bluuebunny/crossref_metadata_2025 huggingface dataset:
High Performance Changeable Dynamic Gentle Random Early Detection (CDGRED) for Congestion Control at Router Buffer
Authors: Jarrah Amin , Alshiab Mohammad Omar , Shurman Mohammad M.
Published: May 2022

The internet is spreading fast and the diversity of its components affects the performance unpredictably. This leads to the continuous examination of internet hardware structure for the purpose of user experience improvement. Network congestion is one of the challenges that affects network performance, which mostly occurs when the arriving packets exceed available network resources. When this occurs, incoming packets face unpredicted losses or delay. Thus, congestion has an impact on worsening the network performance due to an increase in packet loss. Therefore, a high performance approach called CDGRED was proposed to overcome these constraints using adaptive techniques. An optimized implementation with a suitable parameter tuning for CDGRED method was proposed with results showing clearly enhanced outputs. The CDGRED approach performance is empirically tested and compared with existing methods such as GRED, DGRED, and FLRED. Experimental results prove that the proposed approach has higher performance in early congestion detection over existing approaches.
Optimization of Sparse Distributed Computations
Authors: Larbi Olfa Hamdi
Published: May 2022

We address the problem of the optimization of sparse matrix-vector product (SpMV) on homogeneous distributed systems. For this purpose, we propose three approaches based on partitioning the matrix into row blocks. These blocks are defined by a set of a fixed number of rows and a set of contiguous (resp. non-contiguous) rows containing a fixed number of non-zero elements. These approaches lead to solve some specific NP-hard scheduling problems. Thus, adequate heuristics are designed. We analyse the theoretical performance of the proposed approaches and validate them by a series of experiments. This work represents an important step in an overall objective which is to determine the best-balanced distribution for the SpMV computation on a distributed system. In order to validate our approaches for sparse matrix distribution, we compare them to hypergraph model as well as to PETSc library for SpMV distribution on a homogenous multicore cluster. Experimentations show that our approaches provide performances 2 times better than hypergraph and 49 times better than PETSc.
A QoS-Enabled Load Balancing Approach for Cloud Computing Environment Join Minimum Loaded Queue (JMLQ)
Authors: Sharma Minakshi , Kumar Rajneesh , Jain Anurag
Published: May 2022

Cloud computing delivers the on-demand virtualized resources to its consumer for servicing their request on a metered basis. During the high demand of cloud resources the load on system increases that may unbalance the system which affects the quality of service parameters (QoS) adversely that leads to violations of service level agreement (SLA). Role of load balancing is significant in such an environment as it enhances the distribution of workload across multiple devices for example across network links, a cluster of servers, disk drives, etc. The present research work introduced a multi scheduler for balancing the load across the system that aims to optimize the QoS parameters such as response time, resource utilization, and the average waiting time by exploiting these virtual resources in the cloud environment. The performance of the proposed approach analyzed and tested in CloudSim that to optimize these parameters for the current approach. The authors found that our QoS enabled JMLQ approach achieved better results in comparison to our previous JMLQ approach and other variants.
A Traitor Tracking Method Towards Deep Learning Models in Cloud Environments
Authors: Zhang Yu , Wei Linfeng , Li Hailiang , Cai Hexin , Wu Yingxin
Published: May 2022

Cloud computing can speed up the training process of deep learning models. In this process, training data and model parameters stored in the cloud are prone to threats of being stolen. In model protection, model watermarking is a commonly used method. Using the adversarial example as model watermarking can make watermarked images have better concealment. Oriented from the signature mechanism in cryptography, a signature-based scheme is proposed to guarantee the performance of deep learning algorithms via identifying these adversarial examples. In the adversarial example generation stage, the corresponding signature information and classification information will be embedded in the noise space, so that the generated adversarial example will have implicit identity information, which can be verified by the secret key. The experiment using the ImageNet dataset shows that the adversarial examples generated by the authors’ scheme must be correctly recognized by the classifier with the secret key.
A Novel Interpretable Stock Selection Algorithm for Quantitative Trading
Authors: Li Zhengrui , Lin WeiWei , Wang James Z. , Peng Peng , Lin Jianpeng , Chang Victor , Pan Jianghu
Published: May 2022

In recent years, machine learning models have exhibited remarkable performance in the fourth industrial revolution. However, especially in the field of stock forecasting, most of the existing models demonstrate either relatively weak interpretability or unsatisfactory performance. This paper proposes an interpretable stock selection algorithm（ISSA) to achieve accurate prediction results and high interpretability for stock selection. The excellent performance of ISSA lies in its integration of the learning to rank algorithm LambdaMART with the SHapley Additive exPlanations (SHAP) interpretation method. Performance evaluation over the Shanghai Stock Exchange A-share market shows that ISSA outperforms regression and classification models in stock selection performance. Our results also demonstrate that our proposed ISSA solution can effectively filter out the most impactful features, potentially used for investment strategy.
Time Restraint Load Balancing in the Cloud Environment
Authors: Malhotra Nikita , Tyagi Sanjay , Singh Monika
Published: May 2022

