Search

Although speech driven facial animation has been studied extensively in the literature, works focusing on the affective content of the speech are limited. This is mostly due to the scarcity of affective audio-visual data. In this article, we improve the affective facial animation using domain adaptation by partially reducing the data scarcity. We first define a domain adaptation to map affective and neutral speech representations to a common latent space in which cross-domain bias is smaller. Then the domain adaptation is used to augment affective representations for each emotion category, including angry, disgust, fear, happy, sad, surprise, and neutral, so that we can better train emotion-dependent deep audio-to-visual (A2V) mapping models. Based on the emotion-dependent deep A2V models, the proposed affective facial synthesis system is realized in two stages: first, speech emotion recognition extracts soft emotion category likelihoods for the utterances; then a soft fusion of the emotion-dependent A2V mapping outputs form the affective facial synthesis. Experimental evaluations are performed on the SAVEE audio-visual dataset. The proposed models are assessed with objective and subjective evaluations. The proposed affective A2V system achieves significant MSE loss improvements in comparison to the recent literature. Furthermore, the resulting facial animations of the proposed system are preferred over the baseline animations in the subjective evaluations., Scientific and Technological Council of Turkey (TÜBİTAK)

The huge advancement in the field of communication has pushed the innovation pace towards a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility) and widely distributed network need to be carefully addressed. This paper therefore focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new Non-linear Levy Brownian Generalized Normal Distribution Optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and inter-node distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods.

This paper deals with automatic clustering algorithm, which partitions a many of objects to get smaller sets (clusters). Where objects of the one set are related to each other rather than to those in the other sets. The number of clusters in automatic clustering don’t given priori, they determinates automatically, the number resulted of clusters exact closed to the numbers of the real dataset structure. This paper represents a stimulus for the current study to introduce an algorithm that automatically finds the number of clusters based on distance among vertices. The study is based on the hypothesis that the proposed algorithm is able to efficiently find the clustering partitions for the whole dataset.

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one's field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology.

This study introduces a novel matrix determinant feature extraction approach for efficient classification of motor and mental imagery activities from electroencephalography signals. First, the multiscale principal component analysis (MSPCA) was utilized to obtain clean EEG signals. Secondly, denoised data were sequentially arranged to form a square matrix of different orders (e.g.10,13,16,20) and determinant was computed for each order matrix. Finally, the extracted matrix determinant features were provided to several machine learning and neural network classification models for classification. All experiments were carried out using a 10-fold cross-validation approach on three publicly accessible datasets: Dataset IV-a, Dataset IVb, and Dataset V of BCI competition III. Also, this study designs a Computerized Automatic Detection of Motor and Mental Imagery graphical user interface (CADMMI GUI) that can assist physicians/experts to efficiently analyses motor and mental imagery data. Experimental results reveal that the highest average classification accuracy of 99.55% (for dataset IV-a), 99.52% (for dataset IV-b), and 91.80% (for dataset V) were obtained for motor and mental imagery respectively with 20-order matrix determinant using feedforward neural network classifier. The experimental results suggest that the proposed framework provides a robust biomarker with the least computational complexity for the development of automated braincomputer interfaces.

The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.

The commercial fifth-generation (5G) wireless communications networks have already been deployed with the aim of providing high data rates. However, the rapid growth in the number of smart devices and the emergence of the Internet of Everything (IoE) applications, which require an ultra-reliable and low-latency communication, will result in a substantial burden on the 5G wireless networks. As such, the data rate that could be supplied by 5G networks will unlikely sustain the enormous ongoing data traffic explosion. This has motivated research into continuing to advance the existing wireless networks toward the future generation of cellular systems, known as sixth generation (6G). Therefore, it is essential to provide a prospective vision of the 6G and the key enabling technologies for realizing future networks. To this end, this paper presents a comprehensive review/survey of the future evolution of 6G networks. Specifically, the objective of the paper is to provide a comprehensive review/survey about the key enabling technologies for 6G networks, which include a discussion about the main operation principles of each technology, envisioned potential applications, current state-of-the-art research, and the related technical challenges. Overall, this paper provides useful information for industries and academic researchers and discusses the potentials for opening up new research directions.

