Search results for: service learning

Authors: Mihail Nagea, Olivera Lupescu, Elena Taina Avramescu, Cristina Patru

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Classical education of orthopedic surgeons involves lectures, self study, workshops and cadaver dissections, and sometimes supervised practical training within surgery, which quite seldom gives the young surgeons the feeling of being unable to apply what they have learned especially in surgical practice. The purpose of this paper is to present a different approach from the classical one, which enhances the practical skills of the orthopedic trainees and prepare them for future practice. The paper presents the content of the research project 2015-1-RO01-KA202-015230, ERASMUS+ VET ‘Collaborative learning for enhancing practical skills for patient-focused interventions in gait rehabilitation after orthopedic surgery’ which, using e learning as a basic tool , delivers to the trainees not only courses, but especially practical information through videos and case scenarios including gait analysis in order to build patient focused therapeutic plans, adapted to the characteristics of each patient. The outcome of this project is to enhance the practical skills in orthopedic surgery and the results are evaluated following the answers to the questionnaires, but especially the reactions within the case scenarios. The participants will thus follow the idea that any mistake within solving the cases might represent a failure of treating a real patient. This modern approach, besides using interactivity to evaluate the theoretical and practical knowledge of the trainee, increases the sense of responsibility, as well as the ability to react properly in real cases.

Keywords: interdisciplinary approach, gait analysis, orthopedic surgery, vocational training

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Authors: Nadir Ali Mugheri, Shaukat Ali Lohar

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In a multilingual society like Pakistan, code switching is commonly observed in different contexts. Mostly people use L1 (Native Languages) and L2 for common communications and L3 (i.e. English, Urdu, Sindhi) in formal contexts and for academic writings. Such a frequent code switching does affect EFL learners' acquisition of grammar and lexis of the target language which in the long run result in different types of errors in their writings. The current study is to investigate and identify common elements of L1 and L2 (spoken by students of the Universities in Pakistan) which create hindrances for EFL learners. Case study method was used for this research. Formal writings of 400 EFL learners (as participants from various Universities of the country) were observed. Among 400 participants, 200 were female and 200 were male EFL learners having different academic backgrounds. Errors found were categorized into different types according to grammatical items, the difference in meanings, structure of sentences and identifiers of tenses of L1 or L2 in comparison with those of the target language. The findings showed that EFL learners in Pakistani varsities have serious problems in their writings and they committed serious errors related to the grammar and meanings of the target language. After analysis of the committed errors, the results were found in the affirmation of the hypothesis that L1 or L2 does affect EFL learners. The research suggests in the end to adopt natural ways in pedagogy like task-based learning or communicative methods using contextualized material so as to avoid impediments of L1 or L2 in acquisition the target language.

Keywords: multilingualism, L2 acquisition, code switching, language acquisition, communicative language teaching

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Authors: Anantdeep Kaur, Sharanjeet Singh

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Early childhood education and assessment of children is a very essential tool that helps them in their growth and development. Techniques should be developed, and tools should be created in this field as it is a very important learning phase of life. Some information and sources are included for student assessment to provide a record of growth in all developmental areas cognitive, physical, Language, social-emotional, and approaches to learning. As an early childhood educator, it is very important to identify children who need special support and counseling to improve them because they are not mentally mature to discuss with the teacher their problems and needs. It is the duty and responsibility of the educator to assess children from their body language, behavior, and their routine actions about their skills that can be improved and which can take them forward in their future life. And also, children should be assessed with their weaker points because this is the right time to correct them, and they be improved with certain methods and tools by working on them constantly. Observing children regularly with all their facets of development, including intellectual, linguistic, social-emotional, and physical development. Every day, a physical education class should be regulated to check their physical growth activities, which can help to assess their physical activeness and motor abilities. When they are outside on the playgrounds, it is very important to instill environmental understanding among them so that they should know that they are very part of this nature, and it will help them to be one with the universe rather than feeling themselves individually. This technique assists them in living their childhood full of energy all the time. All types of assessments have unique purposes. It is important first to determine what should be measured, then find the program that best assesses those.

