Search results for: brand recall

Authors: Colm Barcoe, Garvan Whelan

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The purpose of this paper is to investigate the impact that US TV and movies have had on Irish tourism. This study examines how a destination marketing organisation (DMO) can use social media channels to capitalise upon the opportunities created by film tourism as it pertains to North American TV and movie productions. The findings are based on a combination of two qualitative methods, in-depth interviews with 20 industry professionals and a Netnographic analysis of social media activity between Tourism Ireland and the North American audience on Facebook and Twitter. The qualitative data were analysed in order to provide insights into the effectiveness of using North American pop culture as part of a digital marketing strategy when creating awareness of Ireland as a brand in the US and Canada. This study addresses a gap in the literature in relation to the use of social media when attracting the North American holidaymaker to Ireland. The findings from this investigation will extend an under-researched body of literature pertaining to Ireland as a destination and the successful digital marketing campaigns that have achieved exponential growth in this sector over the past five years. The empirical evidence presented also illustrates how the innovative use of social media has assisted the DMO to engage with the North American holidaymaker as part of an effective digital marketing strategy. This paper will be of value to academics and industry practitioners interested in film-induced tourism and indeed tourism in general, as well as students.

Keywords: digital marketing, tourism, strategies, movies, US TV

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Authors: Danijela Vranješ, Nataša Vukajlović

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Turning to learner-centered teaching instead of the teacher-centered approach brought a whole new perspective into the process of teaching and learning and set a new goal for improving the educational process itself. However, recently a tremendous decline in students’ performance on various standardized tests can be observed, above all on the PISA-test. The learner-centeredness on its own is not enough anymore: the students’ ability to think is deteriorating. Especially in foreign language learning, one can encounter a lot of learning by heart: whether it is grammar or vocabulary, teachers often seem to judge the students’ success merely on how well they can recall a specific word, phrase, or grammar rule, but they rarely aim to foster their ability to think. Convinced that foreign language teaching can do both, this research aims to discover how two different approaches to teaching foreign language foster the students’ ability to think as well as to what degree they help students get to the state-determined level of foreign language at the end of the semester as defined in the Common European Framework. For this purpose, two different curricula were developed: one is a traditional, learner-centered foreign language curriculum that aims at teaching the four competences as defined in the Common European Framework and serves as a control variable, whereas the second one has been enriched with various thinking routines and aims at teaching the foreign language as a means to communicate ideas and thoughts rather than reducing it to the four competences. Moreover, two types of tests were created for each approach, each based on the content taught during the semester. One aims to test the students’ competences as defined in the CER, and the other aims to test the ability of students to draw on the knowledge gained and come to their own conclusions based on the content taught during the semester. As it is an ongoing study, the results are yet to be interpreted.

Keywords: common european framework of reference, foreign language learning, foreign language teaching, testing and assignment

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Authors: Ramanjaneyulu Mogili, G.V.R.K. Acharyulu

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Indian organized retail sector is growing at a faster pace and gaining popularity. Indian Brand Equity Foundation (IBEF) reveals that the current market size of Indian retail industry is about US$ 520 billion with for growth rate 14 to 15 percent annually by 2018 the Indian retail sector is likely to grow at a CAGR of 13% to reach a size of US$ 950 billion. Developments in Information Technology have enabled online Retail sector that empowers customers to order products, conduct transactions without the need to interact physically with the retailers. In recent years, the online shopping industry has gained popularity to the point where certain categories of customers would consider buying electronic products online rather than visiting the stores. Conventionally the physical location of a store is seen as a source of competitive advantage. Online Retailing service sites provide virtual shopping space to the customers. Online Retail services are gaining momentum in India, with internet penetration improving in the country and smartphones becoming affordable along with changing lifestyles and preferences of customers. Although online shoppers prefer the convenience and choice available in online shopping, certain issues raised due to the occurrence of service failure. The proposed study attempts to measure the service recovery and failure process of electronic goods in Indian retail channels.

