Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven

The paper’s aims are to present a novel concept of linguistic interval-valued Pythagorean fuzzy set (LIVPFS) or called a linguistic interval-valued intuitionistic type-2 fuzzy set, which is a robust and trustworthy tool, and to accomplish the imprecise information while solving the decision-making problems. The presented LIVPFS is a generalization of the linguistic Pythagorean fuzzy set, by characterizing

The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network–based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered

Learning to play and perform a music instrument is a complex cognitive task, requiring high conscious control and coordination of an impressive number of cognitive and sensorimotor skills. For professional violinists, there exists a physical connection with the instrument allowing the player to continuously manage the sound through sophisticated bowing techniques and fine hand movements. Hence, it

Due to the uncertainty and complexity of socioeconomic environments and cognitive diversity of decision makers, the cognitive information over alternatives provided by decision makers is usually uncertain and fuzzy. Two-tuple linguistic Pythagorean fuzzy sets (2TLPFSs) provide useful tools to depict the uncertain and fuzzy cognitions of the decision makers over attributes. To effectively handle such

With the widespread dissemination of user-generated content on different web sites, social networks, and online consumer systems such as Amazon, the quantity of opinionated information available on the Internet has been increased. Sentiment analysis of user-generated content is one of the main cognitive computing branches; hence, it has attracted the attention of many scholars in recent years. One

Reservoir computing (RC) has emerged as an alternative approach for the development of fast trainable recurrent neural networks (RNNs). It is considered to be biologically plausible due to the similarity between randomly designed artificial reservoir structures and cortical structures in the brain. The paper continues our previous research on the application of a member of the family of RC approaches—the

Feature selection is the process of selecting a subset of relevant features from the original feature set, and it plays an important role in handling high-dimensional data. In recent years, sparse learning-based feature selection approaches have been widely studied, and different regularizers have been proposed. Among these regularizers, it has been found that ℓ2,p (0 < p < 1) has a good modeling effect

In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature

Bio-inspired optimization algorithms are capable of resolving a wide variety of challenges in science and technology, including cognitive science. The principles used by the smallest living organisms in the world could be adopted in the decision-based algorithms for artificial intelligence purposes. Bacterial biological functions and behaviors have been the most effective strategies, which have evolved

Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers’ trust in AVs. The personalised AV system is able to identify the driving

Automatic text summarization is the process of generating or extracting a brief representation of an input text. There are several algorithms for extractive summarization in the literature tested by using English and other languages datasets; however, only few extractive Arabic summarizers exist due to the lack of large collection in Arabic language. This paper proposes and assesses new extractive

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography

The complexity of decision environments poses challenges for individuals engaged in decision-making proceedings. Picture fuzzy linguistic sets (PFLSs) are an effective tool for depicting the inherent subjective nature of human cognition. Aiming for the promotion of a general theory on PFLSs, we conduct a critical research to identify some limitations of a recent publication in Cognitive Computation

Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and

As a single hidden layer feed-forward neural network, the extreme learning machine (ELM) has been extensively studied for its short training time and good generalization ability. Recently, with the deep learning algorithm becoming a research hotspot, some deep extreme learning machine algorithms such as multi-layer extreme learning machine (ML-ELM) and hierarchical extreme learning machine (H-ELM)

Semi-supervised learning targets the common situation where labeled data are scarce but unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In practice, it may make sense to utilize active learning in conjunction with semi-supervised learning. That is, we might allow the learning algorithm to pick a set of unlabeled instances to be labeled by a domain expert, which

It is essential to build the effective appearance model for object tracking in computer vision. Most object trackers can be roughly divided into two categories according to the appearance model: the bounding box model and the patch model. The bounding box model cannot handle shape deformation and occlusion of the non-rigid moving object effectively. The patch model is prone to be disturbed by complex

Sentiment analysis is an increasingly evolving field of research in computer science. With the considerable number of studies on innovative sentiment analysis available, it is worth the effort to present a review to understand the research on sentiment analysis comprehensively. This study aimed to investigate issues involved in sentiment analysis; for instance, (1) What types of research topics had

Single-valued trapezoidal neutrosophic numbers (SVTNNs) are very useful tools to describe complex cognitive information because of their advantage in maintaining the completeness and accuracy of information. This paper develops a method based on the single-valued trapezoidal neutrosophic power-weighted aggregation operators and possibility degree of SVTNNs for dealing with multi-criteria group decision-making

Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal

The advancement of cognitive computing for traffic status understanding, powered by machine learning and data analytics, enables prediction of traffic anomalies from continuously generated big GPS trajectory data. Existing methods generally use traffic indicators such as traffic flows and speeds to detect anomalies, but they may over-identify anomalies while missing the critical ones. For example,

Improving robots with self-learning ability is one of the critical challenges for the researchers in the area of cognitive robotics and artificial general intelligence. This robot will decide when, where, and what to learn in a continuous visual environment by itself. Here we focus on the procedural knowledge learning, which is sequential and considered harder to understand compared with declarative