The outlook for cloud computing is growing day by day. It is a developing field which is evolving and giving new ways to build, manage and process data. The most difficult task in cloud computing is to provide best quality parameters like maintaining deadline, minimizing make-span time, increasing utilization of resources etc. Therefore, dynamic scheduling of algorithm is needed by a service provider that executes the task within given time span while reducing make-span time.The proposed algorithm utilizes merits of max min, round robin, and min-min and tries to remove the demerits of them. The proposed algorithm has been simulated in cloud Sim by varying number of tasks and analysis has been made based on make-span, average utilization of resources and balancing of load. The results show that the proposed technique has better results as compared to heuristic techniques such as min-min, round robin and max-min.
Interactive Documentary Practices as an Emerging Tool for Development Communication
Authors: Kusuma Krishna Sankar , Patidar Paoni
Published: May 2022

Interactive documentary is an evolving form of filmmaking which has the potential to become an influential development communication tool to facilitate social change because of its participatory nature. Study explores how can the medium of interactive documentary create participatory interaction that can be used as a tool for development communication? How components of interactive documentaries help in making an effective development communication intervention? The study also identifies technological advancements that have made i-doc’s as a potential tool for social change. The i-doc is an innovative form of documentary films that are interactive, multilinear, and participatory. The i-doc film projects selected are analysed from a participatory development communication approach specifically. Through content analysis of selected i-doc’s from a development communication lens; recommendations are made for the filmmakers, change-makers, and development communication specialists to apply interactive documentary form as a successful development communications tool.
The Use of Management Information Systems in Learning
Authors: Alfahl Husam
Published: May 2022

Information systems are applied to a range of activities by organizations to improve efficiency and effectiveness. They are used in almost all industries, including manufacturing, health, and education. Therefore, organizations must measure and evaluate the usability of their systems to get the most value from the systems. One of the most reliable evaluation scales that can measure the usability of the systems is the system usability scale (SUS) which was used in this study to evaluate the usability of Blackboard at universities in Saudi Arabia. This study showed that Blackboard as LMS had a high SUS score when it was used at Saudi Universities. Indeed, the current study revealed that Blackboard could be used in teaching effectively. Furthermore, the current research findings show that students have higher SUS scores than lecturers, which means they can easily use Blackboard. It is also recommended that lecturers receive more training on Blackboard.
Research on the Policies Responding to the Rise of Platform Monopolies
Authors: Huang Yonggang
Published: May 2022

Platform enterprises operate in the platform business model, which is very different from traditional enterprises. Therefore, the legal regulation of mutual monopoly of Internet platform enterprises has become more complex. Facing the monopoly phenomenon in the platform economy market, we must have appropriate policies to deal with it and alleviate the problem of unfair resource allocation under incomplete market competition. This essay briefly analyses the corresponding policy of platform monopoly in different jurisdictions. From the antitrust experience of the United States and China, it can be concluded that preventing market monopoly is the primary goal to deal with platform monopoly. From the EU's antitrust experience, it can be supposed that preventing data monopoly is a vital goal to deal with platform monopoly. Through the combination of Anti-market monopoly and Anti-data Monopoly policies, we can appropriately deal with the platform's market monopoly, stimulate the platform's economic vitality, and create more economic value for the market.
Exploring Online Shopping Adoption for Innovations in Market Development
Authors: Baporikar Neeta
Published: May 2022

Online shopping technology is important as it helps businesses to gain value from lesser operational expenditures due to the reduced workforce and physical space. Despite its benefits, the adoption of online shopping in Africa, including Namibia, lags. Hence the main objective of this research is to investigate the challenges and issues in adopting online shopping for market growth. Adopting a qualitative approach based on the UTAUT model we determine and analyze predominant challenges and issues, examine the influence of demographic variables on online shopping, define and investigate the key success factors and propose strategies to increase the adoption of online shopping. Findings reflect that lack of security and trustworthiness in the online shopping process is a key challenge in the adoption of online shopping. Recommendations include that the government should regulate online shopping procedures to protect consumers and prevent cybercrime and businesses should offer consumer education to build trust and eliminate fear in the online shopping process.
Understanding Innovation in Organizations and Its Implications
Authors: Mandal Pratap Chandra
Published: May 2022