Keywords: service recovery, customer satisfaction, e-shopping, service failure

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Authors: Mehtab Alam, Mudiarasan Kuppusamy, Puvaneswaran Kunaserkaran

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The purpose of this paper is to bridge the gap and complete the relationship between tourism destinations and image branding as a choice of edifice foreign policy. Such options became a crucial component for individuals interested in leisure and travel activities. The destination management factors have been evaluated and analyzed using the primary and secondary data in a mixed-methods approach (quantitative sample of 384 and qualitative 8 semi-structured interviews at saturated point). The study chose the Environmental Management Accounting (EMA) and Image Restoration (IR) theories, along with a schematic diagram and an analytical framework supported by NVivo software 12, for two locations in Abbottabad, KPK, Pakistan: Shimla Hill and Thandiani. This incorporates the use of PLS-SEM model for assessing validity of data while SPSS for data screening of descriptive statistics. The results show that destination management's promotion of tourism has significantly improved Pakistan's state image. The use of institutional setup, environmental drivers, immigration, security, and hospitality as well as recreational initiatives on destination management is encouraged. The practical ramifications direct the heads of tourism projects, diplomats, directors, and policymakers to complete destination projects before inviting people to Pakistan. The paper provides the extent of knowledge for academic tourism circles to use tourism destinations as brand ambassadors.

Keywords: tourism, management, state image, foreign policy, image branding

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: William Cheng, Rini Sekartini

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Background: Sleep is essential for children because it provides enhancement for the neural system activities that give physiologic effects for the body to support growth and development. One of the modifiable factors that relates with sleep is nutrition, which includes nutritional status, iron intake, and magnesium intake. Nutritional status represents the balance between nutritional intake and expenditure, while iron and magnesium are micronutrients that are related to sleep regulation. The aim of this study is to identify prevalence of sleep disturbance among Indonesian children and to evaluate its relation with aspect to nutrition. Methods : A cross-sectional study involving children aged 5 to 7-years-old in an urban primary health care between 2012 and 2013 was carried out. Related data includes anthropometric status, iron intake, and magnesium intake. Iron and magnesium intake was obtained by 24-hours food recall procedure. Sleep Disturbance Scale for Children (SDSC) was used as the diagnostic tool for sleep disturbance, with score under 39 indicating presence of problem. Results: Out of 128 school-aged children included in this study, 28 (23,1%) of them were found to have sleep disturbance. The majority of children had good nutritional status, with only 15,7% that were severely underweight or underweight, and 12,4% that were identified as stunted. On the contrary, 99 children (81,8%) were identified to have inadequate magnesium intake and 56 children (46,3%) with inadequate iron intake. Our analysis showed there was no significant relation between all of the nutritional status indicators and sleep disturbance (p>0,05%). Moreover, inadequate iron and magnesium intake also failed to prove significant relation with sleep disturbance in this population. Conclusion: Almost fourth of school-aged children in Indonesia were found to have sleep disturbance and further study are needed to overcome this problem. According to our finding, there is no correlation between nutritional status, iron intake, magnesium intake, and sleep disturbance.

Keywords: iron intake, magnesium intake, nutritional status, school-aged children, sleep disturbance

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Authors: A. B. Robert, Adam Pramadia, Calvin Andika

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The main purpose of this study is to explore consumer loyalty characteristic of mobile phone category in Indonesia. Second, this research attempts to identify consumer segment and to explore their profile in each segment as the basis of marketing strategy formulation. This study used some tools of multivariate analysis such as discriminant analysis and cluster analysis. Discriminate analysis used to discriminate consumer loyal and not loyal by using particular variables. Cluster analysis used to reveal various segment in mobile phone category. In addition to having better customer understanding in each segment, this study used descriptive analysis and cross tab analysis in each segment defined by cluster analysis. This study expected several findings. First, consumer can be divided into two large group of loyal versus not loyal by set of variables. Second, this study identifies customer segment in mobile phone category. Third, exploring customer profile in each segment that has been identified. This study answer a call for additional empirical research into different product categories. Therefore, a replication research is advisable. By knowing the customer loyalty characteristic, and deep analysis of their consumption behavior and profile for each segment, this study is very advisable for high impact marketing strategy development. This study contributes body of knowledge by adding empirical study of consumer loyalty, segmentation analysis in mobile phone category by multiple brand analysis.

