Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers

Many AI systems have been developed for clinical diagnoses, in which most of them lack interpretability in both knowledge representation and inference results. The newly developed Dynamic Uncertain Causality Graph (DUCG) is a probabilistic graphical model with strong interpretability. However, existing DUCG is mainly for fault diagnoses of large, complex industrial systems. In this paper, we extend

Sentiment analysis is an area of study that aims to develop computational methods and tools to extract and classify the opinions and emotions expressed by people on social networks, blogs, forums, online shoppings, and others. A lot of research has been developed addressing opinions expressed in the English language. However, studies involving the Portuguese language still need to be advanced to make

This paper demonstrates a recapitulated historic evolution further to a future overview of all vehicular ad-hoc network (VANET) routing problems that concern either directly related routing tasks or targeting a set of diverse routing-related techniques with the aid of the bio-inspired approaches. In this lecture, we serialize, in a synchronous observation, the evolution and tendencies of the VANET

Heuristic algorithms are often used to find solutions to real complex world problems. These algorithms can provide solutions close to the global optimum at an acceptable time for optimization problems. Social Spider Algorithm (SSA) is one of the newly proposed heuristic algorithms and based on the behavior of the spider. Firstly it has been proposed to solve the continuous optimization problems. In

The presentation of learning materials in a sequence, which considers the association of students’ individual characteristics with those of the knowledge domain of interest, is an effective learning strategy in online learning systems, especially if related to traditional approaches. However, this sequencing, called Adaptive Curriculum Sequencing (ACS), represents a problem that falls in the NP-Hard

Analysis of skin lesion images via visual inspection and manual examination to diagnose skin cancer has always been cumbersome. This manual examination of skin lesions in order to detect melanoma can be time-consuming and tedious. With the advancement in technology and rapid increase in computational resources, various machine learning techniques and deep learning models have emerged for the analysis

In this paper, we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation, in and of itself, is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost- and time-intensive. Thus, much work has been put into finding methods which allow a reduction in

Optimal Coalition Structure Generation (CSG) is a significant research problem in multi-agent systems that remains difficult to solve. This problem has many important applications in transportation, eCommerce, distributed sensor networks and others. The CSG problem is NP-complete and finding the optimal result for n agents needs to check \(O (n^n)\) possible partitions. The ODP–IP algorithm (Michalak

In this paper, a novel metaheuristic algorithm called Chaos Game Optimization (CGO) is developed for solving optimization problems. The main concept of the CGO algorithm is based on some principles of chaos theory in which the configuration of fractals by chaos game concept and the fractals self-similarity issues are in perspective. A total number of 239 mathematical functions which are categorized

Age estimation using face images is an exciting and challenging task. The traits from the face images are used to determine age, gender, ethnic background, and emotion of people. Among this set of traits, age estimation can be valuable in several potential real-time applications. The traditional hand-crafted methods relied-on for age estimation, cannot correctly estimate the age. The availability of

The purpose of this study is to introduce an innovative multi-attribute group decision making (MAGDM) based on bipolar fuzzy set (BFS) by unifying“ VIseKriterijumska Optimizacija I Kompromisno Rasenje (VIKOR)” method. The VIKOR method is considered to be a useful MAGDM method, specifically in conditions where an expert is unable to determine his choice correctly at the initiation of designing a system

The dual generalized Bonferroni mean operator is a further extension of the generalized Bonferroni mean operator which can take the interrelationship of different numbers of attributes into account by changing the embedded parameter. The 2-dimensional uncertain linguistic variable (2DULV) adds a second dimensional uncertain linguistic variable (ULV) to express the reliability of the assessment information

The linguistic information can be expressed as a 2-tuple of a linguistic variable and a real number in an interval \([-\frac{1}{2}, \frac{1}{2})\). The intuitionistic 2-tuple linguistic (I2TL) set accurately deals with the imprecise and unpredictable information in those decision-making problems where experts prefer the degree of membership and non-membership values in the form of 2-tuple. The existing

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics

The significant growth of modern technology and smart systems has left a massive production of big data. Not only are the dimensional problems that face the big data, but there are also other emerging problems such as redundancy, irrelevance, or noise of the features. Therefore, feature selection (FS) has become an urgent need to search for the optimal subset of features. This paper presents a hybrid

With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision

In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading

Since the first years of the 90s, recommender systems have emerged as effective tools for automatically selecting items according to user preferences. Traditional recommenders rely on the relevance assessments that users express using a single rating for each item. However, some authors started to suggest that this approach could be limited, as we naturally tend to formulate different judgments according