Organizations incorporate innovation in their processes to grow and to achieve business excellence. Although innovation is a pervasive term, it appears to be elusive because of its different explanations. Organizations should recognize three aspects of innovation to reap its benefits which include: innovation is an outcome, innovation is a process, and innovation is a mindset. The outcome emphasizes what output is sought, including product innovation, process innovation, marketing innovation, business model innovation, supply chain innovation, and organizational innovation. Innovation as a process should be organized so that the outcomes are effective. This includes an overall innovation process and a new product development process. Innovation is a mindset which employees should internalize. Innovation should be ingrained with the creation of a supportive organizational culture. It defines necessary elements, considerations, and vernacular. It allows organizations to adopt effective strategies and to take better decisions which allow them to enable innovation.
Current Status, Requirements, and Challenges of Blockchain Application in Land Registry
Authors: Shuaib Mohammed , Alam Shadab , Ahmed Rafeeq , Qamar S. , Nasir Mohammed Shahnawaz , Alam Mohammad Shabbir
Published: May 2022

The land registry system is one of the crucial aspects of any government. Proper and reliable land management is essential for economic growth and governance. The traditional land registry model lacks many essential requirements and mired with a different type of malpractices and security concerns. Blockchain technology has been proposed as a possible solution to counter these malpractices and security concerns. Even though there are many advantages of using blockchain in the land registry, still there are some concerns and challenges that need to be reviewed and addressed for successful blockchain implementation in the land registry. This paper reviews the current state of blockchain implementation in the land registry and the existing project being implemented in different countries. It further highlights the requirements, implications and challenges for successfully implementing blockchain in the land registry system. This paper provides a future direction for new implementations.
Challenges and Ethical Issues in Data Privacy
Authors: Bala Renu
Published: May 2022

This paper strived to find out the challenges and ethical issues in the stream of data privacy in the present era of the twenty-first century. The present study focuses on empirical studies that were published during the period 2008-2020. During the research, efforts have been made to identify the challenges and ethical issues that the researchers have to face during the research process. In the study, it has been noticed that such kind of matters gained popularity after the year 2011. In the research, it has been found that there are many challenges to data privacy. It becomes challenging to follow ethics to maintain data confidentiality and security in this technology-driven era. In the end, based on available data, it has been concluded that there is an excellent need for stiff punishments for that person who misuse the data for their own sake and demoralize those people who make hard efforts to carry out the research and generate new concepts and ideas. So the government should take necessary measures toward data privacy areas for security and safety purposes.
Credit Card Fraud Prediction Using XGBoost
Authors: Mohbey Krishna Kumar , Khan Mohammad Zubair , Indian Ajay
Published: May 2022

With the development of technology, the internet and eCommerce online payment has become an essential mode of payment. Nowadays, credit card payment is a convenient mode of payment online as well as offline transactions. As online credit card payment increases, fraud transactions are likewise increasing day by day. Increasing fraud transactions in the online payment system became a more significant challenge for banks, companies, and researchers. Therefore, it is essential to have an efficient methodology to detect fraud transactions while payment has completed via credit card. Although many traditional approaches are already available for fraud transaction prediction, however, existing methods lack accuracy, and it can be increased by ensemble techniques such as XGBoost. In this paper, we use an ensemble approach that is XGBoost (eXtreme Gradient Boosting) for credit card fraud prediction. The results are compared with existing machine learning approaches.
A Hybrid Approach of Prediction Using Rating and Review Data
Authors: Srivastava Aseem , Ahmed Rafeeq , Singh Pradeep Kumar , Shuaib Mohammed , Alam Tanweer
Published: May 2022

A collaborative filtering technique has proven to be the preferable approach for personalized recommendations. Traditionally, collaborative filtering recommends target items to those users who have similar tastes. The performance of collaborative filtering degrades significantly when a considerable number of users do not provide ratings on recommended products. In such a scenario, the dataset utilized in recommendation becomes highly sparse, and ratings become very few or none co-rated. To mitigate the problem, as mentioned earlier, and to improve the performance of collaborative filtering, we propose an approach that adopts users' textual reviews and ratings both in the rating prediction. The dataset used is Amazon fine Food Reviews containing rating and text review with 568454 reviews from October 1999 to October 2012. The proposed model is tested on the collected dataset. The experimental results provide the proper evidence that the proposed model outperforms other traditional algorithms of collaborative filtering techniques.
Detecting Sinkhole Attacks in IoT-Based Wireless Sensor Networks Using Distance From Base Station
Authors: Mondal Koushik , Yadav Satyendra Singh , Pal Vipin , Singh Akhilendra Pratap , Yogita Yogita , Singh Mangal
Published: May 2022

