained with an attention module can implicitly highlight the salient regions in the radar signatures while suppressing the irrelevant background regions and consistently improving network predictions. Network visualizations are provided through class activation mapping, providing better insights into how the predictions are made. The visualizations demonstrate how the attention mechanism focusses on the region of interest in the radar signatures.

Intelligent Fault Diagnosis Method for Gearboxes Based on Deep Transfer Learning

The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter α corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis.

Classification and Analysis of Tomato Species with Convolutional Neural Networks

Tomatoes are one of the most used vegetables. There are varieties that can grow in different climates. The taste, usage area and commercial value of each are different from each other. For this reason, identifying and sorting tomato species after the production stage is a problem. In addition, since tomato is a sensitive vegetable, it is extremely important to separate it from a distance. For this purpose, the classification of tomato images belonging to 9 different tomato species was carried out in the study. In total, a dataset containing 6810 tomato images in 9 classes was used. Three different pre-trained Convolutional Neural Network (CNN) models were used with the transfer learning method to classify the images. AlexNet, InceptionV3 and VGG16 models were used for classification. As a result of the classifications made, the highest classification belongs to the AlexNet model with 100%. Evaluation of the performances of the models was also made with precision, recall, F1 Score and specificity performance metrics. It is foreseen that the proposed methods can be used for the separation of tomatoes.

Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.

A pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical treatment. The model was designed using ensemble deep learning (DL). A total of 172 anterior segment images of pterygia were obtained from the Jiangxi Provincial People's Hospital (China) between 2017 and 2022. They were divided by a senior ophthalmologist into the non-surgery group and the surgery group. An artificial intelligence model was then developed based on ensemble DL, which was integrated with four benchmark models: the Resnet18, Alexnet, Googlenet, and Vgg11 model, for detecting the pterygium that requires surgical treatment, and Grad-CAM was used to visualize the DL process. Finally, the performance of the ensemble DL model was compared with the classical Resnet18 model, Alexnet model, Googlenet model, and Vgg11 model. The accuracy and area under the curve (AUC) of the ensemble DL model was higher than all of the other models. In the training set, the accuracy and AUC of the ensemble model was 94.20% and 0.978, respectively. In the testing set, the accuracy and AUC of the ensemble model was 94.12% and 0.980, respectively. This study indicates that this ensemble DL model, coupled with the anterior segment images in our study, might be an automated and cost-saving alternative for detection of the pterygia that require surgery.