This study aimed to provide a comprehensive assessment of upper limb kinetics and kinematics and shoulder movements during wheelchair propulsion while negotiating a speed bump of 6 cm height using four different wheelchair configurations. 16 healthy males aged 30.8 ± 5.7 years participated in the experiment. The kinetic and kinematic data during wheelchair propulsion were recorded. A smart system was used to collect the push forces and a motion capture system was used to collect upper limb movements. The results show that approximately 50% more pushing force was required to negotiate the speed bump than that of level ground propulsion. At the upward-forward axle position, peak total forces were 95.17 ± 5.70 N which resulted in significantly improved propulsion ergonomics, but 129.36 ± 6.68 N was required at the upward-back axle position at the speed bump push. The findings could help manufactures to design protective gloves for wheelchair users and provide useful rehabilitation information to clinicians and patients. Practitioner summary: This study investigated pushing forces and movements during wheelchair propulsion over a speed bump. Approximately 50% more pushing force was required to negotiate the bump than a level surface propulsion. The upper-forward axle position was found to be reasonably better than other positions during wheelchair propulsion. Abbreviations: UF: upper and forward position; UB: upper and back position; DF: down and forward position; DB: down and back position; ROM: range of motion.

The study of the mind and how we behave regarding certain things is tied to psychology. Psychology in itself is merely a field of study that studies the scientific nature of the mental states and processes of the mind. American Psychological Association defines psychology as the study of the mind and behavior. The question of how our minds affect our physical processes is a topic of great concern in psychology. Asserts that psychology is considered mainly a "hub science," having hefty connections to social sciences, medical science, and education. The interactions involve the mutuality between our internal and external states, and what extends from it plays an essential role in shaping the manifestations of our life's endeavors. The outfits of psychology determine how we handle tools, people, processes, etc., even in our places of work; the way we address our fellow workers, devices, and tools has almost everything to do with our psychological makeup.

The Flyback inverter is a single-stage power inverter which represent an attractive solution for photovoltaic (PV) grid-tied inverter application. The main advantages of a current-source flyback inverter are high power density and high efficiency due to its small, as well as low total harmonic distortion (THD) operation. However, a flyback topology works with Discontinuous Conduction Mode (DCM) control strategy has lower efficiency and poor THD values. In this paper, an efficient current-source flyback inverter topology works with Boundary Conduction Mode (BCM) control strategy is presented. Besides, an efficient incremental conductance (IC) maximum power point tracking (MPPT) is used to extract the maximum power from the PV module. To verify the proposed control a power simulator (PSIM) software is used. As a result, the simulation results indicates that the BCM control is more efficient than the DCM method for various weather conditions. Finally, the proposed BCM strategy is compared with DCM control in terms of THD and thus it is achieved lower THD contain in the injection current of grid.

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect individuals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an example of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

This paper proposes an adaptive real‐time energy scheduling method (RT‐EMS) for a microgrid, using a Lyapunov optimization‐based real‐time approach. Inaccuracy in day‐ahead predictions can result in non‐optimal solutions to the energy scheduling problem. Although the real‐time optimization method eliminates the need to deal with the prediction uncertainties, it ignores the valuable statistical information used in day‐ahead stochastic approaches and provides suboptimal solutions to the problem. The proposed adaptive approach combines the advantages of both the stochastic day‐ahead and the RT‐EMS and reduces the real‐time operational cost of the microgrid. The proposed method moves the RT‐EMS solution towards the optimal solution, by adding a penalty term to the objective function. Numerical results are provided to demonstrate the improved performance of the proposed adaptive method.

In this paper, a novel four-dimensional chaotic system has been created, which has characteristics such as high sensitivity to the initial conditions and parameters. It also has two a positive Lyapunov exponents. This means the system is hyper chaotic. In addition, a new algorithm was suggested based on which they constructed an image cryptosystem. In the permutation stage, the pixel positions are scrambled via a chaotic sequence sorting. In the substitution stage, pixel values are mixed with a pseudorandom sequence generated from the 4D chaotic system using XOR operation. A simulation has been conducted to evaluate the algorithm, using the standardized tests such as information entropy, histogram, number of pixel change rate, unified average change intensity, and key space. Experimental results and performance analyses demonstrate that the proposed encryption algorithm achieves high security and efficiency.