Wireless sensor networks (WSNs) are infrastructure-less in nature, which contains numbers of autonomous sensor nodes. These sensor nodes are dedicated to monitoring the physical conditions of the environment and are organizing the collected data at a central location. Application area of WSN, like - heath care, military surveillance, are sensitive with respective to information sensed, that’s why security of WSN needs to be very effective. Providing security to WSN plays a major role as it consists of limited resources. The security system should lie within the boundary of the resource potential as well as should be competent enough to handle attacks. Intrusion detection system (IDS) is one such type of defense system, which can fulfill the measure of limitation of resources. In this paper, a detection technique is proposed against sinkhole attack using the Euclidean distance of each node from base station. The main advantage of the proposed technique is that it doesn't require any extra hardware setup as well as doesn't require extra communication cost.
Research on Collaborative Machine English Translation Using the HIC Technology
Authors: Lv Jingjing
Published: May 2022

Due to the rapid development of smart city, Hybrid Information Centric-Networking (HICN) emerges as a promising technology to enable the power of smart city. One of the most important application is the smart English translation, which becomes more and more popular with the process of Internationalization. In this work, we focus on studying the intelligent English translation is smart city using the HICN technology. Particularly, a method using collaborative machine learning and quality estimation technique is proposed, which sets a fixed threshold to filter pseudo-parallel data during unsupervised neural machine translation training. The quality estimation is used to evaluate and screen the pseudo-parallel data with high performance generated during reverse translation training. The results indicate that the proposed method outperforms the state-of-the-art methods.
A Diffraction Service Composition Approach Based on S-ABCPC
Authors: Min Xunyou , Xu Xiaofei , Wang Zhongjie , Liu Zhizhong
Published: May 2022

In recent years, research on the QoS-aware service composition problem often assumes that each component service in the process to be solved is equally essential. They do not consider the impact of core component services and other component services on problem-solving, or even though their impact is considered, they are not fully considered. So this paper first proposes a diffractive method based on them. Considering the advantages of artificial bee colony (ABC) such as simplicity, this paper chooses it as the basic algorithm. In addition, with the continuous development of service ecosystem, it gradually formed a variety of domain features. They have an important influence on problem-solving, but the existing research has not explored this influence in-depth. Therefore, this paper digs deep into this influence. Given the characteristics of the problem to be solved in this paper, the S-ABCPC algorithm is designed. At last, experiments have proved the effectiveness of the method proposed in this paper. The impact factors of this method have been studied.
QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms
Authors: Pandharbale Priya Bhaskar , Mohanty Sachi Nandan , Jagadev Alok Kumar
Published: May 2022

Service-oriented computing (SOC) activates communication through web services to provide computing as a service for business applications in the service-oriented architecture (SOA). To make SOC successful, finding a needed service to build a system directly depending on the collection of services is a critical confront. In this paper, the authors planned the clustering-based approach called dynamic clustering (DCLUS). The novelty in DCLUS compared to static-based clustering technique is the use of dynamic clustering technique. In existing CLUS, the static various widths clustering method is exploited for the users and services clustering. However, due to the limitations of static clustering, they proposed dynamic clustering to optimize the performance of clustering using data mining to find the associations and patterns, for services, and also the prediction accuracy. The performance of the proposed DCLUS system will be implemented and evaluated facing the existing system in phases of precision, recall, and f-score performance metrics using the research dataset.
Analysis and Prediction of Healthcare Sector Stock Price Using Machine Learning Techniques
Authors: Ahmed Daiyaan , Neema Ronhit , Viswanadha Nishant , Selvanambi Ramani
Published: May 2022

Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis.
Promote Learning Survival Skills Through Technology Integration in Course Design
Authors: van Wyk Mari , Moodley Kimera
Published: May 2022

In an integrated, dynamic, and ever-changing world, it is imperative that students are able to transition between jobs irrespective of qualifications. Studies show that graduates are inadequately prepared for the working world. In an attempt to address this need, a postgraduate certificate course was redesigned to follow a constructionist approach integrated with mobile technology. Data were collected from two learning designers and 29 students, forming a qualitative study. An evaluation of the activities and learning survival skills in the course was done using the technology integration matrix (TIM). The results indicated that technology was integrated on various levels, and students achieved a multitude of skills, making them more confident in their use of technology. Feelings of anxiety, incompetence, and resistance were transformed to resilience, motivation, and comfort. The art to address graduate preparedness for work lies in effective course design and a constructionist forward-thinking approach to learning.
Will the Customer Survive or Not in the Organization?
Authors: Padhy Neelamadhab , Panda Sanskruti , Suraj Jigyashu
Published: May 2022

