FORTHCOMING ARTICLES

These articles have been peer-reviewed and accepted for publication but are pending final changes. The articles will be published in near future as soon as they are assigned to the respective issues.
 
 
Detecting Public Sentiment and Hesitancy Towards Covid-19 Vaccines by Mining Twitter Data
Tina Tian, Emelia Hajdarovic
 
ABSTRACT
This paper presents a process and experimentation where data is collected, processed, and analyzed from the Twitter platform service to detect public sentiment of the COVID-19 vaccines. A collection of more than four million tweets is collected over a period of four months. Using machine learning algorithms, the collection of tweets is grouped into sentiments toward the COVID-19 vaccines. Multiple prediction models are used in this study and analyzed. The model presented to be the most operational with the collection of tweets generates a precision of 80%. This work represents a use case where machine learning methods are performed on data to gather opinions on a current vaccine. Also, this study includes experimentations that involve data analysis of sociological features’ impact on public opinions.
KEYWORDS - IJAE-209, Twitter, Social Media Big Data, Machine Learning
 
     
Fuzzy Expert System: an Intelligence Framework for Diagnosing Malaria
Muzammil Adamu, Muhammad Lawal Jibril
 
ABSTRACT
Malaria is a deadly disease killing millions of people every year. Different countries of the world, governmental and non-governmental organizations including World Health Organization have taken it as a challenge to address the issue of deaths associated with malaria. Prompt and accurate diagnosis is a major key in medical field. This prompts for the need to develop a diagnosis and therapy system that will help in diagnosing of malaria. The fuzzy logic was used, and low, average, high, very high are the linguistic variable used. The system interacts with user using plain English based on some arranged rules which are a typical collection of if/then rules. The system was developed using VB.Net and MySQL database. It is believed that this design can help to reduce the congestion we often see in our hospitals by providing solution for sick patients, irrespective of their locations. It would be of great necessity to provide a computerized system that will provide a complementary medical service, such as medical disease diagnosis in places where accessibility is a problem as well as health care facilities where qualified experts are lacking, hence this topic, Fuzzy Expert System an Intelligent Framework for Diagnosing Malaria.
KEYWORDS - IJAE-205, Diagnose, Expert, Framework
 
     
Action Research: The Bridge Connecting Research, Practice and Theory
Alexan Hagopian
 
ABSTRACT
Action Research as a scientific approach capitalizes on collaboration between researcher and participants to collect information, solve problems, and deliver new results. It is one of the many management research methodologies at the disposal of researchers today though it remains a highly debated one. Literature debates its characteristics using either a thematic approach or case studies without a comprehensive review of themes to answer the question ‘How does Action Research bridge the gap between research, practice, and theory?’. The current work carries out a content-based literature review of published articles on Action Research to highlight its ability as a methodology to outperform others in bridging this gap. It addresses the origin, definition, process, and stance of Action Research and aims to compile supporting evidence on its distinct bridging feature. It concludes that indeed Action Research possesses this distinct feature in more than one way. First, through a clear integration between various social science disciplines uncommon among managers due to higher levels of specialization involved in jobs. Second, through matching the problems faced by managers and the problem addressed by social scientists. Third, through the creation of social integration between social scientists and practitioners by bringing together the former with their academic interests and inclination to make contributions to knowledge and the latter with theircareer interests. Finally, Action Research remains a unique methodology that guides practitioners to understand the workplace and achieve an improvement of a problem situation, while incentivizing them to work better on perceived problems, be more effective and supportive while working collaboratively, and develop their skills.
KEYWORDS - IJAS-117, Action Research, Theory and Practice Gap, Theory and Practice Bridge, Action Research Origin, Action Research Development, Action Research Stance
 
     
Evaluation of material flows in the M River watershed (Ivory Coast)
Serge Ehouman Koffi, Anzoumanan Kamagaté, Koffi Jean Thierry Koffi, Amidou Dao, Dabissi Djibril Noufé, Bamory Kamagaté, Lanciné Droh Goné, Maurice Guilliod, Luc Séguis, Jean Louis Perrin
 
ABSTRACT
Soil erosion by precipitation, rainfall and runoff is a widespread phenomenon in different countries of the world. It becomes disastrous in particular on the slopes because of torrentially of the flow, of the strong vulnerability of the grounds (soft rocks, fragile grounds, steep slopes). The present study has for objective: The analysis of the data of concentrations of sediments in suspension are measured at the station of the rivers highlights relations, linking the concentration (or the solid flow) of the sediments in suspension to the liquid flow and to quantify the seasonal, monthly and interannual and intra annual variation of the surface degradation. Annual tonnage estimates of solids loads to the Mé were derived from the power law for all seasons. From this deduction, the annual quantities of sediment transported by the Mé from 2015/2017 is 7.06.106 t/year, or a specific degradation of 1.79.103 t/km²/year. On the other hand, in 2017, the value of this solid input is 3.06.106 t/year. However, the annual solid input is estimated at 7063.03.103 t/year with a specific degradation of 1784.47 t/km²/year at the Mé from 2015 to 2017.
KEYWORDS - IJAS-120, solid transport, solid flow, Abidjan district
 
     
Identification of Potential Inhibitors against Attachment Glycoprotein G of Nipah Virus using Comprehensive Drug Repurposing Approach
Sangita Ghimire, Sazzad Shahrear, Siddhesh Kishor Saigaonkar, Laura K.Harris
 
ABSTRACT
The emerging zoonotic Nipah virus (NiV) is a major threat to public health because of its potential to cause severe outbreaks from human-to-human transmission and lack of therapeutic options currently. Identification of effective therapeutics to combat NiV infections is needed to contain future outbreaks. This research uses in silico methods to predict putative therapeutic candidates for the NiV attachment glycoprotein G (NiV-G) from existing therapeutic agents. To do this, virtual screening of NiV-G against 1615 FDA approved drugs publicly available from the Zinc 15 database is performed using a molecular docking approach via AutoDock Vina software. Further, a molecular dynamics simulation using WebGRO server is employed to identify top NiV-G inhibitors. Most of the binding for the top three ligands – as determined by binding energy–occurs in the catalytic groove that must contain Phe458, Trp504, Gln559, and Glu579 in order to successfully inhibit NiV-G. The molecular dynamics simulation analysis validates rigidity and stability of the docked complex through the assessment of root mean square deviations, root mean square fluctuations, solvent accessible surface area, radius of gyration, and hydrogen bond analysis from simulation trajectories. Post-molecular dynamics analysis also shows that Alvimopan, Betrixaban, and Ribociclib interact with NiV-G in the same binding pocket. Therefore, Alvimopan, Betrixaban, and Ribociclib are identified as top NiV-G inhibitors that could be used to improve NiV-infected patient outcomes when an outbreak arises.
KEYWORDS - IJBB-268, Nipah Virus Drug Development, Attachment Glycoprotein G, Molecular Docking, Molecular Dynamics Simulation, Drug Repurposing.
 
     
Assessing the key barriers to innovation acceptance in Iranian construction companies
Hadi Sarvari, Daniel W.M. Chan, Reza Soltani
 
ABSTRACT
The emergence of innovation in recent decades has attracted the attention of major companies around the world to its productivity and importance. As a developing country, Iran has great potential in the construction industry, but due to the lack of a proper strategy and the lack of a favorable process in innovation, growth and development has not been appropriate. The purpose of this study is to identify and prioritize the key barriers to innovation in Iranian construction companies, which have been answered by 62 board members, consultants, contractors, supervisor and computational engineers, experts and suppliers in the construction industry. About 60 percent of participants are from the private sector, 29 percent from the public sector, and 11 percent from both sectors. Most of the participants were between 25 and 45 years old and the majority had a master's and bachelor's degree and had a experience of 5 to 20 years. In the questionnaire, the main factors of research, including: cultural, financial, organizational, and managerial, had the most effect, and human, investigate, and research factors had a moderate effect, and external factors had the least effect. The results showed that the main determinants of non-use of innovation in construction companies can be classified into internal and external factors. The factors was evaluated based on the 5-point Likert scale measurement. The results shows that all factors identified as barriers to innovation acceptance in Iranian construction companies.
KEYWORDS - IJBRM-288, Innovation, Barriers, Construction
 
     
On the Reciprocal Relationship between Faith and Management
Volker Kessler
 
ABSTRACT
This article describes the complex reciprocal relationship between faith and management. Firstly, faith-based organizations have to be managed. Some management methods will foster the faith; others will affect the faith negatively. Each faith group will have to look for management methods suited to the specific faith of that group. Secondly, faith has an influence on management. This can happen implicitly or by intention. An example of the latter is when managers just copy concepts from a faith group because of their success, without necessarily sharing their faith. Or it happens when believers want to implement the standards of their faith at work. The concept faith@work can be problematic if it is a single-faith approach within a secular work environment because it might lead to injustice. Due to the reciprocal relationship between faith and management, we can discover the re-entry of religious terms or concepts: these terms originate in Christian faith, enter the management sciences and from there re-enter the Christian faith. The examples servant leadership and vision show the subtle change of meaning that occurs when words wander between the two worlds, thus becoming false friends to the faith group.
KEYWORDS - SIBRM8-1, Economic Theology, Faith-based Organizations, Faith@work, Servant Leadership, Vision.
 
