Commercialization of the wireless body area network (WBAN) envisions future new normal for WBAN devices coexistence in a localized area. The coexistence may allow devices to freely change positions, associate, or dissociate with the neighbours as users interact. Devices’ interaction in a stationary or mobile fashion radiates heat and also competes for the limited network resources resulting to unreliable

This article explores the possibility of disaggregating query/question information in e-learning system online lectures or course recommendations. Information arrangement includes reading, parsing and classification of inquiry/question messages. Data extraction is a kind of shallow content processing. It finds a set of predefined applicable content in the feature language archives and performs common

The Internet of Everything (IoE) is an advanced platform which provides a common network among people, process, data, and things. It provides a strong turning tool which converts the information into actions. The growing penetration of Internet of Things (IoT) into every aspect of our life brings a logical sense regarding the adoption of IoE for the efficient agricultural process. The objective of

This paper presents a novel chaotic augmented Lagrange method for solving constrained optimization problems. The algorithm employs chaotic maps to reduce the search space and to get the best parameters for handling the problem constraints. Then, the first carrier wave method can be applied to obtain a solution as an initial point of simplex method to find optimal solution. To verify the efficiency

It is well known that there are some maternal and fetal issues that directly influence preterm births. However, all the variables provoking it are not completely determined. On the other hand, chronodisruption alters maternal circadian rhythms, with negative consequences for the maturation of the fetus. Thus, the objective of this work is to add other factors related to maternal chronodisruption factors

Electronic health (e-health) is commonly recognized as a promising model for raising the enormous pressure on conventional healthcare systems. In this paper, an improved delay-sensitive medical packet transmission scheduling system has been proposed to manage the medical packet transmission in e-health networks. It focuses on communication through the wireless body area network (over WBAN). Medical

As an essential part of the modern intelligent traffic management system, traffic speed prediction is a challenging task. In recent studies, deep neural networks (LSTM and WaveNet) and graph neural networks (GCN and GNN) have been extensively investigated on traffic networks evaluation, which is better than statistical-based models (MA and ARIMA). However, the demerits existing in these deep learning

Arrhythmias such as Atrial Fibrillation (Afib), Atrial Flutter (Afl), and Ventricular Fibrillation (Vfib) are early indicators of Stroke and Sudden Cardiac Death, which are significant causes of death globally. Therefore, it is vital to detect patients with these conditions early. Manual inspection of ECG signals is tedious, time-consuming, and is limited by inter-observer variabilities. Further, it

Collaborative filtering over the years have emerged as an alternative recommender system to address some of the setbacks of content based filtering. Although, Collaborative filtering has offered some benefits to the majority of the online stores in recommending products to users using users’ ratings of similarity measure, its usage has also raised some doubt in the minds of researchers, regarding its

Collaborative cloud business models enable a new dimension of business by giving option to the third party software vendors to deploy their software in the cloud for offering software as a service (SaaS) to the users. However, the secure provisioning of resources requires scalable architecture with efficient authentication for configuring the collaborative software services in the cloud. In this paper

In this paper, we present an approach for the anticipation of electroencephalography (EEG) seizures using different families of wavelet transform. Different signal attributes are investigated to anticipate the seizure onset based on the wavelet transform. These attributes comprise amplitude, local mean, local median, local variance, derivative, and entropy of the wavelet-transformed signals. Different

The paper aims to present the concept of power aggregation operators for the T-spherical fuzzy sets (T-SFSs). T-SFS is a powerful concept, with four membership functions denoting membership, abstinence, non-membership and refusal degree, to deal with the uncertain information as compared to other existing fuzzy sets. On the other hand, the relationship between the different pairs of the attributes

Zero-shot learning (ZSL) is a learning paradigm that tries to develop a recognition model to recognize mutually exclusive training and testing classes. To recognize mutually exclusive classes, some kind of correlation between training and testing classes are required. This paper proposed an inductive solution of the ZSL problem in two stages: (1) a supervised multiclass classifier is trained on the

The solar distillation process utilizes the abundantly available solar energy to separate pure water from the contaminants. The process takes place in a device called the solar distillation still (SDS). The thermal performance delivered by the SDS mainly depends on the distillate formation rate inside the basin. The distillate formation inside the SDS depends on its basin temperature (BT), basin water

Wireless body area networks (WBANs) are an upcoming technology for achieving effective healthcare. The security and privacy of patient-related data are two essential aspects of WBAN system security. Storing data on a single server is simple but may lead to a single point of failure (whether a typical failure or a failure due to security attacks). In this paper, enhanced security and privacy of patient