Keywords: special needs, motor ability, environmental understanding, physical development

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Authors: Michael Flannery, Annie ó Breacháin

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Teaching the creative arts in preservice teacher education can be challenging. Some students find artistic self-expression and its related creative processes overwhelming. Low creative self-efficacy levels and creative habits of mind can impede their levels of motivation, engagement and persistence. For some, creative arts engagement can induce a state of anxiety and distress as opposed to flow. Flow theory posits that learners are happiest when they are learning in a state of flow. During the flow state, students feel, think and perform their best. They become so involved in the learning experience that nothing else seems to matter. The creative flow state is a crucial conduit of artistic processes to enable learners to explore and produce their best work. Despite the research conducted on flow state across several contexts, the phenomenon of personal flow state remains quite elusive. While some research has examined flow in relation to characteristics, conditions and personality traits, no research has investigated individuals' personal experiences of flow in a visual and tangible manner nor explored a relationship between flow state and teachers’ artistic development. This explorative case study explores preservice teachers’ impressions of flow using an arts-based approach. It identifies, categorizes and discusses patterns of commonality and difference. Grounded by theory concerning flow, self-efficacy and creative habits, this study ponders how emerging findings regarding flow impressions might aid teacher arts educators in helping preservice teachers who struggle with creative self-expression.

Keywords: creative arts, flow theory, presence, self-efficacy, teacher education

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Medha Talpade, Salil Talpade

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The intent of this quantitative project was to compare the values and perceptions of students from a predominantly white college (PWI) to those from a historically black college (HBCU) about culturally relevant teaching and learning practices in the academic realm. The reason for interrelating student culture with teaching practices is to enable a pedagogical response to the low retention rates of African American students and first generation Caucasian students in high schools, colleges, and their low rates of social mobility and educational achievement. Culturally relevant pedagogy, according to related research, is deemed rewarding to students, teachers, the local and national community. Critical race theory (CRT) is the main framework used in this project to explain the ubiquity of a culturally relevant pedagogy. The purpose of this quantitative study was to test the critical race theory that relates the presence of the factors associated with culturally relevant teaching strategies with perceived relevance. The culturally relevant teaching strategies were identified based on the recommendations and findings of past research. Participants in this study included approximately 145 students from a HBCU and 55 students from the PWI. A survey consisting of 37 items related to culturally relevant pedagogy was administered. The themes used to construct the items were: Use of culturally-specific examples in class whenever possible; use of culturally-specific presentational models, use of relational reinforcers, and active engagement. All the items had a likert-type response scale. Participants reported their degree of agreement (5-point scale ranging from strongly disagree to strongly agree) and importance (3-point scale ranging from not at all important to very important) with each survey item. A new variable, Relevance was formed based on the multiplicative function of importance and presence of a teaching and learning strategy. A set of six demographic questions were included in the survey. A consent form based on NIH and APA ethical standards was distributed prior to survey administration to the volunteers. Results of a Factor Analyses on the data from the PWI and the HBCU, and a ANOVA indicated significant differences on ‘Relevance’ related to specific themes. Results of this study are expected to inform educational practices and improve teaching and learning outcomes.

Keywords: culturally relevant pedagogy, college students, cross-cultural, applied psychology

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Authors: Larry Audet

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Creativity is suddenly and fortunately a new educational focus in the United Arab Emirates and around the world. Yet still today many leaders of creativity are not sure how to introduce it to their teachers. It is impossible to simultaneously introduce every aspect of creativity into a work climate and reach any degree of organizational coherence. The number of alternatives to explore is so great; the information teachers need to learn is so vast, that even an approximation to including every concept and theory of creativity into the school organization is hard to conceive. Effective leaders of creativity need evidence-based and practical guidance for introducing and stimulating creativity in others. Machine learning models reveal new findings from KEYS Survey© data about teacher perceptions of stimulants and barriers to their individual and collective creativity. Findings from predictive and causal models provide leaders with a rational for decision-making when introducing creativity into their organization. Leaders should focus on management practices first. Analyses reveal that creative outcomes are more likely to occur when teachers perceive supportive management practices: providing teachers with challenging work that calls for their best efforts; allowing freedom and autonomy in their practice of work; allowing teachers to form creative work-groups; and, recognizing them for their efforts. Once management practices are in place, leaders should focus their efforts on modeling risk-taking, providing optimal amounts of preparation time, and evaluating teachers fairly.

