bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Change in Body Motion With Background Construction and Silhouette Orientation
Authors: Mahajan Rohini , Padha Devanand
Published: May 2022

Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity
A Firefly Algorithm-Based Approach for Web Query Reformulation
Authors: Zeboudj Meriem , Belkadi Khaled
Published: May 2022

A major difficulty in using a web-based information retrieval system is the choice of terms to be used for expressing and processing a query. The user has to examine a lot of data to find the necessary documents or information. The problem that often appears in this situation is that the query is incorrect and does not express those needs. Researchers have come up with various solutions to overcome this problem among them the use of query reformulation. This paper presents an approach called FA-QR based on this technique using the Firefly metaheuristic. This algorithm was applied to frequent itemsets generated by frequent- pattern growth (FP Growth). The algorithmic solution allowed the user to select the best path among all the possible solutions for the initial query. Experimentally, the results demonstrated that our proposed approaches achieved a significant improvement over other different methods on TREC and FIRE datasets.
Automated Classification of Sleep Stages Using Single-Channel EEG
Authors: Satapathy Santosh Kumar , Loganathan D.
Published: May 2022

The main contribution of this paper is to present a novel approach for classifying the sleep stages based on optimal feature selection with ensemble learning stacking model using single-channel EEG signals.To find the suitable features from extracted feature vector, we obtained the ReliefF (ReF), Fisher Score (FS) and Online Stream Feature Selection (OSFS) selection algorithms.The proposed research work was performed on two different subgroups of sleep data of ISRUC-Sleep dataset. The experimental results of the proposed methodology signify that single-channel of EEG signal superior to other machine learning classification models with overall accuracies of 97.93%, 97%, and 95.96% using ISRUC-Sleep subgroup-I (SG-I) data and similarly the proposed model achieved an overall accuracies of 98.16%, 98.78%, and 95.26% using ISRUC-Sleep subgroup-III (SG-III) data with FS, ReF and OSFS respectively.
AI for Health-Related Data Modeling
Authors: Cheng Na
Published: May 2022

Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.
The Correlation Between Green Investment and Enterprise Growth Based on Gray Correlation Analysis
Authors: Li Wei , Wang Qin , Qiu Xiaoxing
Published: May 2022

The green transformation and upgrading of manufacturing enterprises cannot do without the support of green investment. The purpose of this study is to verify whether there is a correlation between the influencing factors of green investment of manufacturing enterprises and the growth of enterprises, and what the correlation degree is. The research methods are about two listed companies of wood flooring industries, energy consumption per unit of production (only Daya Shengxiang's data as reference), the proportion of technical personnel, enterprise support funds, the amount of invention patents, and patent applications with the annual sales of their main products being more than 0.6 and the correlation of the two is stronger. Research conclusion is expected that the influencing factors of green investment of manufacturing enterprises have a positive effect on the growth of the two manufacturing enterprises.
Rakshak
Authors: Dixit Ashutosh , Sethi Preeti , Garg Puneet
Published: May 2022

Almost every country in the world is facing the issue of child trafficking. Besides abduction, children below 10 years sometimes get missed from their homes or other locations due to many reasons. following the criminal record, many of the abducted or missed children got received by police officials where majorly officials face difficulty to get correct information as the founded children are not in their normal state in general due to fear factor or less trust. It is also observed that such children also usually start stammering due to uncontrolled emotions. In this kind of situation, Police officials would not get the exact information of founded children and unable to contact their respective guardians. This paper proposes a possible solution “Rakshak” for this kind of difficult situation. The Rakshak is Machine Learning based software which inputs the recorded voice of such children by police officials and returns the voice as well as text output after being corrected by various techniques of the Rakshak.
A Bio-Inspired DNA Cryptographic-Based Morse Code Ciphering Strategy for Secure Data Transmission
Authors: B. Adithya , G. Santhi
Published: May 2022

In this research, DNA cryptography-based Morse code encoding is proposed to bulwark the delicate knowledge within the demanding environment. The plaintext is changed over to DNA sequences utilizing the Morse code encoding table to create original DNA references. The encoded information is transcribed and translated by the Ribonucleic Acid (RNA) arrangements of Morse code. Translated RNA Morse code is hidden by the standard genetic code utilizing organic compounds, and the hidden DNA Morse code is again ciphered by Morse code encoding table to create bogus Morse DNA references. However, the Morse code encoding pattern is used to compress the generated ciphertext with a reducible size rate of 25% to 30% of the plaintext value. The designed bio explores analysis, and results show that the transmission's security is high compared to the existing techniques in terms of cryptography properties, attacks, CIA triad, and it preserves the biological process without repudiating the nature of the organic forms of DNA.
MANET Proactive and Reactive Routing Protocols
Authors: Shukla Neha , Garg Puneet , Singh Madan
Published: May 2022

