Recent advances in the availability of computational resources allow for more sophisticated approaches to speech recognition than ever before. This study considers Artificial Neural Network and Hidden Markov Model methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet rather than the classical approach of the classification of whole words

Motor vehicle crashes are a leading cause of death in the U.S. In order to reduce death and serious injury, road and traffic engineers manually evaluate road segments and visualize the safety level of roads. These existing risk maps can be confusing and must be manually interpreted by drivers to find the safest path from a source to a given destination; this can result in ignoring the safety of the

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature

The sensitive nature of many data streams necessitates data mining techniques that are privacy-preserving. This paper proposes two data perturbation methods for privacy-preserving stream mining based on a combination of random projection, random translation, and two alternative forms of additive noise: noise generated independently for each record and noise that accumulates over the lifetime of a stream

This paper presents a new version of the TOPSIS method for sorting problems. The proposed method, called Preference Disaggregation on Technique for Order of Preference by Similarity to Ideal Solution - Sort (PDTOPSIS-Sort), is based on nonlinear programming for inferring parameters and uses expert's holistic evaluations. The existing TOPSIS-Sort method demands a significant number of parameters from

Aspect based sentiment analysis (ABSA) is the task of identifying fine-grained opinion polarity towards a specific target in a sentence, which is empowering experts and intelligent systems with enriched interaction capabilities. Most of approaches to date usually capture semantic relations between target and context words based on RNNs (Recurrent Neural Networks) or pre-trained models (e.g. BERT).

Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable knowledge in each specific domain. An action is a new tool in this research area that suggests some changes to the user to gain a profit in his/her domain. Currently, most of action mining methods rely on simple data which describes each object independently

The segmentation of malignant breast tissue from histological images represents a crucial task for the diagnosis of breast cancer (BC). This is a time-consuming process that could be alleviated with the help of computerized segmentation methods, leading to elevated precision and reproducibility results. However, this automated segmentation poses a challenge due to the large size of histological whole-slide

The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence

Light field imagery has attracted increasing attention for its capacity of simultaneously capturing intensity values of light rays from multiple directions. Such imagery technique has become widely accessible with the emergence of consumer-grade devices, e.g. Lytro, and the Virtual Reality (VR) / Augmented Reality (AR) areas. Light field reconstruction is a critical topic to mitigate the trade-off

The use of metaheuristics is widespread for optimization in both scientific and industrial problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf Optimizer (GWO) is one of the most recent and popular algorithms in this area. In this work, opposition-based learning (OBL) is combined with GWO to enhance its exploratory behavior while maintaining a fast convergence

Frog population has been experiencing rapid decreases worldwide, which is regarded as one of the most critical threats to the global biodiversity. Therefore, large volumes of frog recordings have been collected for assessing this decline. Building an automatic frog species classification system is becoming ever more important. The traditional system for classifying frog species consists of four steps:

Assessing performance of different classifiers and selecting the best one is one of the most important tasks in classification problem. The assessment of classifiers becomes more complex when dataset is imbalanced because most of the frequently used performance metrics can be misleading. Many real world classification problems such as fraud detection, churn prediction, medical diagnosis, and cyber-security

Healthcare informatics and analytics (HCI&A), also known as healthcare information technology (HIT), healthcare IS (HIS), and so on, has rapidly evolved with the emerge of advanced data analytics technologies applied to the medical domain. Currently, HCI&A has emerged as an important area of study for both practitioners and academic researchers. Accordingly, this emerging field has prompted for an

Effective information management has long been a problem in organisations that are not of a scale that they can afford their own department dedicated to this task. Growing information overload has made this problem even more pronounced. On the other hand we have recently witnessed the emergence of intelligent tools, packages and resources that made it possible to rapidly transfer knowledge from the

The high prevalence of foot pain, and its relation to foot shape, indicates the need for an expert system to identify foot shape abnormalities. Yet, to date, no such expert system exists that examines the full 3D foot shape and produces an intuitive explanation of why a foot is abnormal. In this work, we present the first such expert system that satisfies those goals. The system is based on the concept

Social bots represent a new generation of bots that make use of online social networks (OSNs) as command and control (C&C) channels. Malicious social bots have been used as tools for launching large-scale spam campaigns, promoting low-cap stocks, manipulating users digital influence, and conducting political astroturfing. Recent studies in this area either focus only on general security issues related

Gravitational waves represent a new opportunity to study and interpret phenomena from the universe. In order to efficiently detect and analyze them, advanced and automatic signal processing and machine learning techniques could help to support standard tools and techniques. Another challenge relates to the large volume of data collected by the detectors on a daily basis, which creates a gap between

This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Lévy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging

Clustering validation and identifying the optimal number of clusters are of great importance in expert and intelligent systems. However, the commonly used similarity measures for validating are not versatile to measure the complex data structure, in reality, some of which are not as effective as that of the used clustering algorithm which gives the clustering results. This paper studies the validity

