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Features Selection (FS) approaches have more attention since they have been applied to several fields primarily to deal with high dimensional data. An increase in the dimension of data can lead to degradation of the accuracy of the machine learning method. Therefore, there are several FS methods based on meta-heuristic (MH) techniques that have been developed to tackle the FS problem and avoid the

The paper aims are to impersonate some robust sine-trigonometric operations laws to determine the group decision-making process under the Pythagorean fuzzy set (PFS) situation. The PFS has a notable feature to trade with the dubious information with a broader membership representation space than the intuitionistic fuzzy set. Based on it, the present paper is classified into three phases. The first

Rivers, as one of the freshwater resources, are generally put in the state of jeopardy in terms of quantity and quality due to the development in industry, agriculture, and urbanization. Management of water quality is inextricably bound up with a reliable prediction of the Water Quality Index (WQI) for various purposes. In this way, an accurate estimation of WQI is one of the most challenging issues

The transmission from offline activities to online activities due to the social disorder evolved from COVID-19 pandemic lockdown has led to increase in the online economic and social activities. In this regard, the Automatic Keyword Extraction (AKE) from textual data has become even more interesting due to its application over different domains of Natural Language Processing (NLP). It is observed that

This paper demonstrates an application of the improved crow search algorithm (I-CSA), which is a modified version of the crow search algorithm (CSA). CSA is a recent, nature-inspired meta-heuristic optimization algorithm. I-CSA differs from CSA by allowing application on a discrete problem, which is P-median and fortifying faster convergence to an optimal or near-optimal solution. Improvements are

Cardiovascular diseases (CVDs) in India and globally are the major cause of mortality, as revealed by the World Health Organization (WHO). The irregularities in the pace of heartbeats, called cardiac arrhythmias or heart arrhythmias, are one of the commonly diagnosed CVDs caused by ischemic heart disease, hypertension, alcohol intake, and stressful lifestyle. Other than the listed CVDs, the abnormality

In an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations

Smart manufacturing is an essential part of fourth industrial revolution in which robotic machines can control and perceive automatically to provide effectiveness and convenience in production process. However, the existence of potential failures and defects not only influence the manufacturing process but also damages the resources and cause negative impacts on environment. Failure modes and effects

Over the last decade, the amount of Arabic content created on websites and social media has grown significantly. Opinions are shared openly and freely on social media and thus provide a rich source for trend analyses, which are accomplished by conventional methods of language interpretation, such as sentiment analysis. Due to its accuracy in studying unstructured data, deep learning has been increasingly

In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively

Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques

In this paper an innovative method of ranking neutrosophic number based on the notions of value and ambiguity of a single-valued neutrosophic number is being developed. The method is based on the convex combination of value and ambiguity of truth-membership function with the sum of values and ambiguities of indeterminacy-membership and falsity-membership functions. This convex combination is also termed

Nowadays, text detection and localization have gained much popularity in the field of text analysis systems as they pave the way for the number of real-time based applications like mobile transliteration technologies, assistive methods for visually impaired persons, etc. Text detection and localization techniques are used to find the position of text area in the image.This paper intends to present

The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in the field

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on

Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis

Community detection aims to partition a set of nodes with more similarities in the set than out of it based on different criteria like neighborhood similarity or vertex connectivity. Most present day community detection methods principally concentrate on the topological structure, largely ignoring the heterogeneous properties of the vertex. This paper proposes a new community detection model, based

Facial feature extraction (FFE) is considered as a challenging area for computer vision and artificial intelligence research community. There are numerous application domains of face recognition such as demographic classification and facial disease classification. In last few years, numerous feature extraction approaches are proposed. This review focuses on studies that exclusively use facial asymmetry

This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent

In recent years, different higher order fuzzy sets have been introduced to better handle the uncertainty in many practical decision making and data mining problems. The recent proposal of higher order fuzzy set is q-rung picture fuzzy set (q-RPFS) modeled by three parameters positive, negative, and neutral membership function. One of the important topics in q-RPFS is distance measures which play a

Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects

Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn higher levels of feature hierarchy established by lower level features by transforming the raw feature space to another complex feature space. Although deep networks are successful in a wide range of problems in different fields, there are some issues affecting their overall performance such as selecting appropriate