A group of wireless nodes forms a configuration that is linked to a remote medium forming dynamic topology. A routing protocol enables packets to travel from the sender to the destination via intermediate nodes. When a device or intermediate node delivers information to other nodes in an Ad hoc network, the node consumes some energy, and data transmission may be interrupted as all of the power is consumed. MANET is often power-driven devices, the difficult component in MANET is to lower the power consumption of energy by the intermediate devices in the network so that the network remains active at the time of data transmission. The characteristics, uses, and problems of MANET are described in this research. In addition, we look at the MANET routing protocols. The performance of alternative routing protocols, such as DSDV, AODV, AOMDV, and DSR, is then compared using performance measures. The NS2.35 simulator is used to conduct the performance assessment.
An Approach for Semi-Supervised Machine Learning-Based Mobile Network Anomaly Detection With Tagging
Authors: Vijaya Kumar B.P.  , Pankaj Chongtham , Naresh E.
Published: May 2022

The world economy has been stable by emerging into online business and activity with increased online users. There is likelihood to escalate the fraud activity and misuse the corporation's network. Hence, strengthening of network security is necessary to prevent such unwanted activities. In this work, Anomaly Detection System (ADS) is proposed to detect the anomalous activities in the network. Firstly, network packets with the tagging are trained with the k-nearest neighbor algorithm (KNN) and Kohonen’s Self-Organizing Maps (KSOM) algorithm clusters the network packets. Initially, the Tagging Application (TA) dataset is created that contains network packets with the labelling of applications by extracting captured live packets using high computing server that is configured in data center which are used for the proposed Fix Weight Kohonen's Self-Organizing Maps (FW-KSOM) to cluster different activities in the network. Implementation of the proposed ADS model for labelling and clustering is carried out in real time networking scenario to identify the applications for anomaly detection.
Classification of Software Defects Using Orthogonal Defect Classification
Authors: Kumar Sushil , Muttoo SK , Singh V. B.
Published: May 2022

Classification of software defects is an important task to know the type of defects. It helps to prioritize the defects, to understand the cause of defects for improving the process of software defect management system by taking the appropriate action. In this paper, we evaluate the performance of naïve Bayes, support vector machine, k nearest neighbor, random forest, and decision tree machine learning algorithm to classify the software defect based on orthogonal defect classification by selecting the relevant features using chi-square score. Standard metrics accuracy, precision, and recall are calculated separately for Cassandra, HBase, and MongoDB datasets. The proposed method improves the existing approach in terms of accuracy by 5%, 20%, 6%, 27%, and 26% for activity, defect impact, target, type, and qualifier respectively, and shows the enhanced performance.
An Empirical Analysis of Inferences From Commit, Fork, and Branch Rates of Top GitHub Projects
Authors: Rashid Ekbal , Prakash Mohan
Published: May 2022

GitHub repositories have been used to understand some important parameters related to quantitative growth of software projects. The rate at which fork, commits and branches have been done in different repositories which are supposed to be the topmost ones when it comes to the number of contributors is the main point of study in this paper. The desirable goal is to find out some sort of similarity in the trends of all these software projects. Similar trends tend to lead to interesting inferences regarding the quantitative growth and even the quality of these software projects. Is the rate of forking, branching, committing anything to do with the success of software projects? The authors have taken up this question in the current study.
SBHDetector
Authors: Garg Ritu , Singh Rakesh Kumar
Published: May 2022

Mining Software Repositories in collaborative environment during software evolution or maintenance faces challenges due to creation of larger than necessary slices or unnecessary splitting of Revision History and detection of edge level changes. Due to these limitations, GIT and Diff & Merge Tools does not accurately detect the similarities and changes between versions due to renaming or shifting. Detection of these similarities accurately helps to detect code clones and change patterns that improves understandability, knowledge transfer and tracking changes. Therefore, the authors proposed fuzzy based hybrid technique to detect the similarities/changes between versions considering RS by enriching the Revision History at three granularities- File, Class and Method level. 30% more entities have been found similar/change by deriving Classification model with f-score and ROC Area more than 0.985 and .994 respectively for all applications. Hence, proposed technique improves productivity, reusability and maintainability with respect to VCA.
Enhancing Clustering Performance Using Topic Modeling-Based Dimensionality Reduction
Authors: Ramathulasi T. , Babu M. Rajasekhara
Published: May 2022

