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Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance. [ABSTRACT FROM AUTHOR]

Glaucoma is an asymptotic condition that damages the optic nerves of a human eye. Glaucoma is frequently caused due to abnormally high pressure in an eye that leads to permanent blindness. Detecting glaucoma at an initial phase has the possibility of curing this disease, but diagnosing accurately is considered as a challenging task. Therefore, this paper proposes a novel method known as a glaucoma detection system that performs the diagnosis of glaucoma by exploiting the prescribed characteristics. The significant intention of this paper involves diagnosing the glaucoma disease present at the top optical nerve of a human eye. The proposed glaucoma detection has used four different phases namely data preprocessing or enhancement phase, segmentation phase, feature extraction phase, and classification phase. Here, a novel classifier named fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) is developed for the effective classification of glaucoma-infected images from the normal image. Finally, the experimental analysis for the proposed approach and various other techniques are performed, and the accuracy rate while diagnosing glaucoma achieved is 98.75%. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the long-lasting Diabetic retinal disorders that leads to vision impairment eventually blindness in most of the working-age population. The process of classifying the severity level of DR has been a great challenging task as the lesion features are hard to analyze. The screening process requires an effective detection method to classify the subtle pathologies of the retina. Deep neural architectures play a vital role in diagnosing eye disease and helps ophthalmologists to provide timely treatment. This paper proposes an efficient, optimized deep neural network with Chronological Tunicate Swarm Algorithm (CTSA) for classifying the severity of DR. Initially, the retinal images captured through the low-quality fundus photography are preprocessed and then subjected to the segmentation process. First, the optic disc and the blood vasculatures are segmented using a U-Net and sparse Fuzzy C-means-based hybrid entropy model. The lesion area is then detected using the Gabor filter banks, and then the features are extracted. The final classification process takes place using a deep Stacked Autoencoder (SAE) jointly optimized with a bio-inspired Tunicate Swarm Algorithm based on the chronological concept. The presented model achieved an average accuracy, sensitivity, specificity and F1-Score values of 95.9%, 88.07%, 96.80% and 85.26% for the DIARETDB0 database and 95.48%, 93.29%, 91.89% and 90.53% for the DIARETDB1 database. The experimental outcome demonstrates the effectiveness and the robustness of the proposed method in the DR classification task. [ABSTRACT FROM AUTHOR]

Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand's shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research. [ABSTRACT FROM AUTHOR]

In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrusion detection. However, their enhancement root causes usually present weak relationships to the differences between normal and attack behaviors in the data samples. Thus, we propose a Classification Tendency Difference Index (CTDI) model for feature selection and extraction in intrusion detection. The CTDI model consists of three indexes: Classification Tendency Frequency Difference (CTFD), Classification Tendency Membership Difference (CTMD), and Classification Tendency Distance Difference (CTDD). In the dataset, each feature has many feature values (FVs). In each FV, the normal and attack samples indicate the FV classification tendency, and CTDI shows the classification tendency differences between the normal and attack samples. CTFD is the frequency difference between the normal and attack samples. By employing fuzzy C means (FCM) to establish the normal and attack clusters, CTMD is the membership difference between the clusters, and CTDD is the distance difference between the cluster centers. CTDI calculates the index score in each FV and summarizes the scores of all FVs in the feature as the feature score for each of the three indexes. CTDI adopts an Auto Encoder for feature extraction to generate new features from the dataset and calculate the three index scores for the new features. CTDI sorts the original and new features for each of the three indexes to select the best features. The selected CTDI features indicate the best classification tendency differences between normal and attack samples. The experiment results demonstrate that the CTDI features achieve better detection accuracy as classified by DNN for the Aegean WiFi Intrusion Dataset than their related works, and the detection enhancements are based on the improved classification tendency differences in the CTDI features. [ABSTRACT FROM AUTHOR]