Forecasting time series has acquired immense research importance and has vast applications in the area of air pollution monitoring. This work attempts to investigate the abilities of various existing techniques when applied for short term, high granular time series forecasting of PM2.5. More specifically, a comparative study has been provided, taking into account both popularly used models and lesser-used

Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders because it provides brain biomarkers. However, only highly trained doctors can interpret EEG signals due to its complexity. Machine learning has been successfully trained with EEG signals for classifying mental disorders, but a time consuming and disorder-dependant feature engineering (FE) and subsampling process

The picture fuzzy sets (PFSs) state or model the voting information accurately without information loss. However, their existing operational laws usually generate unreasonable computing results, especially when the agreement degree (AD) or neutrality degree (ND) or opposition degree (OD) is zero. To tackle this issue, we propose the interactional operational laws (IOLs) to compute picture fuzzy numbers

A bipedal walking robot is a kind of humanoid robot. It is suppose to mimics human behavior and designed to perform human specific tasks. Currently, humanoid robots are not capable to walk like human being. To perform the walking task, in the current work, human gait data of six different walking styles named brisk walk, normal walk, very slow walk, medium walk, jogging and fast walk is collected through

The world has witnessed a surfeit of usage of Artificial Intelligence systems for a long time. Nowadays, most of the problems are transforming from logical solutions into statistical domains. This requires the implementation of machine learning algorithms to mine useful data from the statistical datasets which in turn demands high-end computing. Generally, machine learning algorithms utilize Gradient

Research in metaheuristics for global optimization problems are currently experiencing an overload of wide range of available metaheuristic-based solution approaches. Since the commencement of the first set of classical metaheuristic algorithms namely genetic, particle swarm optimization, ant colony optimization, simulated annealing and tabu search in the early 70s to late 90s, several new advancements

The Web is a source of information for Location-Based Service (LBS) applications. These applications lack postal addresses for the user’s Point of Interests (POIs) such as schools, hospitals, restaurants, etc., as these locations are annotated manually by using the yellow pages or by the location owners (users/companies). Our study in this paper confirms that Google Maps, a common LBS application,

Machine learning can be considered as the current gold standard for predicting deterioration in Intensive Care Unit patients and is in extensive use throughout the world in different fields. As confirmed by many studies, preventing the occurrence of the onset of deterioration in a sufficient time window is a priority in healthcare centers. Also, the significance of enhancing the quality of hospital

In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in a broad range of cyber security applications. Therefore, this paper surveys the existing literature (comprising 54 papers mainly published between 2016 and 2020) on the applications of AI in user access authentication, network situation awareness, dangerous behavior monitoring, and abnormal traffic identification

Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying

In the era of big data, multimedia, hyper-media and social networks are emerging, and the amount of information is growing rapidly. When people participate in the process of massive data processing, they will encounter data with different structures, so data has heterogeneity. How to acquire hidden and valuable knowledge from heterogeneous data and measure its uncertainty is an important problem in

The idea of vehicular communication has evolved dramatically over the past two decades apparently with technological advancements that accord low power and memory consumption devices, and yet providing low latency and high performing abilities. In the 20th century, vehicular communications were primarily done through singling blinkers, tail lights, and hazard lamps, essentially serving rudimentary

Longitudinal datasets of human ageing studies usually have a high volume of missing data, and one way to handle missing values in a dataset is to replace them with estimations. However, there are many methods to estimate missing values, and no single method is the best for all datasets. In this article, we propose a data-driven missing value imputation approach that performs a feature-wise selection

Image denoising methods are of fundamental importance in image processing and artificial intelligence systems. In this review, we analyze the traditional and state of the art mathematical models for computational color image denoising. These algorithms are divided into methods that are based on the partial differential equations, low rank, sparse representation and recent developments based on deep

Shallow neural networks process the features directly, while deep networks extract features automatically along with the training. Both models suffer from overfitting or poor generalization in many cases. Deep networks include more hyper-parameters than shallow ones that increase the overfitting probability. This paper states a systematic review of the overfit controlling methods and categorizes them