Keywords: creativity, leadership, KEYS survey, teaching, work climate

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Authors: Buddhiprabha D. D. Pathirana

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Despite a rapid increase in the number of school counselors in the recent past; procuring the service of school counselors is a luxury that many Sri Lankan schools cannot afford. In addition, school counseling in Sri Lanka also faces new challenges in implementation due to the fact that a generally agreed consensus on training, ethical standards, role identity, counseling model, and structures for school counselors has not been reached. Therefore, this paper has several objectives. First, it reviews a brief history of school counseling in Sri Lanka and describes its current status. Second, it describes current trends/ problems specific to Sri Lankan school counseling milieu which have limited the progress of school counseling as a practice. Third, it discusses societal and cultural issues that are important to consider when implementing school counseling as a practices in Sri Lanka and provides recommendations to improve it.

Keywords: school counseling, Sri Lanka, current situation, recommendations

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Authors: Jonas Mayer, Kai-Frederic Seitz, Thorben Kuprat

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In today’s competitive environment production’s logistic objectives such as ‘delivery reliability’ and ‘delivery time’ and distribution’s logistic objectives such as ‘service level’ and ‘delivery delay’ are attributed great importance. Especially for small and mid-sized enterprises (SME) attaining these objectives pose a key challenge. Within this context, one of the difficulties is that interactions between departments within the enterprise and their specific objectives are insufficiently taken into account and aligned. Interdepartmental independencies along with contradicting targets set within the different departments result in enterprises having sub-optimal logistic performance capability. This paper presents a research project which will systematically describe the interactions between departments and convert them into a quantifiable form.

Keywords: department-specific actuating and control variables, interdepartmental interactions, cybernetic model, logistic objectives

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Authors: Amarjit S Tanda

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Integrated Pest Management Technology (IPMT) offers a crop protection model with sustainable agriculture production with minimum damage to the environment and human health. A concept of agro-ecological crop protection seems unsuitable under dynamic environmental systems. To remedy this, we are proposing Genetically Engineered Crop Protection System (GECPS), as an alternate concept in IPMT that suggests how GE cultivars can be optimally put to the service of crop protection. Genetically engineered cultivars which are developed by gene editing biotechnology may provide a preventive defense against the insect pests and plant diseases, a suitable alternative crop system for blending in IPMT program, in the future agro-industry.

Keywords: integrated, pest, management, technology

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Authors: Jinming Du

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With the COVID-19 pandemic beginning in 2020, many higher education institutions and education systems are turning to hybrid or fully distance online courses to maintain social distance and provide a safe virtual space for learning and teaching. However, the majority of faculty members were not well prepared for the shift to blended or distance learning. Communication frustrations are prevalent in both hybrid and full-distance courses. A systematic literature review was conducted by a comprehensive analysis of 1688 publications that focused on the application of the adoption of chatbots in education. This study aimed to explore instructors' experiences with chatbots in online and blended undergraduate English courses. Language learners are overwhelmed by the variety of information offered by many online sites. The recently emerged chatbots (e.g.: ChatGPT) are slightly superior in performance as compared to those traditional through previous technologies such as tapes, video recorders, and websites. The field of chatbots has been intensively researched, and new methods have been developed to demonstrate how students can best learn and practice a new language in the target language. However, it is believed that among the many areas where chatbots are applied, while chatbots have been used as effective tools for communicating with business customers, in consulting and targeting areas, and in the medical field, chatbots have not yet been fully explored and implemented in the field of language education. This issue is challenging enough for language teachers; they need to study and conduct research carefully to clarify it. Pedagogical chatbots may alleviate the perception of a lack of communication and feedback from instructors by interacting naturally with students through scaffolding the understanding of those learners, much like educators do. However, educators and instructors lack the proficiency to effectively operate this emerging AI chatbot technology and require comprehensive study or structured training to attain competence. There is a gap between language teachers’ perceptions and recent advances in the application of AI chatbots to language learning. The results of the study found that although the teachers felt that the chatbots did the best job of giving feedback, the teachers needed additional training to be able to give better instructions and to help them assist in teaching. Teachers generally perceive the utilization of chatbots to offer substantial assistance to English language instruction.