Mainly in the present times, the description of the services and their working procedure have been established in natural text language. We have obtained service groups based on their similarities to reduce search space and time in service innovation. Major topic models such as LSA, LDA, and CTM policies have not been able to show effective performance due to the short description and limited description of services in text form, the reduction or absence of words that occur. To solve the issues created by brief text, the Dirichlet Multinomial Mixer model (DMM) with features representation using the Gibbs algorithm has been developed to reduce dimensionality in clustering and enhance performance. The launch results prove that DMM-Gibbs can give better results than all other methods with agglomerative or K-means clustering methods by sampling. Evaluations with internal and external criteria were used to calculate clustering performance based on these two values. Using this standard model, the dimensionality can be reduced to 93.13% and better clustering performance can be achieved.
The Public Sector Cloud Service Procurement in Sweden
Authors: Islam M. Sirajul , Karlsson Fredrik
Published: May 2022

This paper investigates the use of cloud services in the public sector and management of information security challenges in the procurement of such services. The findings are based on an exploratory approach that included a systematic literature review and a survey among the public agencies and municipalities in Sweden. The literature review is used to derive a conceptual framework that structures our empirical results into the three groups: a) contractual and legal, b) operational and c) managerial competency. The survey explored all these three groups. Our findings show that the information security challenges are mostly related to the potential breaching of national security and laws applicable to cross-border cloud services. Most of the cloud contracts of public organizations are found to be supplier driven. In this case, lack of knowledge and awareness in managing procurement are mostly raised compared to technical risks.
Information Security Policies in Nigerian Institutions
Authors: Ajibesin Adeyemi Abel , Maharazu Kasim , Ogundapo Olusegun
Published: May 2022

Information systems have become part and parcel of today’s business operations, with competition on its rise. The security challenges related to information systems could have a severe impact on the overall business objectives of an institution if not handled at the right time. A well-meaning institution must take a full-blown step towards information security management to achieve information security. Information security management has been seen as any management system to address the security issues affecting an institution and align security needs to the overall business objectives. Conversely, Information security policy is the foundation upon which institutions base their entire information security management. This study looks at the uptake of information security policy among Nigerian institutions. The survey result created a clear picture of the uptake of information security policy among Nigerian institutions.
The Inevitability of Escalating Energy Usage for Popular Proof-of-Work Cryptocurrencies
Authors: Read Colin
Published: May 2022

Few financial market innovations offer the opportunities to enhance transaction efficiency, as does the potential of the blockchain and cryptocurrencies. The author demonstrates that this industry's unusual demand and supply relationship results in ever-increasing energy consumption if Proof-of-Work currencies rise in price faster than mining rewards decay. This previously undocumented challenge poses global warming risk. In addition, the accessibility and opacity of many cryptocurrencies create financial risks for inexperienced speculators. Digital currencies also reduce the ability of central banks to conduct monetary policy and challenge regulatory authorities to promulgate rules to protect the economy and consumers. The author explores these various risks, demonstrates how the design of Proof-of-Work cryptocurrency mining frustrates the efficiency they promise and describes mechanisms that would allow cryptocurrencies based on the blockchain to live up to their immense potential.
The Role of Mutual Benefit in Informal Risk Management
Authors: Al Balushi Mohammed , Ansell Jake
Published: May 2022

Informal risk management is widely practiced as it can be more agile and flexible compared to formal methods. There are abundant research studies covering areas such as technical and social aspects of informal risk management. Often a holistic approach is advocated integrating inputs coming from informal networks to consider, inter alia, social, cultural and emotional factors. These studies, though, fail to explore the motivation and do not account for the role of mutual benefit. Using ethnographic and interview data, we tackle the issue of how decision makers consider the interests of all relevant stakeholders, the role mutual benefit plays in informal risk management and the impact of the formal structure on informal risk management. The findings show that mutual benefit is an essential pillar for informal risk management by both stimulating the required response and balancing interests. Also, the formal structure impacts the informal network through the influence and ranks it confers on members, and by setting consequential limits.
Fleet Management Practice and Implication for Fleet Performance
Authors: Mengistu Habtamu Solomon , Kitessa Busha Temesgen , Kleineidam Julia
Published: May 2022