Keywords: customer loyalty, segmentation, marketing strategy, discriminant analysis, cluster analysis, mobile phone

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim targets from cluttered backgrounds, and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications, such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in the F1 score over the existing state-of-the-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Sadia Idrees, Gianpaolo Vignali, Simeon Gill

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The study aims to establish the virtual size and fit technology features to enhance fashion online retailing platforms, utilising digital human measurements to provide customised style and function to consumers. A few firms in the UK have launched advanced interactive fashion shopping domains for personalised shopping globally, aided by the latest internet technology. Virtual size and fit interfaces have a great potential to provide a personalised better-fitted garment to promote mass customisation globally. Made-to-measure clothing, consuming unstitched fabric is a common practice offered by fashion brands in Pakistan. This product is regarded as economical and sustainable to be utilised by consumers in Pakistan. Although the manual sizing system is practiced to sell garments online, virtual size and fit visualisation and recommendation technologies are uncommon in Pakistani fashion interfaces. A comparative assessment of Pakistani fashion brand websites and UK technology-driven fashion interfaces was conducted to highlight the vast potential of the virtual size and fit technology. The results indicated that web 2.0 technology adopted by Pakistani apparel brands has limited features, whereas companies practicing web 3.0 technology provide interactive online real-store shopping experience leading to enhanced customer satisfaction and globalisation of brands.

Keywords: e-commerce, mass customization, virtual size and fit, web 3.0 technology

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Authors: James Ormrod

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This paper is concerned with volunteer livestock checking. Based on a pilot study consisting of ‘go-along’ interviews with livestock checkers, it argues that there are limitations to the insights that can be generated from approaches to ‘discourse analysis’ that would focus only on the verbalizable aspects of the practice. Volunteer livestock checking takes place across Europe as part of conservation projects aimed at maintaining particular habitats through the reintroduction of grazing animals. Volunteers are variously called ‘urban shepherds’, because these practices often take place on urban fringes, or ‘lookerers’, as their role is to make visual checks on the animals. Pilot research that took place on the South Downs (a chalk downland habitat on the South Coast of the UK) involved researchers accompanying volunteers as they checked on livestock. They were asked to give an account of what they were doing and then answer semi-structured interview questions. Participants drew on popular discourses on conservation and biodiversity, as framed by the local council who run the programme. They also framed their relationships to the animals in respect to the more formal limitations of their role as identified through the conservation programme. And yet these discourses, significant as they are, do not adequately explain why volunteers are drawn to, and emotionally invested in, lookering. The methodology employed allowed participants instead to gesture to features of the landscape and to recall memories, and for the researchers to see how volunteers interacted with the animals and the landscape in embodied and emotionally loaded ways. The paper argues that a psychosocial perspective that pays attention to the contradictions and tensions made visible through this methodology helps develop a fuller understanding of volunteer livestock checking as a social practice.

Keywords: conservation, human-animal relations, lookering, volunteering

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Authors: Mohammed Abdelsabour-Khalaf, F. Yusuf , B Brand-Saberi

