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It was found that 46.6% of S. aureus were collected from nose swaps followed by ear (41.6%) and catheter tubes (11.8%). Results showed the highest resistance 100% for both classes of Aminopenicillins (Amoxicillin), cephalosporins 3Gr (Cefotaxime, Ceftriaxone, and Cefexime, and Carboxypencillin, as well as one group as 93.5% for class the β-lactam combination agents (Amoxicillin/ clavulanic acid). Many virulence factors and antibiotics resistance genes also being considered in this study, including erm, luk, hlg, fbl, Sea, MecA, seb, vat and tet. The highest frequency was in MecA genes 53 (88%) followed by sea genes 20 (33%), hlg and seb genes 18 (30%) for each one, erm genes 14 (23.3%) and luk genes 2 (3.3%), genes fbI, vat and tet were not detected in any isolate. Results shows no correlation concerning age, gender or the site of infection with either antimicrobial resistance or virulence factors genes.

As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region – convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset.

The adaptability of the convolutional neural network (CNN) technique is probed for aerodynamic meta- modeling task. The primary objective is to develop a suitable architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN structures were trained to learn the lift coefficients of the airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds numbers, and diverse angles of attack. This was conducted to illustrate the concept of the methodology. Multi-layered perceptron (MLP) solutions were also obtained and compared with the CNN results. The newly proposed meta-modeling concept has been found to be comparable with the MLP in learning capability; and more importantly, our CNN model exhibits a competitive prediction accuracy with minimal constraints in geometric representation.

Microarray technology is a novel method to monitor the levels of expression of a huge number of genes simultaneously.this study aims at (1) identifying the most important genes in the molecular senescence of the hippocampus and retina, where both with accelerated neurological senescence (S10 and 8) models were obtainable. By using feature selection to reduce the size of high dimensional data. Hence, the process of gene selection is twofold; removing the irrelevant genes and selecting the informative genes, and (2) The determination of the study is to specify the association among these genes or pathways that would deliver insight into the mechanism for this phenotype which will be greater to the current imperfect state-of-the-art estimates. In this study, gene selection methods have been implemented, including Analysis of Variance (ANOVA). The results are showed that CNN model achieve 0.98 accuracy based on a subset of genes from ANOVA method. Thus, Genes subset selected is achieved a better accuracy at classification and a little time of processing.

Within few weeks, when the first reported COVID-19 case in the city of Wuhan, China, the pandemic has spread quickly across nations and tourism has been regarded as the main case in the spread of corona virus worldwide. With no vaccine and limited medical facilities, it plunged the economy into various economic crisis. Both the purchase and production has come to an end due to factory closures; travel bans and border closures. The most severe disruption of the global economy is the national quarantines and unprecedent global restrictions on traveling, bringing international travel to a standstill. As a consequence, tourism operations of many countries have been ceased in March, 2020. According to UNWTO research, 100% UNWTO countries have imposed restrictions on travelling in response to the pandemic and international tourism has completely been closed and the borders were sealed by 72% nations. Due to the cancellation of business trips, holidays, and weekend gateways, tourism industry is heavily impacted by the COVID-19 crisis. So, in this paper we have discussed the impact evaluation of COVID-19 pandemic on tourism industry and its branches. We have reviewed research papers published in 2020, OECD and UNWTO reports.

In this article, a comprehensive overview of the classic designing of mechatronics-based robotics components like sensors and effectors in a network to solve the problems related to the medical skin of the human body with the help of an Artificial Intelligence has been carried out. Area-based artificial agent is the easiest way to deal with the skin-related diseases. In this artificial agent, we are having autonomy and flexibility to work on the area.