Outdoor localization of non-cooperative moving discrete target tracking is a demanding and challenging due to inherent constraints of “target tracking wireless sensor networks” such as battery capacity, processing capacity, memory capacity. Current methods use either an active sensor or perform additional processing of the data received from multiple passive sensor nodes that increases power consumption

The optimal segmentation of medical images remains important for promoting the intensive use of automatic approaches in decision making, disease diagnosis, and facilitating the sustainable development of computer vision studies. Generally, recent methods tend to minimize human–machine interaction by using multi-agent systems (MAS) and optimize the segmentation systems control. Some of the existing

The main objective of this paper is to introduce some algebraic properties of finite linear Diophantine fuzzy subsets of group, ring and field. Relatedly, we define the concepts of linear Diophantine fuzzy subgroup and normal subgroup of a group, linear Diophantine fuzzy subring and ideal of a ring, and linear Diophantine fuzzy subfield of a field. We investigate their basic properties, relations and

The fast-growing population, great development in the economy and the improvement of urbanization brought about the quick rise in the volume of motor vehicles in the urban areas of India. Therefore, the significance of estimating the travel demand model will be expanded in the ongoing years. Determining the travel demand model includes different phases of excursion age and dissemination, mode choice

In this study, the embedding of picture fuzzy graphs on the surface of spheres is introduced. The picture fuzzy genus graph with its genus value and strong (weak) picture fuzzy genus graphs are defined. Some important properties of picture fuzzy genus graphs are discussed. A relation between genus value and degree of planarity of picture fuzzy graph is established. The isomorphism properties of picture

Non-recurrent congestion, which is mainly due to traffic incidents, may seriously impact the performance and operation of a traffic system. Reacting quickly and in a uniform and structured way is vital. In particular, choosing the appropriate response strategy with only a short delay may mitigate the impact of incidents, improve traffic efficiency, and increase safety in the transportation system.

Flood is one of the devastating natural disaster than anything else. It is a harmful event that can risk human life, damage homes, and have huge economic impacts. Flooding creates garbage and solid waste which includes dead animals, waste products, etc. and this can increase the possibilities of spreading disease and worsening water and sanitation problems in an area and hence the need to warrant a

In the last few years, the mixture of nuclear medicine and the reverse engineering method has confirmed to be an identical key development in the medical field. In contrast, the traditional method has some downsides. For instance, some difficulty presence in matching the well suitable due to the change of patient’s oral state and it takes more attention to meet reliability and comfort. So, the patient-specific

Detecting spatio-temporal patterns as an interesting topic from different perspectives like detecting anomalies, unexpected and high-risk areas is a powerful means to manage future events by describing the current state and giving the possibility to predict the future. Criminology theories explain that the distribution of crime is not random. Although various specifications of crime are necessary for

Some stops on busy bus lines regularly suffer from bus bunching, which refers to a bus arriving with a little headway to its predecessor. This phenomenon increases scheduling difficulties and has a negative impact on the passenger experience due to unreasonable scheduling. The conventional holding strategy aims to alleviate this problem by holding buses at control points. However, the holding strategy

In recent years, multi-modal optimization algorithms have attracted considerable attention, largely because many real-world problems have more than one solution. Multi-modal optimization algorithms are able to find multiple local/global optima (solutions), while unimodal optimization algorithms only find a single global optimum (solution) among the set of the solutions. Niche-based multi-modal optimization

The security of modern IoT Industry 4.0, 5G, 6G, Mobile ad hoc (MANET), narrowband internet of things (NB-IoT) and wireless sensory (WSN) networks and the autonomous computing capabilities of individual devices and self-organizing, greatly influence their applications in smart connected world. To achieve the sufficient security and privacy, autonomous and dynamic adaptive key management scheme and

Decision support systems (DSS) were built using the support of wireless sensors network (WSN) for resolving many real-world issues. Precision agriculture (PA) is the most popular area which requires DSS. Numerous agricultural cropping schemes in arid and semiarid areas practice irrigation process which is a crucial one and also here the main concern is water applications and management. An automatic

Latest studies have exploited sink mobility as a pervasive approach to effectively mitigate the energy hole problem frequently perceived in the clustered wireless sensor network. For mobile sink (MS) based data collection, the selection of MS sojourn location plays a significant role in network performance. Moreover, there are several challenges to WSN in the form of energy preservation and data delivery