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Purpose: Applications of nanotechnology nowadays extended to include a wide range of scientific areas such electron micrscopy and gene expression. The aim of the current study was to investigate the developmental expression pattern of genes involved in human glomerulo-nephropathies associated with massive proteinuria and podocyte differentiation using the chicken mesonephros as a model system. Method: We performed in situ hybridization using chicken specific mRNA probes for genes expressed in the early nephron and slit diaphragm genes. The probes used were cNeph1, cNeph2, cSim1, cLmx1b, and cAtoh8. Chicken embryos from Hamburger Hamilton developmental stage HH19 (E3) to HH 34 (E9) were used for the in situ hybridization (ISH). ISH was performed on whole mount embryos which were sectioned by vibratome. Results: Our result show that Neph1, Neph2, Sim1. Lmx1b and Atoh8 genes are dynamically expressed during nephron morphogenesis and Neph1 and Atoh8 are also specifically expressed in the podocytes during late stages of differentiation. Conclusion: We conclude from our results that the genes implicated in congenital and acquired glomerulo-nephropathies like Neph1 and Neph2 are dynamically expressed during mesonephros development pointing towards a role in the formation of the filtration barrier and the differentiation of the mesonephric podocytes. Thus the avian mesonephros could serve as a model to study human kidney diseases.

Keywords: mesonephros, chicken embryo, gene expression, immunohistochemistry

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Authors: Feng-Tsung Cheng, Shu-Li Wang, Mei-Fang Wu, , Hui-Yu Chuang

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Due to the rapid development of modern technology, the widespread usage of the Internet makes business environment changing quickly. In order to be a leader in the global competitive market and to pursuit survive, “changing” becomes an unspoken rules need to follow for the company survival. The purpose of this paper is to build change model by using SWOT, strategy map, and balance scorecard, KPI and change management theory. The research findings indicate that organizational change plan formulated by the case company should require the employee to resist change factors and performance management system issues into consideration and must be set organizational change related programs, such as performance appraisal reward system, consulting and counseling mechanisms programs to improve motivation and reduce staff negative emotions. Then according to the model revised strategy maps and performance indicators proposed in this paper, such as strategy maps add and modify corporate culture, improve internal processes management, increase the growth rate of net income and other strategies. The performance indicators are based on strategy maps new and modified by adding net income growth rate, to achieve target production rate, manpower training achievement rates and other indicators, through amendments to achieve the company’s goal, be a leading brand of precision machinery industry.

Keywords: organizational change, SWOT analysis, strategy maps, performance indicators

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Authors: Shreyansh Singh, Afroz Ahmed

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Sentiment Analysis intends to naturally reveal the hidden mentality that we hold towards an entity. The total of this assumption over a populace addresses sentiment surveying and has various applications. Current text-based sentiment analysis depends on the development of word embeddings and Machine Learning models that take in conclusion from enormous text corpora. Sentiment Analysis from text is presently generally utilized for consumer loyalty appraisal and brand insight investigation. With the expansion of online media, multimodal assessment investigation is set to carry new freedoms with the appearance of integral information streams for improving and going past text-based feeling examination using the new transforms methods. Since supposition can be distinguished through compelling follows it leaves, like facial and vocal presentations, multimodal opinion investigation offers good roads for examining facial and vocal articulations notwithstanding the record or printed content. These methodologies use the Recurrent Neural Networks (RNNs) with the LSTM modes to increase their performance. In this study, we characterize feeling and the issue of multimodal assessment investigation and audit ongoing advancements in multimodal notion examination in various spaces, including spoken surveys, pictures, video websites, human-machine, and human-human connections. Difficulties and chances of this arising field are additionally examined, promoting our theory that multimodal feeling investigation holds critical undiscovered potential.

Keywords: sentiment analysis, RNN, LSTM, word embeddings

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Authors: Prajwal Eachempati, Laurent Muzellec, Ashish Kumar Jha

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We use Facebook as a case study to investigate the complex relationship between the firm’s public discourse (and actions) surrounding data privacy and the performance of a business model based on monetizing user’s data. We do so by looking at the evolution of public discourse over time (2004–2021) and relate topics to revenue and stock market evolution Drawing from archival sources like Zuckerberg We use LDA topic modelling algorithm to reveal 19 topics regrouped in 6 major themes. We first show how, by using persuasive and convincing language that promises better protection of consumer data usage, but also emphasizes greater user control over their own data, the privacy issue is being reframed as one of greater user control and responsibility. Second, we aim to understand and put a value on the extent to which privacy disclosures have a potential impact on the financial performance of social media firms. There we found significant relationship between the topics pertaining to privacy and social media/technology, sentiment score and stock market prices. Revenue is found to be impacted by topics pertaining to politics and new product and service innovations while number of active users is not impacted by the topics unless moderated by external control variables like Return on Assets and Brand Equity.