Keywords: artificial intelligence in education, chatbots, education and technology, education system, pedagogical chatbot, chatbots and language education

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Authors: Ferah Tesfaye Admasu

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Autism Spectrum Disorder (ASD) often presents significant communication challenges, particularly for non-verbal individuals who struggle to express their needs and emotions effectively. Assistive technologies (AT) have emerged as vital tools in enhancing communication abilities for this population. Recent advancements in artificial intelligence (AI) hold the potential to revolutionize the design and functionality of these technologies. This study explores the application of AI in developing intelligent, adaptive, and user-centered assistive technologies for non-verbal individuals with autism. Through a review of current AI-driven tools, including speech-generating devices, predictive text systems, and emotion-recognition software, this research investigates how AI can bridge communication gaps, improve engagement, and support independence. Machine learning algorithms, natural language processing (NLP), and facial recognition technologies are examined as core components in creating more personalized and responsive communication aids. The study also discusses the challenges and ethical considerations involved in deploying AI-based AT, such as data privacy and the risk of over-reliance on technology. Findings suggest that integrating AI into assistive technologies can significantly enhance the quality of life for non-verbal individuals with autism, providing them with greater opportunities for social interaction and participation in daily activities. However, continued research and development are needed to ensure these technologies are accessible, affordable, and culturally sensitive.

Keywords: artificial intelligence, autism spectrum disorder, non-verbal communication, assistive technology, machine learning

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Authors: Bashir Aliyu Yauri, Rufai Aliyu Yauri

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With the introduction of cash-less society policy by the Central Bank of Nigeria, the concept of e-banking services has experienced a significant improvement over the years. Today quite a number of people are embracing e-banking activities especially ATM, thereby moving away from the conventional banking system. This paper presents a review of the underlying Architectural Layout of Intra-Bank and Inter-Bank ATM connectivity in Nigeria. The paper further investigates and discusses factors affecting the Intra-Bank and Inter-Bank ATM connectivity in Nigeria. And as well possible solutions to these factors affecting ATM Connectivity and Operations are proposed.

Keywords: architectural layout, automated teller machine, e-services, postilion

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Authors: Arif Ahmed Mohammed Al-Ahdal

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Poetry, tales, and fables have always been a part of the L1 repertoire and one that takes the learners to another amazing and fascinating world of imagination. The storytelling class and the genre of poems are activities greatly enjoyed by all age groups. The very significant idea behind their inclusion in the language curriculum is to sensitize young minds to a wide range of human emotions that are believed to greatly contribute to building their social resilience, emotional stability, empathy towards fellow creatures, and literacy. Quite certainly, the learning objective at this stage is not language acquisition (though it happens as an automatic process) but getting the young learners to be acquainted with an entire spectrum of what may be called the ‘noble’ abilities of the human race. They enrich their very existence, inspiring them to unearth ‘selves’ that help them as adults and enable them to co-exist fruitfully and symbiotically with their fellow human beings. By extension, ‘higher’ training in these literature genres shows the universality of human emotions, sufferings, aspirations, and hopes. The current study is anchored on the Reader-Response-Theory in literature learning, which suggests that the reader reconstructs work and re-enacts the author's creative role. Reiteratingly, literary works provide clues or verbal symbols in a linguistic system, widely accepted by everyone who shares the language, but everyone reads their own life experiences and situations into them. The significance of words depends on the reader, even if they have a typical relationship. In every reading, there is an interaction between the reader and the text. The process of reading is an experience in which the reader tries to comprehend the literary work, which surpasses its full potential since it provides emotional and intellectual reactions that are not anticipated from the document but cannot be affirmed just by the reader as a part of the text. The idea is that the text forms the basis of a unifying experience. A reinterpretation of the literary text may transform it into a guiding principle to respond to actual experiences and personal memories. The impulses delivered to the reader vary according to poetry or texts; nevertheless, the readers differ considerably even with the same material. Previous studies confirm that poetry is a useful tool for learning a language. This present paper works on these hypotheses and proposes to study the impetus given to L2 learning as a factor of exposure to poetry and meaningful stories in L1. The driving force behind the choice of this topic is the first-hand experience that the researcher had while teaching a literary text to a group of BA students who, as a reaction to the text, initially burst into tears and ultimately turned the class into an interactive session. The study also intends to compare the performance of male and female students post intervention using pre and post-tests, apart from undertaking a detailed inquiry via interviews with college learners of English to understand how L1 literature plays a great role in the acquisition of L2.