Developing ICT as English Language Teaching (ELT) materials in Indonesia is still being studied in higher education contexts. Nowadays, amid covid 19 there are many tools, apps and technologies are being integration in virtual classroom. ICT can be used as media for teaching and learning process it should be related today’s fact by integrating ICT easy enough to share it and easy to access by the students’ technologies devices (Muthmainnah, et.all, 2021 and Apriani et.al, 2021 The purposes of this study is to investigate the impact of development instructional design incorporating social media- movie based learning project (SMMBLP) in Computer Science Faculty in learning English to engagement student’s online environment. The method of this study is quantitative research and used pre-experimental design to investigates the impact of SMMBLP on students’ English skill and student’s motivation in ELT. The participants in this study were the first semester students of Computer Science Faculty academic year 2020-2021 at Universitas Al Asyariah Mandar consisted 40 students. The data were collected by test; pre-test, post-test and questionnaire. The result of the study implied the impact on integration social media- movie based learning project (SMMBLP) that very effective to enhancing students’ English skill and motivation. The study suggests SMMBL can be integration in online or Hybrid/ Blended environment for the next education teaching model in ELT for EFL students.

Agricultural production is extremely important to the global economy. Agribusiness not only provides food and raw materials, but also provides employment opportunities to a large portion of the population.Increased agricultural production and per-capita income in rural areas, combined with industrialization and urbanization, resulting in increased demand for industrial goods.According to a analysis conducted by the Food and Agriculture Organization, the world's population is projected to increase by another two billion people by 2050, while cropland is only expected to increase by 5%. As a result, to increase agricultural productivity, smart and capable farming approaches are needed. Agriculture land suitability appraisement is a required tools for agriculture advancement. The fast growth of wireless networks has resulted in the development of low-cost Internet of Things (IoT) devices that are favored as a useful methodology for agricultural autonomy and decision making. The proposed model, called the Artificial Intelligence Agriculture Recommendation Model (AIARM), incorporates sensory networks and artificial intelligence programs like neural networks and multi-layer perceptron to determine crop readiness, crop prediction, and fertilizer recommendations. Instead of a binary split, the proposed system divides agricultural land into four categories of decisions, which are fair, appropriate, fairly equitable, and not appropriate to guide farmers accurately.

Chemical energy is not effectively converted into mechanical energy by an internal combustion engine. Major portion of heat energy get dissipated with exhaust and coolant. Use of thermoelectric generator (TEG) technology is regarded as an environment friendly technique for recovering the waste heat, with which heat energy directly converts into electrical energy. In this study, an experiment is conducted in which TEG technology has been used to recover waste heat going out through exhaust gas from the diesel engine. Thermoelectric modules were fixed on the surface of stainless steel made pipe having square cross-section. It has been found that the power output of thermoelectric generator enhances with the increase in engine load. The maximum electrical power output of TEG has been obtained as 37 W at maximum load of 6 kg. Overall thermal efficiency of diesel engine has also been improved with the use of TEG type of waste heat recovery system.

Extensive applications are found in aluminum matrix composites, where high strength to weight ratio, low weight and high corrosion resistance are needed. One of the categories of such materials is the SiC reinforced aluminum matrix composite. This reinforcing material belongs to ceramic group of materials and can withstand high temperature and improve several properties when incorporating in the grain structure of the base matrix. The introduction of SiC enhances wear resistance, hardness value and tensile strength. However, the use of high percentage of SiC material reduces the machinability (ductility) and toughness resistance and the machining of such material becomes difficult. In this current study, an attempt is made to develop an aluminum (LM25 alloy) matrix composite, which is reinforced with 7%SiC and 3%Gr particulates through stir casting route. The fabrication of composite is followed by evaluating the machining behavior of hybrid composite through electric discharge machining. During machining, current, voltage, pulse on time and pulse off time are selected as input controllable variables while material removal rate (MRR), tool wear rate (TWR) and over cut are chosen as responses. The experiments are carried out as per Taguchi's L9 OA, with the aid of ANOVA and significant parameters effecting responses are identified. Main effects plots are drawn and optimum set of input machining conditions are determined.

