bluuebunny/crossref_metadata_2025 huggingface dataset:
A Watermark Approach for Image Transmission: Implementation of Channel Coding Technique with Security
Authors: Et.al G.Aparna
Published: April 2021

In this paper an approach for secured digital image transmission with watermark is being proposed.  The tremendous growth in technology for various applications demand secured communications across the wireless channels. Secured image transmission is the one of the prominent process in digital communication applications. A watermark is embedded in to the image data that is to be protected from unauthorized users.  The cryptographic algorithms chosen for secured transmission led to the need for hardware implementation.  In the process of secured image transmission turbo encoder is proposed for error correction.  The proposed approach is realized in terms of hardware for the digital logic size, area and power consumption using Xilinx 14.2 software. Synthesizing and implementation of verilog code on the target device xc6slx150-2fgg484 for timing constraints, device utilization and performance details.

© 2020 Elsevier Ltd. All rights reserved.

Selection and/or Peer-review under responsibility of International Conference on Mechanical, Electronics and Computer Engineering
Remote Monitoring And Localization: Tools For Smart Parking
Authors: Et.al P Devi
Published: April 2021

The increase in usage of IoT environment is beyond the limits, which use Wireless (WSN).These networks can be used in many environments like logistics, supply chain management,health management,e-governance, smart parking, smart city, and smart appliances. WSN is a collection of sensors spread across them to base station/sink. Remote sensing sensors do sensing of an object remotely and detectthe static or dynamic information.In this paper, discussion is made on how to monitor and track the person’scar within a shopping mall. Remote monitoring and local optimization techniques are used in smart parking architecture, which suits for smart parking environment. Further, description of some of the monitoring and tracking techniques which was used earlier also been discussed with different types of protocols used for this appropriate environment. These systems can be implemented for effective smart parking.
An intelligent Model for Defect Prediction in Spot Welding
Authors: Et.al Christopher Paulraj
Published: April 2021

There are more than 30% defect in the spot welding of cars and randomly chosen cars are performed ultrasound or destructive testing.  This makes the process very vulnerable and unpredictable. This results in huge reworks, productivity, monetary loss and negative impact on brand name. 

This research paper presents the prediction of defect using machine learning models and as well forecasting models in spot welding through optimized methodology.  This defect prediction model is useful in determining the defects that are likely to occur during spot welding. The forecasting model for process parameters data pattern, trends, etc.  helps to identify the link between predicted defects.  This model can evolve and improve over time by considering data from previous phases and history data of the spot welding cycle. Predicting the defects before testing begins improves the quality of the product being delivered and helps in planning and decision making for future spot welding.

The optimized defect prediction methodology in spot welding reduces the defects and predicted sample for testing which reduces the rework and increase the productivity, monetary value and brand name.

The experimental result shows that the spot-welding methodology has shown improvement over existing spot-welding method. Please see the six-sigma (Fig:13) chart for before and after improvement curve and value.
Spatial Management for Solar and Wind Energy in Kuwait
Authors: Et.al Ali Al-Dousari
Published: April 2021

Kuwait situated at the northwestern part of the Arabian Gulf experiences severe arid environmental conditions. Kuwait is facing many challenges in the sustainable development of the energy sector. These challenges are: deposited dust, increase of carbonate and mud percentages in dust, lower solar radiation, high relative humidity and low wind speed. These challenges that affects more the photovoltaic (PV) power stations were carefully monitored in order to special manage the solar and wind energy in Kuwait. Using 14 weather stations, solar radiation, wind speed and directions were monitored during the period (2010-2017). The annual deposited dust represents one of the highest in the region with 216 t km-2 as an average in 2009-2010 and 339 t km-2 in 2010-2011. Also, carbonates and mud content within deposited dust represent the highest percentages compared to the surrounding regions. The annual power production of wind farms in Kuwait is 2.3 times higher than PV unit with similar capacity. Finally, maps that were produces for all environmental challenges for solar and wind energy in this present paper show that the northwestern regions of Kuwait are the most appropriate areas for setting up a wind and/or a PV power stations.
A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification
Authors: Et.al Sunil S S
Published: April 2021