Biometric verification systems are used to recognize people based on their uniqueness or characteristics. Signature is considered as one of the most commonly used biometric that individualizes a human being. It is generally used to keep individual’s privacy in many places such as banking sectors, academic institutes, office premises and trading. But increase of criminal attempts in falsifying an individual’s

We address the problem of facial expression analysis. The proposed approach predicts both basic emotion and valence/arousal values as a continuous measure for the emotional state. Experimental results including cross-database evaluation on the AffectNet, Aff-Wild, and AFEW dataset shows that our approach predicts emotion categories and valence/arousal values with high accuracies and that the simultaneous

The Recurrent Transmission Ratio (RTR), considered as confine for Cognitive Radio Ad-hoc Networks (CRAN), has attracted several researchers’ in the recent past. Among the manifold practices that are trying to lessen the RTR, the CRAN is one of the dimensions. The transmission scheduling channels and allocation of the suitable channel are the essential objectives for attaining CRAN concerning to reduce

The outbreak of Coronavirus (COVID-19) has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but

Assessment the quality of medical services is a crucial strategy to identify the strengths and weaknesses providing actionable insights to improve the healthcare services provided to cancer patients. In Libya, the challenge of medical facilities and treatment in healthcare systems of cancer patients is of high importance. This study investigates the relationships between nurse care, attitude of patient

In this paper, an uncertain competing failure degradation model is proposed, in which the natural degradation process is described by an uncertain process, the time interval of shocks arrival and the size of the shocks have independent and nonidentical uncertainty distributions, respectively. The parameters in the distributions are uncertain variables. The belief reliability function and the mean time

This current research work consists of the development of a new e-health application for the patients that have a cardiac problem and use a pacemaker. It allows: (1) heartbeat measure, (2) blood pressure surveillance, and (3) oxygen consumption surveillance. The main goal of our application is to assist the patients that have a pacemaker at any time and at any place. An IoT application based on remote

Tool wear is a detrimental circumstance in end milling and estimating its occurrence in machinery is an onerous process. Indirect tool monitoring has been actively studied to identify instances of wear on the cutting tool based on the signal from a sensor that represents the tool condition. Runout of a machine spindle during machining as a result of a defective tool commonly occurs in the metal cutting

In this paper, an improved teaching optimization algorithm called monitor system and Gaussian perturbation (GP) teaching–learning-based optimization algorithm (MG-TLBO) is proposed based on several modified variants of TLBO. TLBO is simply divided into two phases: “Teacher phase” and “Learner phase.” To further improve the solution accuracy and efficiency, we introduce two mechanisms in the learner

Speckle noise is a main obstacle for change detection tasks of synthetic aperture radar (SAR) images. However, change detection methods often focus on removing noise and ignore the importance of preserving details of SAR images, which results in a loss of classification accuracy. In order to alleviate the contradiction between removing noise and preserving details, a multi-objective change detection

Opening range breakout (ORB) is a well-known trading strategy in which predetermined price thresholds are used to characterize price movements. However, some researchers have noted that ORB does not make full use of market characteristics and fails to define a cogent closing strategy. Several modified ORB strategies have been optimized using grid-wise algorithms; however, those methods operate within

The lengths of input vectors corresponding to different circuits are often quite different, which makes it difficult to standardize them. This paper proposes a characteristic standardization method for circuit input vectors based on hash algorithm. First, the collision rates of different hash algorithms are analyzed, and four representative hash algorithms are selected to process the input vectors

One of the best biometrics used for human verification and identification is iris recognition. The contrast of its unique characteristics differs from one candidate to another in which the iris pattern has numerous well-known features like uniqueness texture, stability and compactness representation for human identification. Among these facts, several approaches in these areas are localized, but there

A new interesting topic in the Internet application is the Internet of Things (IoT) . Using novel technologies is a common subject, but not that much in the use of service management. We have found only a few studies regarding service management mechanisms discussion in the IoT. So, we have investigated and scrutinized the use of service management mechanisms in the IoT. The main goals of this paper

In order to solve the issue mismatching and structure disconnecting in exemplar-based image inpainting, an image completion algorithm based on improved total variation minimization method had been proposed in the paper, refer as ETVM. The structure of image had been extracted using improved total variation minimization method, and the known information of image is sufficiently used by existing methods

As a convenient and economic computing model, cloud computing promotes the development of intelligence. Solving the workflow scheduling is a significant topic to promote the development of the cloud computing. In this work, an Actor-Critic architecture is utilized to solve this problem achieving the task executive time minimization under the task precedence constraint. It is similar to the list-based