     
COVID-19 and Its impact on the Tourism Sector and Hotel Business in Georgia
Marina Metreveli, Tinatin Dolidze
 
ABSTRACT
The objective of the paper is to study the negative impact of COVID-19 on the Georgian Tourism sector and Hotel business. In particular, the chronology of the booking trends and the level of digitization of hotels in the pandemic and post-pandemic period; also, what impact does the coronavirus in general have on Georgia's tourism business and hotel adaptation process during crisis challenges. The research aims are to study the spread of digitalization and rapid development in the field of hospitality, in particular in the hotel business. Also, to identify tools, that have worked positively in a pandemic, at a time, when direct contact is severely restricted by regulations.

Based on the results of the research, chronological changes in hotel occupancy during the pandemic and post-pandemic periods should be identified, as well as the impact of digitization on changes in the number of employees. Accordingly, to explore innovative ways of hotel adaptation, creative and simple ways of restoring industry, world experience, and new approaches of Georgia in the post-pandemic period. We are talking about innovations that many hotels wanted to use for years, but needed a new "trigger" in the form of covid. We would like to share with you the results of our research on the Georgian experience of such an adaptation, which we connected from European analogs. We have also developed recommendations for measures to be taken to restore the hotels.
KEYWORDS - IJBRM-356, Tourism, Hotel Business, Covid-19, Digitalization, Innovations.
 
     
 
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Ethical theory vs. practical worl-life: implications for organizations
Ana Maria Cabodevila
 
ABSTRACT
In business, the relationship between ethical theory and a practical work-life is challenging, since there is a strong ethics-practice contrast: work as the practical dimension and ethics as the theoretical dimension. There is, thus, a gap between theory and practice, and the imbalance is obvious in a majority of organizations, be they profit or non-profit. How do organizations respond to ethical theories of business? On the one hand, ethical convictions and practices may be understood as being merely subsets of national culture, and so are thus not, or only partially, considered in the workplace. On the other hand, organizations may perceive business ethics as being rather limiting with regard to practice owing to an abstract understanding of ethics (Brügger & Kretzschmar, 2015, p. 3). Business ethics theory is, thus, often perceived as being difficult to put into practice. The purpose of this article is to show that for many centuries, theory and practice, or faith and working life, were not separated in the way that they apparently are today. The aim is to use the examples shown to encourage organizations and responsible persons to break down the artificial divisions introduced by the Enlightenment in such a way that ethical thinking in working life no longer remains a foreign concept but can indeed find its way into daily work.
KEYWORDS - IJBRM-350, Business ethics, Enlightenment, NGO
 
     
 
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Network governance and co-production of community services. The interregional Italian - Swiss program D.E.A. (SIBRM9)
Veronica Giuliani, Davide Maggi, Cinzia Zugolaro
 
ABSTRACT
Over the last decades, the implementation of public networks to address social issues has become more the rule than the exception. Especially in the public sector, the interactions between different actors, and thus the activation of networks, ought to find shared solutions for problems of general interest that neither government nor other spheres of society can meet on their own. Using participative approaches, public networks reshape the traditional redistributive welfare paradigms and responsibilities in the production of social wellbeing. This paper studies the structure, roles, and performance of the network built to implement the interregional Italian-Swiss project D.E.A. (Diversità E Arti performative per una società inclusiva del terzo millennio), a European program that involves public administration, non-profit organization, universities and citizens, to promote and support the participation in civic activities of fragile people at risk of social exclusion. Using a combined approach this paper analyzes the case study both from analytical and governance perspectives: it proposes a unified theoretical framework based on the extant literature, to explore the nature and the related critical factors underlying the success of the public network under study.
KEYWORDS - IJBRM-343, network, governance , participation
 
     
Determinants of halal purchasing behaviour: evidence from Germany
Vincenzo Uli
 
ABSTRACT
What are the main determinants behind halal purchasing behaviour in a non-Muslim dominant country? The paper is aimed at enriching the academic debate about halal products purchase intention, specifically discussing the German context. Drawing from a survey of 772 respondents, the work presents a set of descriptive statistics with results resonating with earlier investigations in the research domains of the Theory of Planned Behavior (TPB) and Religiosity and Halal Supply Chain (HSC). The work confirmed that halal certification, preferably released by a German certification agency, constitutes a major predictor for halal products’ consumption. We also found that halal demand is relatively inelastic to price and distribution.
KEYWORDS - IJBRM-364, Halal Supply Chain, Purchasing Behaviour , Theory of Planned Behaviour
 
     
 
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ADAPTING TO COVID-19: SMES IN ZIMBABWE MANAGING CHANGE
Tongesai Mpofu, Shephard Makurumidze
 
ABSTRACT
The purpose of the paper is to find how SMEs in Zimbabwe managed to implement change with respect to COVID-19. The pandemic has changed the way of doing things in the entire world. To survive in situations, organisations need to change. All the SMEs in Zimbabwe may be trying their level best to implement change successfully however, all of them are facing severe issues. The purpose of this study is to identify the major hurdles in implementing change and identifying a way out to cope with the situation based on available literature on SMEs and change management. The other purpose is to find the challenges faced by these SMEs in managing and implementing change.

This study follows a synthesized literature review methodology and is a kind of review paper. In this research, literature that has been written on crisis management and especially over the COVID-19 has been reviewed. In this review analysis, previous studies regarding change management and the importance of SMEs in economic sustainability have been discussed. The paper is a review of existing literature and will identify how the organizations can survive through implementing change successfully.

This study found out that SMEs in Zimbabwe have being struggling to manage and implement change due to non-availability of financial resources. This study is limited to SMEs in Harare province only it does not cover all SMEs in Zimbabwe. The study is significant for SMEs in the entire world. The study is useful for understanding the issues that are related to managing and implementing change in small and medium enterprises especially while facing contingencies. The findings of the study and the literature review led to the conclusion that without implementing change successfully, it is impossible to survive during the COVID-19 pandemic. In order to deal with the crisis of resistance to change organisations should motivate and train its employees. The study recommends SMEs in Zimbabwe to manage change by training and motivating employees and the government to support the SMEs sector in order for it to survive since it’s a backbone of the Zimbabwean economy.
KEYWORDS - IJBRM-311, Change Management, Implementing Change, Small and Medium Enterprises, COVID-19, Performanc
 
     
THE CHALLENGES AND OPPORTUNITIES OF THE CORONAVIRUS (COVID-19) PANDEMIC FOR SMALL AND MEDIUM ENTERPRISES(SMEs) IN ZIMBABWE
Tongesai Mpofu, Shephard Makurumidze
 
ABSTRACT
COVID-19 pandemic brought a lot of challenges for the global community from the year 2020. The spread of this virus led to unparalleled health crisis in all the countries across the world. The COVID-19 pandemic has also caused unprecedented panic and disruptions for both the public and private sectors and is considered an experiential threat to the global economy with governments and businesses grappling with the effects. In Zimbabwe, the pandemic has caused challenges and opportunities for Small and Medium Enterprises (SMEs) and in some cases threatening their own survival and forcing many SMEs to change their focus in order to manage the crisis. Some SMEs have adopted crisis management and alternative business response efforts. The main objectives of this paper were to determine the challenges of COVID-19 pandemic to SMEs in Zimbabwe and identify the emerging opportunities arising as a result of the pandemic. The methodology of this article is secondary information where various literature on challenges and opportunities for small to medium businesses was analyzed. Secondary data results show that many small businesses are suffering and the COVID-19 caused destruction for many small to medium businesses. It is difficult to survive with low income, jobs were lost and frail marketing performance. Findings from this study show that amongst the crisis caused by COVID-19, numerous opportunities have also emerged for innovative Zimbabwean entrepreneurs to explore. The firms should reduce expenses, adapt to new technology in order to be competitive and survive and utilise other business opportunities that have been opened up by COVID-19 pandemic. The study recommends that SMEs should first assess the damages of the pandemic to the businesses and then find the strategies to mitigate the negative effects of the pandemic and maximize the new opportunities. Furthermore, the study recommends the companies to adopt digital marketing, and use different alternatives to deliver their products and recover from crisis.
KEYWORDS - IJBRM-312, COVID-19, Pandemic, SMEs, Challenges, Emerging Opportunities, Digital
 
     
HOW DO INTRA-ORGANIZATIONAL NETWORKS FOSTER CONTEXTUAL RESILIENCE AND PREPARE MNCs TO THE NEW NORMAL? INSIGHTS FROM A CASE-STUDY
Jessica Geraldo Schwengber
 
