Hand gesture recognition is progressively becoming a mature technology as a result of significant investment in human–machine interaction. The requirements of human–machine interaction are becoming increasingly genuine for non-verbal communication. In this study, we have analyzed and summarized past studies on non-vision (e.g., data glove-based sensor technology) and vision-based gesture recognition. Several tools and technologies available to date for gesture recognition, including hidden Markov model, finite state machine, color modeling, naive Bayes classifier, deep neural networks, histogram-based features, and fuzzy clustering, have been examined in this study. We have reviewed studies on visual gesture identification based on static and dynamic motions. In the literature, the methodologies presented in gesture recognition have been appropriately divided into the phases of detection, tracking, and recognition, with the various algorithms at each stage developed and contrasted. The purpose of this study is to review prospective technologies, methods, and research outcomes, as well as to analyze the benefits and challenges of various hand gesture detection algorithms, in order to contribute to future research.

Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages or youth, and ASD follows a repetitive pattern of behavior in most cases. Accurate diagnosis of ASD has been difficult in clinical practice as there is currently no valid indicator of ASD. Since ASD is regarded as a neurodevelopmental disorder, brain signals specially electroencephalography (EEG) are an effective method for detecting ASD. Therefore, this research aims at developing a method of extracting features from EEG signal for discriminating between ASD and control subjects. This study applies six prominent connectivity features, namely Cross Correlation (XCOR), Phase Locking Value (PLV), Pearson's Correlation Coefficient (PCC), Mutual Information (MI), Normalized Mutual Information (NMI) and Transfer Entropy (TE), for feature extraction. The Connectivity Feature Maps (CFMs) are constructed and used for classification through Convolutional Neural Network (CNN). As CFMs contain spatial information, they are able to distinguish ASD and control subjects better than other features. Rigorous experimentation has been performed on the EEG datasets collected from Italy and Saudi Arabia according to different criteria. MI feature shows the best result for categorizing ASD and control participants with increased sample size and segmentation.

Object detection is a key component in computer vision research, allowing a system to determine the location and type of object within any given scene. YOLOv5 is a modern object detection model, which utilises the advantages of the original YOLO implementation while being built from scratch in Python. In this paper, BiFPN-YOLO is proposed, featuring clear improvements over the existing range of YOLOv5 object detection models; these include replacing the traditional Path-Aggregation Network (PANet) with a higher performing Bi-Directional Feature Pyramid Network (BiFPN), requiring complex adaptation from its original implementation to function with YOLOv5, as well as exploring a replacement to the standard Swish activation function by evaluating the performance against a number of other activation functions. The proposed model showcases state-of-the-art performance, benchmarking against well-known datasets such as the German Traffic Sign Detection Benchmark (GTSDB), improving mAP by 3.1 %, and the RoboFEI@Home dataset, where Mean Average Precision (mAP) is improved by 2 % compared to the base YOLOv5 model. Performance was also improved on MSCOCO by 1.1 % and a custom subset of the OpenImagesV6 dataset by 2.4 %.

The task of visuo-tactile object recognition is key in enabling robots to interact with humans and their environment in an efficient and effective manner. The differing statistical properties of visual images and tactile time-series data make visuo-tactile fusion non-trivial and complex. This work investigates the usage of Transformers to perform feature level fusion for visuo-tactile data, utilising the Transformer to generate temporal relationships between the visual and tactile data through its self-attention structure. The proposed pipeline is tested on the PHAC-2 dataset, and a complex ablation experiment is completed across a collection of leading activation functions. The proposed pipeline is demonstrated to achieve state-of-the-art accuracy for visuo-tactile object recognition on the PHAC-2 dataset, achieving a 94.3% accuracy when data from two tactile actions are considered.

Improvements to human-robot interaction methods could increase the ease of use of robots in manufacturing environments. Many of these environments are noisy and therefore preclude the use of audio communication between humans or in human-robot interactions. Therefore, this paper proposes using a gesture based communication system for robot control. To that end, the VGG16 and VGG19 convolutional neural network (CNN) structures are used for gesture classification along with 3 datasets of American Sign Language (ASL) fingerspelling images. The model performance is evaluated, and modifications made to their parameters to improve performance, before applying them to robot control tasks. The results show that with parameter tuning, test accuracies of up to, 100% are achievable.