This study aims to assess fleet practice and implications for fleet performance in Ethiopian pharmaceutical supply agency (EPSA). EPSA is encountering aging fleets, higher fleet expenses, lack of spare parts, and excessive service delays. EPSA was chosen as it was the major public pharmaceutical procurer and distributor in Ethiopia. Institution-based descriptive and explanatory cross-sectional study designs with mixed approaches were employed. Descriptive findings reveal that vehicle tracking is the most practiced activity. Correlation and regression analysis show that maintenance, fuel, and tracking activities have significant association and predictive power for fleet performance. Qualitative findings show that delay of vehicle maintenance was the major challenge. EPSA higher officials may emphasize having a well-organized workshop. The findings can be considered as input by policymakers in the transport sector. The limitation was the use of a homogenous purposive sampling method for the qualitative study. Thus, future study can be conducted using other reliable methods.
Gender Equality in Business
Authors: Cervantes-Guzmán Jovanna Nathalie
Published: May 2022

The purpose of the research is in illusion of the utility of neuroeconomics in decision making and behavior. Scientific knowledge will be advanced in the need for the application of neuroeconomics focused on one of the services of the information and communication technologies (ICT) of companies that is e-commerce of exponential artisanal SMEs of women entrepreneurs, by developing a proposal for a business model to increase the possibility of growth of their companies at the level national and international level. The methodological approach used was deductive, exploratory, descriptive, correlational and documentary. The findings are neuroeconomics have the potential to explain the phenomena that are considered as a deviation from the prediction or behavioral bias of decision-making models in economic theory. The practical implications are the study up to this point is quantitative using primary and secondary sources for research.
Exposing “Pluralistic Ignorance” to Reduce Youth Violence in Political Spaces
Authors: Dodo Obadiah
Published: May 2022

The study was inspired by the involvement of youths in political violence in Zimbabwe. It attempted to expose pluralistic ignorance concept as a way of reducing violence in political spheres. The concept of Pluralistic Ignorance argues that there are individuals who wrongly see the attitudes and deeds of peers as unlike their own when in fact they are not. It creates misunderstandings, which ensue in relation to problem behaviours which are commonly overrated. The study followed an intensive desk review conducted along a qualitative research paradigm. The concept of anti-violence as defined by the Commonwealth Commission on Respect and Understanding was applied to underpin the analysis. The study noted that the exposure of pluralistic ignorance allowed the affected youths to be able to discern the good from the bad and make sound decisions for their participation in politics. This, if appropriately applied helps reduce youth conflicts in politics in Africa.
Effect of the Textured Surface on the Structural Behavior of a Plain Bearing
Authors: Kadda Mehala , Nadia Bendaoud
Published: May 2022

An elastohydrodynamic model is applied in this work to study the influence of surface texturing on the structural performance of a coated aerodynamic plain bearing and a coated hydrodynamic plain bearing. Shaft rotational speed and radial load are digitally optimized to improve plain bearing performance, such as pressure, bearing inner face displacement, and shear stresses. Results are presented for different operating conditions. This research is carried out in order to predict the elastic behavior of a coated hydrodynamic plain bearing as well as a coated aerodynamic plain bearing at textured surface compared at a non-textured surface, operating at very high speeds. This study is a numerical investigation using a CFD code, by solving the Navier-Stokes equations of motion by the finite volume method and the displacement equations using the finite element method.
Effect of Copper Addition on Tensile Behaviour of Al-Cu Alloy Used in High Temperature Applications
Authors: Tripathy Sasmita , Sutradhar Goutam
Published: May 2022

Aluminium (Al) alloys are now-a-days used starting from aerospace, automotive body panel, to kitchen ware especially due to their high strength to weight ratio. Aluminium having copper as major alloying element exhibit good mechanical properties at room as well as at high temperature working conditions. But presence of silicon in these alloys adversely affect the strength at high temperature. The current paper analyses change in microstructure and it’s effect on mechanical properties of Aluminium-Copper(Al-Cu) alloy with varying wt% of copper. The Al-Cu alloy developed for study contain silicon as trace element only. Tests carried out to evaluate the impact of such variations on tensile and metallurgical behaviour at room temperature. Analysis of microstructure change with varying wt% of Copper is studied which goes well with the tensile data recorded. Effect of heat treatment (T6) on tensile properties was studied. The tensile results both as cast and heat treated suggest Al-Cu alloy containing 8wt% of Copper as the optimum composition.
Hydrogen Sorption Behaviors on Lithium Doped MIL@53-Al at Non-Cryogenic Temperatures
Authors: Konni Madhavi , Karnena Manoj Kumar , Mukkamala Saratchandra Babu
Published: May 2022