ABSTRACT
This paper explores how intra-organizational networks promote contextual organizational resilience in multinational companies (MNCs). Following Lengnick-Hall et al. (2011), the contextual elements of resilience are psychological safety, social capital, power diffusion, and network resources. Since multinational companies are, by definition, geographically dispersed and heterogeneous, the study investigates the extent to which a network structure promotes contextual resilience and thus prepares MNCs for the new normal. The results of a case study conducted in a MNC are presented. The study was conducted during the Covid-19 pandemic (2020), and the pandemic was used as an example of shock to analyze how the network influences resilience during a shock. The results demonstrate that a cohesive network can promote contextual resilience by increasing connection and thus psychological safety, social capital, power diffusion, and access to network resources. With its focus on interaction, exchange, and relationship in addressing challenges and opportunities, this research aims to contribute to a relational view of economy (Wieland 2020).
KEYWORDS - IJBRM-362, Intra-organizational network, organizational resilience, case study
 
     
How has Entrepreneurship Opportunity Formation amongst immigrants been influenced by the Covid-19 pandemic?
Osa-Godwin Osaghae, Thomas. M. Cooney
 
ABSTRACT
A body of evidence exists which suggests that stable economic conditions support entrepreneurial opportunity formation within a national context. However, despite the advent of the COVID-19 Pandemic and recent global economic uncertainty, entrepreneurial activity is continuing to flourish across the globe. This article explores the possible factors driving entrepreneurship opportunity formation in the current climate. The study employed a comparative narrative analysis of literature relating to entrepreneurship opportunity formation, environmental change (e.g. COVID-19 pandemic) and demand expansion (e.g. market extension resulting from environmental changes). The triangulation of the literature from these diverse topics leads to the conclusion that environmental changes and irregularity creates demand expansion that can drive entrepreneurship in any climate. This article contributes to knowledge by suggesting that irregular events within the environment (not stable economic growth alone) can positively influence entrepreneurship opportunity formation.
KEYWORDS - IJBRM-341, Environment, climate change, entrepreneurship opportunity
 
     
Sustainability: Future Orientation Through Engagement of MSME’s
Deepti Prakash, Parul Manchanda, Twinkle Arora
 
ABSTRACT
Purpose: Enterprises have been facing a concern from the government to take an initiative in the various environmental maintenance and in the implementation of various sustainable practices laterally with the satisfaction of the customer demands. Thus, the enterprises are enforced to implement sustainable ways of doing business, which would help them achieve competitive advantage in the long run. This paper intends to institutionalise the various sustainability measures (through the leadership approaches and the theoretical approaches) in the various Micro, Small and Medium Enterprises (MSME’s).

Design/methodology/approach: This research paper is a general review for highlighting the varied reasons and unreason’s behind the various enterprises practising sustainability initiatives in the real business scenario. A thorough and wide exploratory search was made from the existing literature with the help of online databases. The results are presented in the form of descriptive findings.

Findings: The research paper concludes that, sustainability is not a unitary concept, but involves a throng of efforts (to explain, the activities, actors and the resources employed). The MSME’s require explicit thought, in case of business plans for sustainability as it is by one way or another not equivalent for the large firms. It has also been brought about that the MSME’s require a different way to support sustainability in comparison to the various large organisationswhich hold a varied set profile and resources.

Practical implications: Sustainability, today is a concern for everybody in the civilisation, this is because of the changes in the climate that have been observed and the growing problem of global warming. This research work, may enable the MSME managers to reconsider the whole business strategy, and making sustainability as an important inclusive element of the same, and thus practising it too.

Research Limitations: Sustainability, has been an important concern to the society in general which points out that there can be plenty opportunities for various organisations to identify strategies that will have a bearing and may positively advance the – social and environmental performance. However, this research work, does not provide an empirical evidence and support but offers insights on engaging MSME’s in sustainability.

Originality/ Value: This research contributes to the area of literature by providing a review, for the various considerations and occasionsfor the various business strategies for sustainable development and its varied applications to the certainties of business operations in various MSME enterprises.
KEYWORDS - SIBRM11-3, MSME, Small and Medium Enterprises, Sustainable Development, Leadership Approaches, Business Sustainability
 
     
 
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The Importance of Stakeholder Collaboration and Co-creation in the Social Innovation Creation Process
Emmanuel Osigwe
 
ABSTRACT
Over the years social innovation (SI) has been offering useful solutions to social problems in societies. SIs are created by social enterprises (SEs) that may operate as a non-profit, for-profit, or hybrid enterprise with the primary mission to create social impact to benefit societies. The literature on SI is emerging, thus greater attention is given to understanding and conceptualising SI. Recent studies have shown that the process of SI largely remains unexplored. As such, little is known about the importance and the role of external actors in the process. This study examines the importance of stakeholder co-creation within the SI ecosystem through inductive analysis of interview data from 20 SEs in four countries. The study finds SE seek strategic collaborations, engage critical beneficiaries, and explore social networks external to their enterprise when seeking to co-create SI.
KEYWORDS - IJBRM-337, Social innovation, Stakeholder engagement, Collaboration
 
     
Setswana Parts of Speech Tagging: Indirect Relative
Gabofetswe Malema, Ontiretse Ishmael, Boago Okgetheng, Goaletsa Rammidi
 
ABSTRACT
Setswana relatives have been shown to have a wide range of structures compared to other qualificatives. They can take negation, tense and can be recursively extended using other qualificatives, adverbs, noun phrases, and verb phrases. Studies have also shown that the structure of indirect relatives is more challenging as it is less regular compared to that of direct relatives. As a result, proposed Setswana Relatives taggers performed badly on indirect relatives. In this study, we propose the use of noun phrase and verb phrase constructs to represent the structure of indirect relatives at a high level. This approach shows that indirect relatives are also regular making them also amenable to the use of regular expressions for their identification in a sentence. This study investigates the extent to which noun phrases and verb phrases could be used to construct a regular structure for indirect relatives. We developed patterns for indirect relatives which we then implemented in Python NLTK regular expressions. The proposed tagger has a recall of 69% and precision of 62%. The tagger fails in some instances due to challenges in identifying its sub-components of noun phrases, verb phrases, and qualificatives.
KEYWORDS - IJCL-131, Parts of Speech Tagging, Setswana Relative, Rule based POS Tagging.
 
     
Encoding Yangping Tone in Zhangzhou: Going beyond Convention
Yishan Huang
 
ABSTRACT
This study examines the encoding of Yangping tone in three different linguistic contexts in Zhangzhou Southern Min, a Sinitic dialect spoken in south Fujian province of mainland China. The scientifically-acoustically normalized F0 result, based on 21 native speakers’ utterances, falsifies those auditory-based prior studies that document this tone with competing transcriptions. Its F0 contour is changed to be categorically different from corresponding citation form at the non-right-most position, demonstrating the right-dominance of tone sandhi system in this dialect. In the meanwhile, its F0 contour at the right-most position is not exactly the same as its citation value. Instead, it presents variation resulting from its phonetic sensitivity to occurring environments, reflecting the carry-over effect of tonal co-articulation, and the position-final declining effect, while questioning the conventional default-principle on the specification of sandhi dominancy in Sinitic languages. This exploration supersedes existing inconsistency and inadequacy in prior studies, while substantially stretches and advances our knowledge of tonal phonetics and phonology in this dialect. It is expected to serve as a model for a thorough investigation of multiple tonal realizations in Sinitic languages, while contributing vital linguistic data to the typology of tone and tone sandhi as an important language phenomenon in Asian languages. The exploration also enlighten the discussion on how human beings model different variants in their mental grammar, while decode the diversity and complexity in their language practice.
KEYWORDS - IJCL-135, Yangping tone, acoustics, contexts
 
     
Automatic Diacritic Restoration for Northern Sotho
Gabofetswe Alafang Malema, Moffat Motlhanka, Boago Okgetheng
 
ABSTRACT
Diacritic markers are usually not inserted in text for convenience as users type text. However, text without diacritic markers could affect the quality of its analysis as it may affect how it is pronounced and its meaning among others. The number of diacritics and the impact of not inserting them vary from language to language. The processes of restoring diacritics in the text can be looked at as language-dependent and language-independent and also as word-based or syllable based. Northern Sotho language uses two diacritic markers to indicate pronunciation and also distinguish between homographs in some cases. Very little research has been done on diacritics restoration in the Northern Sotho language. In this paper, we show that morphological word transformations are consistent in how they insert or do not insert diacritics in derived words. We focus on the caron diacritic marker.An input word is reduced to its root form by a morphological analyzer. The accented form of the root word is retrieved from the diacritic dictionary. This word, together with morphological rules is used to determine the diacritics of the input word. The implemented tool gave a recall performance of 86% on test data. Most errors were due to failures in the morphological analysis of the input word.
KEYWORDS - IJCL-133, Diacritic Restoration, Northern Sotho.
 