Keywords: SLA, literary text, poetry, tales, affective factors

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Rebecca Zahra, Joseph G. Vella, Ernest Cachia

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The notion of cloud computing is rapidly gaining ground in the IT industry and is appealing mostly due to making computing more adaptable and expedient whilst diminishing the total cost of ownership. This paper focuses on the software as a service (SaaS) architecture of cloud computing which is used for the outsourcing of databases with their associated business processes. One approach for offering SaaS is basing the system’s architecture on multi-tenancy. Multi-tenancy allows multiple tenants (users) to make use of the same single application instance. Their requests and configurations might then differ according to specific requirements met through tenant customisation through the software. Despite the known advantages, companies still feel uneasy to opt for the multi-tenancy with data security being a principle concern. The fact that multiple tenants, possibly competitors, would have their data located on the same server process and share the same database tables heighten the fear of unauthorised access. Security is a vital aspect which needs to be considered by application developers, database administrators, data owners and end users. This is further complicated in cloud-based multi-tenant system where boundaries must be established between tenants and additional access control models must be in place to prevent unauthorised cross-tenant access to data. Moreover, when altering the database state, the transactions need to strictly adhere to the tenant’s known business processes. This paper focuses on the fact that security in cloud databases should not be considered as an isolated issue. Rather it should be included in the initial phases of the database design and monitored continuously throughout the whole development process. This paper aims to identify a number of the most common security risks and threats specifically in the area of multi-tenant cloud systems. Issues and bottlenecks relating to security risks in cloud databases are surveyed. Some techniques which might be utilised to overcome them are then listed and evaluated. After a description and evaluation of the main security threats, this paper produces a list of software requirements to ensure that proper security policies are implemented by a software development team when designing and implementing a multi-tenant based SaaS. This would then assist the cloud service providers to define, implement, and manage security policies as per tenant customisation requirements whilst assuring security for the customers’ data.

Keywords: cloud computing, data management, multi-tenancy, requirements, security

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Authors: Kunyanuth Kularbphettong, Kunnika Tenprakhon, Pattarapan Roonrakwit

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In this paper, we present a recommendation library application on Android system. The objective of this system is to support and advice user to use library resources based on mobile application. We describe the design approaches and functional components of this system. The system was developed based on under association rules, Apriori algorithm. In this project, it was divided the result by the research purposes into 2 parts: developing the Mobile application for online library service and testing and evaluating the system. Questionnaires were used to measure user satisfaction with system usability by specialists and users. The results were satisfactory both specialists and users.