Context: The technology of machine learning and data science is gradually evolving and improving. In this process, we feel the importance of data science to solve a problem. Objective: In this article our main objective is to predict the customer churn, i.e. whether the customer will leave the telecom service or they will continue with the service. In this paper, we have also followed some statistical measures like we have computed the mean, standard deviation, min, max, 25%, 50%, 75% values of the data. Mean is the average value of the data values. The standard deviation is a measure of the amount of variation or dispersion of a set of values. Conclusion: We have done an extensive data pre-processing and built Machine Learning models, and found out that among all the models Logistic regression gives the best performance i.e 81.5%., and hence we chose that as our final model to indicates the churn prediction
Cross-Project Software Refactoring Prediction Using Optimized Deep Learning Neural Network With the Aid of Attribute Selection
Authors: Panighrahi Rasmita , Kuanar Sanjay Kumar , Kumar Lov
Published: May 2022

Cross-project refactoring prediction is prominent research that comprises model training from one project database and testing it for a database under a separate project. While performing the refactoring process on the cross project, software programs want to be restructured by modifying or adding the source code. However, recognizing a piece of code for predicting refactoring purposes is remained to be actual challenge for software designers. To date the entire refactoring procedure is highly dependent on the skills and software inventers. In this manuscript, a deep learning model is utilized to introduce a predictive model for refactoring to highlight classes that need to be refactored. Specifically, the deep learning technique is utilized along with the proposed attribute selection phases to predict refactoring at the class level. The planned optimized deep learning-based method for cross-project refactoring prediction is experimentally conducted on open- source project and accuracy found as 0.9648 as comparison to other mentioned state of the art.
Assessment of Road Maintenance Project Management in Ethiopia
Authors: Kesto Dakito Alemu , Gebre Zerubabel Alemu
Published: May 2022

Addis Ababa City Roads Authority manages and implements the construction and maintenance of roads within the city. Different literature support efficient performance of road infrastructure projects is essential for economic growth and development of the country, and data indicate that there is a high range of new construction of road coverage in Addis Ababa. Despite the growth of new road construction there is decline of road maintenance projects and there are different reasons mentioned as factors that affect road maintenance project management performance. Thus, the aim of this study was to assess approaches to improve road maintenance projects management practice by tackling the challenges for future practices. The results indicated that, even though inadequate funding has higher factor that influence the performance of road maintenance project management, there are other factors such as no privatization of road maintenance work and lot of paper work had significant effects on the performance of road maintenance project management practice.
Emergency Project Management Practice in Ethiopia
Authors: Kesto Dakito Alemu , Tamene Hunde
Published: May 2022

Emergency Project Management is a kind of project management that often attracts the most attention and resources and its management needs application of knowledge, skills, tools, and techniques to project activities to meet the immediate project output and goal. Proper project management practice is critical for the success of Emergency Project. Thus, the main objective of this study was to assess the extent to which Project Management Knowledge area were applied and identify the challenges in Emergency Project management. The study used descriptive research design and data were obtained through questionnaire. The finding revealed Scoping & Scheduling tools and techniques were not utilized properly. Moreover, Procurement management needs improvement. Some of the knowledge areas were managed informal & traditionally. The study recommended that workable policy documents to lead the procurement and overall Emergency Project management are vital.
Estimation of Construction Activity Duration Under Uncertainty Using Discrete Fuzzy Weighted Average Algorithm
Authors: Rezakhani Pejman , Korkmaz Kasim A.
Published: May 2022

This paper presents a qualitative risk assessment tool based on fuzzy set theory to estimate the amount of activity duration overrun under the combinatory effect of multiple risk factors. Applying proposed methodology, a set of duration modifiers to calculate the optimistic, most-likely and pessimistic duration values under uncertainty are calculated. To elaborate the methodology, a simulated bridge project with ten risk factors affecting activities duration is presented. Proposed model contributes to knowledge that could help minimize the schedule overrun and improve risk mitigation strategies through providing risk-based duration estimates instead of discrete values. Advantages of the developed approach compared to existing models include (1) considering the combinatory effect of multiple risk factors on activity duration, (2) accounting for uncertainty in experts’ evaluations by employing the interval-valued fuzzy numbers, and (3) utilizing a discrete fuzzy weighted average algorithm which avoids creation of incorrect fuzzy membership functions.
Philosophical Sediments
Authors: Jackson Ross A. , Heath Brian L. , Hartman Paul , Kumar Shweta
Published: May 2022