     
A survey on neural text generation and degeneration
Elham Madjidi
 
ABSTRACT
In this survey, we provide a comprehensive overview of text generation, from early works to the latest neural models, followed by an in-depth analysis of the most recent neural text generation techniques. Additionally, we explore the problem of text degeneration and present various solutions to mitigate its effects. Through this survey, we aim to offer insights into the current state of neural text generation and the challenges that lie ahead.
KEYWORDS - IJCL-136, Natural Language Processing, neural text degeneration, large language models
 
     
APPLY AGILE WORK METHODS FOR PRODUCT/SERVICE DEVELOPMENT IN A FAST-CHANGING ENVIRONMENT: THE CASE OF APPLICATION DEVELOPMENT IN NIGERIA
Anulika Joy Nwankwo, Thomas Thurner
 
ABSTRACT
There are new trends and waves in the software industry that have made it important for agile work methods to be the only way to overcome strong hits from several directions because of some of the rapid changes we see today. Businesses have upgraded to an online business model as a result of the need to service a broader range of customers. This study aim to understand the process of application development in Nigeria's IT companies, to understand the original application development procedure that is prevalent in developing software and applications and to point toward an outlook of the IT industry and the impact of their work methods.
KEYWORDS - IJSE-186, Agile engineering process, Application development process , Agile methodology
 
     
Text Data Augmentation to Manage Imbalanced Classification: Apply to BERT-based Large Multiclass Classification for Product Sheets
Yu DU, Erwann LAVAREC, Colin LALOUETTE
 
ABSTRACT
Recent studies have showcased the effectiveness of deep pre-trained language models, such as BERT (Bidirectional Encoder Representations from Transformers), in tasks related to natural language processing and understanding. BERT, with its ability to learn contextualized word vectors, has proven highly accurate for binary text classification and basic multiclass classification, where the number of unique labels is relatively small. However, the performance of BERT-based models in more ambitious multiclass classification tasks, involving hundreds of unique labels, is seldom explored, despite the prevalence of such problems in real-world scenarios. Moreover, real-world datasets often exhibit class imbalance issues, with certain classes having significantly fewer corresponding texts than others. This paper makes two primary contributions: first, it examines the performance of BERT-based pre-trained language models in handling tasks of large multiclass classification system within a specific real-world context; second, it investigates the application of text data augmentation techniques to mitigate the class imbalance problem. Through rigorous experiments in a real-world SaaS (Software as a Service) domain, the results demonstrate that: 1) BERT-based models can effectively tackle tasks of large multiclass classification system, delivering reasonable prediction performance; and 2) text data augmentation can significantly enhance prediction performance in terms of accuracy (by 34.7%) and F1-score (by 37.1%).
KEYWORDS - IJCL-134, Text Classification, Imbalanced Classification, Natural Language Processing, BERT, CamemBERT
 
     
A Statistical Study of Arabic Discourse Connectors in a Diachronic Bespoke Corpus of the Years 1950 and 2018
Sarah Ajlan Alajlan
 
ABSTRACT
This paper discusses changes of Arabic discourse connectors in newspaper writing by comparing two distinct time periods: 1950 and 2018. It attempts to provide an answer to the question: How has the usage of Arabic discourse connectors changed, quantitatively and qualitatively, in Arabic newspapers as evidenced in the 1950 and 2018 sub-corpora? A specialized bespoke corpus has been built specifically for this study, Leeds Bespoke Corpus of Arabic Newspaper Writings (LBCANW), that contains rare material from the year 1950 and recent material from the year 2018 (Alajlan, 2019). It is part of an ongoing PhD study that approaches the changes in the lexicon and syntax via ‘lexis’ (Sinclair, 1991). The research methodology includes: recording the frequency of occurrences; normalizing the frequencies to per million words; and calculating the percentage of change by using %DIFF value (Gabrielatos and Marchi, 2011). Discourse connectors are arranged in descending order according to %DIFF value. Discourse connectors that are found in one sub-corpora are placed in separate tables. The results show noticeable degrees of change in most of the 95 discourse connectors included in this study across the two sub-corpora 1950 and 2018. Finally, detailed linguistic discussion of the changes in five selected discourse connectors is included with concordance lines examples from the corpus using the statistical information obtained from this study.
KEYWORDS - IJCL-132, Arabic Newspaper Writing, Language Change, Syntax, Discourse Connectors, Diachronic Corpus, Type of Change
 
     
A PARTIAL NEW PROOF OF PYTHAGORAS THEOREM
Hammad Azzam
 
ABSTRACT
This paper proposes an elegant and simple proof of Pythagoras Theorem. The proof starts by rotating the non-hypotenuse shorter side on an arc towards the other non-hypotenuse side, then computing a value, x, which starts as a negative value, but increases as the rotation happens. When that value hits zero, the identity is at hand. However, although the proposed work provides a fresh perspective on Pythagoras Theorem, it is not complete. Further suggestions to complete the proof are proposed.
KEYWORDS - IJCM-46, Pythagoras Theorem, Integration, Novel Approach
 
     
Security in Wireless Sensor Networks: Comparative Study
Fatimah Khalil Aljwari, Hajer Abdullah Alwadei, Aseel Abdullah Alfaidi
 
ABSTRACT
The security in wireless sensor networks (WSNS) is a very important issue. These networks may be exposed it different attacks. With this in mind, researchers propose in this area variety of security techniques for this purpose, and this article describes security in wireless sensor networks. Discussed threats and attacks of wireless sensor networks. The article also aims to provide the basic information related to determining essential requirements for the protection WSNs. Lastly, we mention some security mechanisms against these threats and attacks in Wireless Sensor Network.
KEYWORDS - IJCSS-1678, Wireless Sensor Networks, Security Requirements, Attacks
 
     
The State of Phishing Attacks and Countermeasures
Sameer Abufardeh, Bouchaib Falah
 
ABSTRACT
Phishing is a cybercrime where criminals employ various deceptive techniques to obtain personal information from individuals. There are multiple facets of phishing attacks. These include what phishing is, known phishing types, and methods used to protect users\\\' personal information. While many tools are being used to protect users from phishing attacks, phishing attacks are increasing, its methods and tactics are changing, and more victims are falling for them. The first line of defense in protecting people from phishing attacks is, understanding the dynamics of phishing and the psychology of both the attacker and the victim, and analyzing users\\\' decision-making strategies in reaction to phishing attacks. This paper is intended to examine the multiple facets of phishing attacks to enhance our understanding of an extremely challenging issue for the IT community as the first step to curb the effects of this persistent crime.
KEYWORDS - IJCSS-1702, Phishing Email, Phishing Types, Phishing Countermeasures.
 
     
Improved Automated Framework to Improve Users' Awareness of Online Social Networks
Sameer Abufardeh, Rahaf Barakat
 
ABSTRACT
The wide spread usage of Online Social Networks (OSN) has introduced new privacy threats. These threats emerge when users intentionally but unknowingly share their information with a broader audience than intended (Ismail et al., 2021), (Zhu et al., n.d.), (Pew Research, 2019), (Pew Forbes, 2022), (Pew Ming, 2021), (Bright et al., 2022), (Cain et al., 2021). This paper introduces an improved framework for Users' Awareness of Online Social Networks. In our initial work, we identified critical privacy issues related to posting on social networks. We proposed a unique two-phase approach to address this problem (Barakat et al., 2016). The first phase involved recognizing key phrases, particularly those that revealed location information, in potential posts. In this paper, we have expanded the detection rules to identify the location and other types of sensitive information, such as work and interests. We have developed a set of detection rules for this purpose and tested them with over 1500 actual Facebook posts. The initial detection system achieved a success rate of 85%, but the new system has improved the detection rate to approximately 88%. Additionally, we conducted experiments with 500 actual Facebook posts from Arabic language users. However, the detection rates were lower due to the presence of English words mixed with the Arabic text. The second phase of our approach involves automatically grouping friends into sets called Circles of Trust. This allows messages containing sensitive information to be restricted to the appropriate Circles of Trust. We discuss an approach for initially assigning friends to circles, and the mechanisms for moving friends among circles as their relationship with the poster changes. This aspect has slight improvement but is still needs improvement.
KEYWORDS - IJCSS-1703, Online Social Networks , privacy, sensitive informaiton
 
     
DEVELOPMENT OF A REAL TIME INTRUDER DETECTION SYSTEM USING FACIAL RECOGNITION
OLAKUNLE SAMUEL OWOLABI, I. B. Asianuba, C. Ezeofor
 
ABSTRACT
The research is aimed at developing a real time intruder detection system using facial recognition and deployable on automated teller machines in Nigerian Banking sector. This research was implemented using python programming language and raspberry pi device. Haar cascade classifiers was used to implement face detection and recognition. A total of six thousand images were used to train the classifier. Five thousand positive images and one thousand negative images. The performance of the result was evaluated using five major metrics; Sensitivity, specificity, accuracy, precision and processing time. The results obtained have sensitivity 90.6%, specificity 94%, precision 94.1%, accuracy 92.2%, a maximum of 16 seconds was taken to detect a face and a maximum of 45 seconds was recorded to recognize a detected face. Achieving a maximum of 45 seconds with 92.2% accuracy real time, indicates this work can be deployed on any real-life system.
KEYWORDS - IJCSS-1644, Image recognition., face detection, raspberry pi
 