The problem of neural decoding is essential for the realization of a neural interface. In this study, the position of the moving finger of a macaque was directly decoded through the neuron spike signals in the motor cortex, instead of relying on the synergy of the related muscle tissues around the body, also known as neural decoding. Currently, supervised learning is the most commonly employed method

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive

Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important issue related with e-health applications is the case where the change in ADL undergoes a linear drift, which occurs in cognitive decline, Alzheimer’s disease or dementia. This work presents

In recent years, deep learning has been attracting substantial attention due to its outstanding forecasting performance. However, the application of deep learning methods in solving the problem of forecasting tourist arrivals has been few. For the efficient allocation of tourism resources, tourist arrivals must be accurately predicted for government and tourism enterprises. In this study, a new hybrid

Human civilizations have performed the art of writing across continents and over different time periods. In order to speed up the writing process, the art of shorthand (brachygraphy) came into existence. Today, the performance of writing does not make an exception in social media platforms. Brachygraphy started to re-emerge in the early 2000s in the form of microtext in order to facilitate faster typing

Autistic spectrum disorder (ASD) is a neurodevelopment condition normally linked with substantial healthcare costs and time-consuming assessments where early detection of ASD traits can help limit the development of the condition. The existing conventional ASD screening methods contain a large number of items and are based on domain expert rules which may be criticized of being lengthy and subjective

The P300 wave has been successfully employed to develop brain-computer interfaces (BCI) for speller applications. However, methods to analyze the P300 require computers with high processing capability because they are computationally complex and require many electrodes. Therefore, this paper proposes a novel BCI speller system based on the P300 wave that employs a few electrodes and a processing method

Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack. Recently, some contributions have proposed techniques to endow the networks with early exits, allowing to obtain predictions at intermediate points of the stack. These multi-output networks have a number of advantages, including (i) significant reductions

Existing tracking methods might suffer from the performance degradation due to insufficient positive samples. A typical network structure is proposed to enrich positive samples by generating masks during the tracking process. Although this structure has achieved good results, it ignores the drift problem that occurs when the tracked object is very similar to the surrounding objects. This problem is

Predicting emotions automatically is an active field of research in affective computing. Considering the property of the individual’s subjectivity, the label of an emotional instance is usually created based on opinions from multiple annotators. That is, the labelled instance is often accompanied with the corresponding inter-rater disagreement information, which we call here the perception uncertainty

Digital watermarking is a significant issue in the field of information security and avoiding the misuse of images in the world of Internet and communication. This paper proposes a novel watermarking method for tamper detection and recovery using semi-fragile data hiding, based on lifting wavelet transform (LWT) and feed-forward neural network (FNN). In this work, first, the host image is decomposed

Background/IntroductionThe deaf community in the world uses a gesture-based language, generally known as sign language. Every country has a different sign language; for instance, USA has American Sign Language (ASL) and UK has British Sign Language (BSL). The deaf community in Pakistan uses Pakistan Sign Language (PSL), which like other natural languages, has a vocabulary, sentence structure, and word

Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when the boundaries have salient objects. In order to address this limitation and enhance saliency detection performance, this paper proposes a novel task-independent

Time expressions and named entities play important roles in data mining, information retrieval, and natural language processing. However, the conventional position-based tagging schemes (e.g., the BIO and BILOU schemes) that previous research used to model time expressions and named entities suffer from the problem of inconsistent tag assignment. To overcome the problem of inconsistent tag assignment

Stress-induced performance degradation in high-pressure situations has been documented empirically and generated different explanations. The existing theories often assume the distinction between implicit and explicit processing but speculate differently on the impact that high-pressure situations have on their interaction. Although few attempts have been made so far at clarifying these underlying

Piezoelectric actuators (PEAs) are the pivotal components of many nanopositioning systems because of their superiorities in bandwidth, mechanical force, and precision. Unfortunately, the intrinsic nonlinear property, hysteresis, makes it difficult to achieve the precise control of PEAs. Considering this drawback, diversified feedback control approaches have been studied in the literature. Inspired

An essential component of any dialogue system is understanding the language which is known as spoken language understanding (SLU). Dialogue act classification (DAC), intent detection (ID) and slot filling (SF) are significant aspects of every dialogue system. In this paper, we propose a deep learning-based multi-task model that can perform DAC, ID and SF tasks together. We use a deep bi-directional

Recently, artificial neural networks (ANNs) have been applied to various robot-related research areas due to their powerful spatial feature abstraction and temporal information prediction abilities. Decision-making has also played a fundamental role in the research area of robotics. How to improve ANNs with the characteristics of decision-making is a challenging research issue. ANNs are connectionist

In the current paper, we have proposed a new multi-modal authorship verification approach for social media texts. Authorship verification is a task of verifying whether an unknown text is written by a suspect or not. Use of social media like Facebook and Twitter is increasing day by day because of digitization. People have grown accustomed to regularly post or tweet about their everyday life, memorable