In this paper, the problem of resource allocation at Al-Raji Company for soft drinks and juices was studied. The company produces several types of tasks to produce juices and soft drinks, which need machines to accomplish these tasks, as it has 6 machines that want to allocate to 4 different tasks to accomplish these tasks. The machines assigned to each task are subject to failure, as these machines are repaired to participate again in the production process. From past records of the company, the probability of failure machines at each task was calculated depending on company data information. Also, the time required for each machine to complete each task was recorded. The aim of this paper is to determine the minimum expected time for the completion of all the machines assigned to perform their tasks in the company by using the dynamic optimization technique over finite and infinite horizons. By comparing the results, it was found that the first and second tasks were better than the third and the fourth tasks because the first task and the second one had completed their tasks in a shorter period than the others, they took 1379.2 hours and 1379.3 respectively during of horizons (stages), while the third task took 1379.4 hours and the fourth task 1379.5 hours. A careful analysis of the situation revealed that the time it takes for each machine to complete its tasks has been reduced, from appropriate planning and quick and effective maintenance can enhance the capacity of the machines and thus reduce time and effort, which contributes to reducing the company's costs and thus maximizing the production capability to increase the company's profits

Software requirements prioritization and selection is an important activity of the requirements elicitation process. The objective of this process is to prioritize the software requirements so that...

Background: The Global disease (COVID-19 pandemic) has impacted our lives in various ways. Online teaching is the strange routine for studying platforms to continue with the process of educating students, Online classes have become a key component in the continuity of education. Aims: 1. This study aims to rank the usefulness of online teaching compared to classroom conventional teaching 2. Socioeconomic parameters related to online teaching in a medical college Study Design: The study is a cross-sectional study done on medical students. A questionnaire was prepared listing the various ways in which they discern online teaching trying to cope up during the pandemic. Materials and Methods: After the institutional Ethics committee clearance, online cross-sectional study was done on the medical students where from the 2 and 3 year. This online survey was done between 10May 2021- 10 June 2021, A total number of 211 students participated out of 250, after completion of 18 months of online classes. A Set of 15 self -designed questionnaire based on 5 point Likert scale was given to the students to rank the effectiveness through online class usefulness levels and satisfaction levels on various parameters. Students were also asked about the demographic factors. Statistical Analysis: The data was analyzed using software SPSS software Results: The study results shows that online classes is less helpful in five parameters and equally helpful in five parameters but was not at all superior to conventional methods, we can come to the interpretation that students think that classroom teaching cannot be replaced by online teaching. Conclusions: We can conclude that online studies can only be a supplement and cannot be an alternate for the established order of learning Keywords: Online teaching, Medical students, Questionnaire

The term "Real-Time Operating System (RTOS)" refers to systems wherein the time component is critical. For example, one or more of a computer's peripheral devices send a signal, and the computer must respond appropriately within a specified period of time. Examples include: the monitoring system in a hospital care unit, the autopilot in the aircraft, and the safety control system in the nuclear reactor. Scheduling is a method that ensures that jobs are performed at certain times. In the real-time systems, accuracy does not only rely on the outcomes of calculation, and also on the time it takes to provide the results. It must be completed within the specified time frame. The scheduling strategy is crucial in any real-time system, which is required to prevent overlapping execution in the system. The paper review classifies several previews works on many characteristics. Also, strategies utilized for scheduling in real time are examined and their features compared.

Distributed systems, which may be utilized to do computations, are being developed as a result of the fast growth of sharing resources. Data mining, which has a huge range of real applications, provides significant techniques for extracting meaningful and usable information from massive amounts of data. Traditional data mining methods, on the other hand, suppose that the data is gathered centrally, stored in memory, and is static. Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. This becomes increasingly costly to centralize them at a single location. Furthermore, traditional data mining methods typically have several issues and limitations, such as memory restrictions, limited processing ability, and insufficient hard drive space, among others. To overcome the following issues, distributed data mining's have emerged as a beneficial option in several applications According to several authors, this research provides a study of state-of-the-art distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection distributed data mining. Furthermore, each work is evaluated and compared to the others.

Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

In this study, a 9-elements hybrid MIMO (multiple input multiple output) antennas system is developed for 5G Smartphone communication.The designed MIMO system is composed of two different antenna parts i.e. the multi-band antenna element, and the eight-elements single-band antenna array. The multi-band antenna element encompasses the GSM 0.9/1.8/1.9 GHz (2G bands), UMTS 2.1 GHz (3G bands), LTE 2.3/2.5 GHz (4G bands), GPS, and WLAN 2.4/5 GHz, while the single-band antenna array covers the C-band (3400–3600 MHz) allocated for 5G cellular communcation. The model of the designed system has been manufactured and the simulation results are validated by conducting experiments with the fabricated prototype. High isolation levels have been observed between the elements. The designed antenna systems showed consistent radiation characteristics with a peak gain value of 5.5 dBi, and efficiency of as high as 90%. Furthermore, good MIMO performance parameters, such as the envelop correlation coefficient (ECC) less than 0.13, and 40 bps/Hz channel capacity have been observed over the desired bandwidth. Owing to the salient performance characteristics, the proposed antenna system could be used in modern mobile communications.

Speaker recognition is one of the field topics widely used in the field of speech technology, many research works has been conducted and little progress has been made in the past five to six years, and due to the advancement of deep learning techniques in most areas of machine learning, it has been replaced previous research methods in speaking recognition and verification. The topic of deep learning is now the most advanced solution to verifying and identifying a speaker's identity. The algorithms used are (x-vectors) and (i-vectors) which are considered the baseline in modern work. The aim of this study is to review deep learning methods applied in identifying speakers and tasks for validating older solutions (Gaussian mixture model, Gaussian mixture super vector model and i-vector model) to new solutions using deep neural networks (deep belief network, deep corrective learning network). ) As well as the types of metrics to verify the speaker (cosine distance, probabilistic linear discrimination analysis) as well as the databases used for neural network training (TIMIT, VCTK, VoxCeleb2, LibriSpeech).

Speaker identification techniques are one of those most advanced modern technologies and there are many different systems had been developed, from methods that used to extract characteristics and classification. The applications of Speech identification are quite difficult and requires modern technologies with a large number of audio samples and resources. In this research, the system of speaker identification had been designed based on a text (the word or sentences are pre-defined) which give the system the capability to identify the speaker in the least time, number of training samples and resources. The system consists four main parts, the first one is to create audio databases. In the study, two audio databases were relied upon, the first being a database (QS- Dataset) and the second database (audioMNIST_meta). The databases were processed and configured in a way that was explained in the body of the research later. The second part of the research is to extract the characteristics through the pitch coefficients algorithm, while the third part is the use of the neural network as a classifier. And the last part of the research is to verify the work and results of the system. The test results showed the ability of the MNN network to deal with the smallest number of data, as it achieved a percentage of 100%. As for large data, it ranged from 80% to 81%. Unlike CNN network, the results were not good for the few data, from 60% to 76%, and with large data it was The results are excellent, from 91% to 96%.

