SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters C and \(\gamma \) to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated

Implicit Learning (IL) involves the fundamental problem of human potential development, and it has been a hot and difficult topic for many years. Traditional artificial neural networks can simulate IL, but there are some shortcomings. A few years ago, people used a morphological neural network (MNN) to simulate IL, but the support in theory and practice is weak. The contribution of this study is threefold

Digital tamper detection is a substantial research area of image analysis that identifies the manipulation in the image. This domain has matured with time and incredible accuracy in the last five years using machine learning and deep learning-based approaches. Now, it is time for the evolution of fusion and reinforcement-based learning techniques. Nevertheless, before commencing any experimentation

Ensuring proper punctuation and letter casing is a key pre-processing step towards applying complex natural language processing algorithms. This is especially significant for textual sources where punctuation and casing are missing, such as the raw output of automatic speech recognition systems. Additionally, short text messages and micro-blogging platforms offer unreliable and often wrong punctuation

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced

Since the initial reports of the Coronavirus surfacing in Wuhan, China, the novel virus currently without a cure has spread like wildfire across the globe, the virus spread exponentially across all inhabited continent, catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community

Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related documents. The QAS system is composed of three main modules; question analysis, passage retrieval, and answer extraction. Over the years, numerous QASs have

The requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this problem are not suitable, it can cause undesirable damage to the patient when exercising. Normally, the problem of Inverse Kinematics in the robotics field, as well as the natural field

The heuristic algorithms are often used to find solutions to real complex world problems. In this paper, the Social Spider Algorithm (SSA) and Social Spider Optimization (SSO) which are heuristic algorithms created upon spider behaviors are considered. Performances of both algorithms are compared with each other from six different items. These are; fitness values of spider population which are obtained

Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital

Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities

This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL)

With the increase in the usage of the Internet, a large amount of information is exchanged between different communicating devices. The data should be communicated securely between the communicating devices and therefore, network security is one of the dominant research areas for the current network scenario. Intrusion detection systems (IDSs) are therefore widely used along with other security mechanisms

Classification, which means discrimination between examples belonging to different classes, is a fundamental aspect of most scientific and engineering activities. Machine Learning (ML) tools have proved to be very performing in this task, in the sense that they can achieve very high success rates. However, both “realism” and interpretability of their models are low, leading to modest increases of knowledge

Over the past few years, neural networks have exhibited remarkable results for various applications in machine learning and computer vision. Weight initialization is a significant step employed before training any neural network. The weights of a network are initialized and then adjusted repeatedly while training the network. This is done till the loss converges to a minimum value and an ideal weight

This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content

This paper formalizes a novel model that is able to use both interval representations, parameterizations, partial memberships and multi-polarity. These are differing modalities of uncertain knowledge that are supported by many models in the literature. The new structure that embraces all these features simultaneously is called interval-valued multi-polar fuzzy soft set (IVmFSS, for short). An enhanced

Picture fuzzy graphs are an extension of intuitionistic fuzzy graphs. Balanced picture fuzzy graph is a special type of picture fuzzy graph (PFG). In this study, the definition and important properties of PFG like, average PFG, balanced PFG, size, order, density of a PFG, isomorphism, the direct product of two PFG, etc have been studied. The necessary and sufficient conditions for balanced picture

Conjunctive normal forms (CNF) of structured satisfiability problems contain logic gate patterns. So Boolean circuits (BC) by and large can be obtained from them and thus structural information that is lost in the CNF can be recovered. However, it is not known which logic gates are useful for local search on BCs or which logic gates in particular help local search the most and why. In this article

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other

Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical

The concept of sponge city receives more and more attention by Chinese government, and the evaluation of sponge city construction is an important aspect. To cope with the complexity and uncertainty of the evaluation process, this paper adopts interval type-2 trapezoidal fuzzy numbers (IT2TFNs) to express decision-making information and develops an approach for evaluating sponge city construction. To

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration

The mysteries of deep-sea ecosystems can be unlocked to reveal new sources, for developing medical drugs, food and energy resources, and products of renewable energy. Research in the area of underwater image processing has increased significantly in the last decade. This is primarily due to the dependence of human beings on the valuable resources existing underwater. Effective work of exploring the

Metaheuristics are widely applied to solve optimization problems. Numerous metaheuristic algorithms inspired by natural processes have been introduced in the past years. Studying and comparing the convergence of metaheuristics is helpful in future algorithmic development and applications. This study focuses on bee-inspired metaheuristics and identifies seven basic or root algorithms applied to solve

Game theory has found successful applications in different areas to handle competitive situations among different persons or organizations. Several extensions of ordinary game theory have been studied by the researchers to accommodate the uncertainty and vagueness in terms of payoffs and goals. Matrix games with payoffs represented by interval numbers, fuzzy numbers, and intuitionistic fuzzy numbers

Alzheimer disease is a neurodegenerative brain disorder leading to gradual loss of memory. Multiple biomarkers have been accepted for identifying the Alzheimer’s disease namely Neuroimaging, Cerebrospinal fluid proteins, blood and urine tests, genetic risk profilers. In this study, an extensive review has been done for Alzheimer disease prediction using diverse brain-imaging biomarkers through varied

Several methods for dynamically adapting the local consistency property applied by a CP solver during search have been put forward in recent and older literature. We propose the classification of such methods in three categories depending on the level of granularity where decisions about which local consistency property to apply are taken: node, variable, and value oriented. We then present a detailed

In the current competitive corporate world, organizations rely on their products’ advertisements for surpassing competitors in reaching out to a larger pool of customers. This forces companies to focus on advertisement quality. This work presents a content-based advertisement viewability prediction framework using Artificial Intelligence (AI) methods. The primary focus here is on the web-advertisements

In the original publication of the article, the corresponding author name and the e-mail ID was incorrect. The correct name and e-mail ID is given in this correction.

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