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A large component of the Health Information Systems now comprises numerous independent apps created in the past that need to be merged to provide a more uniform service. In addition to affecting the Intelligent Health Board Functionality and dependability, the quality of these additional apps may also have an impact. A critical characteristic of the SHS's management and upkeep is the SHS's reliance on the real benefits provided to it. In speaking, an HMIS (Healthcare Management Information System) is a computer-based device that benefits medical practitioners to perform their duties more efficiently by coordinating all of their data. Even though these systems are widely used by most of the world, there is a significant need to comprehend these technologies and indeed the potential they provide. Healthcare data warehouses in Saudi Arabia have evolved through time, and this research examines how key service improvements in Saudi present varied viewpoints on how premium initiative help may be attained in health as well as how this could be done. When it comes to understanding how different types of medical professionals interact with healthcare systems throughout history, researchers developed stages of the maturity model. [ABSTRACT FROM AUTHOR]

Bioactive peptides are naturally found in various foods and were shown to have various distinct physiological as well as medicinal benefits. In this study shrimp peptide hydrolysate (SPH) was prepared to investigate its immunomodulatory effect against cyclophosphamide (CTX) induced immunosuppressed mice. The SPH effect was also analyzed on murine macrophage (RAW264.7 cells). The findings show that SPH stimulates macrophages to form multiple pseudopodia, has no cytotoxic effect, and increases phagocytic activity in RAW264.7 cells. Furthermore, the immunosuppressed in‐vivo model illustrates the improvement in various aspects, that is body weight, escalation in immune organ index, and ameliorates histopathological transformation of thymus along with the spleen. SPH enhances cell‐mediated immunity by facilitating splenocyte proliferation and inhibit excessive apoptosis. Moreover, the significant outcome had been observed with the upregulation of cytokines interferon‐gamma (IFN‐ϒ), interleukin‐2 (IL‐2) level and simultaneously downregulate certain genes include interleukin‐4 (IL‐4) and interleukin‐10 (IL‐10). Additionally, SPH expedites cellular immunity by enhancing the regulation of immunoglobulin A (IgA) and immunoglobulin M (IgM). However, these findings support the hypothesis that SPH is an effective immunomodulatory agent capable of preventing immune system hypofunction. It is necessary to investigate the detailed mechanism to rule out any unforeseen effects of SPH in future research. Practical applications: Chemotherapy medications, despite their dominating detrimental effects of damaging immunological organs such as the spleen and thymus, extend the treatment process as well as the destruction of the self‐immune system. This study found that SPH is an effective immunomodulatory agent capable of avoiding immune organ hypofunction and improving cell mediate immunity by enhancing macrophage activation, phagocytosis, spleenocyte proliferation, suppressing apoptosis, and elevating cytokines and antibodies. As a result, SPH can be utilized as a nutritional and functional dietary supplement to boost immunological modulation in combination with chemotherapy medications in order to lessen their adverse effects. [ABSTRACT FROM AUTHOR]

The estimated 30 million children and adults are suffering with diabetes across the world. A person with diabetes can recognize several symptoms, and it can also be tested using retina image as diabetes also affects the human eye. The doctor is usually able to detect retinal changes quickly and can help prevent vision loss. Therefore, regular eye examinations are very important. Diabetes is a chronic disease that affects various parts of the human body including the retina. It can also be considered as major cause for blindness in developed countries. This paper deals with classification of retinal image into diabetes or not with the help of deep learning algorithms and architecture. Hence, deep learning is beneficial for classification of medical images specifically such a complex image of human retina. A large number of image data are considered throughout the project on which classification is performed by using binary classifier. On applying certain deep learning algorithms, model results into the training accuracy of 96.68% and validation accuracy of 66.82%. Diabetic retinopathy can be considered as an effective and efficient method for diabetes detection. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to introduce a new analogue of Phillips-type Bernstein operators B m , q u f u , v and B n , q v f u , v , their products P m n , q f u , v and Q n m , q f u , v , their Boolean sums S m n , q f u , v and T n m , q f u , v on triangle T h , which interpolate a given function on the edges, respectively, at the vertices of triangle using quantum analogue. Based on Peano's theorem and using modulus of continuity, the remainders of the approximation formula of corresponding operators are evaluated. Graphical representations are added to demonstrate consistency to theoretical findings. It has been shown that parameter q provides flexibility for approximation and reduces to its classical case for q = 1. [ABSTRACT FROM AUTHOR]

Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters' network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters' network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This is done using social network analysis and semantic analysis. The proposed method (1) detects and removes fake supporter(s), (2) mines communities in the politicians' social network(s), (3) investigates the supporters' reply network for conversations between supporters about each leader, and, finally, (4) analyses the retweet network for information diffusion of each political leader. Furthermore, sentiment analysis of the supporters of politicians is done using machine learning techniques, which ultimately predicted and revealed the strongest supporter network(s) among the three political leaders. Analysis of this data reveals that as of October 2017 (1) IK was the most renowned of the three politicians and had the strongest supporter's community while using Twitter in a very controlled manner, (2) BBZ had the weakest supporters' network on Twitter, and (3) the supporters of the political leaders in Pakistan are flexible on Twitter, communicating with each other, and that any group of supporters has a low level of isolation. [ABSTRACT FROM AUTHOR]

The main intent of this paper is to innovate a new construction of modified Lupaş-Jain operators with weights of some Beta basis functions whose construction depends on σ such that σ 0 = 0 and inf x ∈ 0 , ∞ σ ′ x ≥ 1. Primarily, for the sequence of operators, the convergence is discussed for functions belong to weighted spaces. Further, to prove pointwise convergence Voronovskaya type theorem is taken into consideration. Finally, quantitative estimates for the local approximation are discussed. [ABSTRACT FROM AUTHOR]

The most common use of robots is to effectively decrease the human's effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Summary: Successful integration of wind energy in an electrical grid system requires prior information of the generated wind power from a turbine or a wind farm. That is a challenging task given the unpredictable, intermittent, and nonlinear nature of wind speed. We present a novel method for short‐term wind speed prediction by using a hybrid approach based on variational mode decomposition (VMD) in conjunction with both linear and nonlinear prediction models. The VMD is a fully data‐adaptive signal decomposition method that offers significant improvement over existing data‐adaptive techniques, such as empirical mode decomposition, in terms of its sound mathematical framework and accuracy of its decomposition. In our work, VMD is used to decompose wind speed data into multiple intrinsic narrow band components to facilitate their forecasting/prediction. We report results from extensive experiments performed on a large wind speed data set obtained from multiple sites in Pakistan to demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

In the year 2003, McD Mercer established an interesting variation of Jensen’s inequality and later in 2009 Mercer’s result was generalized to higher dimensions by M. Niezgoda. Recently, Asif et al. has stated an integral version of Niezgoda’s result for convex functions. We further generalize Niezgoda’s integral result for functions with nondecreasing increments and give some refinements with applications. In the way, we generalize an important result, Jensen-Boas inequality, using functions with nondecreasing increments. These results would constitute a valuable addition to Jensen-type inequalities in the literature. [ABSTRACT FROM AUTHOR]

ABSTRACT Organic-inorganic nanocomposites poly- o-anisidine and poly- o-toluidine Sn(IV) tungstate were chemically synthesized by mixing organic polymers that is, o-anisidine and o-toluidine with inorganic gels of Sn(IV) tungstate in different mixing ratios. Ion-exchange kinetics for few divalent metal ions was evaluated by particle diffusion-controlled ion-exchange phenomenon at four different temperatures. The particle diffusion mechanism was confirmed by linear τ (dimensionless time parameter) versus t (time) plots. The exchange processes were controlled by the diffusion of ions within the exchanger particle for the systems under studies. Some physical parameters such as self-diffusion coefficient ( [ABSTRACT FROM AUTHOR]

We study statistical approximation properties of q-Bernstein-Shurer operators and establish some direct theorems. Furthermore, we compute error estimation and show graphically the convergence for a function f by operators and give its algorithm. [ABSTRACT FROM AUTHOR]

Poly-o-anisidine Sn(IV) arsenophosphate is a newly synthesized nanocomposite material and has been characterized on the basis of its chemical composition, ion exchange capacity, TGA-DTA, FTIR, X-RAY, SEM, and TEM studies. On the basis of distribution studies, the exchanger was found to be highly selective for lead that is an environmental pollutant. For the detection of lead in water a heterogeneous precipitate based ion-selective membrane electrode was developed by means of this composite cation exchanger as electroactive material. The membrane electrode is mechanically stable, with a quick response time, and can be operated over a wide pH range. The selectivity coefficients were determined by mixed solution method and revealed that the electrode is sensitive for Pb(II) in presence of interfering cations. The practical utility of this membrane electrode has been established by employing it as an indicator electrode in the potentiometric titration of Pb(II). [ABSTRACT FROM AUTHOR]