     
Enhancing User Authentication Using Keystroke Dynamics & Machine Learning Techniques in Critical Information System’s Architecture
Jesse Barima Afrifa, Kwame Ofosuhene Peasah, Dennis Redeemer Korda
 
ABSTRACT
Standard authentication methods, such as passwords, are often criticized for their inherent vulnerabilities, including the use of common or popular words and password reuse. To address these glaring weaknesses, research efforts have been aimed and targeted at introducing schemes that enhance password security. A combination of keystroke dynamics and machine learning techniques has sprung up as a promising area of research to not only strengthen passwords but ultimately help reduce breaches resulting from lost or stolen credentials. This study presents a behavioral biometric-based authentication model utilizing machine learning. We conducted experiments using a range of machine learning techniques, and our results demonstrate the effectiveness of models based on Extra Trees, Convolutional Neural Networks (CNN), Random Forest, and Multilayer Perceptrons (MLP). In particular, the Extra Trees algorithm achieved a remarkable accuracy rate of 96.08% in discriminating between legitimate users and imposters, based on analysis of the CMU public keystroke dataset. This represents a significant improvement compared to other models utilized in previous studies. Additionally, we evaluated our models using performance metrics such as precision, recall, and F1-scores. By leveraging machine learning and keystroke dynamics, this research contributes to the advancement of user authentication systems. The proposed model demonstrates its potential to enhance password security and mitigate the risks associated with compromised credentials. Further exploration and refinement of these techniques hold promise for developing robust authentication systems in the future.
KEYWORDS - IJCSS-1701, Keystrokes, Authentication, Extra Trees
 
     
Performance evaluation of Denial of Service Attack Detection on SIP based VoIP.
Abdirisaq, Othman O. Khalifa, Nantha Kumar Subramaniam
 
ABSTRACT
Recent trends have revealed that SIP based IP-PBX DoS attacks contribute to most overall IP-PBX attacks which is resulting in loss of revenues and quality of service in telecommunication providers. IP-PBX face challenges in detecting and mitigating malicious traffic. In this research, Support Vector Machine (SVM) machine learning detection & prevention algorithm were developed to detect this type of attacks Two other techniques were benchmarked decision tree and Naïve Bayes. The training phase of the machine learning algorithm used proposed real-time training datasets benchmarked with two training datasets from CICIDS and NSL-KDD. Proposed real-time training dataset for SVM algorithm achieved highest detection rate of 99.13% while decision tree and Naïve Bayes has 93.28% & 86.41% of attack detection rate, respectively. For CICIDS dataset, SVM algorithm achieved highest detection rate of 76.47% while decision tree and Naïve Bayes has 63.71% & 41.58% of detection rate, respectively. Using NSL-KDD training dataset, SVM achieved 65.17%, while decision tree and Naïve Bayes has 51.96% & 38.26% of detection rate, respectively. The time taken by the algorithms to classify the attack is very important. SVM gives less time (2.9 minutes) for detecting attacks while decision tree and naïve Bayes gives 13.6 minutes 26.2 minutes, respectively. Proposed SVM algorithm achieved the lowest false negative value of (87 messages) while decision table and Naïve Bayes achieved false negative messages of 672 and 1359, respectively.
KEYWORDS - IJCSS-1621, Voice over IP, , Session Initiation Protocol, Denial of Service
 
     
Improved Automated Framework to Improve Users' Awareness of Online Social Networks
Sameer Abufardeh, Rahaf Barakat
 
ABSTRACT
The wide spread usage of Online Social Networks (OSN) has introduced new privacy threats. These threats emerge when users intentionally but unknowingly share their information with a broader audience than intended (Ismail et al., 2021), (Zhu et al., n.d.), (Pew Research, 2019), (Pew Forbes, 2022), (Pew Ming, 2021), (Bright et al., 2022), (Cain et al., 2021). This paper introduces an improved framework for Users' Awareness of Online Social Networks. In our initial work, we identified critical privacy issues related to posting on social networks. We proposed a unique two-phase approach to address this problem (Barakat et al., 2016). The first phase involved recognizing key phrases, particularly those that revealed location information, in potential posts. In this paper, we have expanded the detection rules to identify the location and other types of sensitive information, such as work and interests. We have developed a set of detection rules for this purpose and tested them with over 1500 actual Facebook posts. The initial detection system achieved a success rate of 85%, but the new system has improved the detection rate to approximately 88%. Additionally, we conducted experiments with 500 actual Facebook posts from Arabic language users. However, the detection rates were lower due to the presence of English words mixed with the Arabic text. The second phase of our approach involves automatically grouping friends into sets called Circles of Trust. This allows messages containing sensitive information to be restricted to the appropriate Circles of Trust. We discuss an approach for initially assigning friends to circles, and the mechanisms for moving friends among circles as their relationship with the poster changes. This aspect has slight improvement but is still needs improvement.
KEYWORDS - IJCSS-1703, Online Social Networks , privacy, sensitive informaiton
 
     
Analysing the Behaviour of High Uncertainty Avoidance towards online educational learning Interface Design Elements
 
ABSTRACT
With the exponential growth of technology, online educational games has become an intrinsic part of our daily lives, shaping the way we communicate, share information, and interact with one another. Uncertainty avoidance refers to a society\\\\\\\'s tolerance for ambiguity and the unknown. Cultures characterized by high uncertainty avoidance tend to favor clear rules, stability, and predictability. Within the sphere of the game based platforms, this cultural trait can greatly influence design preferences. Platforms targeting audiences from such cultures might benefit from intuitive interfaces, clear navigation, and comprehensive user guidelines. By offering consistent layouts, routine confirmatory feedback, and a lucid user experience, the comfort level of these users is enhanced. To delve deeper into this aspect, a questionnaire was sent out to 152 Saudi students. Their responses provided insights into the dynamics of their engagement with the online educational gaming platforms. By comprehending their preferences, behaviors, and challenges on educational and technologists can develop strategies, tools, and educational programs tailored specifically to their needs. This ensures that Saudi students can navigate through these platforms seamlessly and safely, empowering them to harness its potential without encountering the pitfalls it might present. This is not just about understanding their trends, but also about fostering an environment where they can benefit from global connectivity without compromising their cultural values and personal security. As the online educational game platforms expand globally, addressing these cultural nuances becomes paramount for optimal user engagement and retention.
KEYWORDS - IJCSS-1704, Educational game, Uncertainty Avoidance, User interface
 
     
Product Sentiment Analysis for Amazon Reviews
Arwa M. AlQahtani
 
ABSTRACT
Recently, eCommerce has witnessed rapid development. As a result, online purchasing has grown, and that has led to growth in online customer reviews of products. The implied opinions in customer reviews have a massive influence on customer's decision purchasing, since the customer's opinion about the product is influenced by other consumers' recommendations or complaints. Accordingly, product purchases could increase or not based on consumers' reviews. Also, customer opinions could help firms improve insights into customer interests, hence improve their products or services. This research provides an analysis of the Amazon reviews dataset and studies and sentiment classification with machine learning approaches. First, the reviews were transformed into vector representation using different techniques, i.e., bag-of-words, TF-IDF, and GloVe. Then, we trained various machine learning algorithms, i.e., Logistic Regression, Random Forest, Naïve Bayes, Bidirectional Long-Short Term Memory, and BERT. After that, we evaluated the models using accuracy, F1-score, precision, recall, and cross-entropy loss function. The experiment was conducted on multiclass classifications, then we selected the best performing model and re-trained it on the binary classification.
KEYWORDS - IJCSS-1619, Amazon, Data Analytics, Product Sentiment
 
     
PERFORMANCE EVALUATION OF DATA MINING ALGORITHM FOR DETECTING WINDOWS KERNEL ROOTKIT
Amit Mishra
 
ABSTRACT
Rootkits tends to allow an attacker operate in the host system unnoticed. The stealthy nature of the Rootkits makes them difficult to detect especially when they operate in the Kernel. One of the ways which Malware enters the Kernel is through the Kernel Driver. This research work therefore, used a Static analysis approach to extract features from various Windows Kernel Drivers. 500 Rootkit drivers and 500 benign drivers were collected from various infected computers and Internet respectively. The drivers were first dissembled to extract the native API functions and other functionalities to generate a set of features after which Feature selection is performed using Information gain algorithm. The selected features were then used to train the Naïve Bayes and Decision tree Algorithm. An Accuracy of 98.3% was achieved in distinguishing the malicious driver from its legitimate counterpart using Decision tree while an accuracy of 95.3% was achieved using Naïve Bayes. The performances of the two model were then compared using Accuracy, Precision, AUC, and Error rate.
KEYWORDS - IJCSS-1686, Performance Evaluation,, Datamining Algorithm, , SVMS, Network Security, Virus, Malware
 