Diabetes Retinopathy (DR) is an eye disorder that affects the human retina due to increased insulin levels in the blood. Early detection and diagnosis of DR is essential in the optimal treatment of diabetic patients. The current research is to develop controls for identifying different characteristics and differences in colour  retina and using different classifications. This therapeutic approach describes diabetes recovery from data collected from multiple fields including DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Here  machine learning, neural networks and deep learning algorithms issues are addressed with related topics such as Sensitivity, Precision, Accuracy, Error,   Specificity and F1-score, Mathews Correlation Coefficient (MCC) and coefficient of kappa are compared. Finally due to the deep learning strategy the results were more effective compared to other methods. The system can help ophthalmologists, to identify the symptoms of diabetes at an early stage, for better treatment and to improve the quality of life biology.
Awareness On Healthy Dietary Habits Among Prospective Teachers In Tirunelveli District
Authors: Et.al Dr.M.Maria Saroja
Published: April 2021

Unhealthy dietary habits are related to diabetes, cardiovascular disease, and increased obesity risk over the past decades. It’s mainly due to the dietary factors and lack of physical activities among students. Unhealthy food eating habits and lack of food knowledge and health products leads to severe health problems in the young generation. They prefer food based on their taste, look especially in their psychological point of view not based on their healthy aspects. They often prefer outside foods especially from hotels and local food shops this may affect their immunity and increase the risk of various disease. The main objective of the present study was to find out the awareness on healthy dietary habits among prospective teachers in Tirunelveli district. Survey method was adopted by the investigators to conduct this study. The sample consists of 250 prospective teachers in Tirunelveli District. Healthy Dietary Habits Awareness Scale was developed by Maria Saroja, M and Michael Jeya Priya, E (2020) has been used for collecting data. Mean, SD, ‘t’- test and ꭓ2 was used for analysis the data. The investigators found that  i) there is significant difference between rural and urban, nuclear and joint family prospective teachers in their awareness on the healthy dietary habits (ii) there is no significant difference between girls and co-education prospective teachers in their awareness on the healthy dietary habits  (iii) there is no significant association between prospective teachers in their awareness on the healthy dietary habits with reference to the  father’s educational qualification and mothers educational qualification.
Determinants Of Foreign Direct Investment In The Manufacturing Industry: A VectorAutoregression Model Approach
Authors: Et.al Symon Elli R. Blas
Published: April 2021

This paper aimed to identify the factors that affect Foreign Direct Investment (FDI) in the manufacturing industry using a Vector Autoregression (VAR) model. The researcher selected possible economic indicators (Agriculture, Consumer Price Index, Government Expenditure and Unit Labour Cost) through theories and empirical evidences from various researches, covering quarterly period of 2004-2018. The result showed that there is a significant relationship between FDI and the selected economic indicators. It also revealed that shocks agriculture produces a positive impact, as well as government expenditure. However, consumer price index when introduced to shocks showed an immediate negative impact. On the other hand, unit labor cost did not have a significant impact on the foreign investments. The future variability of FDI primarily depends on itself, while the selected indicators affect roughly one-tenth of the future values of FDI
A Model for Developing of Surveillance, Prevention and Control the COVID-19 and Protection of People's Rights in a New Normal Way by a Virtual Community of Primary Health Care Service Network, Mahasarakham Province, Thailand
Authors: Et.al Rungrueng Kitphati
Published: April 2021

Epidemic disease is extremely dangerous for humanity; coronavirus 2019 is another epidemic that has caused great damage to the nation. Every country tries to prevent, treat and resolve to overcome an epidemic situation like this. Therefore, this study aims to (1) Study the success elements of surveillance, prevention, and control of COVID-19 systems and protect citizens' rights in a new normal, (2) Analyze conditional factors that influence the success of surveillance, prevention, and control of COVID-19 and protection of citizens' rights. Ans (3) Developing a model for the development of surveillance, prevention, and control of COVID-19 and protect the rights of people in a new way by the virtual community of primary health service networks. This research method was Mixed Methods Research which was held at Mahasarakham Province, Thailand, and was divided into 3 phases; The first: Application of the Future Search Conference (FSC) technique to study the surveillance, prevention, and control of COVID-19 and protect the rights of people at the provincial, district, and sub-district levels, divided into 2 groups as follows (1) 60 the coach lecturer, and (2) 690 virtual community members in the provincial area,The content is then analyzed for theoretical conclusions.
Factors Affecting Millenials’ Acceptance of E-Money Application in Jakarta
Authors: Et.al Muhammad Reiza Indrawan
Published: April 2021