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

PurposeThis study proposes a self-regulatory framework to enhance safety performance at the construction stage among building developers.Design/methodology/approachExtant literature identified 137 potential factors that influence the construction safety performances of building developers. Focus group discussions and interviews were conducted with 11 panels of experts and professionals. The Relative Importance Index (RII) was used to analyse the response feedback described in a similar paper. In this study, the survey tool used was set up with 40 variables grouped into eight latent variables in the framework, which were agreed and certified as “extremely important” by the panel. Based on random sampling, data were collected from 229 valid respondents. Structural equation modelling (SEM) technique using Smart PLS software was then used to analyse the respondent's feedback.FindingsThe results show that safety administration and processes, effective communication of safety behaviour, significantly influenced safety performance on a construction site with β values of 0.330 and 0.431 along with t values of 3.005 and 2.547 at p Research limitations/implicationsThe study may not have covered all possible factors that influence the construction safety performance of building developers. Also, the generalizability and transferability of the research outcome to the construction industry wide use is also limited when reference is made to the characteristics of the research respondents and/or participants. In addition, validation of the framework by five professionals is rather small.Practical implicationsTheoretically, the framework through the identified factors provide a distinct approach to understanding and improving on-site construction safety through voluntary adherence to self-regulatory standard where there are no enforceable laws and regulations to promote safety. The study findings will potentially benefit building professionals and other stakeholders by improving awareness of the health and safety practices of the construction industry.Originality/valueMany research efforts have developed frameworks and models for construction safety. However, the particularity of these frameworks to countries other than Nigeria requires similar research to be conducted to enhance the safety performance of building developers.

Software requirements prioritization (SRP) is one of the key activities of the requirements elicitation process in which several decision makers are involved for the evaluation of the software requirements (SRs) based on various criteria. Different fuzzy based methods have been developed to compute the ranking values of the SRs. In these methods different types of membership functions are employed to model the linguistic variables. It is hard to decide the boundary of the fuzzy set accurately because membership functions are selected on the basis of subjective judgement. The fuzzy based methods may affect the ranking order of the SRs because it focuses on subjectivity and lacks objectivity. To address this issue, we present a method for the prioritization of SRs using rough set theory. The applicability of the proposed method is explained using the examination system of an educational institute. Based on our analysis we find that the proposed method captures the exact opinion of the decision makers during the SRP process and no prior information is needed to compute the ranking values of the SRs.

Blasting outcomes have significant impacts on downstream mining operations such as loading, hauling, crushing, milling, and mineral processing. An ideal blasting plan can result in decrease in undesirable blast-induced impacts and in supplementary costs of downstream operations. This study developed a new mathematical model using a risk-based probability distribution and mixed integer programming concept to minimize the total costs associated with blasting operation. Exponential and Poisson functions were used to consider the probability of undesirable blast-induced impacts in the model. Costs for balancing undesirable blast-induced consequences were considered by relative values based on probabilistic conditions of the consequences. The base code of firefly algorithm was developed to solve the mathematical model and find a new blasting plan based on the data of several blasting plans executed in a limestone mine. Comparison between the results of the mathematical modeling and the literature indicates the efficiency of the former by a reduction of 23% in total costs. The mathematical model and the risk-based probabilistic part of the methodology can be updated and applied easily to extract the best possible blasting plan for the same geomechanical conditions.

A hybrid multiple pulse amplitude and position modulation (H‐MPAPM) scheme based on gradual multi‐pulse pulse‐position modulation (G‐MPPM) and multi‐level pulse amplitude modulation (M‐PAM) schemes is proposed for deep‐space optical communication. In this scheme, transmitted information is conveyed by different combinations of the positions and amplitudes of multiple optical pulses. The performance of the proposed scheme with multi‐pulse pulse‐position modulation (MPPM), G‐MPPM and multiple pulse amplitude and position modulation (M‐PAPM) schemes in terms of the number of transmitted symbols, data rate, symbol error rate (SER), bit error rate (BER), bandwidth utilization efficiency and power requirements are studied in this work. Our results reveal that, at the same frame size, the proposed scheme allows much more symbols per frame transmission, achieves higher transmission energy and further enhance the bandwidth utilization efficiency of MPPM, G‐MPPM and M‐PAPM schemes. Moreover, compared to the MPPM and G‐MPPM, the proposed scheme achieves lower levels of SERs but at the expense of peak power levels. Furthermore, the proposed scheme outperforms G‐MPPM and MPPM schemes by approximately 1 and 2 dB at a BER of 10−6, respectively. In addition, the analytical expressions for the BER and SER of the proposed scheme are validated using Monte Carlo (MC) simulations.