Automatic image captioning, a highly challenging research problem, aims to understand and describe the contents of the complex scene in human understandable natural language. The majority of the recent solutions are based on holistic approaches where the scene is described as a whole, potentially losing the important semantic relationship of objects in the scene. We propose Dense-CaptionNet, a region-based

The deep learning–based computer-aided diagnosis (CADx) approaches of dementia often require a lot of manual intervention. Although deep learning has a good effect on feature extraction, the current deep learning methods usually need to set a large number of parameters manually, which is time consuming. Hierarchical extreme learning machine (H-ELM) needs only less manual intervention and can extract

This work explores the relationship between a person’s demographic/psychological traits (e.g., gender and personality) and self-identity images and captions. We use a dataset of images and captions provided by N ≈ 1350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with individual

A group decision-making (GDM) process is a social cognition process, which is a sub-topic of cognitive computation. The normalization of attribute values plays an important role in multi-attribute decision-making (MADM) and GDM problems. However, this research finds that the existing normalization methods are not always reasonable for GDM problems. To solve the problem of attribute normalization in

Dialogue act classification (DAC) gives a significant insight into understanding the communicative intention of the user. Numerous machine learning (ML) and deep learning (DL) approaches have been proposed over the years in these regards for task-oriented/independent conversations in the form of texts. However, the affect of emotional state in determining the dialogue acts (DAs) has not been studied

Modeling emotions in negotiations is an open challenge that attracted an increasing amount of attention from researchers. Bargainers look for achieving an agreement with the opposing parties and, at the same time, try to reach their own goals. This process consists of both bargaining and (game theory) problem solving. Game theory models seek to enlighten the rational negotiations between players, but

Various types of theoretical algorithms have been proposed for 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. However, they are still far from the performance of our natural biological systems, which can undertake 6D pose estimation of multi-objects efficiently, especially with severe occlusion. With the inspiration of the Müller-Lyer

Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this

Every 3 seconds, someone develops dementia worldwide. Brain-training exercises, preferably involving also physical activity, have shown their potential to monitor and improve the brain function of people affected by Alzheimer disease (AD) or mild cognitive impairment (MCI). This paper presents a cognitive robotic system designed to assist mild dementia patients during brain-training sessions of sorting

Cognitive frameworks best represent cognitive information and knowledge. Several classical probability theory (CPT)-based cognitive frameworks were proposed in the literature. Recently, CPT has failed in explaining certain cognitive processes while quantum theories were successful in explaining the same. In this work, we integrate two cognitive frameworks namely conceptual spaces and 3-way formal concept

Deep neural networks have achieved outstanding progress in many fields, such as computer vision, speech recognition and natural language processing. However, large deep neural networks often need huge storage space and long training time, making them difficult to apply to resource restricted devices. In this paper, we propose a method for compressing the structure of deep neural networks. Specifically

In order to discriminate different spikes in an extracellular recording, a multitude of successful spike sorting algorithms has been proposed up to now. However, new implantable neuroprosthetics containing a spike sorting block necessitate the use of a real-time and a preferably unsupervised method. The aim of this article is to propose a new unsupervised spike sorting algorithm which could work in

In recent years, non-iterative learning approaches for kernel have received quite an attention by researchers and kernel ridge regression (KRR) approach is one of them. Recently, KRR-based Auto-Encoder is developed for the one-class classification (OCC) task and named as AEKOC. OCC is generally used for outlier or novelty detection. The brain can detect outlier just by learning from only normal samples

Linguistic neutrosophic number (LNN) is a specific form of neutrosophic number whose elements are expressed by linguistic terms. Maclaurin symmetric mean (MSM) operator is one of the basic collection operators in the modern knowledge fusion theory. Its most important feature is to consider the interrelationships among multiple input arguments. Multiple attribute group decision-making (MAGDM) with linguistic

Millions of people in the world suffer from vision impairment or vision loss. Traditionally, they rely on guide sticks or dogs to move around and avoid potential obstacles. However, both guide sticks and dogs are passive. They are unable to provide conceptual knowledge or semantic contents of an environment. To address this issue, this paper presents a vision-based cognitive system to support the independence

Deep learning (DL), regarded as a breakthrough machine learning technique, has proven to be effective for a variety of real-world applications. However, DL has not been actively applied to condition monitoring of industrial assets, such as gas turbine combustors. We propose a deep semi-supervised anomaly detection (deepSSAD) that has two key components: (1) using DL to learn representations or features

Complex intuitionistic fuzzy set (CIFS) is a special intuitionistic fuzzy set where the membership and non-membership degrees are expressed by a complex-valued membership degree and can more easily describe the vagueness and uncertainty in the real world. Archimedean t-conorm and t-norm (ATT), as an important class of the t-norm (TN) and t-conorm (TC), have greater flexibility in the information fusion