The use of e-money applications such as OVO, Gopay, Dana, LinkAja and other e-money applications have significantly increased the volume of non-cash transactions in Indonesia, but, Indonesia’s non-cash transaction is still below its region’s countries. On the other hand, Millennial generations (Millenials) is reported  as the largest users of e-money applications as well as the largest potential market in Indonesia, however, limited researches have been conducted to study the acceptance of e-money applications by Millennials. This research is intended to determine factors that have significantly influenced Millenial’s acceptance of e-money applications based on the theory of Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), which covers variables include Perceived Usefulness, Perceived Ease Of Use, Trust, Subjective Norms, Attitude Toward Use and Behavior Intention. This research examined 100 Millenials in Jakarta, which selected with purposive sampling technique, and tested using PLS SEM statistical analysis method. The results show that Perceived Usefulness has the strongest positive effect on Attitude Toward Use, followed by Trust. While Attitude Toward Use and Subjective Norms have positive effect on Behavior Intention. Whereas, Perceived Ease of Use and Subjective Norms have no significant effect on Attitude Toward Use. It is concluded that Perceived Usefulness and Trust are the two main factors which influenced Millenial’s acceptance of e-money applications in Jakarta.
Multiclass Classifier for Stock Price Prediction
Authors: Nabi Rebwar M.
Published: April 2021

The stock market has been a crucial factor of investments in the financial domain. Risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock Price forecasting is complex that could have a significant influence on the financial market. The Machine Learning (ML) type of artificial intelligence (AI) provides a more accurate forecast for binary and multiclass classification. Different effective methods have been recommended to resolve the problem in the binary classification case but the multiclass classification case is a more delicate one. This paper discusses the application of multiclass classifier mappings such as One v/s All (OvA) and One v/s One (OvO) for stock movement prediction. The proposed approach comprises four main steps: data collection, assign a multi-label (up, down, or same), discover the best classifier methods, and comparison of classifiers on evaluation metrics of 10k cross-validation for stock price movement. In this study, a stock NASDAQ dataset for about ten years of ten companies from yahoo finance on daily basis is used. The resultant Stock Price prediction uncovers Neural Network classifier has good performance in some case whereas Multiclass (One V/s One) and (One V/s All) have overall better performance among all other classifiers as AdaBoost, Support Vector Machine, OneR, Bagging, Simple Logistic, Hoeffding trees, PART, Decision Tree and Random Forest. The Precision, Recall, F-Measure, and ROC area comparison results show that Multiclass (One V/s All) is better than Multiclass (One V/s one). The proposed method Multiclass classification (One v/s All) yields an accuracy of 97.63% for average prediction performance on all ten stock companies, also the highest accuracy achieved as 98.7% for QCOM. The individual stock-wise evaluation of the Multiclass (One V/s All) classifier is found to achieve the highest accuracy among all other classifiers which is outperforming all the recent proposals.
Financial Leverage and Financial Performance of Oil and Gas Companies in Nigeria: A Re-examination
Authors: Abubakar Ahmadu
Published: April 2021

This study assessed the effect of financial leverage on the financial performance, using data from the annual reports of 7 quoted oil and gas firms in Nigeria, as well as from the Nigerian Stock Exchange (NSE) daily official lists over the period 2005- 2018. Descriptive statistics were used in data presentation, while random effects panel estimator was applied in determining the effect of financial leverage variables as short-term debt ratio (STDR), long-term debt ratio (LTDR) and total-debt equity ratio (TDER) on the financial performance, measured by the return on equity (ROE). The regression results from the random effects model (REM) indicate that STDR and LTDR have no significant effect on the financial performance, and TDER has a negative but significant effect on the financial performance denoted by ROE. The study concludes that higher financial leverage of quoted oil and gas companies in Nigeria attenuates shareholders’ wealth. The investment implication of this conclusion is that oil and gas companies should look more carefully at the utility maximization value of debt vis-à-vis equity in their capital structure.
Drivers of Engagement among Full-time Faculty from Private University
Authors: Et.al Gladys T. Tumbali
Published: April 2021

This study determined the drivers of engagement among full time faculty members of University of Saint Louis, one of the Private Higher Educational Institution in the Northern Philippines. The descriptive-survey method of research was employed using a validated questionnaire. The questionnaire was administered among ninety-eight (98) full-time faculty members in the University. Data was analyzed using descriptive and inferential statistics. Findings of the study revealed that the five drivers of faculty engagement which are (1) Administration and Management, (2) Workplace and Resources, (3) Compensation and Benefit, (4) Interpersonal Relationship, and (5) Achievement and Recognition, are all important factors that drive faculty to engage themselves in their jobs. Furthermore, results showed that Compensation and Benefits was found to be the most significant driver of faculty engagement, followed by Achievement and Recognition then Administration and Management. Meanwhile, Workplace and Resources as drivers of faculty engagement was found to be less important. Furthermore, results showed that the drivers of engagement among faculty vary by department and by the highest educational attainment
A Novel Technique for IDS in Distributed Data Environment Using Merkel Based Security Mechanism for Secure User Allocation
Authors: Et.al D. Priyadarshini
Published: April 2021