Recent advances in artificial intelligence demand an automated framework for the development of versatile brain–computer interface (BCI) systems. In this article, we proposed a novel automated framework that reveals the importance of multidomain features with feature selection to increase the performance of a learning algorithm for motor imagery electroencephalogram task classification on the utility of signal decomposition methods. A framework is explored by investigating several combinations of signal decomposition methods with feature selection techniques. Thus, this article also provides a comprehensive comparison among the aforementioned modalities and validates them with several performance measures, robust ranking, and statistical analysis (Wilcoxon and Friedman) on public benchmark databases. Among all the combinations, the variational mode decomposition, multidomain features obtained with linear regression, and the cascade-forward neural network provide better classification accuracy results for both subject-dependent and independent BCI systems in comparison with other state-of-the-art methods. Impact Statement —The brain–computer interface (BCI) is a revolutionary device that utilizes cognitive function explicitly for the interaction of external devices without any motor intervention. BCI systems based on motor imagery have shown efficacy for stroke patient treatment, but poor performance, nonflexible characteristics, and lengthy training sessions have limited their use in clinical practice. The proposed automated framework overcomes these limitations. With the significant improvement of up to 26.1% and 26.4% in comparison with the available literature, the proposed automated framework could offer help to BCI device developers to develop flexible BCI devices and provide interaction for motor-disabled users.

The task of network security is to keep services available at all times by dealing with hacker attacks. One of the mechanisms obtainable is the Intrusion Detection System (IDS) which is used to sense and classify any abnormal actions. Therefore, the IDS system should always be up-to-date with the latest hacker attack signatures to keep services confidential, safe, and available. IDS speed is a very important issue in addition to learning new attacks. A modified selection strategy based on features was proposed in this paper one of the important swarm intelligent algorithms is the Meerkat Clan Algorithm (MCA). Meerkat Clan Algorithm has good diversity solutions through its neighboring generation conduct and it was used to solve several problems. The proposed strategy benefitted from mutual information to increase the performance and decrease the consumed time. Two datasets (NSL-KDD & UNSW-NB15) for Network Intrusion Detection Systems (NIDS) have been used to verify the performance of the proposed algorithm. The experimental findings indicate that, compared to other approaches, the proposed algorithm produces good results in a minimum of time.

The process of deploying the two dimensional data in to the three dimensional format is three dimensional imaging. Now a days three dimensional imaging is developing as an emerging factor in the field of dentistry. Three dimensional imaging is useful in the field of orthodontics, to view the anatomy along with the morphology of the bone for desired tooth movement, it is useful in the field of prosthodontics to check or verify the bone morphology, exact height and width of the bone for the process placing implant, three dimensional imaging is quite useful in the field of endodontics along with oral surgery to view the anatomy of the root curvature, to confirm the presence of accessory canals to confirm the bony architectural defect in oral surgical procedures. So in present days three dimensional imaging plays an important role in diagnosis as well as treatment planning in the field of dentistry. Keywords: Cone beam computed tomography (CBCT).

In this paper a triple-band dielectric resonator antenna (DRA) is designed using a 0.8 mm thicker Arlan FR-25 substrate, having relative dielectric constant of 3.58 and loss tangent of 0.0035. The antenna works in three unique frequency bands of 15, 16.3 and 18.5 GHz, lying in the sub-20 GHz spectrum. A stack of ten radiating elements of AR100 material, are installed at the centre of the proposed antenna structure. The DRA is excited through an elliptical aperture in the metallic plane via a 50 Ohm microstrip feed line, etched on the rear side of the Arlan FR-25 substrate. Stacked parasitic elements are incorporated around the central radiating element for achieving enhanced gain. The proposed DRA gives satisfactory gain and fractional bandwidth of (8.06 dB, 12%), (10.7 dB, 3%) and (7.03 dB, 2.73%) at 15, 16.3 and 18.3 GHz bands, respectively. The simulation results of the DRA are compared and validated using three different analysis techniques i.e., FIT (Finite Integration Technique), FDTD (Finite Difference Time Domain) Method and FEM (Finite Element Method). The designed antenna can be used for future fifth generation (5G) systems (15 GHz), internet of things (IoT) based device-to-device (D2D) communication and satellite applications (16.3–18.3 GHz).