Sentence Pair Modeling is a critical and challenging problem in natural language processing (NLP). It aims to seek the underlying semantic relationship between two sentences. Inspired by human learning, attention mechanisms are widely used in NLP. Previous attentive CNNs mainly focus on attentive pooling which only compute the matching scores between sentence pairs with the same filter size and lack

This paper proposes an ordinal decision-tree model, which applies a new weighted information-gain ratio (WIGR) measure for selecting the classifying attributes in the tree. The proposed measure utilizes a weighted entropy function that is defined proportionally to the value deviation of different classes and thus reflects the consequences of the magnitude of potential classification errors. The WIGR

The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint

Crossover is one of the most important operators in a genetic algorithm by which offspring production for the next generation is performed. There are a number of crossover operators for each type of chromosome representation of solutions that are closely related to different types of optimisation problems. Crossover operation in genetic algorithms, aimed at solving permutation-based combinatorial optimisation

Organizations rely on accurate demand forecasts to make production and ordering decisions in a variety of supply chain positions. Significant research in time series forecasting techniques and a variety of forecasting methods are available in the market. However, selecting the most accurate forecasting model for a given time series has become a complicated decision. Prior studies of forecasting methods

We consider the scenario of multiple RFID-tagged objects that simultaneously move across an indoor space where several RFID antennas are placed. We assume that a logical partition of the indoor space into a set of locations is given, along with a set of hard and weak integrity constraints describing both the valid movements of the objects and the capacity of the locations. In this setting, we address

Consumption of traditional fossil energy has promoted rapid economic development and caused effects such as climate warming and environmental degradation. In order to solve the problem of environmental economic dispatch (EED), this paper proposes a DE-CQPSO (Differential Evolution-Crossover Quantum Particle Swarm Optimization) algorithm based on the fast convergence of differential evolution algorithms

The primary aim of this study is concerned with the structural optimization of data-driven fuzzy rule-based systems (FRBS), with the intent of their complexity management. This is accomplished in two ways: the first one involves a structuralization of the antecedents and the second one deals with a structuralization of the consequents of the fuzzy rules. More specifically, this study contributes to

In classification, regression, and other data mining applications, feature selection (FS) is an important pre-process step which helps avoid advert effect of noisy, misleading, and inconsistent features on the model performance. Formulating it into a global combinatorial optimization problem, researchers have employed metaheuristic algorithms for selecting the prominent features to simplify and enhance

This study investigates a new two-machine flow shop group scheduling problem with job-related blocking and transportation times, which is derived from the realistic pipe-making process of steel pipe products in the modern steel manufacturing industry. In contrast to the traditional blocking constraint, the attributes of jobs, not the quantity of jobs in the buffer area, are used to determine the need

In this paper, we propose a new definition of hesitant fuzzy numbers (HFNs) and study some essential properties of these numbers. We show (α, k)-cuts that were discussed in the recent literature for hesitant fuzzy sets (HFSs), on HFNs have resulted in compact intervals. In the following, we propose a new binary operation on these numbers. It has shown that the outcome of the proposed operation is a

Heart failure (HF) is among the most costly diseases to our society, and the prevalence keeps on increasing these days. Early detection of HF plays a vital role in saving lives through adjusting lifestyles and drug interventions that can slow down disease progression or prevent HF. There are many cardiovascular risk factors associated with HF, and they often coexist. In this paper, we assess the predictive

In expert systems, complex optimization problems are usually nonlinear, nonconvex, multimodal and discontinuous. As an efficient and simple optimization algorithm, particle swarm optimization(PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the

Lenders, such as banks and credit card companies, use credit scoring models to evaluate the potential risk posed by lending money to customers, and therefore to mitigate losses due to bad credit. The profitability of the banks thus highly depends on the models used to decide on the customer’s loans. State-of-the-art credit scoring models are based on machine learning and statistical methods. One of

Dimensionality reduction techniques aim to transform the high-dimensional data into a meaningful reduced representation and have been consistently playing a fundamental role in the study of intrinsic dimensionality estimation and the design of an intelligent expert system towards real-world applications. From the perspective of manifold learning, locality preserving projections is a classical and commonly

With the development of e-commerce, various methodologies have studied to improve recommendation performance. Recently, many deep learning based network embedding approaches are applied to the recommendation domain. However, these approaches still have several limitations, such as the problem of data sparseness and the changing in user preference over time, which cannot be considered. In this paper

The explosion of reusable Web services (e.g., open APIs, open data sources, and cloud/IoT services), has become a new opportunity for modern service-composition based applications development. However, this enormous growth of Web services increases the difficulty of selecting the best suitable Web services for a particular application. Hence, the design of an effective and efficient Web service recommendation

This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this paper

Cloud-based expert systems are highly emerging nowadays. However, the data owners and cloud service providers are not in the same trusted domain in practice. For the sake of data privacy, sensitive data usually has to be encrypted before outsourcing which makes effective cloud utilization a challenging task. Taking this concern into account, we propose a novel cloud-based approach to securely recognize

