https://digitalcommons.aaru.edu.jo/cgi/viewcontent.cgi?article=1048&context=fcij

Classification using deep learning neural networks for brain tumors

Structural crack detection using deep convolutional neural networks

COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

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A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications.

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A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

Many machine learning softwares are available which help the researchers to accomplish various tasks. These software packages have various conventional algorithms which perform well if the training and test data are independent and identically distributed. However, this might not be the case in the real world. The training data may not be available at one time. In the case of neural networks, the architecture has to be retrained with new data that are made available subsequently. In this paper, we present a novel training algorithm which can avoid complete retraining of any neural network architecture meant for visual pattern recognition. To show the utility of the algorithm, we have investigated the performance of convolutional neural network (CNN) architecture for a face recognition task under transfer learning. The proposed training algorithm may be used for enhancing the utility of machine learning software by providing researchers with an approach that can reduce the training time under transfer learning.

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A novel training algorithm for convolutional neural network

Component-based face recognition under transfer learning for forensic applications

Automatically verifying a person from a video frame or a digital image using computer application is known as a Face Recognition system. With changes in facial pose, face appearance changes drastically. Recognition of faces under pose variations is much more challenging. Now a day's recognizing human faces in unconstrained or wild environment is emerging as a critically important and technically challenging computer vision problem. Recently face recognition community is gradually shifting its focus on much more challenging unconstrained setting. A new unconstrained human face Database called as “My unconstrained Database” has been developed in this paper. A model based approach is used and the Moment based feature extraction techniques (Hu's, Zernike and Legendre Moments) are implemented on three different face databases containing different poses of the faces. This paper proposes a modified method called “Genetic Algorithm based Transfer Vectors” for generation of features of a frontal face from the features of different poses of image. Next, the generated frontal features are matched with the actual frontal features. Extracted feature are classified by three different methods: kNN classifier, LDA and Transform Vector with Distance Metric and finally Correct Recognition Rate is determined.

A framework for face classification under pose variations

Social Media has become a vital component of every individual's life in society opening a preferred spectrum of virtual communication which provides an individual with a freedom to express their views and thoughts. While virtual communication through social media platforms is highly desirable and has become an inevitable component, the dark side of social media is observed in form of detrimental/objectionable content. The reported detrimental contents are fake news, rumors, hate speech, aggressive, and cyberbullying which raise up as a major concern in the society. Such detrimental content is affecting person's mental health and also resulted in loss which cannot be always recovered. So, detecting and moderating such content is a prime need of time. All social media platforms including Facebook, Twitter, and YouTube have made huge investments and also framed policies to detect and moderate such detrimental content. It is of paramount importance in the first place to detect such content. After successful detection, it should be moderated. With an overflowing increase in detrimental content on social media platforms, the current manual method to identify such content will never be enough. Manual and semi-automated moderation methods have reported limited success. A fully automated detection and moderation is a need of time to come up with the alarming detrimental content on social media. Artificial Intelligence (AI) has reached across all sectors and provided solutions to almost all problems, social media content detection and moderation is not an exception. So, AI-based methods like Natural Language Processing (NLP) with Machine Learning (ML) algorithms and Deep Neural Networks is rigorously deployed for detection and moderation of detrimental content on social media platforms. While detection of such content has been receiving good attention in the research community, moderation has received less attention. This research study spans into three parts wherein the first part emphasizes on the methods to detect the detrimental components using NLP. The second section describes about methods to moderate such content. The third part summarizes all observations to provide identified research gaps, unreported problems and provide research directions. 

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Detection and moderation of detrimental content on social media platforms: current status and future directions

Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.

Alwin D. Anuse

Papers

A novel training algorithm for convolutional neural network

Component-based face recognition under transfer learning for forensic applications

A framework for face classification under pose variations

Detection and moderation of detrimental content on social media platforms: current status and future directions

Transfer learning for face recognition using fingerprint biometrics