Hybrid Feature Vector for Screening of Autistic People using Deep Learning

Micro blogging platforms like Twitter generate a wealth of information during a disaster. Data can be in the form of sound, image, text, video etc. by way of tweets. Tweets produced during a disaster are not always educational. Information tweets can provide useful information about affected people, infrastructure damage, civilized organizations etc. Studies show that when it comes to sharing emergency information during a natural disaster, time is everything. Research on Twitter use during hurricanes, floods and floods provide potentially life-saving data on how information is disseminated in emergencies. The proposed system outlines how to distinguish sensitive and non-useful tweets during a disaster. The proposed method is based on the use of Word2Vec and the Convolutional Neural Network (CNN). Word2vec provides a feature vector and CNN is used to classify tweets.

Download Full-text

Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with Stacked Autoencoder

Remote Sensing ◽

10.3390/rs13091732 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1732

Author(s):

Hadis Madani ◽

Kenneth McIsaac

Keyword(s):

Deep Learning ◽

Spatial Information ◽

Hyperspectral Image ◽

Feature Vector ◽

Remote Sensing Data ◽

Distance Transform ◽

Hyperspectral Image Classification ◽

Spatial Feature ◽

Learning Techniques

Pixel-wise classification of hyperspectral images (HSIs) from remote sensing data is a common approach for extracting information about scenes. In recent years, approaches based on deep learning techniques have gained wide applicability. An HSI dataset can be viewed either as a collection of images, each one captured at a different wavelength, or as a collection of spectra, each one associated with a specific point (pixel). Enhanced classification accuracy is enabled if the spectral and spatial information are combined in the input vector. This allows simultaneous classification according to spectral type but also according to geometric relationships. In this study, we proposed a novel spatial feature vector which improves accuracies in pixel-wise classification. Our proposed feature vector is based on the distance transform of the pixels with respect to the dominant edges in the input HSI. In other words, we allow the location of pixels within geometric subdivisions of the dataset to modify the contribution of each pixel to the spatial feature vector. Moreover, we used the extended multi attribute profile (EMAP) features to add more geometric features to the proposed spatial feature vector. We have performed experiments with three hyperspectral datasets. In addition to the Salinas and University of Pavia datasets, which are commonly used in HSI research, we include samples from our Surrey BC dataset. Our proposed method results compares favorably to traditional algorithms as well as to some recently published deep learning-based algorithms.

Download Full-text

Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

10.54216/jisiot.040102 ◽

2021 ◽

pp. 26-40

Author(s):

Ahmed N. Al Al-Masri ◽

◽

Hamam Mokayed

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Time Domain ◽

Feature Vector ◽

Particle Swarm ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Spectral Features ◽

State Recognition

Gear faults have always been a problem encountered in mechanical processing. For gear fault diagnosis, using mathematical-statistical feature extraction methods, deep learning neural networks (DLNN), particle swarm algorithm (PSA), and support vector machines (SVM), etc. According to the feature extraction of deep learning and particle swarm SVM state recognition, the intelligent diagnosis model is established, and the reliability of the model is verified by experiments. The model uses the combination of spectral features extracted by deep learning adaptively and the time domain features extracted by mathematical statistics methods to form a joint feature vector and then uses particle swarm SVM to diagnose the joint feature vector. After research, this paper draws a classification fitness curve combining the fault spectrum features extracted by DLNN and traditional time-domain statistical features. The classification result obtained by using this method is 95.3%. The reliability of the model is verified, and satisfactory diagnosis results are obtained. In addition, the application results also verify the effectiveness of adaptively extracting spectral features based on deep learning.

Download Full-text

Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

Complexity ◽

10.1155/2021/6170416 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Mosleh Hmoud Al-Adhaileh ◽

Ebrahim Mohammed Senan ◽

Waselallah Alsaade ◽

Theyazn H. H Aldhyani ◽

Nizar Alsharif ◽

...

Keyword(s):

Deep Learning ◽

Feature Vector ◽

Gastrointestinal Diseases ◽

Activation Function ◽

Learning Networks ◽

Video Endoscopy ◽

Deep Feature ◽

Short Period

Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

Download Full-text

Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

Wireless Communications and Mobile Computing ◽

10.1155/2021/8024953 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Zhijun Guo ◽

Shuai Liu

Keyword(s):

Deep Learning ◽

Feature Vector ◽

Recognition Performance ◽

Image Transmission ◽

Recognition System ◽

Interference Signal ◽

Signal Recognition ◽

Interference Signals ◽

The Time Domain ◽

Wireless Image Transmission

In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

Download Full-text

Deep learning model for Pathogen Classification using Feature fusion and Data augmentation

Current Bioinformatics ◽

10.2174/1574893615999200707143535 ◽

2020 ◽

Vol 15 ◽

Author(s):

Fareed Ahmad ◽

Amjad Farooq ◽

Muhammad Usman Ghani Khan

Keyword(s):