Clickbait indicates the type of content with an intending goal to attract the attention of readers. It has grown to become a nuisance to social media users. The purpose of clickbait is to bring an appealing link in front of users. Clickbaits seen in the form of headlines influence people to get attracted and curious to read the inside content. The content seen in the form of text on clickbait posts

This paper focuses on an energy-efficient job-shop scheduling problem within a machine speed scaling framework, where productivity is affected by deterioration. To alleviate the deterioration effect, necessary maintenance activities must be put in place during the scheduling process. In addition to sequencing operations on machines, the problem at hand aims to determine the appropriate speeds of machines

Video skimming is one of the recently, getting popular technique for preparing preview for long watching video sequences. Most of the video skimming techniques developed in the literature uses manual intervention of users to prepare the review. Mostly the literature reported video skimming for sports and movie industries. In sports the portion of video where audience claps their hands or in movie important

Recommender systems help users to find information that fits their preferences in an overloaded search space. Collaborative filtering systems suffer from increasingly severe data sparsity problem because more and more products are sold in commercial websites, which largely constrains the performance of recommendation algorithms. User clustering has already been applied to recommendation on sparse data

The manufacturing cell formation represents one of the important stages of the construction of cellular manufacturing systems. It focuses on grouping machines, parts and workers and assigning them to the corresponding cells. This assignment is guided by multiple objectives, and is subject to many constraints. In this paper, the focus is made on a variant of the cell formation problem, which is the

Clickstream data is an important source to enhance user experience and pursue business objectives in e-commerce. The paper uses clickstream data to predict online shopping behavior and target marketing interventions in real-time. Such AI-driven targeting has proven to save huge amounts of marketing costs and raise shop revenue. Previous user behavior prediction models rely on supervised machine learning

Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA’s search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a

In this paper, we propose a novel method for automatic k-complex (KC) detection in human sleep EEG, named MT-KCD. KCs are slow oscillations in the EEG signal characterized by a well-delineated, negative, sharp waves immediately followed by a positive component standing out from the background, with high-amplitude and total duration ≥ 0.5 seconds. Among the important aspects of the KC are its homeostatic

The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods

This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal

Due to the non-cognition of real-time data, rare loss-based risk warning methods can effectively respond to unexpected emergencies. Machine learning has powerful data processing capabilities and real-time computing functions and thus is suitable for offsetting the shortcomings of traditional risk methods. Risk analysis can be easily employed to perform risk-based data classification for a set of process

This paper presents a comparison among several sentiment analysis classifiers using three different techniques – machine learning, deep learning, and an evolutionary approach called EvoMSA – for the classification of educational opinions in an Intelligent Learning Environment called ILE-Java. To make this comparison, we develop two corpora of expressions into the programming languages domain, which

Given that the main task of process monitoring (i.e., fault detection) is actually a classical one-class classification problem, the generalized regression neural network (GRNN) is directly inapplicable for handling process modeling and monitoring issues. Through the selection of only one variable to be the output while the others serve as the corresponding input, a GRNN model can then be constructed

Deep neural networks are potentially suitable tools for time series forecasting due to their ability to extract complex patterns of nonlinear data and their versatility in terms of models and applications. Even though they are powerful instruments and well-behaved approaches for certain tasks, they are sometimes surpassed by data complexity, and thus struggle to find an error that generalizes well

Simulation optimization is a tool commonly used as a decision-making support system on industrial problems in order to find the best resource allocation, which has a direct influence on costs and revenues. The present study proposed an open-source framework developed on Python, integrating different strategies for a novel optimization algorithm. The framework includes multicore parallelism (tested

The permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies, multiple objectives related to production efficiency have been considered simultaneously. However, studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work

Grasshopper optimization algorithm (GOA) is a newly proposed meta-heuristic algorithm that simulates the biological habits of grasshopper seeking for food sources. Nonetheless, some shortcomings exist in the basic version of GOA. It may quickly drop into local optima and show slow convergence rates when facing some complex basins. In this work, an improved GOA is proposed to alleviate the core shortcomings

Dimensionality reduction is one basic and critical technology for data mining, especially in current “big data” era. As two different types of methods, feature selection and feature extraction each have their pros and cons. In this paper, we combine multi-strategy feature selection and grouped feature extraction and propose a novel fast hybrid dimension reduction method, incorporating their advantages

Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position

Non-numerical classification plays an essential role in many real-world applications such as DNA analysis, recommendation systems and expert systems. The nearest neighbor classifier is one of the most popular and flexible models for performing classification tasks in these applications. However, due to the complexity of non-numerical data, existing nearest neighbor classifiers that use the overlap

Due to its simplicity and intuitive interpretability, spherical k-means is often used for clustering a large number of documents. However, there exist a number of drawbacks that need to be addressed for much effective document clustering. Without well-dispersed initial points, spherical k-means fails to converge quickly, which is critical for clustering a large number of documents. Furthermore, its