Dialogue agent, a derivative of intelligent agent in the field of computational linguistics, is a computer program that is capable of generating responses and performing conversation in natural language. The field of computational linguistics is flourishing due to the intensive growth of dialogue agents; the most potential one is providing voice controlled smart personal assistant service for handsets

Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path

Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly

Medicinal plants are gaining attention in the pharmaceutical industry due to having less harmful effects reactions and cheaper than modern medicine. Based on these facts, many researchers have shown considerable interest in the research of automatic medicinal plants recognition. There are various opportunities for advancement in producing a robust classifier that has the ability to classify medicinal

One of the most fundamental approaches to learn and understand from any type of data is by organizing it into meaningful groups (or clusters) and then analyzing them, which is a process known as cluster analysis. During this process of grouping, proximity measures play a significant role in deciding the similarity level of two objects. Moreover, before applying any learning algorithm on a dataset,

Searching for a specific and meaningful piece of information in the humongous textual data volumes found on the internet and knowledge repositories is a very challenging task. This problem is usually constrained to answering simple, factoid questions by resorting to a question answering (QA) system built on top of complex approaches such as heuristics, information retrieval, and machine learning. More

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability

Nowadays, software engineers have a great tendency to use design patterns (DPs) because they are considered to have an important role in software engineering in the sense that they can make the understanding of nonentities easier. However, most of the systems have no document helping engineers recognize DPs from the codes. As a result, different approaches for design pattern detection have been suggested

Image synthesis using representations learned by deep neural networks has gained wide attention in recent years. Among the different categories of natural images, face images are very important because of their broad range of applications. However, it is very challenging to synthesize face images due to their highly complicated hierarchical structure and the uniqueness of information contained in individual

Ensemble learning is an effective technique to improve performance and stability compared to single classifiers. This work proposes a selective ensemble classification strategy to handle missing data classification, where an uncertain extreme learning machine with probability constraints is used as individual (or base) classifiers. Then, three selective ensemble frameworks are developed to optimize

Image segmentation is a prerequisite for image processing. There are many methods for image segmentation, and as a result, a great number of methods for evaluating segmentation results have also been proposed. How to effectively evaluate the quality of image segmentation is very important. In this paper, the existing image segmentation quality evaluation methods are summarized, mainly including unsupervised

Machine learning and artificial intelligence are the two fields of computer science dealing with the innovative idea of inducing smartness and intelligence in machines and automating complex tasks and operations through modern learning algorithms. While the rest of the operational fields have been diligent in developing new technologies, the mining industry has been lacking when it comes to applying

Vacuum glass is widely used in many construction applications, including single-family homes, as a proven energy-saving method with outstanding heat preservation characteristics. The thermal insulation performance of vacuum glass is closely related to its heat transfer coefficient. In this study, we applied neural network methods to predict the heat transfer coefficients of vacuum glass. Using MATLAB

This study aims at automatic processing and knowledge extraction from large amounts of oncology-related content from online social networks (OSN). In this context, a large number of OSN textual posts concerning major cancer types are automatically scraped and structured using natural language processing techniques. Machines are trained to assign multiple labels to these posts based on the type of knowledge

Text stemming is one of the basic preprocessing step for Natural Language Processing applications which is used to transform different word forms into a standard root form. For Arabic script based languages, adequate analysis of text by stemmers is a challenging task due to large number of ambigious structures of the language. In literature, multiple performance evaluation metrics exist for stemmers

Predicting traffic disturbances is a challenging problem in urban cities. Emergency vehicles (EV) is one of the biggest disturbances that affect traffic fluidity. The goal of this paper is to provide a machine learning application to deal with emergency cases in traffic networks. Particularly, we investigate the use of deep learning techniques coupled with Artificial Immune System to tackle the issue

Optical character recognition (OCR) is an important research area in the field of pattern recognition. A lot of research has been done on OCR in the last 60 years. There is a large volume of paper-based data in various libraries and offices. Also, there is a wealth of knowledge in the form of ancient text documents. It is a challenge to maintain and search from this paper-based data. At many places

The metaheuristic approaches inspired by the nature are becoming powerful optimizing algorithms for solving NP-complete problems. This paper presents five nature-inspired metaheuristic optimization algorithms to find near-optimal Golomb ruler (OGR) sequences in a reasonable time. In order to improve the search space and further improve the convergence speed and optimization precision of the metaheuristic

Visual question answering (VQA) is a task that has received immense consideration from two major research communities: computer vision and natural language processing. Recently it has been widely accepted as an AI-complete task which can be used as an alternative to visual turing test. In its most common form, it is a multi-modal challenging task where a computer is required to provide the correct

