Abstract Employing cognitive robots, capable of throwing and catching, is a strategy aimed at expediting the logistics process within Industry 4.0’s smart manufacturing plants, specifically for the transportation of small-sized manufacturing parts. Since the flight of mechanically thrown objects is inherently unpredictable, it is crucial for the catching robot to observe the initial trajectory with utmost precision and intelligently forecast the final catching position to ensure accurate real-time grasping. This study utilizes multi-camera tracking to monitor mechanically thrown objects. It involves the creation of a 3D simulation that facilitates controlled mechanical throwing of objects within the internal logistics environment of Industry 4.0. The developed simulation empowers users to define the attributes of the thrown object and capture its trajectory using a simulated pinhole camera, which can be positioned at any desired location and orientation within the in-plant logistics environment of flexible manufacturing systems. The simulation facilitated ample experimentation to be conducted for determining the optimal camera positions for accurately observing the 3D interception positions of a flying object based on its apparent size on the camera’s sensor plane. Subsequently, a variety of calibrated multi-camera setups were experimented while placing cameras at identified optimal positions. Based on the obtained results, the most effective multi-camera configuration setup is derived. Finally, a training dataset is prepared for 3000 simulated throwing experiments where the initial part of the trajectory consists of observed interception positions, through derived best multi-camera setup, and the final part consists of actual positions. The encoder–decoder Bi-LSTM deep neural network is proposed and trained on this dataset. The trained model outperformed the current state-of-the-art by accurately predicting the final 3D catching point, achieving a mean average error of 5 mm and a root-mean-square error of 7 mm in 200 real-world test experiments.

Abstract Optical coherence tomography (OCT) is one of the principal imaging modalities for retinal eye disease detection and classification. Different retinal eye diseases are the leading cause of blindness that can be overcome by early detection. However, ophthalmologists are currently carrying out retinal eye disease detection manually with the help of OCT images that may be erroneous and subjective. Different methods have been presented to automate the manual retinal eye disease detection process that needs further improvement in detection accuracy. This research proposed an automatic method for retinal eye disease detection and classification from OCT images using fusion and selection techniques. First, the modified‐Alexnet and ResNet‐50 are utilized for deep feature vector extraction. In the next step, these vectors are fused serially and rectified by the proposed feature selection framework and passed as input to different machine learning classifiers for retinal disease diagnosis. For this purpose, a publicly available dataset of retinal eye diseases with four classes is utilized. The proposed retinal eye disease detection method achieved an overall average accuracy index of greater than 99.95%, higher than the top one in the literature, that is, 99.39%. Experimental results authenticated that the proposed retinal eye disease detection methodology can reliably be used for automatic eye disease detection from OCT images. Furthermore, the proposed deep feature and selection‐based retinal eye disease detection methodology achieved state‐of‐the‐art performance.

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.

Abstract Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre‐trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well‐known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising.

Deep learning is a growing scientific research trend in machine learning and artificial intelligence due to its better performance compared to other machine learning techniques. This special issue focuses on recent advances in deep learning for human health care applications such as biometrics, medical imaging, and data science. The following articles were carefully reviewed and selected for this special issue: Bhurane, Dhok, Sharma, Yuvaraj, Murugappan and Acharya, ‘Diagnosis of Parkinson's disease from electroencephalography signals using linear and self-similarity features’. In this paper, the authors propose a natural (time) domain technique for diagnosing Parkinson's disease (PD). The presented computer-aided diagnosis system can act as an assistive tool to confirm the finding of PD for the clinicians. They demonstrate that using the support vector machines (SVM) classifier, the feature ranking, and the principal component analysis technique, the proposed system can detect the PD signals automatically with maximum accuracy of 99.1% ± 0.1%. Khan, Sharif, Raza, Anjum, Saba and Shad, ‘Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection.’ This paper addresses the problem of automated skin lesion diagnosis from dermoscopic images overcoming challenges such as hairs, irregularities, lesion shape, and irrelevant feature extraction. The authors propose a hybrid approach combining optimized colour feature of lesion segmentation improved by an existing saliency approach fused with a novel pixel-based method and deep convolutional neural network (DCNN)-based skin lesion classification. Experimental results of the proposed approach demonstrate remarkable performance on three different datasets. Sivan, Sellappa and Peter J, ‘Proximity-based cloud resource provisioning for deep learning applications in smart healthcare.’ Health professionals can use smart mobile devices to convey recordings of patients and use machine learning-based approaches to process results and get predictions through smart mobile healthcare applications. Due to the nature of deep learning techniques, learning and prediction processes are moved to the cloud. This paper proposes a proximity-based resource provisioning technique that guarantees minimal delay in obtaining inference results with a local mobile cloud system. The authors implemented a healthcare cloud-based system that outperforms the state-of-the-art methods in terms of response time, deadline meeting percentage and system utilization. Arshad, Khan, Sharif, Yasmin, Tavares, Zhang and Satapathy, ‘A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition.’ This paper proposes an integrated framework for human gait recognition using deep neural network features fusion and fuzzy entropy controlled skewness approach for best feature selection. Pre-trained CNN models (VGG19 and AlexNet) are used, and their information is mixed by the parallel fusion approach. Remarkable results on four gait analysis datasets show that the fusion of multiple CNN frameworks improves the recognition accuracy and the selection of the best features enhances the system accuracy and even minimizes the execution time. Alizadehsani, Roshanzamir, Abdar, Beykikhoshk, Khosravi, Nahavandi, Plawiak, Tan and Acharya, ‘Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries.’ The authors address the uncertainty coming from noise in the data used for automated coronary artery disease (CAD) detection. They propose a novel new feature selection algorithm for CAD prediction. Authors use the genetic algorithm to determine the hyper-parameters of the SVM kernels. The system with the proposed approach demonstrates high accuracy for the stenosis diagnosis of each main coronary artery, which can help the clinicians validate their manual stenosis diagnosis of right coronary artery (RCA), right coronary artery (RCA), left circumflex (LCX) and artery and left anterior descending (LAD) coronary arteries. The results show that discretization and assurance feature selection can significantly improve the efficiency of classification algorithms. Sampathila, Pavithra and Martis, ‘Computational approach for content-based image retrieval of K-Similar images from brain MR image database.’ The task of retrieving medical images from a large image database becomes more tedious due to variations in the size and shape of the images. This paper proposes a system for content-based medical image retrieval that are relevant to a given query image. Various features such as colour, shape, and texture are exploited using the K-nearest neighbour algorithm to find the minimum distance between query and database images. The authors focus on the application of retrieving the brain MRI images of different planes (coronal, sagittal and transverse) from a dataset of normal and demented subjects. The results demonstrate high accuracy of 95%. Such a tool can be helpful in radiology image retrieval and classification.