With 35,000 km of bicycle pathways, cycling is common among persons of all ages less than 65 years in the Netherlands. Bicycle is often seen as a standalone travel mode but when integrated as part of a multimodal trip with train, it can be an important solution for long distance journeys, offering increased flexibility and faster access time compared to other travel modes. In this paper we investigate

Wireless sensor network (WSN) is used for data collection and transmission in IoT environment. Since it consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which substantially deteriorate the network performance. Data aggregation is needed to reduce energy consumption and prolong the lifetime of WSN. In this paper a novel data aggregation scheme

In this paper, we propose a novel approach for accurate detection of vowel onset points (VOPs). A VOP is the instant at which a vowel begins in a speech signal. Precise identification of VOPs is important for various speech applications such as speech segmentation and speech rate modification. Existing methods detect the majority of VOPs to an accuracy of 40 ms deviation, which may not be appropriate

Human activity recognition is concerned with detecting different types of human movements and actions using data gathered from various types of sensors. Deep learning approaches, when applied on time series data, offer promising results over intensive handcrafted feature extraction techniques that are highly reliant on the quality of defined domain parameters. In this paper, we investigate the benefits

A novel facial expression recognition system has been proposed in this paper. The objective of this paper is to recognize the types of expressions in the human face region. The implementation of the proposed system has been divided into four components. In the first component, a region of interest as face detection has been performed from the captured input image. For extracting more distinctive and

The Internet of Things (IoT) combines miscellaneous technologies, which make it more diverse and applicable to different domains than a single technology. Semantic web technologies combined with IoT facilitate ubiquitous computing through machine-to-machine communication and semantic data management. Reusable domain ontologies, which provide a common semantic description for resources, are potential

The objective of this study is to analysis of accident of motorcyclists on Bogotá roads in Colombia. For detection of conditions related to crashes and their severity, the proposed model develops the strategies to enhance road safety. In this context, data mining and machine learning techniques are used to investigate 34,232 accidents by motorcyclists during January 2013 to February 2018. Both the

IoT entities amass a massive quantity of sensing data and transmit it to the cloud for processing as well as reasoning. Such disparate networks raise numerous research questions, say processing, storage, and management of massive data. Though numerous prevailing methodologies are presented, it brings about restricted memory, battery-centered processes, privacy, safety, low processing ability, along

Automatic abnormal detection of video homework is an effective method to improve the efficiency of homework marking. Based on the video homework review of “big data acquisition and processing project of actual combat” and other courses, this paper found some student upload their videos with poor images, face loss or abnormal video direction. However, it is time-consuming for teachers to pick out the

Diabetic retinopathy (DR) is one of the world’s most significant difficulties of diabetes identified with eye illness which happens when veins in the retina become swollen and releases liquid which at last prompts vision misfortune. Early discovery of DR can anticipate the harm to the retina and vision misfortune or atleast moderate its movement. There are various strategies used to distinguish DR

As a precursor of optical character recognition (OCR) technology, script identification finds many applications like sorting and indexing of document images. Classifying these scripts, especially at different scales and orientations, is one of the interesting and vital problems in the field of document image analysis. In this paper, an algorithm is proposed for the identification of scripts using scale

This paper offers a fuzzy-based manoeuvring model labeled as Fuzzy-based Midvehicle Collision Avoidance System (FMCAS) resolving two road crash scenarios. The first scenario covers dual situations (a) Mid vehicle collision avoidance with the rear vehicle under no front vehicle condition and (b) Curvilinear path strategy based on real road conditions. While the suitable curvilinear motion to fit the

Acrylamide is a carcinogenic chemical compound found in carbohydrate rich foods when fried and baked at high temperatures, like potato chips. Identification of such toxic substances in food items is of tremendous significance. Conventional identification approaches like liquid chromatography-mass spectrometry (LC–MS) are time-consuming, destructive and require trained manpower. Traditional machine

Sparse representation method has been widely applied to visual tracking. Most of existing tracking algorithms based on sparse representation exploit the l0 or l1-norm for solving the sparse coefficients. However, it makes the execution of solution very time consuming. In this paper, we propose an effective dual dictionary learning model for visual tracking. The dictionary model is composed of discriminative

The Industrial Internet of Things (IIoT) integrated with cloud computing resources offers effective advancements in the field of industrial automation with open connectivity and emergent computing. With such advancements, the supplementary services evolves in order to augment the automation process in manufacturing and production industries. However, the integration and transformation of cloud and