Keywords: online library, Apriori algorithm, Android application, black box

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Authors: Norah Alosayl

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English is considered the most important foreign language in the Kingdom of Saudi Arabia (KSA) because of the usefulness of English as a global language compared to Arabic. As students’ desire to improve their English language skills has grown, English writing has been identified as the most difficult problem for Saudi students in their language learning. Although the English language in Saudi Arabia is taught beginning in the seventh grade, many students have problems at the university level, especially in writing, due to a gap between what is taught in secondary and high schools and university expectations- pupils generally study English at school, based on one book with few exercises in vocabulary and grammar exercises, and there are no specific writing lessons. Moreover, from personal teaching experience at King Saud bin Abdulaziz University, students face real problems with their writing. This paper revolves around the blackboard-based collaborative writing to help the undergraduate Saudi EFL students, in their first year enrolled in two sections of ENGL 101 in the first semester of 2021 at King Saud bin Abdulaziz University, practice the most difficult skill they found in their writing through a small group. Therefore, a sequential mixed methods design will be suited. The first phase of the study aims to highlight the most difficult skill experienced by students from an official writing exam that is evaluated by their teachers through an official rubric used in King Saud bin Abdulaziz University. In the second phase, this study will intend to investigate the benefits of social interaction on the process of learning writing. Students will be provided with five collaborative writing tasks via discussion feature on Blackboard to practice a skill that they found difficult in writing. the tasks will be formed based on social constructivist theory and pedagogic frameworks. The interaction will take place between peers and their teachers. The frequencies of students’ participation and the quality of their interaction will be observed through manual counting, screenshotting. This will help the researcher understand how students actively work on the task through the amount of their participation and will also distinguish the type of interaction (on task, about task, or off-task). Semi-structured interviews will be conducted with students to understand their perceptions about the blackboard-based collaborative writing tasks, and questionnaires will be distributed to identify students’ attitudes with the tasks.

Keywords: writing difficulties, blackboard-based collaborative writing, process of learning writing, interaction, participations

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Authors: Shohreh Ghasemi

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Introduction: Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Keywords: artificial intelligence, melanoma, accuracy, prognosis prediction, image analysis, personalized medicine

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Authors: Amir Rashid

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Today is the world of innovations where everyone is looking for a change. Organizations are now looking toward virtualization in order to minimize their computing cost. Cloud Computing has also introduced itself by the means of reducing computing cost. It offers different approach to make computing better by improving utilization and reducing infrastructure and administrative costs. Cloud Computing is basically the amalgamation of Utility Computing and SaaS (Software as a Service). Cloud Computing is quite new to organizations as it is still at its deploying stage. Due to this reason, organizations are not confident whether to adopt it or not. This thesis investigates the problem for organization concerning the security and cost issues. Benefits and drawbacks are being highlighted which organizations can have or suffer in order to adopt Cloud Computing. In Conclusion, Cloud Computing is a better option available for small and medium organizations with a comparison to large companies both in terms of data security and cost.

Keywords: cloud computing, security, cost, elasticity, PaaS, IaaS, SaaS

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Ainur Bulasheva

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During the last decade, the dual learning system (DLS) has been actively introduced in various industries in Kazakhstan, including both vocational, post-secondary, and higher education levels. It is a relatively new practice-oriented approach to training qualified personnel in Kazakhstan, officially introduced in 2012. Dual learning was integrated from the German vocational education and training system, combining practical training with part-time work in production and training in an educational institution. The policy of DLS has increasingly focused on decreasing youth unemployment and the shortage of mid-level professionals by providing incentives for employers to involve in this system. By participating directly in the educational process, the enterprise strives to train its future personnel to meet fast-changing market demands. This study examines the effectiveness of DLS from the perspective of employers to understand the motivations of businesses to participate (invest) in this program. The human capital theory of Backer, which predicts that employers will invest in training their workers (in our case, dual students) when they expect that the return on investment will be greater than the cost - acts as a starting point. Further extensionists of this theory will be considered to understand investing intentions of businesses. By comparing perceptions of DLS employers and non-dual practices, this study determines the efficiency of promoted training approach for enterprises in the Kazakhstan agri-food industry.

Keywords: vocational and technical education, dualeducation, human capital theory, argi-food industry

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Authors: Junaid C, Sabique MK

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Introduction: MES Mamapd College is an autonomous college established in 1964 affiliated with the University of Calicut run by the Muslim Educational Society Kerala. The department of Arabic of the college is having a pivotal role in promoting Arabic language learning, teaching, research, and other allied academic activities. State of Problem: Department of Arabic of the college introduced before the academic committee the culture of international seminars. The department connected the academic community with foreign scholars and introduced industry-academia collaboration programs which are beneficial to the job seekers. These practices and innovations should be documented. Objectives: Create awareness of innovative practices implemented for the promotion of the Arabic language. Infuse confidence in learners in learning of Arabic language. Showcase the distinctive academic programs initiated by the department Methodology: Data will be collected from archives, souvenirs, and reports. Survey methods and interviews with authorities and beneficiaries will be collected for the data analysis. Major results: MES Mampad College introduced before its stakeholders different unique academic practices related to the Arabic language and literature. When the unprecedented pandemic situation pulled back all of the academic community, the department come forward with numerous academic initiatives utilizing the virtual space. Both arenas will be documented. Conclusion: This study will help to make awareness on the promotion of the Arabic language studies and related practices initiated by the department of Arabic MES Mampad College. These practices and innovations can be modeled and replicated.