Multiple corporations and people frequently launching their data in the cloud environment. With the huge growth of data mining and the cloud storage paradigm without checking protection policies and procedures that can pose a great risk to their sector. The data backup in the cloud storage would not only be problematic for the cloud user but also the Cloud Service Provider (CSP). The unencrypted handling of confidential data is likely to make access simpler for unauthorized individuals and also by the CSP. Normal encryption algorithms need more primitive computing, space and costs for storage. It is also of utmost importance to secure cloud data with limited measurement and storage capacity. Till now, different methods and frameworks to maintain a degree of protection that meets the requirements of modern life have been created. Within those systems, Intrusion Detection Systems (IDS) appear to find suspicious actions or events which are vulnerable to a system's proper activity. Today, because of the intermittent rise in network traffic, the IDS face problems for detecting attacks in broad streams of links. In existing the Two-Stage Ensemble Classifier for IDS (TSE-IDS) had been implemented. For detecting trends on big data, the irrelevant data characteristics appear to decrease both the velocity of attack detection and accuracy. The computing resource available for training and testing of the IDS models is also increased. We have put forward a novel strategy in this research paper to the above issues to improve the balance of the server load effectively with protected user allocation to a server, and thereby minimize resource complexity on the cloud data storage device, by integrating the Authentication based User-Allocation with Merkle based Hashing-Tree (AUA-MHT) technique. Through this, the authentication attack and flood attack are detected and restrict unauthorized users. By this proposed model the cloud server verifies, by resolving such attacks, that only approved users are accessing the cloud info. The proposed framework AUA-MHT performs better than the existing model TSE-IDS for parameters such as User Allocation Rate, Intrusion Detection Rate and Space Complexity
Impact of Service Environment for effective consumer behavior in Retails Industry with reference to Heritage Super Market
Authors: Et.al Mr. L Komagan
Published: April 2021

Consumer behavior is dynamic and it needs to be evaluated by using scientific research methods. In this regard market research for Heritage had been conducted in this research work. Consumer behavior is complex subject and it is tough to predict the human internal thinking unless they express their opinions. Customers are sometimes loyal to the existing brands and some customers always show interest to adopt disruptive technology. Retailing business is more traditional in India and it had transformed in the past once decade. At the same time consumption also increased with growth of organized retailing business in India. The survey was conducted in selected Heritage super markets and parlous in Hyderabad. It was descriptive research & Quantity research type and the samples were collect through convincing samples & Simple random samples. The hypothesis was tested using the Correlations, Regression and ANOVA testing. There was a significant effect on the service environment and consumer behavior in the retail market.
An Empirical Analysis on Various Techniques Used to Detect the Polarity of Customer Satisfaction in Sentiment Analysis
Authors: Et.al A.Ilavendhan
Published: April 2021

Sentiment analysis is an emerging application of NLP (Natural Language Processing). This is also called opinion mining or attitude detection. In the text mining field, Sentiment Analysis is continuous area of research. It is a procedural treatment of attitude, feelings and textual content. The fundamental thought is to discover text polarity and order it as positive, negative, or neutral. It supports human to take a good judgment. This survey paper gives an extensive summary of the previous updates in this area. Several currently proposed algorithms and numerous upgrades to different SA applications have been investigated and summed up in this review. These articles are classified by their commitment to different SA techniques. Areas identified with SA (business monitoring, polarity observation, social media monitoring) that has recently attracted researchers is discussed. The fundamental objective of this survey is to provide a complete picture of SA practices and associated fields with brief explanation. The significant role of this study includes a refined classification of current papers and a depiction of ongoing patterns in sentiment analysis and research in its associated fields.
Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis
Authors: Et.al R Kanthavel
Published: April 2021

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machine-learning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.
Modelling Under Uncertainty Business Competitive With Robust Estimation Measurement Model
Authors: Et.al Marischa Elveny
Published: April 2021