     
Detecting drifts in data streams using Kullback-Leibler (KL) divergence measure for data engineering applications
Joe Francis Kurian
 
ABSTRACT
The exponential growth of data coupled with the widespread application of artificial intelligence presents organizations with challenges in upholding data accuracy, especially within data engineering functions. While the Extraction, Transformation, and Loading process addresses errorfree data ingestion, validating the content within data streams remains a challenge. This study focuses on the detection of such drifts in data streams and proposes the use of a Kullback-Leibler (KL) divergence measure called Population Stability Index (PSI). By applying KL divergence to compare data streams from different periods and utilizing PSI with varying bin sizes, this study aims to identify and measure the extent of data drift. Through simulations, the effectiveness of PSI in detecting distortions in data streams is demonstrated. The findings contribute to enhancing data validation processes within data engineering functions, promoting consistency and stability within analytical workflows.
KEYWORDS - IJDE-126, Data Distribution Drifts, Kullback-Leibler (KL) divergence, Population Stability Index (PSI)
 
     
Assessment of Solar Energy Availability and Its Potential Applications in NEOM Region
Hossam AbdelMeguid, Zaid Aljohani, Abdulkarim Asiri, Salem Al-Awlaqi, Turki Aljohani
 
ABSTRACT
NEOM is a proposed megacity and business zone in Saudi Arabia. It was announced in 2017 by Crown Prince Mohammed bin Salman with the goal of creating a hub for innovation and a hub for the future of living. NEOM is planned to cover an area of over 26,500 square miles and will include a focus on sustainability and cutting-edge technology. The project is being backed by the Saudi Arabian government and private investment. The primary objective of KSA is to utilize the renewable energy resources in the NEOM region sustainably. This study evaluates the availability of solar energy in the NEOM region on a quantitative and qualitative basis, and a database of weather conditions such as temperatures and wind speed is collected and processed. NEOM has favorable climate conditions with an average annual radiation incident energy of 12.54 GJ/m2, wind speed of 15.68 km/h, and temperatures ranging from 16 to 38°C. Based on the analyzed data, the study investigates the potential of solar energy as a sustainable source and alternative to conventional fossil fuels. The utilization of solar energy could be applied in various ways including seawater HDH desalination with productivity of 26-33 l/day/m2, solar cooling with an average load of 15 MJ/day/m2, green hydrogen production with rate of 41-47 mole/day/m2, and electrical power generation with rate 4.2-6.8 MJ/day/m2..
KEYWORDS - IJE-517, Solar Energy, Desalination, Green H2
 
     
Eye Detection Applying a Probabilistic Algorithm Inspired to the Butterfly Flight
Donatella Giuliani
 
ABSTRACT
Eye state analysis is relevant to detect fatigue and drowsiness when driving, it is therefore essential to ensure safety of people. In this work, we propose an eye detection method applied to color images, recurring to a random exploration performed by multiple research agents simultaneously. The color image is represented in YCbCr color space, because in this space the luminance component is separated by chrominance components. At first, an algorithm is used to recognize the face area in the original image. Afterwards, a normalized EyeMap is generated to distinguish and enhance eye regions in order to make them more evident. For comparison, we performed the procedure on the YCbCr and the Chromaticity Space xy, recurring to a generalized equation. The novelty of this approach is the introduction of the z component of the Chromaticity Space. This choice is justified by its relationship with the Tristimulus Z component, which represents mostly blue wavelengths in turn strictly related to the bluish color of the sclera.The search of ocular areas is conducted through an algorithm that simulates the flight of butterflies. Initially a butterfly gives rise to a random research but, when a foraging zone is discovered, it makes non-random dispersal movements around the food source. Similarly, in the applied algorithm, if a small region containing eyes is detected, even if partially, a more circumscribed research gets started through helicoidal paths around the identified area. In this way, we avoid the arbitrariness of choosing the position and size of any search window.
KEYWORDS - IJIP-1236, Image Segmentation, Eye Detection, Skin Detector, EyeMap, Nature-inspired Algorithm.
 
     
Advancements in Green Hydrogen Production using Seawater Electrolysis in Tabuk, Saudi Arabia
Hossam AbdelMeguid, Hossam Al-johani , Zakariya Saleh, Abdulmalk Almalki , Abdulaziz Almalki
 
ABSTRACT
The transition to green hydrogen holds immense potential for addressing the pressing challenges of climate change, energy security, and sustainable development. Firstly, green hydrogen production relies on renewable sources such as wind, solar, and hydroelectric power, ensuring a significant reduction in greenhouse gas emissions compared to traditional fossil fuel-based hydrogen production methods. By embracing green hydrogen, nations can effectively mitigate climate change and achieve their emissions reduction targets outlined in the Paris Agreement The production of green hydrogen, while promising in terms of its environmental benefits, faces a significant hurdle in the form of high production costs. This paper aims to explore the potential cost reduction in hydrogen production by designing an efficient electrolyzer that utilizes seawater as a feedstock. The use of seawater offers numerous advantages, including its abundance and easy accessibility, which can significantly lower production costs. Using MATHLAB, the mathematical models were solved in order to better understand the equipment and determine a system that is efficient. Overall, the work provides illuminating information about the use of green hydrogen systems and highlights the critical role that theoretical and experimental research plays in lowering the cost of such systems.
KEYWORDS - IJE-519, Solar Energy, Green Hydrogen, Seawater Electrolysis
 
     
Design of an Omni-Direction Robot with Spherical Wheels
Dominic Campbell, Emanuele Lindo Secco
 
ABSTRACT
We proposed the design of an omni-direction robot which embeds 4 spherical wheels. The wheels are connected to 4 DC motors and controlled through an L298N boards which is powered with 2 LIPO batteries. A low-cost Arduino board oversees the system by controlling the motion and speed of the motor.

The wheels of the robot integrate an omni-directional mechanism and all components have been designed in Fusion 360 (Autodesk ®), manufactured with a 3D printer (Ender 5) an then assemble and integrated with the hardware and software of the system.

Preliminary tests show that the proposed solution is promising and provide a good reference for the manufacturing of low-cost robot.
KEYWORDS - IJE-514, Omni-directional Wheel, Osaka Wheel Mechanism, Wheeled Robotics.
 
     
Macroergonomic Approach to Development Of Sustainable Tourism Village
Ahmad Padhil, Hari
 
ABSTRACT
Sector development has strong relevance to regional development. Regions can develop through the development of leading sectors in a region that encourage other sectors. Gunung Condong Village, located in Bruno sub-district, Purwerojo Regency, is one of the regions that has natural beauty and abundance natural resources. This study aims to develop Gunung Condong Village into a tourism village by using macroergonomic approach. This approach uses Focus Group Discussion (FGD) method in Gunung Condong Village that developed in accordance with the conditions of the land and the capacity of its nature. In addition, the application of rules and community involvement in managing the institutional matter of tourism village is a social responsibility for the entire community. This study resulted in the suitability of tourism development based on rainfall factors, topography, environmental feasibility and the existence of supporting facilities in Gunung Condong Village.
KEYWORDS - IJEG-68, Tourism Village, Macroergonomic, Focus Group Discussion (FGD)
 
     
Reviewing Assistive Human-Robot Experiences for Inclusive Human-Robot Interaction
Aishah Shah, Saira Iftikhar, Naila Kamran Chaudhry
 
ABSTRACT
Human-Robot Interaction (HRI) and collaboration have gained immense popularity recently, owing to the new mechanisms and advancements in the field of computing and the symbiotic nature of the involved processes. It relates to the ever-dynamic means of communication between humans and robots. Such interactions work both ways, i.e., inputs and commands from humans and the expression of the robot's interpretations. A significant amount of work has been done in this area; however, there are still challenges in assistive human-robot experiences. This study aims to review the literature on assistive robots to bring the current research together, identify persistent gaps and challenges, and recommend ways to enhance human-robot interaction. This paper covers critical aspects of assistive robots in social environments for sociophysical needs and assistance for children and the elderly with special needs. Given advancements in elements like sensor technologies, manufacturing materials, machine learning, control methodologies, and computer capacity, the subject of assistive robots is bound to see tremendous results. The findings in this paper present gaps, issues, and challenges in today’s assistive robots that hinder human-robot interaction. The findings can be used to summarize the current works and provide a base for technological innovations to enhance the interactions between humans and their partner robots.
KEYWORDS - IJHCI-159, Human-Robot Collaboration, Assistive Human-Robot Experiences, Inclusion.
 