In this research, the design and simulation of 2000VA inverter is presented. The method implemented for the design was the d.c. – a.c. inverter topologies. The d.c. – a.c. inverter circuit made use of a 555 timer, to digitally pulse the transistors on the inverter side, and the oscillating circuit, to drive the inverter. The system consists of the main inverter stage and the charging unit. These units are further subdivided into different stages. The main inverter performs the basic operation of converting the input d.c. voltage from the battery into an a.c voltage. The charging unit contains an automatic switch that charges the battery when it senses no supply from the mains. Each unit of the project was simulated individually at every different stage of the design by using a simulating software (every circuit). The waveform given at the final stage of the simulation shows that it is a modified sine wave inverter, and it successfully converts a 12V d.c. into a 220V a.c. with a load capacity of 2000VA. The inverter should be able to power the electronic device connected for a reasonable amount of hours

Contractive auto-encoder (CAE) is a type of auto-encoders and a deep learning algorithm that is based on multilayer training approach. It is considered as one of the most powerful, efficient and robust classification techniques, more specifically feature reduction. The problem independence, easy implementation and intelligence of solving sophisticated problems make it distinct from other deep learning approaches. However, CAE fails in data dimensionality reduction that cause difficulty to capture the useful information within the features space. In order to resolve the issues of CAE, restricted Boltzmann machine (RBM) layers have been integrated with CAE to enhance the dimensionality reduction and a randomized factor for hidden layer parameters. The proposed model has been evaluated on four benchmark variant datasets of MNIST. The results have been compared with four well-known multiclass class classification approaches including standard CAE, RBM, AlexNet and artificial neural network. A considerable amount of improvement has been observed in the performance of proposed model as compared to other classification techniques. The proposed CAE–RBM showed an improvement of 2–4% on MNIST(basic), 9–12% for MNIST(rot), 7–12% for MNIST(bg-rand) and 7–10% for MNIST(bg-img) dataset in term of final accuracy.

Background: We need drugs to decrease inflammation as well as have very less side effects. Review of literature mentioned anti-inflammatory action of Omega 3 fatty acids. Hence, we selected this drug. Aim: Our aim was to find the anti-inflammatory effect of Omega 3 fatty acid using animal model. Study Design: The study is objective animal study where measurement of inflammation was done by observational methodology, for the present study four groups were made- 6 Guinea pigs in each group. Control guinea pigs were fed distilled water, second group standard drug indomethacin 4mg/ kg body wt, test group 1 received 1200mg/kg body wt, test group II received 2400mg/kg body wt of Omega 3 fatty acid. All drugs given orally 30 minutes before exposure to UV radiation of 30 seconds duration was given and inflammation was observed at 6, 12 and 24 hrs after UV exposure in the form of scoring. Statistical significance was calculated using Anova method. For statistical analysis we have used SPSS system version 20. Results: Inflammation was observed in all the four groups of Guinea pigs, there was significant antiinflammatory effect of omega 3 fatty acid in the dose of 1200mg/kg body wt and 2400mg/kg body wt at 12 and 24 hours. Conclusions: Omega 3 fatty acids 1200mg/kg body wt orally produced significant anti-inflammatory effect against UV radiation induced inflammation in Guinea pigs at 12 and 24 hrs. However, standard drug could produce significant anti-inflammatory effect only at 24 hrs in comparison to control group. The anti-inflammatory effects of test 1, test 2 groups and standard group are comparable. Keywords: Inflammation, Omega 3 fatty acid, UV radiation.

Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimization algorithms is crucial for this task. In this paper, a recently invented grasshopper optimization algorithm is upgraded from its original version. The method is improved by adding an elite opposition-based learning methodology to an elite opposition-based learning grasshopper optimization algorithm. The new optimizer, which is elite opposition-based learning grasshopper optimization method (EOBL-GOA), is validated with several engineering design probles such as a welded beam design problem, car side crash problem, multiple clutch disc problem, hydrostatic thrust bearing problem, three-bar truss, and cantilever beam problem, and finally used for the optimization of a suspension arm of the vehicles. The optimum results reveal that the EOBL-GOA is among the best algorithms reported in the literature.