Business insight can be characterized as a bunch of procedures and apparatuses for the obtaining and change of crude information into significant and valuable data for business examination purposes. Information requires strategies and programs that competing organizations in which e-metrics cannot ignore it. Electronic-Metrics or commonly known as e-metrics are data created based on electronic-based customer behavior (e-customer behavior). Extracting information on a competitive organization involves a similarity that is used to find a connection or linkage between organizational behavior. Various new ways are used to achieve success, one of which is with an electronic-based business, but with such a large number of variations, the uncertainty in business is also increasingly difficult to predict. Especially in predicting what activities will often occur in the next few years. Prediction is a process of systematically estimating something that is most likely to happen in the future based on past and present information. To keep up with the development of the company, it is necessary to optimize the metrics for the business. The purpose of optimization is to find the minimum or maximum value of a problem, whether the value of a company produces the desired results. Optimization can be done with robust nonparametric regression, where robust regression is used to detect outliers and provide results that are resistant to outliers. MM (Method of Moment) estimation is one of the estimates in robust regression that can be used for detected outlier data on the independent and dependent variables. Based on the parameter significance test, it was found that merchants who sell food are more profitable than other merchants. With a difference in iteration value 23.9 and a comparison of iteration value 53.1. Based on the parameter significance test conducted, it was found that merchants who sell food are more.
Implementation Android Based E-Commerce for Improving Business Process and Increasing Revenue
Authors: Et.al Fergyanto E. Gunawan
Published: April 2021

E-commerce is one of the solutions to make a business known by people and gaining new customers. E-commerce makes transaction between seller and customer convenient because people no longer need to come to the store if they want to buy something. Retail Businesses that still serve their customers using old method or only serve the customers who come to the store will lose with the competitors that use e-commerce because many people right now using e-commerce in their smartphone to find the products they wanted rather than to go to the store and it makes the business can’t get any new customers and can’t increase their revenue. Retail Businesses needs to change their old method to a new one by using android based e-commerce. Android based e-commerce is designed using Extreme Programming method. The old method that the business used will be replaced into a new one where the business will serve the customers online by using android based e-commerce and also serve the customers who came to the store. The changes from implementing android based e-commerce is expected to make the business process in retail business will be more efficient and effective to increase revenue and customer’s satisfaction.
Social Network Extraction Unsupervised
Authors: Et.al Mahyuddin K. M. Nasution
Published: April 2021

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.
A Selective reliable communication to reduce broadcasting for cluster based VANET
Authors: Et.al R. Shiddharthy
Published: April 2021

Vehicular Ad hoc Network (VANET) is one of the subset of Mobile Ad hoc Network (MANET) and it is a self-organised system with a group of vehicles, which are capable of short-range communication using On Board Unit (OBU). This unit is comprised with the vehicles that are possible to communicate with the nearby vehicles. VANETs rely on heavy broadcast transmission due to sharing data (messages) between the nearby vehicles about the traffic, collision and so on. This redundant information spoils the nature of VANET that affects the inter-vehicular communication, rebroadcasting and information on collision. This message transmission increases largely as the number of vehicles increases. This problem is typically named as broadcast storm and it is relatively reduced through the proposed Selective Reliable Communication (SRC) Protocol.  Through a reliable communication, packets are retransmitted to reduce a number of transmission in the network within the acceptable level of QoS. The proposed SRC protocol automatically detect the vehicle clusters as “Zone of Interest”. Generally, the proposed protocol forwards the packets to the cluster-heads and the cluster-head forwards the packets to the cluster-members. The proposed protocol outperforms than the existing protocols in terms of Throughput, Packet Delivery Ratio (PDR) and Average delay.
Influx of money and the Manchester City Success Story
Authors: Kelkar Ajinkya
Published: April 2021

The Premier League in the 21st century has been the most popular league in football history. Known for its jaw dropping matches and unpredictability, the teams in this league are quickly establishing themselves as some of Europe’s finest football teams. One such team is Manchester City. In 2008 Man City’s fortune was overturned when the Abu Dhabi United Group decided to purchase Manchester city under its subsidiary City Football group. The new investors provided city with much needed financial backing because of which they were able to improve their training centre, grounds and medical facilities and It also enabled them tobuy young and established talent. In this study we will attempt to establish a relationship between the money invested by City Football Group in Manchester City and try to relate it to City’s success over the last couple of years
Burnout Syndrome In The Covid-19 Pandemic And The Virtualization Of Education
Authors: Carmen Rosa Mosquera Murillo, Judith Soledad Yangali Vicente Bellanire Ospina Orejarena,
Published: April 2021