The current research work reports the methods that have been developed to dope the Lithium nanoparticles to the MIL@53-Al surface frameworks without inducing the structures. The prepared MIL@53-Al MOFs and Li/MIL/53-Al were characterized by XRD, TEM, SEM, BET, and TGA. The developed Lithium doped MIL@53-Al and MIL@53-Al were measured for the hydrogen sorption capacities at 298 and 253 K under a pressure of 75 bar. The study reports revealed that sorption capacities of MIL@53-Al enhanced significantly due to the doping of lithium ions; however, doping of these ions should be controlled for obtaining good uptake capacities as the higher concentrations of lithium might damage the frameworks of the synthesized materials. The lithium doping enhances the hydrogen uptake from 1.37 to 1.75 wt % at 253 K and 75 bar pressure.
Impact of Time Trend and Heterogeneity on EKC in Asian Countries
Authors: Dutta Abhishek , Adhikary Maniklal
Published: May 2022

The basic objective of the study is to investigate the shape of EKC for 39 Asian countries and income groups during the period of 1990-2015 with and without the use of a linear time trend variable. The study tests the validity condition of the mathematical model depicting an ‘N’ shaped EKC. Besides, the status of each country group is compared as per the expected emissions of GHG. The empirical investigation reveals that for both the overall and the high-income Asian group, the presence of a linear time trend variable renders EKC to be inverted U-shaped. However, eliminating the effect of time, the EKC becomes an ‘N’ shaped curve in agreement with a valid mathematical model. The middle-income Asian countries follow a monotonic increasing EKC relationship both in the presence and absence of a linear time trend. The computation of expected emissions of GHG demonstrates that the lower-middle-income group is expected to emit more among the three income groups followed by the high-income group and the upper-middle-income group.
The Influence of Remittances at the Macroeconomic Level in Romania
Authors: Oprea Mihaela
Published: May 2022

The impact of remittance in local and national economy is not known enough, even though the migration process amplified, due to few studies regarding the patterns and impact of migration, conditions and processes which lead to migration decision, contribution of remittance revenues for sustainable development of local economies and the potential of employment growth lead to a decrease in rural-urban migration and a potential increase in number of emigrants returning to Romania. The present research proposes the analysis of the impact of funds transfers made by Romanian emigrants at macroeconomic level.
Do Exchange Rate Volatility and Public Expenditures Influence Selected Crop Output in Nigeria?
Authors: Ali Ayuba , Obekpa Hephzibah Onyeje , Adejo Moses Adejo
Published: May 2022

This study assessed the impact of exchange rate volatility and public expenditure on selected crop output in Nigeria from 1981 to 2018. Time series data for the study were obtained from the Central Bank of Nigeria (CBN). Inferential statistics such as Co-integration analysis, Error Correction Model (ECM) and Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model were employed alongside some preliminary unit root tests. All the variables achieved stationarity after the first difference, which prompted the application of co-integration analysis. The Co-integration tests showed the existence of long-run equilibrium relationships among the variables. The result of ECM indicates that the exchange rate is positive and significant. Government expenditure showed a positive effect on crop output, and the ARCH and GARCH test for exchange rate volatility reveals that the exchange rate is weakly volatile in Nigeria within the study period. Policies and recommendations are also discussed.
Impact of Reservation Policy in Higher Education
Authors: Rashmi  , Sharma Anju
Published: May 2022

The study was designed to find out how extensively Agra District, popularly known as Taj City globally, which is active in terms of the utility of reservation policy for SCs/STs in higher education system that represents accurate and authentic assessment of those students belong to the SCs and STs and to explore if some other similar districts in the state offer examples of utility of best practices while adopting reservation policy at higher education level. Present research reveals that the concept of reservation policy is important in higher education particularly for the community of SCs/STs, which remains highly relevant in today’s context, although the claim of awareness, effectiveness, literacy rates, education level have not found same in the higher education sector. In modern world and digital age, the education system in itself has widened the scope for the students both in India and International universities.
ANN-Based Reference Voltage Generation Scheme for Control of Dynamic Voltage Restorer
Authors: Patel Meet R. , Sant Amit Vilas
Published: May 2022

Dynamic voltage restorer (DVR) is usually employed to mitigate sag/swell in supply voltages so that load voltage is regulated at nominal value. This paper proposes artificial neural network (ANN) based reference voltage generation (RVG) scheme for the control of 3-phase DVR. ANN replaces the traditional control of DVR, which involves abc-dq0 and dq0-abc transformations, estimation of d-q axes voltage errors and proportional-integral (PI) controllers along with their tuning. In proposed control scheme, the feedforward ANN utilizes present and previous samples of supply voltage and peak magnitude of load voltage for RVG, which when impressed across the injection transformer results in sag/swell mitigation. It is important to note that the proposed scheme is free from transformations and controller tuning. The performance of 3-phase DVR with the proposed ANN based RVG scheme results in standard IEEE-519 compliant operation with load voltage regulated at nominal value even under sag/swell in supply voltage. This is verified through MATLAB/SIMULINK based simulaiton studies.
An Evolutionary Optimization Technique for Time Domain Modelling
Authors: Kumari Abha , Vishwakarma C. B.
Published: May 2022

