Circuit Testing, in general, is one of the most important projects in the microelectronics industry in which a product's actual performance will be compared with its expected performance. Built-In Self-Test (BIST), is a test by which the Test Pattern Generator (TPG), the Output Response Analyzer (ORA), and the main circuits under test are built on one chip. One of the most important problems in-circuit

In this paper, modified dragonfly algorithm (MDA) with ant lion optimization (ALO) based Load Flow Analysis (LFA) with hybrid power flow controller in comparison with unified power flow controller is established. The proposed technique is exemplify at enhanced search ability, random reduction and decreased complexity. Here, search behavior of dragonflies is updated to ALO algorithm. Hence, the combination

Affective gaming has been an active research field recently. This is due to the importance of the player’s emotions while playing computer games. Emotions can be detected from various modalities such as facial, voice, and physiological signals. In this study, we evaluate an XGBoost ensemble method and deep neural network for detecting naturalistic expressions of emotions of video game players using

As the most common cancer disease in the world, breast cancer is the main cause of death of female cancer patients. The number of women in China is far greater than that in other countries. Therefore, the absolute death toll is often high. Even a small increase in incidence rate will lead to a severe increase in deaths. Therefore, accurate preoperative diagnosis of benign and malignant breast cancer

In the recent decade, multilevel inverters (MLI) are in favour of academia as well as industry for high power and medium voltage applications. MLI can synthesize switched waveforms with low harmonic distortions than two level converter. The MLI are commonly deployed in grid connected renewable energy sources (RES) to providing better reliability and reducing the harmonics. This paper uses 31 level

High Utility Item-set Mining (HUIM) is the futuristic remodel version of Frequent Item-set Mining (FIM). It discovers customer purchase trends in the retail market. This knowledge is useful to retailers to incorporate various innovative schemes in their businesses to attract the customers such as discounts, cross-marketing, seasonal sale offers…etc. Even though many HUIM algorithms are available to

Fog computing is an emerging computing paradigm that extends traditional cloud computing by leveraging the resources at the user premises for providing better services. It is preferred for many real-time applications because of its advantages such as reduced network latency, improved security, and reduced operational costs. Due to the inherent heterogeneity among the fog devices, resource allocation

In this study, a home dental care system consisting of an oral image acquisition device and deep learning models for maxillary and mandibular teeth images is proposed. The presented method not only classifies tooth diseases, but also determines whether a professional dental treatment (NPDT) is required. Additionally, a specially designed oral image acquisition device was developed to perform image

Bioelectrical time signals are the signals that can be measured through the electrical potential difference across an organ over the time. Electroencephalography (EEG) signals and Electromyography (EMG) signals are among the best-known bioelectrical signals used for medical diagnosis and motion classification. In traditional machine learning methods, the task of extracting unique patterns and features

Cognitive radio (CR) is an emergent communication platform that offers solutions for spectrum scarcity issues. Cognitive radio networks (CRNs) will offer increased bandwidth to mobile consumers through wireless heterogeneous architectures and dynamic spectrum acquisition mechanisms. However, CRNs enforce challenges because of the fluctuating behaviour of the spectrum available and the diverse requirements

Signature identification and verification are some of the biometric systems used for personal identification. Signatures can be considered as authentication of an individual by the analysis of handwriting style, subjected to inter-personal and intra-personal variations. This paper presents an extensive systematic overview of online and offline signature identification and verification techniques. In

Remote deep learning paradigm raises important privacy concerns related to clients sensitive data and deep learning models. However, dealing with such concerns may come at the expense of more client-side overhead, which does not fit applications relying on constrained environments. In this paper, we propose a privacy-preserving solution for deep-learning-based inference, which ensures effectiveness

It has been gradually recognized that mobile phones can be used as a practical and promising way to identify individual travel trajectories. Researchers have developed various approaches to detecting human mobility and trip characteristics including trip origin–destination, travel modes, trip purposes based on mobile phone data. Among these researches, trip purpose detection has drawn less attention

This study presents a new knowledge-based fuzzy clustering called semi-supervised collaborative fuzzy clustering (S\(^{2}\)CFC), which emphasizes aggregating diverse knowledge sources rather than using them in separate steps to revealing structures of data objects stored across multiple data sites while maintaining data sharing restrictions. The proposed method aggregates fuzzy logic, semi-supervised