In light of the pandemic, the identification and processing of COVID-19-related text have emerged as critical research areas within the field of Natural Language Processing (NLP). With a growing reliance on online portals and social media for information exchange and interaction, a surge in online textual content, comprising disinformation, misinformation, fake news, and rumors has led to the phenomenon of an infodemic on the World Wide Web. Arabic, spoken by over 420 million people worldwide, stands as a significant low-resource language, lacking efficient tools or applications for the detection of COVID-19-related text. Additionally, the identification of COVID-19 text is an essential prerequisite task for detecting fake and toxic content associated with COVID-19. This gap hampers crucial COVID information retrieval and processing necessary for policymakers and health authorities. Addressing this issue, this paper introduces an intelligent Arabic COVID-19 text identification system named ‘AraCovTexFinder,’ leveraging a fine-tuned fusion-based transformer model. Recognizing the challenges posed by a scarcity of related text corpora, substantial morphological variations in the language, and a deficiency of well-tuned hyperparameters, the proposed system aims to mitigate these hurdles. To support the proposed method, two corpora are developed: an Arabic embedding corpus (AraEC) and an Arabic COVID-19 text identification corpus (AraCoV). The study evaluates the performance of six transformer-based language models (mBERT, XML-RoBERTa, mDeBERTa-V3, mDistilBERT, BERT-Arabic, and AraBERT), 12 deep learning models (combining Word2Vec, GloVe, and FastText embedding with CNN, LSTM, VDCNN, and BiLSTM), and the newly introduced model AraCovTexFinder. Through extensive evaluation, AraCovTexFinder achieves a high accuracy of 98.89 ± 0.001%, outperforming other baseline models, including transformer-based language and deep learning models. This research highlights the importance of specialized tools in low-resource languages to combat the infodemic relating to COVID-19, which can assist policymakers and health authorities in making informed decisions.

Sign language is the comprehensive medium of mass communication for hearing and speaking impaired individuals. As they cannot speak or hear, they are not able to use sound or vocal signals as an information medium for their communication. Rather, they are bound to exchange visual signals to express their feeling in their day-to-day life. For this, they use various body language mainly hand gestures as sign language. Sign language fundamentals can be largely divided into two parts namely digits (numerals) and characters (alphabetical). In this paper, we proposed a hybrid model consisting of a deep transfer learning-based convolutional neural network with a random forest classifier for the automatic recognition of Bangla Sign Language (numerals and alphabets). The overall performance of the presented system is verified on ‘Ishara-Bochon’ and ‘Ishara-Lipi’ datasets. ‘Ishara-Bochon’ and ‘Ishara-Lipi’ are datasets of isolated numerals and alphabets respectively which are the first complete multipurpose open-access dataset for Bangla Sign Language (BSL). Besides, we also proposed a background elimination algorithm that removes unnecessary features from the sign images. Along with the proposed background elimination technique, the system is able to achieve accuracy, precision, recall, f1-score values of 91.67%, 93.64%, 91.67%, 91.47% for character recognition and 97.33%, 97.89%, 97.33%, 97.37% for digit recognition respectively. The detailed experimental analysis assures the feasibility and effectiveness of the proposed system for BSL recognition.

Covid text identification (CTI) is a crucial research concern in natural language processing (NLP). Social and electronic media are simultaneously adding a large volume of Covid-affiliated text on the World Wide Web due to the effortless access to the Internet, electronic gadgets and the Covid outbreak. Most of these texts are uninformative and contain misinformation, disinformation and malinformation that create an infodemic. Thus, Covid text identification is essential for controlling societal distrust and panic. Though very little Covid-related research (such as Covid disinformation, misinformation and fake news) has been reported in high-resource languages (e.g. English), CTI in low-resource languages (like Bengali) is in the preliminary stage to date. However, automatic CTI in Bengali text is challenging due to the deficit of benchmark corpora, complex linguistic constructs, immense verb inflexions and scarcity of NLP tools. On the other hand, the manual processing of Bengali Covid texts is arduous and costly due to their messy or unstructured forms. This research proposes a deep learning-based network (CovTiNet) to identify Covid text in Bengali. The CovTiNet incorporates an attention-based position embedding feature fusion for text-to-feature representation and attention-based CNN for Covid text identification. Experimental results show that the proposed CovTiNet achieved the highest accuracy of 96.61±.001% on the developed dataset ( BCovC ) compared to the other methods and baselines (i.e. BERT-M, IndicBERT, ELECTRA-Bengali, DistilBERT-M, BiLSTM, DCNN, CNN, LSTM, VDCNN and ACNN).