Ethics rest on philosophical groundings. This holds implications for international business as the philosophers selected as the basis of ethics are informed by culture. Business publications provide a source for exploring this phenomenon. Articles from the CNKI database were downloaded and analyzed in KNIME. An author-created ontology was used to identify business ethics articles. Corpus linguistic techniques established philosophical sediments. From a Marxian perspective, Marx and Mao Zedong figured prominently, where Plato and Aristotle were frequent non-Marxian philosophers. A Mann-Whitney U test showed the median frequency of Marxian philosophers is significantly greater than non-Marxists. Dyadic analysis revealed more frequent reference to socialism over capitalism. These results suggest the philosophical sediments of these articles rest primarily on Marxian philosophers and collectivist constructs. As nations increasingly use artificial intelligence, they will use different philosophical lenses engendering distinctive results culminating in dissimilar system-level outcomes.
Application of Cognitive Network Analysis to Mine Operations
Authors: Lavanderos Leonardo P. , Morales Ramón , Araya Abelardo
Published: May 2022

A faster pace of change in business strategies and the adoption of new mining models on the part of mining companies is their response to commodity price decrease, global demand change, operating costs increase, ore grade declining, the need to extend asset life cycle, community empowerment, and increased mining operating licenses. One of the mainstream solutions is the organization's automation and digital transformation aiming to achieve new levels of performance throughout the process network. However, the barrier that hinders any transformation process is the network culture supporting productive/administrative processes. This paper shows how relational change determines the amount of organizational waste production, so trying to introduce digital change excluding network culture results in a futile effort. The data shown comes from assessments in the Occupational Safety and Health Management (OSHM) of Chuquicamata Division, Codelco, Chile.
Influence of Temperature and Applied Load on Adhesion and Abrasion Wear Characteristics of Ti6Al4V Alloy
Authors: Lal Basant , Dey Abhijit , Wani Mohammad. Farooq
Published: May 2022

Over the last decade, research on high-temperature tribological characteristics of Ti6Al4V alloy has been gathered significant interest due to their sustainable properties in high-temperature applications. Adhesion and abrasion have been identified as the most prevalent wear mechanism in the tribological system operated under high temperatures. Hence, the present study aimed to investigate the high-temperature wear behavior of titanium alloys under a vacuum environment and various operating conditions in order to explore the formation of no-oxide tribo layers and the governing wear mechanisms. The transition of materials from both the worn surfaces (Pin and Disc) has been demonstrated. The microstructural characteristics of the worn surfaces and cross-sections revealed the formation of no-oxide tribo-layers which significantly influenced the wear rates as well as governed wear mechanisms. Due to the formation of no-oxide layers, the coefficient of friction was observed to increase with increasing the temperature and load.
Transforming Public Sector Organisations in Non-Western Economies Through a Sustainability Leadership Approach
Authors: Donkor Francis
Published: May 2022

Dynamics in the business environment today is affecting every facet of human endeavor. The traditional leadership behavior has had its own share. Several researchers have written on sustainable development and corporate governance practices as a sure way of profitability and growth in a contemporary business organization. In this article, the author ignites the debate to transform public sector organizational leadership in the non-western economies to embrace the sustainability leadership approach. This is as a result of poor corporate governance and unethical corporate cultural practices and lack of stewardship theoretical approach that has brought seven domestic banks to their knees in Ghana in 2017 and 2018. The study further proposes a strong organizational culture that keeps values and ethical behavior as well as regulatory framework in checks for long-term survival and profitability in the public sector organizations that take away the vulnerability and unethical practices.
Visibility Dimension of Strategic Corporate Social Responsibility
Authors: Arora Sanjeev , Kaur Jasveen , Arora Gitika
Published: May 2022

Being socially responsible is the prerequisite for any business to be sustainable, and it is incidental for a company to reveal whatever socially responsible activities they are doing. The purpose of this paper is to examine Corporate Social Responsibility (CSR) activities under the visibility parameter of Strategic CSR. This helps to provide information on different ways being used by companies in highlighting their CSR activities and motivates other companies to create a culture of CSR. The paper is exploratory and descriptive in nature. The approach used to display the visibility parameter described in this document is a qualitative analysis of the websites, sustainability reports and annual reports of companies for four years from 2014-15 to 2017-18, with an extant review of the literature of CSR. The study proposed six levels for measuring the visibility of CSR activities of companies.
E-Development and Sustainable Management Education for Effective Leadership and Sustainable Society
Authors: Podder Suplab Kanti , Samanta Debabrata
Published: May 2022