Keywords: public discourses, data protection, social media, privacy, topic modeling, business models, financial performance

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Authors: Jungbin Lim, Bohee Kang, Heelim Lee, Sunjin Kim, GeumHee Choi, Jae-Suk Jung, Suk Ji

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Nonsurgical periodontal therapies have, for several decades, been the basis of periodontal treatment concepts. The aim of this paper is to investigate the effectiveness of non-surgical periodontal therapy according to the severity of periodontitis disease. Methods: Retrospective data of patients who visited Department of periodontics in Ajou University Medical Center from 2016 to 2022 were collected. Among the patients, those who took full mouth examination of clinical parameters and non-surgical periodontal therapy were chosen for this study. Selected patients were divided into initial, moderate, and severe periodontitis based on severity and complexity of management (2018 World Workshop EFP/AAP consensus). Recall visits with clinical periodontal examination were scheduled for 1,2,3 months or 1,3,6 months after the treatment. The results were evaluated by recordings of mean probing pocket depth (mean PD), mean clinical attachment levels (mean CAL), bleeding on probing (BOP%), mean gingival index (mean GI), mean regression, mean sulcus bleeding index (mean SBI), mean plaque scores (mean PI). All statistical analyses were performed with R software, version 4.3.0. A level of significance, P<0.05, was considered to be statistically significant. Results: A total of 92 patients were included in this study. 15 patients were diagnosed as initial periodontitis, 14 moderate periodontitis, and 63 severe periodontitis. The all parameters except for mean recession decreased over time in all groups. The amount of mean PD decreased were the greatest in severe periodontitis group followed by moderate and initial, which was found to be statistically significant. The changes of mean PD were 0.15±0.05 mm, 0.37±0.06 mm, and 1.01±0.07 mm (initial, moderate, and severe, respectively, P<0.001). When comparing before and after treatment, the reductions in BOP(%), mean GI, mean SBI, and mean PI were statistically significant. Conclusion: All patients who received non-surgical periodontal therapy showed periodontal healing in terms of improvements in clinical parameters, and it was greater in the severe group.

Keywords: periodontology, clinical periodontology, oral treatment, comprehensive preventive dentistry, non-surgical periodontal therapy

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Authors: Yan-Wen Wang, Chu-Yang Chou, Jen-Cheng Wang, Min-Sheng Liao, Hsuan-Hsiang Hsu, Cheng-Ying Chou, Chen-Kang Huang, Kun-Chang Kuo, Joe-Air Jiang

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Performance-related parameters of high concentration photovoltaic (HCPV) modules (e.g. current and voltage) are required when estimating the maximum power point using numerical and approximation methods. The maximum power point on the characteristic curve for a photovoltaic module varies when temperature or solar radiation is different. It is also difficult to estimate the output performance and maximum power point (MPP) due to the special characteristics of HCPV modules. Based on the p-n junction semiconductor theory, a brand new and simple method is presented in this study to directly evaluate the MPP of HCPV modules. The MPP of HCPV modules can be determined from an irradiated I-V characteristic curve, because there is a non-linear relationship between the temperature of a solar cell and solar radiation. Numerical simulations and field tests are conducted to examine the characteristics of HCPV modules during maximum output power tracking. The performance of the presented method is evaluated by examining the dependence of temperature and irradiation intensity on the MPP characteristics of HCPV modules. These results show that the presented method allows HCPV modules to achieve their maximum power and perform power tracking under various operation conditions. A 0.1% error is found between the estimated and the real maximum power point.

Keywords: energy performance, high concentrated photovoltaic, maximum power point, p-n junction semiconductor

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Authors: Temnov D., Volgina E., Gerasimov D.