The authors proposed an evolutionary-optimization technique known as the Genetic Algorithm (GA) for the optimization and order reduction of high order systems (HOSs) in the time domain. As we know a lot of optimization techniques are available in the frequency domain but limited in the time domain. The proposed technique is applicable for time-domain systems. The reduced model (RM) obtained by the proposed technique can be replaced with the original HOS as it retains all the important time and frequency response specifications of the original HOS. The efficacy of the proposed method has been tested on few numerical examples from the literature. The important time and frequency response specifications of the proposed RM are compared with RM obtained by recent techniques using MATLAB/Simulink. The proposed algorithm is applicable for Single-input single-output (SISO) and multi-input multi-output (MIMO) linear, nonlinear, time-invariant, and time-variant systems.
A Case Study Approach for Low-Cost Diversity Leadership Coaching for Healthcare Managers
Authors: Sabie-Aridi Amalisha S. , Burrell Darrell Norman , Richardson Kevin
Published: May 2022

Racial attacks during COVID-19 towards Asian American healthcare workers, the Black Lives Matter Protest, and the #ME TOO anti-sexual harassment movement have caused more healthcare organizations to realize the importance of diversity and inclusion training and leadership coaching for their managers. This study employs an action research and action learning approach of a diversity leadership coaching program using ZOOM. The value of this real-world case study is to create a blueprint that other healthcare organizations with limited resources are struggling with where to start in engaging in diversity and inclusion.
The Educ-Entrepreneur Model for Student Learning and Teaching
Authors: Bengueddach Asmaa , Bouamrane Karim
Published: May 2022

The Algerian Ministry of Microenterprise's action plan, which has been in operation since 2020, aims to support the creation of new start-ups (Ministerial Council, 2020). Indeed, the ministry is working to create a legal, institutional, and functional framework for start-ups, as well as a roadmap for financing these enterprises. In this context, our contribution is to propose the educ-entrepreneur model for formalizing entrepreneurship education in Algerian universities. In this process, the authors have included two key topics to work on: 1) promote educational innovation in higher education; and 2) create concrete projects centered on a key economic sector to encourage the formation of new businesses, thereby promoting local, regional, and national economic development. In this paper, the researchers investigate the implementation of the educ-entrepreneur model at Oran 1 University and present preliminary findings.
Education Concerning the Complex Nature of Diversity and Inclusion Through an Exploration of Health Disparities
Authors: Aridi Amalisha Sabie , Burrell Darrell Norman , Wright Jorja B. , Richardson Kevin , Lewis Eugene J.
Published: May 2022

According to the US Centers for Disease Control, Health disparities are preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health experienced by socially disadvantaged populations and minority communities. COVID-19 has had a disproportionate impact on communities of color. Since 2020, communities of color in the US have experienced tremendous uncertainty, fear, and anger concerning trustworthy information about the origins, treatments, and prevention methods regarding COVID-19. This paper explores diversity and inclusion and its connection to health disparities through the kaleidoscope of COVID-19 and through the use of health theories and education solutions developed from the literature.
Keystroke-Based Biometric Authentication in Mobile Applications
Authors: Kabli Fatima , Nawal Sad houari , Afaf Matoug
Published: May 2022

Keystroke dynamics is the study of how people can be distinguished based on their typing rhythms. It is considered as a real alternative to several authentication mechanisms. In order to improve authentication security in mobile applications, especially mobile chat applications , we propose in this work an approach of user keystroke-based authentication which is based on two important mechanisms, firstly the authentication is done on the basis of a supervised classification model trained on the dynamic keystroke database (android platform) the prediction accuracy has reached high values. When the attacker used the same behavior as the legitimate user we are strengthening security by another authentication mechanism based on three parameters during the opening of a communication session between two users (the number of messages per session, connection time and inter-click time). The proposed system has shown satisfactory results by combining user behaviours and connection parameters during an open session between two users to reinforce the authentication aspect.
A Hybrid Optimal Feature Extraction for Brain Tumor Segmentation
Authors: Kumar P. Santhosh , V. P. Sakthivel.  , Raju Manda , Sathya P. D.
Published: May 2022