Human Action Recognition (HAR) has gained considerable attention due to its various applications such as monitoring activities, robotics, visual surveillance, to name a few. An action recognition task consists of feature extraction, dimensionality reduction, and action classification. The paper proposes an action recognition approach for depth-based input by designing Single Layer Feed forward Network

Liu process is a basic process in fuzzy environment. As an extension of Liu process, fractional Liu process has attracted the attention of many scholars. In this paper, a fuzzy stock model driven by fractional Liu process is established, and its European, American, Asian, power options pricing formulas are given. In order to better understand these formulas, we give a few numerical examples to illustrate

Automated guided vehicles are widely used in various applications, especially in manufacturing. In this paper, we present a novel parallel algorithm for multi-AGV systems. The overall structure is composed of three parts: task assignment, path planning, and vehicle navigation. According to the priorities of AGVs, a greedy method is introduced to assign jobs. For path planning, a mixed-integer programming

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction has been widely concerned. The dynamic behavior of wind power time series is the external performance of complex nonlinear and multi-scale phenomena. Before choosing an appropriate prediction model

In order to improve the performance of recommender systems, user social information and item attribute information should be integrated when building the prediction model, which is a hotspot and difficulty in the field of recommender systems. In this paper, we propose an extended matrix factorization model based on network representation learning. To characterize users and items comprehensively, we

We present a new classification algorithm for machine learning numerical data based on direct and inverse fuzzy transforms. In our previous work fuzzy transforms were used for numerical attribute dependency in data analysis: the multi-dimensional inverse fuzzy transform was used to approximate the regression function. Also here the classification method presented is based on this operator. Strictly

A simple method is proposed in this paper for estimating the fuzzy logistic regression model adopted with support vector machines. The proposed method is robust against the outliers in cases that the predictors are exact quantities and the responses are fuzzy data. For this purpose, the unknown center, left, and right spreads of fuzzy regression coefficients were estimated via a separated three-stage

Object detection is a classic problem in computer vision. The main bottleneck of object detection lies in the fusion of multi-scale features. In this paper, we systematically study the design choices of neural network architecture for real-time object detection, and propose an Align-Yolact to improve the instance segmentation accuracy. Firstly, we propose a weighted bounding box, which improves the

As the first step of vehicle detection and recognition system, how to quickly and accurately detect the vehicle in a picture is directly related to the subsequent vehicle application research. In order to improve the processing speed of vehicle detection, reduce the false alarm rate of detection, and get better results, the method is applied in real scene, this paper carried out in-depth research on

Mobile payments makeup one of the fastest-growing mobile services available today and are widely used by smartphones for utility payments, bill payments, and online shopping, among other applications. Mobile payments are playing a vital role in the fast growth of online markets and are revolutionizing the supply chain of businesses and industries. Mobile payments are becoming dominant compared to conventional

Multicriteria Decision Making (MCDM) has a huge role to play while ruling out one suitable alternative among a pool of alternatives governed by predefined multiple criteria. Some of the factors like imprecision, lack of information/data, etc., which are present in traditional MCDM processes have showcased their lack of efficiency and hence eventually it has paved the ways for the development of Fuzzy

The fifth generation mobile network is planned not only to enhance capacity up to 20 Gbps but also able to connect a variety of devices ranging up to one million per square km and having diversified requirements and applications with the latency of less than one millisecond. Hence it becomes very important to utilize all available network resources judiciously to obtain the vision of IMT 2020. Performance

Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract

The aim of this study is to establish an objective evaluation method for the VR glasses in the design process using fit test panels. Based on head width-length index and head height-length index in Chinese head and face 3D database, we get 9 categories using Fuzzy C-Means Clustering (FCM). Then, a fit test panels including 20 samples was established. According to the subjective evaluation of 20 subjects

Student employability is crucial for educational institutions as it is often used as a metric for their success. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help

In the context of smart communities, it is essential an active and continuous collaboration between citizens, organizations and institutions. There are several cases where citizens may be asked to participate such as in public decision-making process by informing, voting or proposing projects or in crisis management by sharing precise and timely information with other citizens and emergency organizations