With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary

With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification

This paper studies the impact of lightweight face models on real applications. Lightweight architectures proposed for face recognition are analyzed and evaluated on different scenarios. In particular, we evaluate the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face recognition, as well

Feature Selection (FS) methods alleviate key problems in classification procedures as they are used to improve classification accuracy, reduce data dimensionality, and remove irrelevant data. FS methods have received a great deal of attention from the text classification community. However, only a few literature surveys include them focusing on text classification, and the ones available are either

In this paper, a novel optimal scale selection method in complete multi-scale decision tables has been proposed. Unlike the existing approaches in the literature, we employ the tools of granularity trees and cuts for each attribute. Each granularity tree has many different local cuts, which represent various scale selection methods under a specific attribute. Different local cuts collectively forms

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones

Retrieving relevant documents from a large set using the original query is a formidable challenge. A generic approach to improve the retrieval process is realized using pseudo-relevance feedback techniques. This technique allows the expansion of original queries with conducive keywords that returns the most relevant documents corresponding to the original query. In this paper, five different hybrid

Heart valve disorders (HVDs) are the major causes of cardiovascular diseases (CVD), which may be detected at the early stage using routine auscultation examination. The phonocardiogram (PCG) is a graphical representation of the physiological condition of the heart, which differs with respect to heart diseases. It is closely related to valve functionality which provides vital information for the diagnosis

The purpose of writing this manuscript is to point out some limitations of existing associated immediate probability intuitionistic fuzzy geometric aggregation operators as these existing operators fail under some conditions such as the existing operators cannot handle the information given in Pythagorean fuzzy sets, picture fuzzy sets, spherical fuzzy sets, and T-spherical fuzzy sets and the existing

In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive, normal or negative or intermediate stages. Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an

With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which attracts worldwide researchers from different fields to its increasingly wide applications. Furthermore

An indeterminacy economic environment includes uncertainty during adopting experts knowledge for the analysis of stock returns. The main goal in this paper is to discuss the problem of portfolio selection with uncertain environment; because, the experts alone has the ability to evaluate the security returns and not with the historical data. However, the uncertain variables considered have shown the

This paper designed an advanced hybrid algorithm (haDEPSO) to solve the optimization problems, based on multi-population approach. It integrated with suggested advanced DE (aDE) and PSO (aPSO). Where in aDE a novel mutation strategy and crossover probability along with the slightly changed selection scheme are introduced, to avoid premature convergence. And aPSO consists of the novel gradually varying

Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic text summarization systems are developed. Text summarization is a process of extracting

In the last few decades, information security has gained huge importance owing to the massive growth in digital communication; hence, driving steganography to the forefront for secure communication. Steganography is a practice of concealing information or message in covert communication which involves hiding the information in any multimedia file such as text, image, or video. Many contributions have

We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training

Multi-Focus Image Fusion (MFIF) is a method that combines two or more source images to obtain a single image which is focused, has improved quality and more information than the source images. Due to limited Depth-of-Field of the imagining system, extracting all the useful information from a single image is challenging. Thus two or more defocused source images are fused together to obtain a composite

This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology

In group decision-making problems, decision makers prefer to use several linguistic terms to describe their own perception and knowledge, and give their preference intensity of each possible linguistic terms based on their own understanding and interpretation. Due to the nonlinearity of decision maker’s cognition, the gaps between adjacent linguistic terms are unbalanced. The unbalanced probabilistic

Since municipalities are an essential part of urban management and are considered as the most important hub for offering urban services, they are expected to develop e-services rapidly and offer high-quality services to citizens. As the websites are the main infrastructure of e-services, therefore constant assessment of the websites assists to provide high-quality services. In this paper, a model was

Biological computation involves the design and development of computational techniques inspired by natural biota. On the other hand, computational biology involves the development and application of computational techniques to study biological systems. We present a comprehensive review showcasing how biology and computer science can guide and benefit each other, resulting in improved understanding

With the financial liberalization and globalization, China’s financial system has been deepening, the scale of the stock market has been expanding, and the stock market bubble problem has gradually emerged. The overinflated stock market bubble will undoubtedly increase the risk of the stock market, seriously threaten the stability of the financial system, and even bring disastrous consequences to the