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Relaxation processes in polyvinylidene fluoride (PVDF) films were studied by the method of thermally stimulated fractional polarization currents (TSTF). The films were obtained by extrusion of a polymer melt followed by isometric annealing. PVDF granules of the Kynar-720 brand (Atofina Chemicals, USA) with a molecular weight of Mw=190,000 g•mol-1 were used for the manufacture of films. The annealing temperature was varied in the range from 120 °C to 170 °C in increments of 10 °C. The dependences of the degree of crystallinity of films (χ) and the intensity of thermally stimulated depolarization currents on the annealing temperature (Toc) are investigated. The TSTF spectra were obtained at the TSC II facility (Setaram, France). Measurements were carried out in a helium atmosphere, and the values of currents were determined by a Keithley electrometer. The annealed PVDF films were polarized at an electric field strength of 100 V/mm at a temperature of 31°C, after which they were cooled to 26°C, at which they were kept for 1 minute. During depolarization, the external field was removed, and the short-circuit sample was cooled to 0°C. The thermally stimulated short-circuit current was recorded during linear heating. Relaxation processes in PVDF films were studied in the temperature range from 0 – 70 °C. It is shown that the intensity curve of the peaks of TST FP has a course that is the reverse of the dependence of the degree of crystallinity on the annealing temperature. This allows us to conclude that the relaxation processes occurring in PVDF in the 35°C region are associated with the amorphous part of the structure of PVDF films between the layers of the spherulite crystalline phase.

Keywords: molecular and supramolecular structure, thermally stimulated currents, polyvinylidene fluoride films, relaxation processes

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Authors: Saman Shafei

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Tourism has improved for several reasons, with the main objective of producing economic benefits, including foreign exchange earnings, income generation, employment, rising government incomes, and contributing to the financing of tourism infrastructure, which also has public consumption. Although today the interests of the tourism industry are not overlooked by anyone, the expansion and development of tourism services and products can make it competitive, and in this competition, those who bring creativity and diversity are ahead of other competitors. Developing creative tourism as third-generation tourism can help to attract visitors, increasing demand and diversifying it, achieving new markets and boosting growth. Creative tourism is a journey aimed at achieving a brand –new experience and is along with collaborative learning of arts, cultural heritage, or specific features of a place, and provides useful communication with the inhabitants of the tourism destination who is creators of the living culture of that place. The present study aims to identify and introduce the capabilities of the city of Isfahan in IRAN for the development of creative tourism and the role of creative tourism on the destination and the local community of this city. The research method is descriptive-analytical and field method, interviewing tool and questionnaire have been applied to obtain research findings. The results indicate that the city of Isfahan has the potential to develop creative tourism in the field of traditional handicrafts and traditional foods, and developing this kind of tourism will lead to the development of sustainable tourism in this destination and will bring numerous benefits for the local community.

Keywords: creative tourism, tourism, Isfahan city, sustainable tourism development

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Istijabatul Aliyah, Bambang Setioko, Rara Sugiarti

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Surakarta is one of the cities which are formed with the concept of Javanese cosmology. As a traditional town of Java, Surakarta is known as ‘the paradise’ of traditional markets. Since its establishment, Surakarta is formed with Catur Gatra Tunggal or Four Single-Slot concept (palace, square, mosques, and markets). Current development in Surakarta downtown today indicates that traditional markets have improved themselves in both physical and non-physical aspects. The efforts start from the market façade revitalization, restoration and the overall development of market; up to social activities, competition between traders or large celebrations in the neighbourhood market. This research was conducted in Surakarta, which is aimed at: identifying the role of traditional market revitalization efforts in the development of a city. This study employs several methods of analysis, namely: 1) Spatial analysis for mapping the distribution of traditional markets in the city constellation, 2) Category-Based Analysis (CBA) to classify the revitalization of traditional markets that has an influence in the development of the city, and 3) Interactive Method of Analysis. The results of this research indicate that the presence of a constellation of traditional markets in Surakarta is dominated by the presence of Gede Market, not only as the oldest traditional market, but also as a center of economic and socio-cultural activities of the community. The role of traditional market revitalization in the development of a town is as an Urban Catalyst towards a MICE city in the sense that the revitalization effort, even done in a relatively short time and not yet covering the overall objects, is able to establish brand image of Surakarta as a city of culture which is friendly and ready to be MICE tourism city.