For flexible job-shop scheduling problem with low carbon emission constraints, an improved quantum genetic algorithm based on double chains coding is proposed. Firstly, a mathematical model is established to minimize makespan, total workload of machines and carbon emissions of machines. Secondly, carbon emission equations in job shop scheduling process are inducted and designed. Based on the selected

Mobile crowd-sensing (MCS) is a solution to provide spectrum availability information for dynamic spectrum access in cognitive radio systems. In MCS-based spectrum monitoring, participants should report the location and time of spectrum sensing in addition to the status of the spectrum bands, which raises the need for privacy-preserving. On the other hand, it is required to mitigate the possibility

Processing and analysis of the expedited volume of data are considered significant challenges for the Internet of Things (IoT) systems in which devices are constantly generating data. The Fog architecture will allow delay-sensitive applications to run everywhere. In IoT, users with smart devices can sense tasks and contribute to their performance. Mobile Crowdsensing (MCS) utilizes users’ crowds as

The selection of sustainable building materials has attracted much attention from society because it is essential for environment, economy, and human health. To meet the need of the building development, the identified criteria for optimal sustainable building materials are determined based on sustainability building standards and previous studies including economic, environmental, social, and technological

Cloud Service Providers (CSP) allow the users to store their data in the cloud storage servers. However, outsourcing the confidential data increases the security vulnerabilities as the data owner loses the physical on-premise control over the data. In order to verify the integrity of the outsourced data, auditing has to be performed frequently. Existing public auditing schemes completely depend on

Driving behavior optimization can not only reduce energy consumption and the probability of traffic accidents but also improve the riding experience of passengers. Unfortunately, the low estimation accuracy resulting from the poor performance of prediction models greatly influences bus service performance. In this paper, a time cycle neural network, the long short-term memory (LSTM) network, is used

The rate of recidivism by criminals after their release from prison is high, which is harmful to society. Thus, it is socially significant to reduce their recidivism rate. This article uses public data from the state of Iowa in the United States. According to the data characteristics, such as having redundant samples and mixed attributes, we propose the following methods. First, we use a rough set

Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. Therefore, there are many security management problems. The existing solutions can only solve the problems of a small-scale crowd, which contains a simple abnormal behavior and a clear surveillance video. However, the performance hasn’t reached a satisfactory result for a large-scale crowd. Therefore, we propose

This paper proposes a novel method based on Fuzzy Logic Systems (FLSs) optimized to maximize the area coverage of a tiling robot by reconfiguring per an obstacle considering the “Infi” concept. The consideration of an infinite number of reconfigurable shapes by a tiling robot is defined as the “Infi” concept. The major advantage of the proposed method over state-of-the-art “Infi” methods is the ability

Digital twin (DT) is a hot topic in information engineering, which has been introduced into the intelligent solution of the power grid system to deal with the reliability assurance issues of complex systems. Due to the lack of an operational application architecture model, it is impossible to map the complex system comprehensively. Based on the review of the concept model of DT and the current research

Real-time data processing and distributed messaging are problems that have been worked on for a long time. As the amount of spatial data being produced has increased, coupled with increasingly complex software solutions being developed, there is a need for platforms that address these needs. In this paper, we present a distributed and light streaming system for combating pandemics and give a case study

Authentication is the process of keeping the user’s personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication.

Collaborative Filtering, though a successful recommendation technique is vulnerable to shilling attacks due to its open nature. These attacks alter recommendations being generated for the user by inserting fake user profiles in the database. To minimize the bias introduced in the recommendation process, many machine learning methods have been explored and shown excellent results. However, supervised

Driver drowsiness is the major cause of many traffic accidents. A study by the National Institute of Sleep and Vigilance showed that the majority of the accidents took place on fast tracks. 34% of these accidents are mainly due to lack of sleep. Indeed, the drowsiness of drivers appears mainly by a fall of vigilance with the appearance of various behaviors that represent clues for the decrease of reflexes

With the popularity of social networks, crisis information has brought huge harm to social stability and people’s lives. The identification of crisis information in social networks plays a crucial role in avoiding the occurrence of crisis events and reducing the harm caused by crisis information. Thus, this paper proposes a crisis information recognition model based on Back Propagation neural network