Abstract Automated skin lesion diagnosis from dermoscopic images is a difficult process due to several notable problems such as artefacts (hairs), irregularity, lesion shape, and irrelevant features extraction. These problems make the segmentation and classification process difficult. In this research, we proposed an optimized colour feature (OCF) of lesion segmentation and deep convolutional neural network (DCNN)‐based skin lesion classification. A hybrid technique is proposed to remove the artefacts and improve the lesion contrast. Then, colour segmentation technique is presented known as OCFs. The OCF approach is further improved by an existing saliency approach, which is fused by a novel pixel‐based method. A DCNN‐9 model is implemented to extract deep features and fused with OCFs by a novel parallel fusion approach. After this, a normal distribution‐based high‐ranking feature selection technique is utilized to select the most robust features for classification. The suggested method is evaluated on ISBI series (2016, 2017, and 2018) datasets. The experiments are performed in two steps and achieved average segmentation accuracy of more than 90% on selected datasets. Moreover, the achieve classification accuracy of 92.1%, 96.5%, and 85.1%, respectively, on all three datasets shows that the presented method has remarkable performance.

Medical imaging systems installed in different hospitals and labs generate images in bulk, which could support medics to analyze infections or injuries. Manual inspection becomes difficult when there exist more images, therefore, intelligent systems are usually required for real-time diagnosis. Melanoma is one of the most common and severe forms of skin cancer that begins from the cells beneath the skin. Through dermoscopic images, it is possible to diagnose the infection at the early stages. In this regard, different approaches have been exploited for improved results. In this study, we propose a two-stream deep neural network information fusion framework for multiclass skin cancer classification. The proposed technique follows two streams: initially, a fusion-based contrast enhancement technique is proposed, which feeds enhanced images to the pretrained DenseNet201 architecture. The extracted features are later optimized using a skewness-controlled moth–flame optimization algorithm. In the second stream, deep features from the fine-tuned MobileNetV2 pretrained network are extracted and down-sampled using the proposed feature selection framework. Finally, most discriminant features from both networks are fused using a new parallel multimax coefficient correlation method. A multiclass extreme learning machine classifier is used to classify lesion images. The testing process is initiated on three imbalanced skin data sets—HAM10000, ISBI2018, and ISIC2019. The simulations are performed without performing any data augmentation step in achieving an accuracy of 96.5%, 98%, and 89%, respectively. A fair comparison with the existing techniques reveals the improved performance of our proposed algorithm.