Keywords: traditional market revitalization, urban catalyst, MICE tourism, Surakarta

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Authors: Ahmed A. A. Fadol, Guangji Tong, Wlaa Mohamed

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This paper provides a comprehensive analysis of food insecurity in Sudan, focusing on consumption patterns and their implications, employing the Integrated Food Security Phase Classification (IPC) assessment framework. Years of conflict and economic instability have driven large segments of the population in Sudan into crisis levels of acute food insecurity according to the (IPC). A substantial number of people are estimated to currently face emergency conditions, with an additional sizeable portion categorized under less severe but still extreme hunger levels. In this study, we explore the multifaceted nature of food insecurity in Sudan, considering its historical, political, economic, and social dimensions. An analysis of consumption patterns and trends was conducted, taking into account cultural influences, dietary shifts, and demographic changes. Furthermore, we employ logistic regression and random forest analysis to identify significant independent variables influencing food security status in Sudan. Random forest clearly outperforms logistic regression in terms of area under curve (AUC), accuracy, precision and recall. Forward projections of the IPC for Sudan estimate that 15 million individuals are anticipated to face Crisis level (IPC Phase 3) or worse acute food insecurity conditions between October 2023 and February 2024. Of this, 60% are concentrated in Greater Darfur, Greater Kordofan, and Khartoum State, with Greater Darfur alone representing 29% of this total. These findings emphasize the urgent need for both short-term humanitarian aid and long-term strategies to address Sudan's deepening food insecurity crisis.

Keywords: food insecurity, consumption patterns, logistic regression, random forest analysis

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Authors: Mohammad Tipu Sultan, Farzana Sharmin, Ke Xue

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Social media create a better opportunity for travelers to search for travel information, select destination and share their personal experiences of the travel. This study proposes a framework which describes the relationships between social media, and positive or negative tourism experience sharing impact on destination choice. To find out new trends of travelers behavioral intention, we propose an extended theoretical model, the Theory of Reasoned Action (TRA). We conducted a survey to analyze three external factors, subjective norms, and positive and negative experience influence on travel destination choice. Structural questionnaire analysis was employed to confirm the proposed research hypothesis within the relationship between consumer influences on the shared experience of social media. The results of the study confirm that sharing positive experiences influence the positive effect of destination choice, while negative experiences decrease the destination selection option. The results indicate that attitudes, subjective norms are passively influenced by shared experience. Moreover, we find that sharing live pictures of travel experiences through social media helps to reduce negative perceptions of the destination brand. This research contribution is useable to the research field as a new determination factor and the findings could be used by destination organization management (DMO) to enhancing their tourism promotion through social media.

Keywords: destination choice, tourism experience sharing, Theory of Reasoned Action, TRA, social media

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Authors: Natalie M. Khazaal

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The effects of the civil war on Lebanese women have been challenging to conceptualize. For some, war is a liberating and empowering force for women, while for others it is one that subjugates women and disempowers them in new ways. Scholars have explored the impact on the Lebanese civil war (1975-1990) on women in the fields of labor history, political activism and literary production. In all these arenas, women’s role and visibility were contested and negotiated in diverse ways. But probably the most visible arena where this contestation took place was television. Dramatized entertainment series were crucial sites where fictional women battled out the gender question, and which reflected and participated in the negotiations of gender politics. Even more stunningly, actual television stations became part of this battle through the plots and portrayals of women that they created. The state-backed Tele-Liban (TL) peddled patriarchal articulations of gender that directly competed with the edgy vision of liberated, independent women on the pirate Lebanese Broadcasting Corporation (LBC). This presentation explores how LBC used gender to distinguish its brand against the retrograde TL programing. Television series are an important medium for creating, testing and reenacting gender politics. They are even more consequential in another way. They are the sites where a dramatic shift in the relationship between Arab television and Arab publics—from benign neglect of public concerns towards engagement with audiences—took place for the first time. As this shift is at the heart of why Arab media was seen as a participant in the Arab uprisings, it is important to explore the roots of the shift in the dramas and comedy series of the mid-1980s Lebanese television. This presentation argues that television battles over gender were consequential and need serious consideration as sites of unexpected meaning.