A correction to this paper has been published: https://doi.org/10.1007/s12652-021-03300-y

Sometimes, the conscious act of decision-making in humans is dramatically interrupted by situations that warrant an immediate response (e.g. when there is an imminent risk). The human body somatizes this interruption such that an action could be taken without a rational analysis. The above is known as a somatic marker. According to the somatic marker hypothesis, somatic markers could directly influence

Fierce competition in the global market forces companies to satisfy all aspects of customers’ needs during the product design stage. Among the customers’ needs, affective needs are difficult to satisfy since understanding the affective needs of customers is a challenging task. Therefore, Kansei Engineering (KE), which is capable of transforming the affective needs of customers into product design form

The goals of emergency management are to restore human safety and security, and to help the authorities understand what causes such events. It requires information that is both highly accurate, and can be generated very quickly. This research addresses these concerns with a machine learning model based on cause-and-effect using a Bayesian belief network. This employs human critical thinking and amplified

Notwithstanding the superior performance of the Whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDWOA) is presented where the conventional WOA is combined

E-healthcare requires communication of patient report to a specialized doctor in a real time scenario. Therefore, any harm to patient medical data can lead to a faulty diagnosis that can be lethal for the patient. To ensure safe and secure communication in E-healthcare framework, a high capacity dual image separable reversible data hiding algorithm in the encrypted domain has been presented in this

With the exposure of various social security issues, the issue of ownership traceability and anti-counterfeiting has received more and more attention. This paper builds a design and implementation of traceability anti-counterfeiting system based on the ownership of edge computing on the blockchain. Based on the ownership of edge computing on the blockchain, this article builds a traceability anti-counterfeiting

Detect cane events in real-life walking situations is needed to assess indirectly human gait without using wearable systems which may be undesirable, uncomfortable, or difficult to wear, especially for patients who are undergoing rehabilitation. This article fosters two reliable kinematic data-based approaches—a machine learning classifier and an adaptive rule-based finite-state machine (FSM)—to detect

An Internet of Things (IoT) botnet is a collection of infected smart devices that are remotely managed by a botmaster. The injection of multiple attacks into the infrastructure, high permeability, and vulnerability of IoT security interfaces are the most significant challenges in the field of IoT security. Therefore, it is essential to provide an efficient solution that can detect intrusion into the

Different respiratory infections cause abnormal symptoms in lung parenchyma that show in chest computed tomography. Since December 2019, the SARS-COV-2 virus, which is the causative agent of COVID-19, has invaded the world causing high numbers of infections and deaths. The infection with SARS-COV-2 virus shows an abnormality in lung parenchyma that can be effectively detected using Computed Tomography

Automatic speech recognition is the main form of man–machine communication. Recently, several studies have shown the ability to automatically recognize speech based on electromyography (EMG) signals of the facial muscles using machine learning methods. The objective of this study was to utilize machine learning methods for automatic identification of speech based on EMG signals. EMG signals from three

Combined with the objective fact that China’s logistics industry inward foreign direct investment (IFDI) and outward foreign direct investment (OFDI) and total factor productivity (TFP) of space development is not balanced, this study used the system generalized method of moments (GMM) and geographically weighted regression (GWR) model respectively to explore the overall influence and spatial difference

Innovations in the Internet of Things (IoT) technology have revolutionized several industrial domains for smart decision-modeling. The capacity to perceive data about ubiquitous instances has resulted in numerous innovations in sensitive sectors like national security, and police departments. In this paper, an extensive IoT-based framework is introduced for assessing the integrity of police personnel

With the advent of recent trends in micro miniature sensor technologies, the wireless body area networks (WBANs) have become prevalent in health monitoring applications. The IEEE 802.15.6 standard has been designed to cater to the vital requirements of the wireless healthcare services, such as WBANs. However, this standard suffers from the static allocation of slots for the scheduled access mechanism

In automatic cancer detection, nuclei segmentation is a very essential step which enables the classification task simpler and computationally more efficient. However, automatic nuclei detection is fraught with the problems of inter-class variability of nuclei size and shapes. In this research article, a novel unsupervised edge detection technique, is proposed for segmenting the nuclei regions in liver