In mobile ad hoc networks (MANETs), cell hubs can not only be a piece of the system but also leave or change their position inside it, so the network’s topology can change unexpectedly and in unusual ways. A MANET is a self-arranging, versatile framework. Ad hoc on-demand distance vector routing (AODV) does not help quality of service (QoS) and has no heap adjusting component. The QoS steering highlight is simple in a constant, independent multi-jump cell network for real-time applications and, furthermore, in a cell network to interconnect systems with QoS help. In this research, we improve the QoS directing of AODV convention by utilizing the area-based abilities of the Distance Effect Routing Algorithm for Mobility (DREAM). In this DREAM convention, the exact records of every hub are kept in the network, considering different hubs which may be taking part in the steering system. The portable basis of the network is anticipated with the guidance of the DREAM convention, so the flooding of foundation bundles is best brought to that zone by ensuring that the overhead for discovering excursion spots is limited. The DREAM convention improves the QoS of the AODV steering convention. In this investigation, we're thinking about the excellent situation of a cell-hubs approach, at least as far as an assortment of hubs in the network. In addition, the demonstration of the proposed QoS steering is better with the limited overhead.

People in this day and age like to keep up with the most recent news on social media since it is economical, simple, and can be obtained in a short amount of time. However, social media can be a double-edged sword, since it may propagate fake news or information that cannot be relied upon. The dissemination of false information could adversely affect both individuals and society as a whole. Therefore, researchers gave their all to devise a system that could identify fake news before its publication. This article gives an overview of the most major attempts that have been made to construct a system that can filter online news and detect false news with a decent amount of accuracy. Data mining approaches were reviewed in the detection phase, including feature extraction and model construction.

Graphical abstract, Introduction The Internet of Things (IoT) comprises of various smart devices for the sharing of sensed data through online services. People will be directly contacted to check their health parameters and the reports will be collected centrally through smart devices. The requirement is protection of messages during the exchange of data between sender and receiver in order to tackle human malicious attacks. Various signature-based schemes are discussed in the literature to provide secure communication. Smart devices however require lightweight tasks by ensuring critical safety strengths. An important problem in the signature based method is that it incurs more computational expenses for signing and verification process in large numbers. Objectives In this study, we introduced an efficient Short Signature Scheme (SSS) that uses Fractional Chaotic Map (FCM) for secure communication in IoT based smart devices, the security of which is closely related to a random oracle based on FCM assumption. Methods In this study, we have designed new short signature scheme using FCM. The presented scheme consist of four sub-algorithm as follows: setup, key generation, signing and verification. We have used less rigorous operations based on the FCM to carry out signing and verification procedures, similar to human signing on valid documents and then verifying them as per witness. Results The proposed SSS offers a better security assurance than currently established signature schemes. The key advantage of the SSS over the DSA schemes is that at the verification stage and signing period it takes less computation; it retains the degree of protection. The presented SSS takes less bandwidth for storage, communication, and computing resources; particularly applicable to wireless devices and smart cards. Conclusion We concluded that the uses of fractional chaotic maps is more effective for secure communication in human-centered IoT to present a provably secure short signature technique.

This manuscript introduces the designing and emulation of a wireless, intelligent health surveillance device based on the internet of medical things, with GSM alert technology by health caregivers. In hospitals or medical centers where the body temperature of the patient needs to be constantly checked or other vital signs have to be checked, this is normally achieved by a specialist or another paramedicine personnel by continuously checking the temperature, pulse rate and keeping records of these arguments. This approach is really repetitive and can be very tiring, especially in medical centers crowded with patients. The main system task is to surveillance the patient's body pulse rate and the patient's body temperature and sending the information to the specialists wirelessly via the liquid crystal display. The transmitting unit of this suggested system continuously surveillance the patient's body temperature, as well as the heart rate with digital sensors, and ultimately, displays the outcomes on the liquid crystal display transmitter to present to the specialists. The receiver device is placed in the specialist chamber for continuous wireless surveillance of the patient's body temperature and heart rate. At the receiver end where the specialist will be, an alarm is often triggered and triggered When the patient's heart rate or temperature is below/above the normal human threshold of 37 ° C and 70 beats/min (bpm), then, send an SMS to the specialist if he or she is absent as a means of continuous update. The suggested framework was implemented utilizing Proteus Tools and programs, composed in Embedded C. The obtained outcomes indicate an effective way to relay details on duty to the specialists for urgent patient care.

Electrocardiogram (ECG) signal utilization for medical image encryption (ImgEnc) algorithm was proposed in this manuscript. It will utilize the chaotic logistic map and the generalized Arnold map (...