The brain is the central nervous system of a human being. Brain tumor disease is considered the significant cause of death in many people. The core idea of deep learning is the comprehensive feature representations that will be learned efficiently along with the deep architectures, which are composed of trainable non-linear operations. Learning effective feature representations directly from the MRI becomes harder. Therefore, in the present study, a hybrid and optimal method are proposed. Grey Wolf Optimization algorithm is used for feature selection which reduces the more numbered features and then the classification of an image with the tumor type is done by the classifier Recurrent Neural Networks. The segmentation process is performed after the classification process, here segmentation is done by the MRG method with threshold optimization. The performance analysis is performed in terms of sensitivity, specificity, and accuracy which is done for the proposed techniques. Performance accuracy is obtained from this study is 98.16% using the proposed GWO technique.
Time-Critical Data Transmission Scheme in Wireless Sensor Networks Using Machine Learning Approach
Authors: Raut Archana R. , Khandait Sunanda P. , Dongre Snehalata S.
Published: May 2022

Wireless sensor network has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.
Ensemble Deep Learning Intrusion Detection Model for Fog Computing Environments
Authors: Kalaivani K.  , Chinnadurai M.
Published: May 2022

Fog computing is decentralized architecture located between the cloud and devices that produce data. It acts as an intermediate layer between IoT devices and Cloud. Fog computing can perform substantial processing for the time sensitive IoT applications to reduce the latency. At the same time the Fog layer is exposed to various kinds of attacks. Deep learning-based intrusion detection system (IDS) can be suitable for fog computing paradigms for protecting the fog nodes from attacks. In this paper we have proposed a novel ensemble deep learning intrusion detection architecture for fog computing by combining two deep learning models such as traditional CNN and IDS-AlexNet model and showed this model gives high accuracy of attack detection. The respective model implementations were applied on the UNSW-NB15 datasets. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for intrusion detection. Our proposed model shows that it outperformed various other traditional and recent models.
Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting
Authors: Sahu Kishore Kumar , Nayak Sarat Chandra , Behera Himansu Sekhar
Published: May 2022

Model with better learning ability and lower structural complexity is desirous for accurate exchange rate forecasting. Faster convergence to optimal solutions has always been a goal for the researcher in building forecasting models. And this is achieved by extreme learning machines (ELMs) due to their single hidden layer architecture and superior generalization ability. ELM is a simple training algorithm used to find the hidden-output layer weights by a random selection of input-hidden layer weights. Metaheuristics algorithms like Fireworks algorithm (FWA), Chemical reaction optimization (CRO), and Teaching learning-based optimization (TLBO) are employed to pre-train the ELM owing to their fewer optimizing parameters. This article aims to pre-train ELM using the said metaheuristics separately, ensuring the optimal solution of a single feedforward network (SLFN) with improved accuracy. The pre-trained ELMs provide accurate results. The same was verified using other primitive optimization algorithms
Metaheuristic-Based Optimization Methods for the Segmentation of Tuberculosis Sputum Smear Images
Authors: Priya E.
Published: May 2022

Tuberculosis (TB) is a worldwide health crisis and second primary infectious disease that causes death. An attempt has been made to detect the presence of bacilli in sputum smears. The smear images recorded under standard image acquisition protocol are segmented by metaheuristic-based methods. Morphological operators are embedded in Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) segmentation to retain concavity of rod-shaped bacilli. Results demonstrate that hybrid ACO segmentation is able to retain the shape of bacilli in TB images. Segmented images are validated with ground truth using overlap, distance and probability-based measures. Significant shape-based features such as area, perimeter, compactness, shape factor and tortuosity are extracted from the segmented images. It is observed that hybrid method preserves more edges, detects the presence of bacilli and facilitates direct segmentation with reduced number of redundant searches to generate edges. Thus this hybrid ACO-morphology segmentation technique aid in the diagnostic relevance of TB images.
An Image Examination System for Retinal Optic Disc Mining and Analysis With Social Group Optimization Algorithm
Authors: Mishra Pradipta Kumar , Satapathy Suresh Chandra
Published: May 2022

This work aims to develop a hybrid image examination system to extract and evaluate the Optic Disc (OD) from the Age-related Macular Degeneration (AMD) and Non-AMD class Digital Fundus Retinal Image (DFRI). This work implements an image pre-processing through Shannon’s Entropy and Social Group Optimization (SE+SGO) based thresholding and image post-processing with Level Set Segmentation (LSS). A relative study among the extracted OD and the ground-truth is then executed to compute the vital Picture Similarity Parameters (PSP). This study also presents a detailed pixel level data analysis practice on the extracted OD. Finally, the performance of the LSS is then validated against the existing segmentation techniques, such as Chan-Vese, Active-Contour and k-means clustering. The proposed work is executed on the iChallenge-AMD-2018 DFRI (400 images) and the results confirm that, proposed hybrid tool helps to achieve better values of Jaccard (86.82%), Dice (91.78%), Accuracy (98.94%), Precision (92.86%), Sensitivity (94.06%), and Specificity (99.46%).
Moth-Flame Optimization Algorithm for Efficient Cluster Head Selection in Wireless Sensor Networks
Authors: Bose Pitchaimanickam
Published: May 2022