Keywords: teaching Arabic language, MES mampad college, Arabic webinars, pandemic impacts in literature

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Authors: Eliza Torrigue, Efrelyn Iellamo

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Maternal death is one of the rising health issues in the Philippines. It is alarming to know that in every hour of each day, a mother gives birth to a child who may not live to see the next day. Statistics shows that intrapartum period and third stage of labor are the very crucial periods for the expectant mother, as well as the first six hours of life for the newborn. To address the issue, The Essential Intrapartum Newborn Care (EINC) was developed. Through this, Obstetric Delivery Room (OB-DR) Nurses shall be updated with the evidence-based maternal and newborn care to ensure patient safety, thus, reducing maternal and child mortality. This study aims to describe the compliance of hospitals, especially of OB-DR nurses, to the EINC Protocols. The researcher aims to link the profile variables of the respondents in terms of age, length of service and formal training to their compliance on the EINC Protocols. The outcome of the study is geared towards the development of appropriate training program for OB-DR Nurses assigned in the delivery room of the hospitals based on the study’s results to sustain the EINC standards. A descriptive correlational method was used. The sample consists of 75 Obstetric Delivery Room (OB-DR) Nurses from three government hospitals in the City of Manila namely, Ospital ng Maynila Medical Center, Tondo Medical Center, and Gat Andres Bonifacio Memorial Medical Center. Data were collected using an evaluative checklist. Ranking, weighted mean, Chi-square and Pearson’s R were used to analyze data. The level of compliance to the EINC Protocols by the respondents was evaluated with an overall mean score of 4.768 implying that OB-DR Nurses have a high regard in complying with the step by step procedure of the EINC. Furthermore, data shows that formal training on EINC have a significant relationship with OB-DR Nurses’ level of compliance during cord care, AMTSL, and immediate newborn care until the first ninety minutes to six hours of life. However, the respondents’ age and length of service do not have a significant relationship with the compliance of OB-DR Nurses on EINC Protocols. In the pursuit of decreasing the maternal mortality in the Philippines, EINC Protocols have been widely implemented in the country especially in the government hospitals where most of the deliveries happen. In this study, it was found out that OB-DR Nurses adhere and are highly compliant to the standards in order to assure that optimum level of care is delivered to the mother and newborn. Formal training on EINC, on the other hand, create the most impact on the compliance of nurses. It is therefore recommended that there must be a structured enhancement training program to plan, implement and evaluate the EINC protocols in these government hospitals.

Keywords: compliance, intrapartum, newborn care, nurses

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Authors: Justyna Żywiołek, Marek Matulewski

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Data, is information processed by enterprises for primary and secondary purposes as processes. Thanks to processing, the sales process takes place; in the case of the surveyed companies, sales take place online. However, this indirect form of contact with the customer causes many problems for both customers and furniture manufacturers. The article presents solutions that would solve problems related to the analysis of data and information in the order fulfillment process sent to post-warranty service. The article also presents an analysis of threats to the security of this information, both for customers and the enterprise.