Deep Learning ◽

Cross Validation ◽

Data Augmentation ◽

Feature Vector ◽

Feature Fusion ◽

High Capacity ◽

Automatic Classification ◽

Fine Tuning ◽

Correct Identification ◽

Learning Models

Background: Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled with their high capacity for ailment and death in infected individuals, makes them a threat to society. Objective: Due to high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to differentiate between them. Their automatic classification using deep-learning models can help in reliable, and accurate outcomes. Method: Deep-learning models, namely; AlexNet, GoogleNet, ResNet101, and InceptionV3 are used with numerous variations including training model from scratch, fine-tuning without pre-trained weights, fine-tuning along with freezing weights of initial layers, fine-tuning along with adjusting weights of all layers and augmenting the dataset by random translation and reflection. Moreover, as the dataset is small, fine-tuning and data augmentation strategies are applied to avoid overfitting and produce a generalized model. A merged feature vector is produced using two best-performing models and accuracy is calculated by xgboost algorithm on the feature vector by applying cross-validation. Results: Fine-tuned models where augmentation is applied produces the best results. Out of these, two-best-performing deep models i.e. (ResNet101, and InceptionV3) selected for feature fusion, produced a similar validation accuracy of 95.83 with a loss of 0.0213 and 0.1066, and a testing accuracy of 97.92 and 93.75, respectively. The proposed model used xgboost to attained a classification accuracy of 98.17% by using 35-folds cross-validation. Conclusion: The automatic classification using these models can help experts in the correct identification of pathogens. Consequently, they can help in controlling epidemics and thereby minimizing the socio-economic impact on the community.

Download Full-text

Artificial intelligence reveals environmental constraints on colour diversity in insects

Nature Communications ◽

10.1038/s41467-019-12500-2 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Shipher Wu ◽

Chun-Min Chang ◽

Guan-Shuo Mai ◽

Dustin R. Rubenstein ◽

Chen-Ming Yang ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Structural Equation ◽

Feature Vector ◽

High Elevation ◽

Image Feature ◽

Equation Modeling ◽

Ecological Gradient ◽

Biogeographic Pattern ◽

Underlying Mechanisms

Abstract Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning—a form of artificial intelligence—can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species’ mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.

Download Full-text

Application of Deep Learning Model Convolution Neural Network for Effective Web Information Retrieval

Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3299-7.ch007 ◽

2021 ◽

pp. 100-120

Author(s):

Suruchi Chawla

Keyword(s):

Neural Network ◽

Information Retrieval ◽

Deep Learning ◽

Language Processing ◽

Feature Vector ◽

Convolution Neural Network ◽

Web Information Retrieval ◽

Fixed Length ◽

Web Information ◽

The Rich

Convolution neural network (CNN) is the most popular deep learning method that has been used for various applications like image recognition, computer vision, and natural language processing. In this chapter, application of CNN in web query session mining for effective information retrieval is explained. CNN has been used for document analysis to capture the rich contextual structure in a search query or document content. The document content represented in matrix form using Word2Vec is applied to CNN for convolution as well as maxpooling operations to generate the fixed length document feature vector. This fixed length document feature vector is input to fully connected neural network (FNN) and generates the semantic document vector. These semantic document vectors are clustered to group similar document for effective web information retrieval. An experiment was performed on the data set of web query sessions, and results confirm the effectiveness of CNN in web query session mining for effective information retrieval.

Download Full-text

CROWD: Crow Search and Deep Learning based Feature Extractor for Classification of Parkinson’s Disease

ACM Transactions on Internet Technology ◽

10.1145/3418500 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1-18

Author(s):

Mehedi Masud ◽

Parminder Singh ◽

Gurjot Singh Gaba ◽

Avinash Kaur ◽

Roobaea Alrobaea Alghamdi ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Feature Selection ◽

Deep Learning ◽

Missing Values ◽

Feature Vector ◽

Feature Selection Method ◽

Healthcare Sector ◽

Brain Cells ◽

Speech Impairment

Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson’s motor symptoms appear on the loss of 60% to 80% of cells, due to the non-production of appropriate dopamine. Recent research found a close connection between the speech impairment and PD. Many researchers have developed a classification algorithm to identify the PD from speech signals. In this article, Adaptive Crow Search Algorithm (ACSA) and Deep Learning (DL)–based optimal feature selection method are introduced. The proposed model is the combination of CROW Search and Deep learning (CROWD) stack sparse autoencoder neural network. Parkinson’s dataset is taken for the experiment from the Irvine dataset repository at the University of California (UCI). In the first phase, dataset cleaning is performed to handle the missing values in the dataset. After that, the proposed ACSA algorithm is employed to find the scrunched feature vector. Furthermore, stack spare autoencoder with seven hidden layers is employed to generate the compressed feature vector. The performance of the proposed CROWD autoencoder model is compared with three feature selection approaches for six supervised classification techniques. The experiment result demonstrates that the performance of the proposed CROWD autoencoder feature selection model has outperformed the benchmarked feature selection techniques: (i) Maximum Relevance (mRMR) (ii) Recursive Feature Elimination (RFE), and (iii) Correlation-based Feature Selection (CFS), to classify Parkinson’s disease. This research has significance in the healthcare sector for the enhancement of classification accuracy up to 0.96%.

Download Full-text