The aim of the research is to determine the relationship between Burnout Syndrome in the framework of the COVID-19 pandemic and the virtualization of education in Colombian official school teachers during the year 2020. The study responds to the mixed approach and sequential design. In its quantitative phase it was deductive-hypothetical, applied, with a non-experimental cross-sectional design, and in its qualitative phase it was inductive, applied with a phenomenological-hermeneutic design. The data collection techniques used were the survey and the interview. The study population consisted of 1093 teachers from official schools in Floridablanca. For the quantitative approach, a non-probabilistic accidental sample of 110 teachers was used, and for the qualitative approach, a non-probabilistic convenience sample of 10 teachers was used. Among the final considerations it can be said that there is an inverse mean correlation between Burnout syndrome and the virtualization of education in this time of the Covid-19 pandemic; it is therefore considered that the effects of the pandemic have maximized the effects of the aforementioned syndrome on educational actors.
Optimal workflow scheduling in cloud computing based on hybrid bacterial evolutionary and bees mating optimization algorithm
Authors: Dr Sunil Kumar Dinesh Kumar,
Published: April 2021

Distributed computing is the most recent developing pattern in disseminated processing that conveys equipment framework and programming applications as administrations. The clients can devour these administrations dependent on a SLA which characterizes their required QoS parameters. By using the cloud computing technique it is possible to reduce the investment on various resources like computer hardware and software. The application or processes that are hosted and executed using clouds consist of set of tasks and it is considered that this task will form the workflow. Therefore scheduling the task is considered as a major issue as resource usage has to be maximized without affecting the services that are facilitated by the cloud. In order to execute different virtual machine application of the tasks are assigned and it is termed as enterprise arranging. In the scheduling process the inter-dependent tasks are mapped and managed in the distributed resources. For additional improvement, this paper proposes a hybrid optimization algorithm for workflow scheduling (HOWS) in cloud environment. In the proposed algorithm the first contribution is the bees mating optimization (BMO) algorithm used to share physical infrastructure to enable multiple service providers to optimize scheduling. The second contribution in the proposed algorithm is the bacterial evolutionary algorithm used to flexible access of the resources in order to optimize the network resources. By combining the hybrid optimization algorithm provides the better improvement in terms of task scheduling and optimal resource allocation. The result and performance analysis shows that the proposed technique performs very efficient in terms of energy efficiency and scalability without compromising security. The performance is obtained using cloudSim tool
Dual-Band, Dual-Polarized Two Element Slot Antenna for Fifth Generation Mobile Devices
Authors: Vaswani Jitendra
Published: April 2021

This paper presents a two-element dual-band MIMO antenna with dual-polarization for 5G user equipment. Both operating frequencies are of sub-6 GHz band centered at 3.6 GHz and 5.5 GHz respectively and are independent of each other. Antenna polarizations depend on the placement of the feed elements on the PCB. The antenna is designed on FR-4 substrate to ensure its easy availability and keeping it cost-effective. The resultant radiation pattern of the antenna set is bi-directional with good gain and efficiency.
Creation theActivity Modelsfor the New Generation Thai Teacher’s Characteristics Conserved the Environments and the LivelihoodFollowedthe Sufficient Economy Philosophy ofthe Frist-Year Students, Faculty of Education at RattanakosinRajabhat University
Authors: Pavaboot Sommai
Published: April 2021

Learners realized how to protect,conserve the natural resources and value environments. Their livelihoodfollowed thesufficient economy philosophyand realized to their conscious for changing the globalizations. They must follow the rules of the professional education in 2013 for the self- management standard. The purpose of this research article was to study results of the experiments on creation theactivity models for the new generation Thai teacher’s characteristics conserved the environments and the livelihoodfollowed thesufficient economy philosophy to the first-year students, faculty of education at RattanakosinRajabhat Universityand the different between before and after experiments.This research was used by Randomized Control Group Pretest- Posttest Design.Statistical hypothesis testing wast - test for Dependent Sample andt-test for Independent Sample: Difference Scores.This researchwas found that after the experiment was the higher scores than before experiment. It issignificant at .05. Further Suggestions of this research showed that the activities should be continuously practiced by the learners themselves and adjust to their worthy lives.The teacher’s dutiesmeans providing the facilities, suggestions, inspiration, praise, complementary, making goodness, supporting positive thinking and esteem to Thai social activities and Thai cultures.
Covariance Kalman Geometric Graph Based Feature Extraction And Bernoulli Kernel Classifier For Plant Leaf Disease Prediction
Authors: Et. al. Mohammed Zabeeulla A N
Published: April 2021