Keywords: ordering furniture online, information security, furniture industry, enterprise security, risk analysis

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Authors: Omil Nady Mahrous Maximous

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The construction industry is currently experiencing a shift towards digitisation. This transformation is driven by adopting technologies like Building Information Modelling (BIM), drones, and augmented reality (AR). These advancements are revolutionizing the process of designing, constructing, and operating projects. BIM, for instance, is a new way of communicating and exploiting technology such as software and machinery. It enables the creation of a replica or virtual model of buildings or infrastructure projects. It facilitates simulating construction procedures, identifying issues beforehand, and optimizing designs accordingly. Drones are another tool in this revolution, as they can be utilized for site surveys, inspections, and even deliveries. Moreover, AR technology provides real-time information to workers involved in the project. Implementing these technologies in the construction industry has brought about improvements in efficiency, safety measures, and sustainable practices. BIM helps minimize rework and waste materials, while drones contribute to safety by reducing workers' exposure to areas. Additionally, AR plays a role in worker safety by delivering instructions and guidance during operations. Although the digital transformation within the construction industry is still in its early stages, it holds the potential to reshape project delivery methods entirely. By embracing these technologies, construction companies can boost their profitability while simultaneously reducing their environmental impact and ensuring safer practices.

Keywords: architectural education, construction industry, digital learning environments, immersive learning BIM, digital construction, construction technologies, digital transformation artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

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Authors: Nahid Nariman

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The prosperity of any nation depends on its ability to use human potential, in particular, to offer an education that builds learners' competencies to become effective workforce participants and true citizens of the world. Ever since the Second World War, the United States has been a dominant player in the world politically, economically, socially, and culturally. The rapid rise of technological advancement and consumer technologies have made it clear that science, technology, engineering, and math (STEM) play a crucial role in today’s world economy. Exploring the top qualities demanded from new hires in the industry—i.e., problem-solving skills, teamwork, dependability, adaptability, technical and communication skills— sheds light on the kind of path that is needed for a successful educational system to effectively support STEM. The focus of 21st century education has been to build student competencies by preparing them to acquire and apply knowledge, to think critically and creatively, to competently use information, be able to work in teams, to demonstrate intellectual and moral values as well as cultural awareness, and to be able to communicate. Many educational reforms pinpoint various 'ideal' pathways toward STEM that educators, policy makers, and business leaders have identified for educating the workforce of tomorrow. This study will explore how problem-based learning (PBL), an instructional strategy developed in the medical field and adopted with many successful results in K-12 through higher education, is the proper approach to stimulate underrepresented high school students' interest in pursuing STEM careers. In the current study, the effect of a problem-based STEM model on students' attitudes and career interests was investigated using qualitative and quantitative methods. The participants were 71 low-income, native Hawaiian high school students who would be first-generation college students. They were attending a summer STEM camp developed as the result of a collaboration between the University of Hawaii and the Upward Bound Program. The project, funded by the National Science Foundation's Innovative Technology Experiences for Students and Teachers (ITEST) program, used PBL as an approach in challenging students to engage in solving hands-on, real-world problems in their communities. Pre-surveys were used before camp and post-surveys on the last day of the program to learn about the implementation of the PBL STEM model. A Career Interest Questionnaire provided a way to investigate students’ career interests. After the summer camp, a representative selection of students participated in focus group interviews to discuss their opinions about the PBL STEM camp. The findings revealed a significantly positive increase in students' attitudes towards STEM disciplines and STEM careers. The students' interview results also revealed that students identified PBL to be an effective form of instruction in their learning and in the development of their 21st-century skills. PBL was acknowledged for making the class more enjoyable and for raising students' interest in STEM careers, while also helping them develop teamwork and communication skills in addition to scientific knowledge. As a result, the integration of PBL and a STEM learning experience was shown to positively affect students’ interest in STEM careers.

Keywords: problem-based learning, science education, STEM, underrepresented students

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Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: Hatim Elhag

Abstract:

Banking and Financial Service Institutions are possibly the most advanced in terms of technology adoption and use it as a key differentiator. With high levels of business process automation, maturity in the functional portfolio, straight through processing and proven technology outsourcing benefits, Banking sector stand to benefit significantly from Cloud computing capabilities. Additionally, with complex Compliance and Regulatory policies, combined with expansive products and geography coverage, the business impact is even greater. While the benefits are exponential, there are also significant challenges in adopting this model– including Legal, Security, Performance, Reliability, Transformation complexity, Operating control and Governance and most importantly proof for the promised cost benefits. However, new architecture designed should be implemented to align this approach.

Keywords: security, cloud, banking sector, cloud computing

Procedia PDF Downloads 499