Network lifetime and energy consumption are the important requirement of wireless sensor networks. The sensor network is mainly used for the military and civil applications, habitat monitoring. These tasks consume more energy for the data processing and directly affect the network lifetime. Clustering methodology provides a better solution for prolonging the network lifetime and reducing the energy consumption. In this paper, moth flame optimization algorithm is proposed in LEACH-C algorithm for identifying the suitable cluster head in wireless sensor networks. The proposed methodology uses the navigation method of moths for balancing the exploration and exploitation phases in the optimization process. The residual energy of the node and distance between the cluster head and sensor node are utilized to calculate the fitness function. The proposed methodology is evaluated with the help of performance metrics of network lifetime, energy consumption and number of alive nodes. The proposed methodology prolongs the network lifetime and reduces the energy consumption.
Improved Wolf Pack Algorithm for UAV Path Planning Problem
Authors: Chen Yueyi , Wu Husheng , Xiao Renbin
Published: May 2022

In order to obtain the global optimal path of UAV flight path planning in low altitude penetration mode, this paper studies a method of UAV 3d flight path planning. Aiming at the shortcomings of original wolf pack algorithm, such as poor local search ability, invariable step size and slow convergence rate, this paper proposes an improved wolf pack algorithm based on the fluorescein guide mechanism in the artificial glowworm swarm optimization algorithm, which also improves the searching mode in wandering behavior, update the individual position in wolf pack with the teaching and learning optimization algorithms with feedback mechanism. In addition, dynamic mutation strategy was introduced to jump out of the local optimal solution. The results show that the improved algorithm is more effective in solving the problem of UAV 3d trajectory planning.
Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals
Authors: Rathod Vasundhara , Tiwari Ashish , Kakde Omprakash G.
Published: May 2022

The conventional BCI system experiences several issues such as background noise interference, lower precision rate and high cost. Hence, a novel speech recognition model which is based on the optimized Deep-CNN is proposed in this research article so as to restrain the issues related to the conventional speech recognition method. The significance of the research relies on the proposed method algorithm known as Aquila-eagle optimization, which effectively tunes the parameters of Deep-CNN. The most significant features are extracted in the feature selection process, which enhance the precision of the speech recognition model. Further unwanted noises in the EEG signals are constructively removed in the pre-processing stage to boost the accuracy of the Deep-CNN classifier.From the experimental outcomes it is demonstrated that the proposed Aquila-eagle-based DeepCNN outperformed other state-of-the-art techniques in terms of accuracy, precision, and recall with the values of 93.11%, 90.89%, and 93.11%, respectively.
Grey Wolf Shuffled Shepherd Optimization Algorithm-Based Hybrid Deep Learning Classifier for Big Data Classification
Authors: Banchhor Chitrakant , Srinivasu N.
Published: May 2022

In recent days, big data is a vital role in information knowledge analysis, predicting, and manipulating process. Moreover, big data is well-known for organized extraction and analysis of large or difficult databases. Furthermore, it is widely useful in data management as compared with the conventional data processing approach. The development in big data is highly increasing gradually, such that traditional software tools faced various issues during big data handling. However, data imbalance in huge databases is a main limitation in the research area. In this paper, the Grey wolf Shuffled Shepherd Optimization Algorithm (GWSSOA)-based Deep Recurrent Neural Network (DRNN) algorithm is devised to classify the big data. In this technique, for classifying the big data a hybrid classifier, termed as Holoentropy driven Correlative Naive Bayes classifier (HCNB) and DRNN classifier is introduced. In addition, the developed hybrid classification model utilizes the MapReduce structure to solve big data issues. Here, the training process of the DRNN classifier is employed using GWSSOA. However, the developed GWSSOA is devised by integrating Shuffled Shepherd Optimization Algorithm (SSOA) and Grey Wolf Optimizer (GWO) algorithms. The developed GWSSOA-based DRNN model outperforms other big data classification techniques with regards to accuracy, specificity, and sensitivity of 0.966, 0.964, 0.870, and 209837ms.