Electronic development is the process of systematic evolution for mankind and society at large that ensures the overall progress of the electronic mode of learning, education, healthcare, society, and corporate governance. The main objective of the chapter was to address the impacts of e-development and sustainable management education for effective leadership that leads to constructing a sustainable society. The required data were collected both from primary and secondary sources. Primary data were collected from 120 respondents. The secondary data sources included Official Websites. The study is empirical and various statistical tools like Mean, Standard Deviation, and t-test were executed for data analysis. The results of the research study were indicated the high degree and low degree of contribution from E-development and Sustainable Management Education are not significant between Effective Leadership and Sustainable Society. E-development can be effective for creating a Sustainable Society with the goal-setting of improving Effective Leadership Skills.
A Hybrid Approach for Simplification Using Logarithmic Clustering and Moments Matching
Authors: Sharma Namrta , Vishwakarma C. B.
Published: May 2022

A simplification method is suggested in this paper to simplify a large-scale dynamic system using logarithmic pole clustering and moments matching using Pade approximations. The denominator polynomial of the simplified model is computed by using logarithmic pole clustering while the numerator coefficients of the same model are determined by matching the time-moments of the original large-scale system. The viability of the proposed method has been tested on few large-scale systems taken from the literature.
Lowest Tariff Load Shifting Demand Side Management Technique in Smart Grid Environment
Authors: Yadav Ravindra Kumar , Hrishikesha P. N. , Bhadoria Vikas Singh
Published: May 2022

Electrical energy is playing an important role in our day-to-day life. The burden on utility is increasing continuously due to huge utilization of electrical energy thereby utilities are suffering from peak shortage. The concept of demand side management can be applied to relieve the utilities from suffering peak load burden. In this paper, a Lowest Tariff Load Shifting (LTLS) approach of DSM is suggested to flatten the load curve as desired by the utilities. Residential and commercial loads are considered for validation of proposed algorithm. The use of DSM techniques can delay the expansion of power system for short duration such as few months or years. This paper produces a flatten load curve by applying LTLS technique of DSM and results demonstrates cost saving and peak load reduction in residential as well as commercial area.
Development of PLC and SCADA-Based Automation Systems for Control of Irrigation Dam Gates
Authors: Lata Suman , Dhakal Sandip , Verma H. K. , Jaiswal Shiva Pujan , Bhadoria Vikas Singh
Published: May 2022

The control and monitoring system presented is based on Supervisory Control and Data Acquisition concept. It provides GUI service for operators and data visualization capability to display data trends using an appropriate DBMS (database management system). The SCADA system also incorporates Alarm and PAS (public address system) for operational and functional safety. The remote control of the gates is carried out through PC based SCADA software from a centrally located control room. The SCADA system consists of a Master Terminal Unit (MTU) located in the control room, MicroLogix 1400 PLC-based Remote Terminal Units (RTUs) located in the control panels of dam gates, and ultrasonic sensors equipped with MODBUS/ RTU interface to measure upstream and downstream water levels. RTUs-MTU communication is realized using a dedicated LAN. This paper presents the system design along with hardware layout and software programs for both, RTUs and MTU. The program for PLC has been written in ladder logic using RSLogix 500, a powerful development software from Rockwell Automation.
An Enhanced Black Widow Optimization Algorithm for the Deployment of Wireless Sensor Networks
Authors: Deghbouch Hicham , Debbat Fatima
Published: May 2022

In order to solve the deployment problem, which is considered a major issue that faces the design of efficient Wireless Sensor Networks (WSNs), a novel deployment algorithm based on an Enhanced Black Widow Optimization algorithm (EBWO) is proposed. The EBWO algorithm aims to determine the optimal number of sensors and their locations for optimizing both the coverage and the deployment cost. The BWO algorithm is adapted to solve the deployment problem by introducing a set of enhancements, which improves the search capability and the run time of the algorithm. A chaotic initialization is employed in the EBWO algorithm to strengthen the exploration capability of the initial population. Moreover, a modified reproduction mechanism is designed to assist the algorithm in optimizing the number of deployed sensors. Comparisons with modern state-of-the-art deployment methods show that the EBWO algorithm can deliver excellent solutions, where it is ranked first during all the simulations with a coverage difference varying between 3.34% and 7.94% from the other competitors.
Mobile Robot Path Planning Based on Angle-Guided Ant Colony Algorithm
Authors: Li Yongsheng , Huang Yinjuan , Ge Lina , Li Xi
Published: May 2022

Ant colony algorithm is easy to fall into local optimum and its convergent speed is slow in solving mobile robot path planning. Therefore, an ant colony algorithm based on angle guided is proposed in this paper to solve the problems. In the choice of nodes, integrate the angle factor into the heuristic information of the ant colony algorithm to guide the ants' search direction and improve the search efficiency. The pheromone differential updating is carried out for different quality paths and the pheromone chaotic disturbance updating mechanism is introduced, then the algorithm can make full use of the better path information and maintain a better global search ability. According to simulations, its global search is strong and it can range out of local optimum and it is fast convergence to the global optimum. The improved algorithm is feasible and effective.
Optimal Trained Hybrid Classifier for Border Gateway Protocol Anomaly Detection
Authors: M. Sunita , Mallapur Sujata V.
Published: May 2022