In recent years, the amount of digital text contents or documents in the Bengali language has increased enormously on online platforms due to the effortless access of the Internet via electronic gadgets. As a result, an enormous amount of unstructured data is created that demands much time and effort to organize, search or manipulate. To manage such a massive number of documents effectively, an intelligent text document classification system is proposed in this paper. Intelligent classification of text document in a resource-constrained language (like Bengali) is challenging due to unavailability of linguistic resources, intelligent NLP tools, and larger text corpora. Moreover, Bengali texts are available in two morphological variants (i.e., Sadhu-bhasha and Cholito-bhasha) making the classification task more complicated. The proposed intelligent text classification model comprises GloVe embedding and Very Deep Convolution Neural Network (VDCNN) classifier. Due to the unavailability of standard corpus, this work develops a large Embedding Corpus (EC) containing 969,000 unlabelled texts and Bengali Text Classification Corpus (BDTC) containing 156,207 labelled documents arranged into 13 categories. Moreover, this work proposes the Embedding Parameters Identification (EPI) Algorithm, which selects the best embedding parameters for low-resource languages (including Bengali). Evaluation of 165 embedding models with intrinsic evaluators (semantic & syntactic similarity measures) shows that the GloVe model is more suitable (regarding Spearman & Pearson correlation) than other embeddings (Word2Vec, FastText, m-BERT) in Bengali text. Experimental results on the test dataset confirm that the proposed GloVe + VDCNN model outperformed (achieving the highest 96.96% accuracy) the other classification models and existing methods to perform the Bengali text classification task.

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive–pruning strategy, and different training samples for individual NN’s learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble; (2) maintaining accuracy and diversity of NNs at the same time; and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.

Eye image recognition from a face image acquired at a distant is a promising physical biometric technique to use for human identification. This contemporary field of research depends on image preprocessing, feature extraction, and reliable classification techniques. In this work, we separate eye images from an image of the entire face of a subject and then extract features from these eye images utilizing a convolutional neural network (CNN) model. In general, CNN models convolve images in different layers to extract effective features and then use the softmax function to produce a probability output in the final layer. In our approach, we use CNN features and a kernel extreme learning machine (KELM) classifier instead of softmax to modify the original CNN model. The modified CNN-KELM model has been verified using the publicly available CASIA.v4 distance image database. The experimental results demonstrate that our proposed approach obtains a satisfactory recognition result when compared with several current state-of-the-art human identification approaches.

Removing artefacts from electroencephalographic (EEG) recordings normally increases their low signal-to-noise ratio and enables more reliable interpretation of brain activity. In this paper we present an evaluation of an automatic independent component analysis (ICA) procedure, a hybrid ICA - wavelet transform technique (ICA-W), for artefact removal from EEG correlated to emotional-state. Spectral and statistical features were classified with support vector machines (SVM) to assess the performance of ICA-W against the regular ICA, in terms of the accuracy of classifying emotional states from EEG. Accuracies on data from 14 subjects are reported and the results indicate that ICA-W performs better than traditional ICA in statistical and wavelet based features whilst the best overall performance is achieved when combining ICA-W with statistical features with an average accuracy across subjects of 74.11% for classifying four categories of emotion. ICA-W is therefore demonstrated to enhance EEG-based emotion recognition applications in terms of performance and ease of application.

Reconstruction of the three-dimensional (3D) trajectory of an imagined limb movement using electro-encephalography (EEG) poses many challenges. However, if achieved, more advanced non-invasive brain-computer interfaces (BCIs) for the physically impaired could be realized. The most common motion trajectory prediction (MTP) BCI employs a time-series of band-pass filtered EEG potentials for reconstructing the 3D trajectory of limb movement using multiple linear regression (mLR). Most MTP BCI studies report the best accuracy using low delta (0.5-2Hz) band-pass filtered EEG potentials. In a recent study, we showed spatiotemporal power distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) EEG frequency bands contain richer information associated with movement trajectory. This finding is in line with the results in the extensive literature on traditional sensorimotor rhythm (SMR) based multiclass (MC) BCI studies, which report the best accuracy of limb movement classification using power values of mu and beta frequency bands. Here, we show the reconstruction of actual and imagined 3D limb movement trajectory with an MTP BCI using a time-series of bandpower values (BTS model). Furthermore, we show the proposed BTS model outperforms the standard potential time-series model (PTS model). The BTS model yielded best results in the mu and beta bands (R~0.5 for actual and R~0.2 for imagined movement reconstruction) and not in the low delta band, as previously reported for MTP studies using the PTS model. Our results show for the first time how mu and beta activity can be used for decoding imagined 3D hand movement from EEG.

Abstract This paper presents a review of recently developed physics‐based search and optimization algorithms that have been inspired by natural phenomena. They include Big Bang–Big Crunch, black hole search, galaxy‐based search, artificial physics optimization, electromagnetism optimization, charged system search, colliding bodies optimization, and particle collision algorithm.

Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In GSA, a collection of objects interacts with each other under the Newtonian gravity and the laws of motion. The performances of objects are measured by masses. All these objects attract each other by the gravity force, while this force causes a global movement of all objects toward the objects with heavier masses. The position of the object corresponds to a solution of the problem. The positions of the objects are updated every iteration and the best fitness along with its corresponding object is stored. Heavier masses move slowly than lighter ones. The algorithm terminates after a specified number of iterations after which the best fitness becomes the global fitness for a particular problem and the positions of the corresponding object becomes the global solution of that problem. This paper presents a review of GSA and its variants.

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

In this work, an optimization technique is adopted to manipulate the input and output scaling factors of a modular fuzzy logic controller (MFC) for lifting and stabilizing the front wheels of a wheelchair in two-wheeled mode. A virtual wheelchair (WC) model is developed within Visual Nastran (VN) software environment where the model is further linked with Matlab/Simulink for control purposes. The lifting of the chair is done by transforming the first link (Link1), attached to the front wheels (casters) to the upright position while maintaining stability of the second link (Link2) where the payload is attached. General rules of thumb allow heuristic tuning (trial and error) of the parameters but such heuristic method does not guarantee that the system tuned with current data set will represent future system states. A global optimization mechanism such as genetic algorithm is necessary to improve the system performance. Due to its significant advantages over other searching methods, a genetic algorithm approach is used to optimize the scaling factors of the MFC and results show that the optimized parameters give better system performance for such a complex, highly nonlinear two-wheeled wheelchair system.

This paper presents a Spiking Neural Network (SNN) architecture for mobile robot navigation. The SNN contains 4 layers where dynamic synapses route information to the appropriate neurons in each layer and the neurons are modeled using the Leaky Integrate and Fire (LIF) model. The SNN learns by self-organizing its connectivity as new environmental conditions are experienced and consequently knowledge about its environment is stored in the connectivity. Also a novel feature of the proposed SNN architecture is that it uses working memory, where present and previous sensor states are stored. Results are presented for a wall following application.

Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect complex/spiral-shaped clusters. In our previous works, we proposed several evolutionary multi-objective clustering algorithms and achieved promising results. Still, they suffer from this usual problem exhibited by evolutionary and unsupervised clustering approaches. In this paper, we proposed an improved multi-objective evolutionary clustering approach (EMCOC) to resolve the overlapping problems in complex shape data. Experimental results based on several artificial and real-world data show that the proposed EMCOC can successfully identify overlapping clusters. It also succeeds obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. The superiority of the EMCOC over some other multi-objective evolutionary clustering algorithms is also confirmed by the experimental results.

This paper proposes a novel scheme of a Takagi-Sugeno (T-S) fuzzy based adaptive critic for the optimal control of the continuous-time input affine nonlinear system. A novel learning strategy is proposed to update the weights of critic network which resolves the issue of under-determined weight update equations discussed in [1]. The T-S Fuzzy based critic network approximates the global optimal cost as fuzzy average of local costs associated with local linear subsystems. This work clearly demonstrates that the optimal cost of a nonlinear system can be represented as the fuzzy cluster of optimal costs of locally valid linear models in a T-S framework. The proposed scheme has been simulated for four different dynamic systems. Simulation results clearly demonstrate that the T-S Fuzzy approximates the optimal cost, with subsystems in each fuzzy zone represents the optimal cost of locally valid linear model.

An algorithm inspired by the biological phenomenon of symbiosis is presented in this paper. The genetic diversity obtained from the creation of symbiotic relationships is investigated and the symbiosis algorithm is applied to a robotic forward kinematics control problem. Compared with other evolutionary optimisation techniques, the symbiosis algorithm is shown to be an effective paradigm in discovering optimal solutions. Genetic diversity was found to be maintained in the absence of conventional genetic operators.

There are several key attributes within the medical packaging industry, such as, anchorage, seal strength etc, which characterize the quality of the medical packaging product. This paper presents a new approach to determine the anchorage of the packaging material. The conventional approach to anchorage measurement involves a manual visual inspection procedure, which is very subjective and has led to inconsistencies in quality control. A new proposed method to improve this procedure involves image-processing technique, which eliminates operator dependencies. A fuzzy reasoning approach is then used to quantify the results generated from image processing which replaces the human decision process. This fusion of these techniques results in a more automated systematic approach to anchorage measurement. Experimental studies confirm that this novel approach leads to a more robust and improved quality control system for the medical packaging company.