As far as the agricultural domain is concerned, one of the most hot research areas of analysis is accurate prediction of leaf disease from the leaf images of a plant. The prediction of agricultural plant diseases bymeans of the image processing techniques will hence reduce the dependence on the farmers to safeguard their agricultural land and also their products. However, with the presence of noise, the leaf disease prediction is said to be hindered. To address this issue, in this paper, Covariance Kalman Geometric Graph-basedBernoulliClassifier (CKGG-BC) for Plant leaf disease prediction is proposed. The CKGG-BC method is split into three parts. To start with the plant leaf image provided as input, the Covariance Kalman Filtered Preprocessing modelintroduced for the image enhancement. Second, Geometric Graph-based Segmented Co-occurrence Feature Extraction model is applied to the preprocessed image to accurately segment the infected leaf areas and followed by which extracting the accurate infected leaf areas. Finally, Bernoulli Online Multiple Kernel Learning Classifier is applied for accurate plant leaf disease prediction with minimum classification error. The proposed method provides a significant refinement with respect to state-of-the-art methods. Even under complex background conditions, i.e., in the presence of noise, the averageaccuracy of the proposed method is said to be improved and hence paves mechanism for prediction of plant leaf disease in a significant manner. Experimentalresults exhibit the effectiveness of the proposed method in terms of computational overhead, accuracy, true positive rate and classification error respectively.
Detecting Misleading Information on COVID-19 : A Machine Learning Perspective
Authors: Et. al. M.Arunkrishna
Published: April 2021

Online Social networks become a popular way for sharing information among people. With increasing technology like Wi-Fi, Wi-Max ,3G/4G along with handheld devices like smartphones and tablets, popular applications such as Instagram, Facebook, Twitter and YouTube, becomes a dominant platform for  news and entertainment. The extensive use of these social networks has an incredible influence on sharing news among people It holds both positive and negative effects of its own. Because of it’s high popularity,Online Social Networks(OSNs),has become the target for spammers. Also, false news for different political and commercial purpose has been evolving in the large count and spread worldwide. After the spread of COVID-19, there had been a lot of confusion and pitfalls on the topic of who to believe and who should be rejected. With the advent of time, several companies like Facebook, and Twitter joined hands to identify the news and regard it authentic or not. This effort was very hard for people, as the news are spreading at a rapid pace, no matter how many people are upon the task, the rate of expansion of news is always faster than the rate of evaluation of whether the news is authentic or not. Additionally, it can be observed that the news cannot be regarded as fake or true before careful evaluation. This evaluation is based on the results. So it is important to create a method for identifying fake news and distinguishing it from individuals. Thus, the paper evaluates several models in order to find the best fit with the highest level of accuracy.
Detection of Malicious Data in Twitter Using Machine Learning Approaches
Authors: Et. al. B.Mukunthan
Published: April 2021

: Unlike traditional media social media is populated by unknown individuals who can broadcast whatever they like. This online social media culture is dynamic in its nature and transition to digital media is becoming a trend among people. In upcoming years the use of traditional media will decline, and the increasing use of Online Social Networks(OSNs) blur the actual information of the traditional media. The information generated by the authentic users gives useful information to the general users, on the other hand,Spammers spread irrelevant or misleading information that makes social media a plot for false news. So unwanted text or vulnerable links can be distributed to specific users. These false texts are anonymous and sometimes linked with potential URLs. Due to data restrictions and communication categories, the current systems do not deserve an exact statistical classification for a piece of news. We will study different research papers using various techniques for master training in the prediction and detection of malicious data on social networks online. We tried to find spam tweets from the tweets collected by using Enhanced Random forest classifications and NaiveBayes in this research. To evaluate the work, different validation metrics such as F1-scoring, accurcy and precision values are calculated.
When do we fly again? Managing Airlines in a Pandemic: Challenges and Recommendations
Authors: Et. al. Mark Loo
Published: April 2021

This paper aimed to identify the challenges and propose recommendations to manage sustainability of the airline industry in a pandemic.  When the World Health Organization stated Covid-19 as pandemic on March 11, 2020, governments in all countries ordered lockdowns, imposed travel restrictions, and required quarantine of 14 days for visitors and citizens upon arrival at the airport.   The airlines industry came to a standstill.  As Covid-19 is a recent and evolving phenomenon, the methodology employs secondary research based on data from authoritative sources such as the World Health Organization, International Civil Aviation Organization (ICAO) and International Air Transport Association (IATA) to identify the challenges.  Peer reviewed research on past pandemics, especially the Severe Acute Respiratory Syndrome (SARS) in 2002-2003 help formulate recommendations to manage sustainability.   The challenges are financial crisis, travel restrictions and customer distrust.  The recommendations are positioning customer safety first, customer engagement, pricing strategy and collaboration with government.  Limitations of the research and future research suggestions are presented
Energy Efficient Scheduling Algorithm for Structural Health Building Monitoring System(Shbm) to Increase the Battery Lifetime
Authors: Et. al. Mr.Pradeep K G M
Published: April 2021