At present, governments and private business operations are highly dependent on relational data applications such as bank accounts, citizen registration, etc. These relational data dependent operations require reliable integrity protection while utilising the cloud computing storage infrastructure. Identification and recovery of stolen bits are a major assistance to the reliable integrity protection services for the sensitive relational data applications. To deal with the problems of detecting and recovering tampering in large relational data at minimum computational complexity, in this paper, N8WA (briefed in Section 2.1) coding-based scheme is presented. Overall the scheme is comprised of two cross functional modules. The first module is labelled as compact code generation using N8WA coding and code registration at registration module (RM). In the second module which is called accurate locating/restoring tampering, utilising the mismatching of different compact codes based on N8WA from RM, the major/minor tampered data is accurately located and restored. Investigational outcome indicates that the scheme ensures the computational complexity of O(n2) while minimum to maximum alterations is accurately localised and restored successfully.

Gastrointestinal tract (GIT) infections such as ulcers, bleeding, polyps, Crohn’s disease and cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient means of investigation of GIT diseases. However, still several challenges exist in this domain, such as lesion shape, colour, texture, size and irregularity. To deal with these problems, several computer-based methods are introduced in computer vision domain but they used only hand-crafted features which produced wrong predictions several times. In this research, a new technique is applied based on the fusion of deep convolutional (CNN) and geometric features. Initially, disease regions are extracted from given WCE images using a new approach named contrast-enhanced colour features . Geometric features are extracted from segmented disease region. Thereafter, unique VGG16 and VGG19 deep CNN features fusion are performed based on Euclidean Fisher Vector . The unique features are fused with geometric features which are later fed to conditional entropy approach for best features selection. The selected features are finally classified by K-Nearest Neighbour . A privately collected database which consists of 5500 WCE images is utilised for the evaluation of the proposed method and achieved best classification accuracy of 99.42% and precision rate of 99.51%. The classification accuracy proves the authenticity of the proposed approach.

Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this field, telecommunication technologies are utilized to transfer medical information to the experts. Due to the skin's visual nature, teledermatology is an effective tool for the diagnosis of skin lesions especially in rural areas. Furthermore, it can also be useful to limit gratuitous clinical referrals and triage dermatology cases. The objective of this research is to classify the skin lesion image samples, received from different servers. The proposed framework is comprised of two module, which include the skin lesion localization/segmentation and the classification. In the localization module, we propose a hybrid strategy that fuses the binary images generated from the designed 16-layered convolutional neural network model and an improved high dimension contrast transform (HDCT) based saliency segmentation. To utilize maximum information extracted from the binary images, a maximal mutual information method is proposed, which returns the segmented RGB lesion image. In the classification module, a pre-trained DenseNet201 model is re-trained on the segmented lesion images using transfer learning. Afterward, the extracted features from the two fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) method. These resultant features are finally fused using a multi canonical correlation (MCCA) approach and are passed to a multi-class ELM classifier. Four datasets (i.e., ISBI2016, ISIC2017, PH2, and ISBI2018) are employed for the evaluation of the segmentation task, while HAM10000, the most challenging dataset, is used for the classification task. The experimental results in comparison with the state-of-the-art methods affirm the strength of our proposed framework.

Human motion analysis has received a lot of attention in the computer vision community during the last few years. This research domain is supported by a wide spectrum of applications including video surveillance, patient monitoring systems, and pedestrian detection, to name a few. In this study, an improved cascaded design for human motion analysis is presented; it consolidates four phases: (i) acquisition and preprocessing, (ii) frame segmentation, (iii) features extraction and dimensionality reduction, and (iv) classification. The implemented architecture takes advantage of CIE‐Lab and National Television System Committee colour spaces, and also performs contrast stretching using the proposed red–green–blue* colour space enhancement technique. A parallel design utilising attention‐based motion estimation and segmentation module is also proposed in order to avoid the detection of false moving regions. In addition to these contributions, the proposed feature selection technique called entropy controlled principal components with weights minimisation, further improves the classification accuracy. The authors claims are supported with a comparison between six state‐of‐the‐art classifiers tested on five standard benchmark data sets including Weizmann, KTH, UIUC, Muhavi, and WVU, where the results reveal an improved correct classification rate of 96.55, 99.50, 99.40, 100, and 100%, respectively.

License plate recognition (LPR) system plays a vital role in security applications which include road traffic monitoring, street activity monitoring, identification of potential threats, and so on. Numerous methods were adopted for LPR but still, there is enough space for a single standard approach which can be able to deal with all sorts of problems such as light variations, occlusion, and multi‐views. The proposed approach is an effort to deal under such conditions by incorporating multiple features extraction and fusion. The proposed architecture is comprised of four primary steps: (i) selection of luminance channel from CIE‐Lab colour space, (ii) binary segmentation of selected channel followed by image refinement, (iii) a fusion of Histogram of oriented gradients (HOG) and geometric features followed by a selection of appropriate features using a novel entropy‐based method, and (iv) features classification with support vector machine (SVM). To authenticate the results of proposed approach, different performance measures are considered. The selected measures are False positive rate (FPR), False negative rate (FNR), and accuracy which is achieved maximum up to 99.5%. Simulation results reveal that the proposed method performs exceptionally better compared with existing works.