Keywords: gender, Lebanon, television, war, women

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Silke Bustamante, Andrea Pelzeter, Andreas Deckmann, Rudi Ehlscheidt, Franziska Freudenberger

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CSR is associated with a great number of positive effects. This also includes the positive impact of CSR on the commitment of its employees. The internal CSR communication here takes the essential function as a mediator of the CSR performance of a company to the employees. The importance of CSR communication is, therefore, essential. Employees can usually only perceive the CSR efforts of a company if it is also communicated to them. Only if the employees perceive the CSR of their employer positively, the employer-CSR can also have a positive impact on their affective commitment. Therefore, organizational and individual factors are crucial and thus need to consider. This relationship between the organizational and individual factors was investigated in a qualitative case study in six companies of the German service sector. Expert interviews and focus group interviews were conducted and questionnaire-based ratings by company representatives were raised. Among the individual factors, in terms of CSR, the expectations and relevance of its employees, as well as the perception of CSR by the staff, are included. The organizational factors include the actual CSR performance and its communication. Ultimately, the impact of CSR on the commitment is examined with this holistic approach. The results show that the individual CSR perception does not always match the corporate CSR performance and its depiction in internal communication. Furthermore, employees have given suggestions on how CSR should be communicated by their employer. Knowledge memory systems (e.g. wiki) on the on hand and media-based information, on the other hand, were highlighted. Primarily the employee-related CSR is most important for the employees, whereas ecological CSR activities hardly play a role. The findings indicate the importance of CSR communication in the CSR concept as it provides the missing link between CSR performance and appreciation by an increase in commitment. It should only be communicated, what is done. CSR communication should also be carried out in a plausible and transparent way.

Keywords: CSR, employee commitment, employer brand, internal communication

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: Wenjuan Lu

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China and Hungary have risen from a friendly and comprehensive cooperative relationship to a comprehensive strategic partnership in recent years, and the economic and trade relations between the two countries have developed smoothly. As an important country along the ‘Belt and Road’, Hungary and China have strong economic complementarities and have unique advantages in carrying China's industrial transfer and economic transformation and development. The construction of the China-Hungary Economic and Trade Cooperation Zone, which was initiated by the ‘Sino-Hungarian Borsod Industrial Zone’ and the ‘Hungarian Central European Trade and Logistics Cooperation Park’ has promoted infrastructure construction, optimized production capacity, promoted industrial restructuring, and formed brand and agglomeration effects. Enhancing the influence of Chinese companies in the European market has also promoted economic development in Hungary and even in Central and Eastern Europe. However, as the China-Hungary Economic and Trade Cooperation Zone is still in its infancy, there are still shortcomings such as small scale, single function, and no prominent platform. In the future, based on the needs of China's cooperation with ‘17+1’ and China-Hungary cooperation, on the basis of appropriately expanding the scale of economic and trade cooperation zones and appropriately increasing the number of economic and trade cooperation zones, it is better to focus on optimizing and adjusting its functions and highlighting different economic and trade cooperation. The differentiated function of the trade zones strengthens the multi-faceted cooperation of economic and trade cooperation zones and highlights its role as a platform for cooperation in information, capital, and services.

Keywords: ‘One Belt, One Road’ Initiative, China-Hungary economic and trade cooperation zone, function optimization, Central and Eastern Europe

Procedia PDF Downloads 155