A structural health building monitoring system is invented, achieved and attempted using Wireless Sensor Networks .Ambient vibration of the structure is audited and is implemented to identify the health status of the structure. With WSN, low cost monitoring is possible without intruding with the action of the structure. In this cluster head is heavily loaded since it gathers information from all other member nodes and integrate then and forward to sink either directly or through intermediate other cluster head. I near future cluster head drain and we need to select another cluster head via re clustering. The re clustering also led to energy loss to select the cluster head based on weight age value.WSN consumes less power and it is used to track slow targets .WSNs covers only small distance. WSN has high failure rate and we have to plant excessive nodes. Hence redundant nodes dissipate more power. To avoid this, sleep mode and scheduling algorithm is used in this paper. The objective of the paper is to design a scheduling algorithm to trace data with target rate. The proposed scheme is a kind of an adaptive/Cyclic on–off scheduling scheme in where sensors nodes use only local information to make scheduling decisions.
Research Intuitions of Asymmetric Crypto System
Authors: Et. al. Rojasree V.
Published: April 2021

The fast moving world full of data exchange and communication technology, with all sensitive information of an individual virtually available anywhere and anytime, make the Internet world more critical in security aspects. The areas of risks are attended and assured to be safe by means of some sought of crypto mechanisms. The strength and vulnerability of the crypto mechanism defines the durability of the system. The encryption on the communication channel can implement either public or private key algorithms based on the area of applications. The public key cryptography is specifically designed to keep the key itself safe between the sender and receiver themselves. There are plenty of public key cryptographic algorithms but only a few are renowned. This paper is aimed to collect all possible public key cryptographic methods and analyze its pros and cons so as to find a better algorithm to suite almost all conditions in Internet communication world and e-commerce. Research in quantum computers is booming now and it is anticipated that the supremacy of quantum computers will crack the present public key crypto algorithms. This paper highlights issues and challenges to be faced from quantum computing and draws the attention of network researchers to march towards researching on quantum-safe algorithms.
Formulation of Nao Equation According to Nao Framework
Authors: Et. al. A. H. Abdul Rasib
Published: April 2021

Companies in manufacturing often find strategies to increase production efficiency and quality to be competitive in the long run. These strategies make companies remain profitable in a highly competitive market. Nonetheless, attempting to maintain a shorter production lead time is also vital as efficiency becomes a competitive priority. Whenever there are longer lead times, overtime is taken into account to meet the target. Overtime can be the most cost-effective way for companies to achieve their quality needs.

Nevertheless, if poorly managed, overtime could quickly outstrip financial gains. This study aimed to establish the manufacturing industry model of non-value-added overtime (NAO) and formulate NAO equations. In this regard, the NAO equations were acquired from the critical factors of NAO. The vital aspects of NAO were then presented through the activities flow in the input/output manufacturing concept. The study results indicated that the highest critical factors contributed to the three processes: pre-process, in-process, and post-process.
Relationship between Covid-19 pandemic and firm’s performance towards unemployment across countries
Authors: Saad Noriza Mohd
Published: April 2021

As a result of the consequences of the Covid-19 pandemic, stock market players issued their quick response on how coronavirus has affected the performance of firms whereby most of the affected sectors are airlines, tourism, hotels, transportation and many more. This has led to an increase in the unemployment rate worldwide.  Considering this global issue, this study was aimed at investigating the relationship between unemployment with a number of confirmed cases of Covid-19 and firms’ performance as well as the controlling factors across seven (7) countries. Secondary data were utilized with the data obtained from Bloomberg and Worldometers website. The dataset for dependent as proxy by unemployment rate and explanatory variables proxies by COVID-19 and firm’s performance were then tested using multivariate regression approach. Finally, from the study, it is suggested that an increase in the number of Covid-19 cases will affect business performance as controlled by macroeconomics factors which lead to a significant positive relationship between the unemployment rates in all the countries of this study. This factor may affect the government’s decision to implement the Movement Control Order (MCO), for instance by the Malaysian Government. By Covid-19 vaccine development in the market nowadays, hopefully, the spread can be mitigated and firms’ performance can return to normalcy to avoid increasing rate of unemployment in the world.