     
Reconstruction of a Multiscale Filter for Edge Preserving Speckle Suppression of Ultrasound Images
Mehedi Hasan Talukder, Md. Masudur Rahman, Shisir Mia, Mohammad Motiur Rahman
 
ABSTRACT
Speckle noise tends to reduce the diagnostic value of ultrasound imaging modalities by degrading image quality. Edge-preserving noise-suppression can play an important role for accurate diagnosis.Therefore edge-preserving speckle suppression is the ultimate demand for accurate diagnosisby healthcare industries. In this study, a new hybrid filtering technique, namely, multiscale filter is proposed and analyzed to suppress the speckle noise in ultrasound images by preserving the image edges. Linear filtering speeds are high, but cannot preserve the edges of images efficiently, and this is a major limitation. Conversely, nonlinear filtering can handle edges more effectively; a Gabor filter preserves edges well but fails at suppressing noise. The method proposed here combines the concept of three linear and nonlinear filters with a Gabor filter to counter the limitations. In particular, when it is filtered, a 33 image kernel is divided into three segments and three linear and non-linear techniques are applied to each segment. Finally, the results of each section are integrated and processing is performed with a Gabor filter to obtain the results. The performance of the multiscale filter is analyzed for various ultrasound images of kidney, breast, abdomen, prostrate, orthopedic, and liver. The proposed multiscale filter provides superior results than other widely used de-speckling filters.
KEYWORDS - IJIP-1237, Linear Filter, Non-linear Filter, Speckle Noise, Gabor Filter, Medical Images.
 
     
Enhanced Matched Filter Theory and Applications
Kaveh Heidary
 
ABSTRACT
Enhanced matched filter (EMF) comprises a distortion tolerant correlation filter and its associated threshold. It is an effective signal detection tool with superior immunity to noise, distortion, and clutter, used for imagery-based authentication, classification, recognition, and tracking of targets of interest. The EMF is synthesized by combining multiple image templates of the target of interest, acquired under prescribed target states and view conditions. In autonomous vision and tracking systems, one EMF can potentially replace copious manifold of exemplar images without adversely affecting the classifier precision. This leads to proportional reduction of operation phase computational load, and concomitant smaller footprint, lighter, faster, and more power efficient smart vision systems. This paper develops the underlying theory of the EMF and provides analytical models of its operation. Filter performance results based on analytical formulations and empirical studies are presented and are compared to the performance data using virtual and real test images.
KEYWORDS - IJIP-1216, Enhanced Matched Filter, Correlation Filtering, Distortion Tolerant
 
     
A Hybrid Face Recognition Method based on Face Feature Descriptors and Support Vector Machine Classifier
Rafika Harrabi Harrabi
 
ABSTRACT
Face recognition is a technique used to identify/verify human identity based on their facial features. A technique allows, based on facial features to authenticate / identify a person. However, for human identification or identity authentication based on face recognition technology, the appropriate determination of the face features plays a crucial role, since the identification of the Human is given directly by the classification of these characteristics.

In this paper, we propose a new face recognition method based on face feature descriptors and Support Vector Machine (SVM) algorithm. The face feature descriptors are used to extract and select the statistical features, whereas, the SVM algorithm is employed to classify the different features and to obtain optimal Human face recognition.

The feature extraction step is the major phase of the recognition cycle. It is employed to extract the features for any human face located in the first step. The accomplishment of this step controls the success of subsequent steps. For that, the main objective of this work is to determine of the best method of feature extraction.

To do the indexation of person’s face, the Histogram of Oriented Gradient features (HOG), Gabor features and Discrete Cosine Transform features (DCT) are employed to extract the feature vectors for any human face.

In addition, the face recognition method, proposed in this paper, is conceptually different and explores a new strategy. In fact, instead of considering an existing face recognition procedure, the proposed technique rather explores the benefit of combining several approaches.This method is a hybrid face recognition technique, which integrates both the results of the HOG, and the SVM technique, in which the HOG method is used as the initial seed for the classification procedure.

Experimental results from the proposed method are validated and the face recognition rate for the ''ORL'' and cropped ''Yale B'' datasets is evaluated, and then a comparative study versus existing techniques is presented. The highest face recognition rate of the used dataset is obtained by the proposed method. In addition, the use of the proposed HOG_SVM method to build face recognition systems can achieve excellent results when the dataset size is large, and therefore it can be used in different security and authentication systems.
KEYWORDS - IJIP-1225, Face Recognition, Feature Extraction, HOG, SVM, DCT, Gabor, Classification.
 
     
Kernel-Induced Fuzzy C-Means Approach with Adaptive Mean Filter in Spatial Constraints for SAR Image Segmentation
Sicong Li
 
ABSTRACT
Fuzzy C-Means (FCM) has been broadly used for semantic image segmentation as one typical approach of unsupervised learning. However, in terms of speckle noise corrupted data like SAR images, traditional FCM algorithms perform poorly in robustness in segmentation tasks. This paper proposes a novel improved FCM algorithm to enhance the robustness to speckle noise in semantic image segmentation. First, our proposed method incorporates adaptive mean filter into spatial constraint. Second, any pixel and its neighborhoods are taken into consideration together in spatial constraint in our objective function. Third, in the final step, a voting algorithm is implemented to further remove the remained noise so as to improve the segmentation accuracy. Besides, Gaussian kernel distance is adopted in our proposed method instead of Euclidean distance and our results reconfirm that Gaussian kernel distance is more effective in segmentation under speckle noise. Our experimental results show the robustness to speckle noise in segmentation result get effectively enhanced throughout such improvements.
KEYWORDS - IJIP-1231, Fuzzy C-Means (FCM), Mean Filter, Spatial Constraints
 
     
DESIGN AND IMPLEMENTATION OF SMART TRAFFIC CONTROL SYSTEM USING IMAGE PROCESSING
Jidnyasa Madhukar Bhoge
 
ABSTRACT
One of the fundamental issues in any metropolitan area is traffic. The primary challenges are the rise in vehicle traffic, a lack of adequate road infrastructure, and difficulty in traffic indicators. In this paper, we suggest a strategy to address one of the problems associated with traffic congestion. The proposed system is designed to regulate the green light signal at four-way intersections. The density and number of cars at the junction are determined using a image processing technique. Every ten seconds, videos are recorded on cameras mounted at each junction. Videos are converted to frames using software, and then image processing techniques are utilized to determine the vehicle's density and provide a suitable window for controlling the red and green lights.
KEYWORDS - IJIP-1233, Smart Traffic, Image Processing, Background subtraction
 
     
Contribution to the Pre-processing Method for Image Quality Improving: Application to Mammographic Images
Y. Nadji, J. Mbainaibeye, G. Toussaint
 
ABSTRACT
Breast cancer is the most common type of cancer of women worldwide, but it can be cured if diagnosed at an early stage. Mammography is the main means of cancer screening and provides useful information on the signs of cancer, such as microcalcifications, masses, architectural distortion etc., which are not easy to distinguish due to certain defects in mammographic images, including low contrast, high noise, blurring and confusion. These challenges could be overcome by proposing a new preprocessing model. This work proposes a pre-processing method using different techniques and their combination in order to minimize the above-mentioned defects in mammographic images and make them usable for further processing. The different techniques range from filtering, thresholding, histogram, blur masking, morphological operations, thresholding and cropping. The aim is to put the mammographic images into a representation that will facilitate the detection of microcalcifications and the classification of healthy and cancerous images. Algorithms were developed and tested using the publicly available international database of the Mammographic Image Analysis Society (MIAS), which contains 322 samples. The results obtained on the regions of interest using four samples clearly show the background of the image and the objects (the part of the pectoral muscle and the suspicious area). These results show that much of the adipose tissue, fat mass and some of the features observed in the zoomed-in part of the image are significantly reduced. Furthermore, the results obtained in terms of visual quality compared to the literature show that they are better.
KEYWORDS - IJIP-1232, Breast Cancer, Digital Mammography, Region of Interest.
 
     
An Experiment of Randomized Hints on an Axiom of Infinite-Valued Lukasiewicz Logic
RUO ANDO, Yoshiyasu Takefuji
 
ABSTRACT
In this paper, we present an experiment of our randomized hints strategy of automated reasoning for yielding Axiom(5) from Axiom(1)(2)(3)(4) of Infinite-Valued Lukasiewicz Logic. In the experiment, we randomly generated a set of hints with size ranging from 30 to 60 for guiding hyper-resolution based search by the theorem prover OTTER. We have successfully found the most useful hints list (with 30 clauses) among 150 * 6 hints lists. Also, we discuss a curious non-linear increase of generated clauses in deducing Axiom(5) by applying our randomized hints strategy.
KEYWORDS - IJLP-35, Lukasiewicz Logic, Hints strategy, OTTER
 
     
A Proposed Approach for Unique Random Key Generation
Dalal N. Hamood, Abdulrahman Q. Hammod
 
ABSTRACT
Regarding network security, many cryptographic techniques use random numbers. To boost its robustness, a precise quantity of randomness must be used to make encryption and decryption unexpected. This paper offered a mechanism that chooses a distinctive number based on data instead of a temporal seed, which is what the bulk of applications now do. The suggested method is the basis for Unique Random Generation and uses the parallel technique. This approach has been tried, and the findings demonstrate that it is easy to use and yields the best results because it chooses a distinct number based purely on data. The random key is better than regular keys since it is dynamic whereas regular keys are static. After testing the proposed work, concluded when using seeds with the appropriate levels of entropy, this method can generate sequences whose randomness cannot be distinguished from that of an ideal random generator, with a confidence level of 99%. Additionally, the proposed method used base=2 to produce the highest entropy, the lowest space complexity, and the highest time complexity than methods based time seed.the proposed method success in all NIST tests (high randomness) and has a short time in generation (faster method). This method appropriates for high security applications.
KEYWORDS - IJS-165, Random Number Generator, Randomness, Binary Tree List, Ranges, Time Generation.
 