An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and seismology

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma

Covering-based rough set is an important extended type of classical rough set model. In this model, concepts are approximated through substitution of a partition in classical rough set theory with a covering in covering-based rough set theory. Various generalized covering-based rough sets have been investigated, however, little work has been done on extending four classical covering-based rough set

Image analysis is a prolific field of research which has been broadly studied in the last decades, successfully applied to a great number of disciplines. Since the apparition of Big Data, the number of digital images is explosively growing, and a large amount of multimedia data is publicly available. Not only is it necessary to deal with this increasing number of images, but also to know which features

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal

Computational modeling of empathy has recently become an increasingly popular way of studying human relations. It provides a way to increase our understanding of the link between affective and cognitive processes and enhance our interaction with artificial agents. However, the variety of fields contributing to empathy research has resulted in isolated approaches to modeling empathy, and this has led

Spoken keyword spotting refers to the detection of all occurrences of desired words in continuous speech utterances. This paper includes a comprehensive review on various spoken keyword spotting (especially discriminative spoken keyword spotting) approaches. The most common datasets and evaluation measures for training and evaluating the spoken keyword spotting systems are reviewed in this paper. Moreover

Due to the high flow velocity over dam spillways and outlets, severe cavitation damage might occur to the structures. Aeration (introducing air into the passing flow) is a useful remedy for preventing or decreasing cavitation, however, proper estimation of aerators air demand is a complex problem. On that account, the standard GMDH model, integrated GMDH-HS (with the harmony search algorithm) model

Abstract Dissimilarity representation is a very interesting alternative for the traditional feature space representation when addressing large multi-class problems or even problems with a small number of training samples. This paper describes the existing possibilities in terms of dissimilarity representation through some comprehensive examples. The justification for using such a problem representation

Abstract When multiple granulated knowledge in multigranulation spaces are involved in decision making, protocol principles are adopted to arrive at the final consensus. Multigranulation rough set theory utilizes a voting principle to combine the decision options derived from individual granulated knowledge. Note that those knowledge may provide different degrees of support to the final results, some

Abstract These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications

Abstract Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features is selected out for effective data representation. In this paper, we proposes a novel robust unsupervised features selection method based on graph-preserving feature selection embedding LLE. Specifically, we integrate the graph matrix learning and the low-dimensional space learning

Abstract This paper proposes a new version of the shuffled complex evolution (SCE) algorithm for solving parameter identification problems. The SCE divides a population into several parallel subsets called complex and then improves each sub-complex through an evolutionary process using a Nelder–Mead (NM) simplex search method. This algorithm applies its evolutionary process only on the worst member

Abstract In the age of big data, we often face the huge Volume, high Velocity, rich Variety, accuracy Veracity and high Value (5Vs) with complicated structures. Big data industrial decision making is crucial for a country or society to improve the effectiveness of leadership, which can remarkably promote scale development and industrialization. Under such circumstance of big data industrial decision

Abstract In automated screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis, urgent need of precise extraction of these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total

Abstract Computer vision systems open a new challenge to recognize human faces under varied poses in similar capacity and capability as human-beings perform naturally. For surveillance applications, pose-invariant face recognition (PIFR) will become a major break-through by presenting the solution of this unique challenge. In recent decade, several techniques are presented to address this challenge

Abstract Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review

Abstract This work bespeaks an insight into a real imperfect multibody systems synthesis, wherein the flexible behaviour of its components is considered. Based on the end-effector mechanism velocity, acceleration and defined transversal deflection for a flexible component, the mechanism optimal design variables have been investigated. Subsequently, the three aforementioned responses have been involved

Abstract The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic

Abstract An accurate mathematical model has a vital role in controlling and synchronization of different systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. In this paper, an improved shuffled complex evolution (SCE) is proposed for parameter identification of engineering problems. The SCE by employing parallel search efficiently finds neighborhoods

Abstract Smart grids enable the residential consumers to have an active role in the management of their electricity consumption through home energy management (HEM) systems. HEM systems adjust the ON–OFF status and/or operation modes of home appliances under demand response programs, typically in a way that the electricity bill of the home is minimised and/or the peak load is minimised. This represents

Abstract Fuzzy permutation graph (FPG) plays a significant role in solving real-life problems where the scope and application of crisp permutation graph get limited due to the fuzziness involved in real situations. In this article an algorithm is designed to find out all maximal fuzzy cliques of a FPG. A similar algorithm is developed at the same time to find out all maximal fuzzy independent sets