The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such hazards is crucial in the early phases of the distribution. In this paper, a detailed review of modern approaches for the identification of DC arc faults in PV is presented. In addition, a thorough comparison is performed between various DC arc-fault models, characteristics, and approaches used for the identification of the faults.

Photovoltaic energy is considered highly favorable due to the environment's pleasant nature. After analyzing different maximum power point tracking (MPPT) algorithms, an effective control scheme is proposed to obtain stabilized maximum output power throughout the PV system. Therefore, this article presents an efficient control algorithm for the extraction of maximum power through a PV system under severe climatic drifts. The modified fuzzy logic controller sustains the maximum output power of the system by defining fuzzy rules to control the duty cycle appropriately. A DC-DC boost converter is also modeled to stabilize and maintain output power under variant climatic uncertainties. Furthermore, charging management control is also implemented on lead-acid battery bank to store PV energy for backup usage. It defines charging-discharging time and state of charge for keeping the battery bank healthier.

In this work, the optimization of structural and mechanical problems is carried out using the equilibrium optimizer (EO), which is a recent physical-based algorithm.The the ten-bar planar truss structure, planetary gearbox, hydrostatic thrust bearing, and robot gripper mechanism problems are solved using the EO algorithm. The results achieved using the EO in solving these problems are compared with those of recent algorithms. The computational results show that EO yields better outcomes and competitive results that can also be applied for the other problems studied.

In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.

In this letter, we introduce accurate approximation expressions for composite fading channel model and outage probability of multiple-input multiple-output (MIMO) free space optical (FSO) link between two unmanned aerial vehicles (UAVs) in hovering state. In our derivations, we consider the effects of weather attenuation, random fluctuations in UAVs' positions and orientations, and atmospheric turbulence. Our proposed model is unified with respect to turbulence effect, which means it is valid for different atmospheric turbulence models. We show the accuracy of the proposed model through comparisons with Monte Carlo simulations under different channel conditions.

PT. Guna Kemas Indah in determining the supplier of raw materials, but the existing criteria are not sufficient to meet the expected criteria. By using Analytical networking process (ANP) and goal programming in determining quality. In addition, it can also be seen how much raw material purchases are based on the selected supplier. Selection of suppliers is based on several criteria using vendor performance indicators, which are arranged based on sub-criteria. Where the existing subcriteria can influence each criterion. Based on the weight calculation for the criteria are success-quality (0.3868), cost (0.3668), delivery (0.1256), flexibility (0.0619), responsiveness (0.0586) so that 3 supplier candidates will be obtained.

Survival systems are difficult to analyze in the presence of extreme observations and multicollinearity. Finding appropriate models that provide a robust description of such survival systems and that address the smooth hazards in the context of covariates can be challenging given the sheer number of possibilities. Survival time algorithms that evaluate the efficiency of models in the presence of extreme observations over different datasets provide an effective tool to identify robust systems. However, the existing algorithms addressing the analysis of survival systems are limited in long-term evaluations. Therefore, an algorithm that can analyze survival time response on high-dimensional complex survival systems having extreme observations is developed which explores large margins dynamically. This algorithm is developed as a conjugate of flexible parametric models and partial least squares to estimate smooth, flexible, and robust functions to extrapolate the survival model in long-term evaluations in the presence of extreme observations. The algorithm is tested and validated using four distributions based on a simulated dataset generated from the Weibull distribution and compared with partial least squares-Cox regression. The comparison shows its flexibility and efficiency in handling different survival systems in the presence of extreme values. The algorithm is also used to analyze four real datasets of breast cancer survival time, each containing seven gene signatures. The coefficients of significant genes for each dataset are estimated. The flexibility in handling various distributions as parametric survival models supports the application of the algorithm to a large variety of different survival problems and represents a robust statistical framework for survival analysis in the presence of extreme observations.

This paper presents a rapid, effective, and linear incremental conductance (IC) algorithm for chasing the maximum power point (MPP) of the grid-tied photovoltaic (PV) array. A technique has been pr...