     
Flare – A Community Based SOS Application
Sanil Arun Chawla
 
ABSTRACT
This paper presents a mobile based application that will enable to solve numerous problems with a simple solution. The project - ‘ Flare ‘ focuses on creating a community based application wherein the community helps and grows together. The application is called Flare , keeping in mind the actual flare gun that is used by like sailors or like army-men in times of need and they launch an actual flare. Similarly any user can launch a flare – a call for help.
KEYWORDS - IJSE-182, SOS , Android , Software
 
     
Developing Cross-Platform Library using Multi-OS Engine
Dilkhaz Yaseen Mohammed, Peter Cooper
 
ABSTRACT
Libraries are of great importance in the development of mobile apps. Mobile application development services have reached a higher level with APIs. When developers develop applications for mobile devices, they often rely on APIs for connectivity. In fact, APIs accelerate mobile development and provide exceptional agility for organizations undergoing their own digital transformation. As a result of developing a library and then sharing it with the world, others can benefit from it in their own projects. The programmer needs to make sure that he uses APIs available for both the Android and iOS platforms. The programmer\\\\\\\'s creation of an interface for accessing platform-specific functions from the library and creating Android and iOS applications in its projects accelerates the development of software projects. In this paper, the programmer uses Intel’s Multi-OS Engine Technology to give the possibility to use Java capabilities to develop native mobile applications for Apple iOS and Android devices, providing the native look, feel, and performance. This technology provides a stand-alone plug-in that integrates into Android Studio on Windows and Apple Mac OS development machines. When developing a Java open source project, the common conclusion the programmer always ends up with is to share the produced outcomes with the developer community, which should be the least objective in the Java world then utilize the library. Then utilize the library in both Android and iOS apps.
KEYWORDS - IJSE-178, Cross-Platform, Java, Retrofit
 
     
A systematic literature review on effort estimation in agile software development using machine learning techniques
Pranay Tandon, Ugrasen Suman
 
ABSTRACT
Agile software development is a way of frequent or continuous delivery of software. Nowadays many software industries have adopted agile for software development. The predictability and stability of traditional methods were replaced with flexibility, adaptability and agility to generate maximum value with collaboration and interaction, as quickly as possible. Effort estimation is the focused area in agile software development to achieve customer collaboration, respond to change and deliver a working software on time. Machine learning is an advanced tool to obtain effort estimation with available project data and widely used in IT industries to get accurate estimations. In this paper, we report our findings through systematic literature review that aimed at identifying the applicability, limitations and individual result of most used machine learning techniques for effort estimation in agile software development. We have also discussed suggested attributes of a robust machine learning model to achieve more accurate effort estimation.
KEYWORDS - IJSE-179, Agile software development, Effort estimation, Machine learning
 
     
Hybrid Pair Programming in Global Software Development
Irdina Wanda Syahputri, Ridi Ferdiana
 
ABSTRACT
Pair programming exhibits direct communication in the same place, same time, and same context. In global software development or distributed software development, pair programming cannot be applied in full-time development because of the limited interaction, space, and time between developers. We aim to understand the pair programming pattern in global software development. We pinpoint the developer's activity based on their work habits between solo and pair activities. Furthermore, we propose hybrid pair programming to implement pair programming and solo programming in global software development. In this experiment, we construct an experiment to understand how pair programming and solo programming are applied in a software project in the academic context. We have three groups. The first group will use solo programming to develop their project. The second group will use pair programming to develop their project. The third group will use a combination of solo and pair programming. We do triangulation to understand how the implementation of hybrid pair programming in global software development affects. Our results show that pair programming cannot be applied in global software development activity. Some activities work well in pairs, and some activities work well solo. In conjunction, As a result, the organization may implement pair programming and solo programming in a global software development environment through our proposed practices, namely hybrid pair programming. These basic findings are consistent with research showing that hybrid pair programming can work well in a global software development environment. However, pair programming provides better productivity than solo programming (55% better). Hybrid pair programming can improve coordination, scheduling, and technical issues and boost solo programming productivity.
KEYWORDS - IJSE-180, Global Software Development, Pair Programming, Distributed Software Development
 
     
Factors Affecting Software Maintenance Cost of Python Programs
Catherine Wambui Mukunga, John Gichuki Ndia, Geoffrey Mariga Wambugu
 
ABSTRACT
One of the primary areas of software project management is cost estimation. The cost estimation problem remains unsolved today because of the ineffective cost estimation techniques which are unsuitable for handling current development methods. Software maintenance costs can be estimated using a variety of models such as the Construction Cost Model (COCOMO), Software Life Cycle Management (SLIM), Software maintenance project effort estimation model and others but more work needs to be done in developing models that can accommodate programs from new programming paradigms. The primary objective of this research was to identify factors affecting the software maintenance cost of python programs and rank them according to their relevance. To achieve the objective, a literature review study was done to identify factors that influence software maintenance costs followed by an expert opinion survey to ascertain which of the factors were relevant for Python programs. Fifty two (52) Python developers and project managers were identified using snowballing technique and asked to rate the cost drivers in order of relevance using a five point scale. Descriptive statistics were used to carry out the analysis of the results. The results indicated that all the eighteen (18) factors affected the maintenance cost of Python programs. The factors were ranked based on the percentage mean of frequencies. Six additional factors were also identified by the experts and ranked. The factors will be considered as input parameters for a cost estimation model to be developed in the near future for estimating the cost of maintaining python programs.
KEYWORDS - IJSE-185, Software Maintenance, Cost Drivers, Expert Opinion, Cost Estimation.
 
     
Covid-19 Data Analysis in Tarakan With Poisson Regression and Spatial Poisson Process
Ika Nurwanitantya Wardani, A’yunin Sofro, Khusnia Nurul Khikmah
 
ABSTRACT
Coronavirus-2019 Disease or COVID-19 had entered Indonesia since March 2020 and continues to spread until now. This included a small town on the edge of North Kalimantan Province, namely Tarakan. COVID-19 cases have outspread in Tarakan, until June 8, 2020, there have been 46 cases of such exposure. The cause of the outspread and how its outspread patterns weren’t known clearly yet. This case encouraged researchers to conduct this research. One relevant approach was to use Generalized Linear Models (GLM). This method was divided into two, including deterministic namely Poisson Regression and Stochastic with Spatial Poisson Process. The variables used in this study were rainfall, population density, and temperature in each village in Tarakan. From the Poisson Regression analysis, it was found that only one factor affected, namely temperature. The results were then refined with the Spatial Poisson Process, where in addition to the influencing factors also the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria, then the model of the case density intensity was obtained using regression. From the model obtained, it was known that the covariate variables that significantly influence the rainfall and temperature. When compared with general Poisson regression analysis, the results showed the variables that have a significant effect ware only the average temperature. Thus, a better method was used namely the Spatial Poisson Process. It was also shown by the two models IAC value, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.
KEYWORDS - IJSSC-62, Generalized Linear Models (GLM), Poisson Regression, Spatial Poisson Process
 
     
Evaluation of Signal Denoising Filtering Techniques using Dual-mode Scramjet data from Optical Emission Spectroscopy Sensors
Darrien Hunt, Janett Walters-Willliams, Qiang Le
 
ABSTRACT
Improving the operability of and control of Dual-mode Scramjets (DMSJ) is very important and requires the measurement and analyzation of spectral emissions data and the transition from one steady state to another. Studying these states provide performance information of these engines. Currently research now examines the use of light emission measured by Optical Emission Spectroscopy (OES) to determine where transition occurs, thus enabling gain-scheduling in the OES controller which helps with DMSJ fuel control. Real time OES signals are not free of noise and this corruption can lead to inefficiency at high speeds and as well as “unstarts” in the DMSJs. Eliminating the noise and recovering the original signal is a task of significance. This paper investigates the performance of several popular denoising filtering algorithms (Wavelet, Median, Savitzky-Golay, and Moving Average Filters) on OES signals. The study yields encouraging results in both the qualitative and quantitative analyses, with wavelet transformation producing the most satisfactory results.
KEYWORDS - SPIJ-303, Signal Processing, Digital Filtering Techniques, Optical Emission Spectroscopy