In the 21st century, transportation brought great convenience to people, but at the same time, automobile transportation is the major factor causing greenhouse gas emissions and climate change. Movements of the world towards green environments, there is hike in use and production of electric vehicles (energy vehicles). However, with the continuous growth in the number of energy vehicles, it is necessary for the government to provide strong support in the construction of charging piles. Real-time and effective management has become a practical problem for the relevant departments which needs to be solved. This paper uses the information research method to fuse the huge amount of heterogeneous data generated by the charging pile resultant to the new energy electric vehicle in the vehicle network and introduces cloud computing as its storage module to facilitate the storage and related expansion of the big data. This paper proposes a system scheme of heterogeneous data fusion based on cloud computing for the acquisition, storage, and fusion of heterogeneous data in the vehicle network. After testing the results, it shows that the system is stable and effective in practical application, which can meet the design requirements of the system. What is the significance of analyzing big data of charging point? Considering from the supply side, obtaining the user's charging behaviour data is helpful to build a digital map of the charging pile of new energy vehicles, connect the service information between the vehicle enterprises and the charging pile enterprises, and provide the most comprehensive and effective real-time charging information covering the widest range of vehicles, which can solve many problems of information asymmetry in the current charging information service. [ABSTRACT FROM AUTHOR]

With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction. This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state-of-the-art work that proposed this method. Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner practitioners seeking knowledge on this subject. Moreover, the limitations of the proposed approaches and future challenges were highlighted, and possible solutions were proposed. [ABSTRACT FROM AUTHOR]

Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation. [ABSTRACT FROM AUTHOR]

Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%. [ABSTRACT FROM AUTHOR]

The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, analyze, and predict landslide distant areas, etc. A wireless sensor network comprises autonomous sensors geographically dispersed for monitoring physical or environmental variables, comprising temperature, sound, pressure, etc. This remote management service contains a monitoring system with more information and helps the user grasp the problem and work hard when WSN is a catastrophic event tracking prospect. This paper illustrates the effectiveness of Wireless Sensor Networks (WSN) and artificial intelligence (AI) algorithms (i.e., Logistic Regression) for landslide monitoring in real-time. The WSN system monitors landslide causative factors such as precipitation, Earth moisture, pore-water-pressure (PWP), and motion in real-time. The problems associated with land life surveillance and the context generated by data are given to address these issues. The Wireless Sensors Network (WSN) and Artificial Intelligence (AI) give the option of monitoring fast landslides in real-time conditions. A proposed system in this paper shows real-time monitoring of landslides to preternaturally inform people through an alerting system to risky situations. [ABSTRACT FROM AUTHOR]

Waste management is a critical problem for every country, whether it is developed or developing. Selecting and managing waste are a critical part of preserving the environment and maximizing resource efficiency. In addition to reducing trash and disposal, reusable items are predicted to be of great benefit since they lessen our dependence on raw materials. The usage of compostable trash may be expanded outside fertilizers and dung after the metallic, chemicals, and glass items have been recycled. After a good scrubbing, the glass may be broken down and remelted to create new items. Reusing waste items via garbage recovery is one of the best methods to do so. This document outlines the steps that must be taken to maximize the use of garbage. This work describes a reusable industrial robot arm for grasping and sorting things depending on the resources they contain. Gripping, motion control, and object material categorization are all integrated into a full-automation, reusable system architecture in this study. LeNet also was adjusted to classify garbage into cartons and plastics using an artificial intelligent technique, with the use of a customized LeNet model. Movement in terms of moving the robot in the most efficient way possible, the robot's grabbing, and categorization were incorporated into the movement design process. The system's grabbing and object categorization success rates and computation time are calculated as metrics for evaluation. [ABSTRACT FROM AUTHOR]

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods. [ABSTRACT FROM AUTHOR]

The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess proficiency of radiologists and radiology residents in managing adult life support in cardiopulmonary arrest and acute anaphylactic reaction. [ABSTRACT FROM AUTHOR]