Border Gateway Protocol (BGP) anomalies have interrupted network connection on a large scale, henceforth; recognizing them is of very important. Machine learning algorithms play a vital role in detecting the BGP anomalies in network. This paper intends to propose a new BGP anomaly detection method under certain processes (i) Feature Extraction and (ii) Classification. In feature extraction, certain features like "Number of Exterior Gateway Protocol (EGP) packets, Number of Interior Gateway Protocol (IGP) packets, Number of incomplete packets, Maximum Autonomous System (AS) path length, average AS-path length, packet size" etc. are extracted. Along with this, the statistical features such as mean, mode, variance, median, standard deviation, and higher-order statistical features such as kurtosis, skewness, second moment, entropy, and percentiles are also extracted. Subsequently, the classification is carried out by a hybrid classifier model that merges the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) models.
Particle Rider Optimization-Driven Classification for Brain-Computer Interface
Authors: Wankhade Megha M. , Chorage Suvarna S.
Published: May 2022

The emerging technology for translating the intention of human into control signals is the Brain–computer interface (BCI). The BCI helps the patients with complete motor dysfunction to interact with the people. In this research, a method for abnormality assessment in humans from the perspective of the BCI was proposed by developing a hybrid optimization algorithm based on Electroencephalography (EEG). The hybrid optimization algorithm, called Particle Rider Optimization Algorithm (PROA) is designed through the incorporation of Particle Swarm Optimization (PSO) and Rider Optimization algorithm (ROA). The pre-processing is done for filtering the noise and removal of artefact. In pre-processing, the noise is removed through the Common Average Referencing (CAR) and Laplacian filters, whereas the artifacts are eliminated by Principle component analysis (PCA).
Optimization of System Reliability in the Imprecise Environment via Genetic Algorithm
Authors: Sahoo Laxminarayan , Mahato Sanat Kumar , Bhunia Asoke Kumar
Published: May 2022

This paper presents a genetic algorithm (GA) approach to solve the redundancy allocation problem (RAP) with imprecise parameters. The impreciseness has been considered here to tackle the unpredictable environment in real-life affairs which is directly striking the design parameters. Imprecise parameters are represented by several types of representations, viz. fuzzy, stochastic, and deterministic interval-valued. In the case of fuzzy and stochastic representations, the corresponding problems have been transformed into a deterministic interval optimization problem. Hence, to solve the problem, a GA based penalty function technique is proposed. The constraint(s) of the same are handled by constraints satisfaction rule which has been proposed in this paper. In the proposed approach, the constrained optimization problem has been converted into an unconstrained optimization problem by use of Big-M penalty technique. To solve the RAP with imprecise parameters, GA has been implemented. Finally, numerical experiments have been done for illustration purpose.
Non-Contact Fingerprint Template Protection Using DFT Combined Random Projection
Authors: Zannou Boris Jerson , Djara Tahirou , Vianou Antoine
Published: May 2022

In view of the different dangers to which users of contactless biometric systems are exposed, we have developed a contactless secure revocable model based on random projection and DFT (Discret Fourier Tansformation) to enhance contactless fingerprint authentication. Two matrices emerge, namely that corresponding to the terminations and that corresponding to the bifurcations. These matrices are then transformed in a first time thanks to the random projection. In a second time we apply to them the Discret Fourier Transformation called the DFT.This proposed non-contact revocable fingerprint model meets the requirements of revocability, diversity, security and non-reversibility. The evaluation of our model through its results gives the most promising results compared to those existing. The equal error rate (EER) obtained are respectively equal to 0.19% for FVC2002 DB1, 1% for FVC2002 DB2, 4.29% for FVC2002 DB3 and 9.01% for FVC2004 DB2.
DNA-Based LSB Steganography
Authors: Khobzaoui Abdelkader , Mansouri Boualem , Benyahia Kadda
Published: May 2022

The integrity and confidentiality of transmitted data are the main requirements of any data security system. To achieve these goals, many techniques have been developed, including cryptography and steganography. Recently, DNA-based steganography has emerged as a very powerful and promising approach to ensure the safety of sensitive information transmitted over an untrusted channel. In this paper, a two-level encryption/decryption scheme combining cryptography and steganography is proposed. First, the plain-text is concealed in a cover image by scattering its letters over randomly selected pixels. Then, the modified image is encrypted by encoding its pixels in a DNA sequence using a symmetric key. Simulations have shown that the proposed scheme is more robust than any other system based on standalone techniques and requires less computing resources.