Search results for: learning behaviour

Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

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Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

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Authors: Roseanne Sharon Borromeo, Mariana Carvalho, Mariia Karizhenskaia

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Healthcare providers need to comprehend cultural diversity for optimal patient-centered care, especially near the end of life. Although a universal method for navigating cultural differences would be ideal, culture’s high complexity makes this strategy impossible. Adding cultural humility, a process of self-reflection to understand personal and systemic biases and humbly acknowledging oneself as a learner when it comes to understanding another's experience leads to a meaningful process in palliative care generating respectful, honest, and trustworthy relationships. This study is a systematic review of the literature on cultural humility in palliative care research and best practices. Race, religion, language, values, and beliefs can affect an individual’s access to palliative care, underscoring the importance of culture in palliative care. Cultural influences affect end-of-life care perceptions, impacting bereavement rituals, decision-making, and attitudes toward death. Cultural factors affecting the delivery of care identified in a scoping review of Canadian literature include cultural competency, cultural sensitivity, and cultural accessibility. As the different parts of the world become exponentially diverse and multicultural, healthcare providers have been encouraged to give culturally competent care at the bedside. Therefore, many organizations have made cultural competence training required to expose professionals to the special needs and vulnerability of diverse populations. Cultural competence is easily standardized, taught, and implemented; however, this theoretically finite form of knowledge can dangerously lead to false assumptions or stereotyping, generating poor communication, loss of bonds and trust, and poor healthcare provider-patient relationship. In contrast, Cultural humility is a dynamic process that includes self-reflection, personal critique, and growth, allowing healthcare providers to respond to these differences with an open mind, curiosity, and awareness that one is never truly a “cultural” expert and requires life-long learning to overcome common biases and ingrained societal influences. Cultural humility concepts include self-awareness and power imbalances. While being culturally competent requires being skilled and knowledgeable in one’s culture, being culturally humble involves the sometimes-uncomfortable position of healthcare providers as students of the patient. Incorporating cultural humility emphasizes the need to approach end-of-life care with openness and responsiveness to various cultural perspectives. Thus, healthcare workers need to embrace lifelong learning in individual beliefs and values on suffering, death, and dying. There have been different approaches to this as well. Some adopt strategies for cultural humility, addressing conflicts and challenges through relational and health system approaches. In practice and research, clinicians and researchers must embrace cultural humility to advance palliative care practices, using qualitative methods to capture culturally nuanced experiences. Cultural diversity significantly impacts patient-centered care, particularly in end-of-life contexts. Cultural factors also shape end-of-life perceptions, impacting rituals, decision-making, and attitudes toward death. Cultural humility encourages openness and acknowledges the limitations of expertise in one’s culture. A consistent self-awareness and a desire to understand patients’ beliefs drive the practice of cultural humility. This dynamic process requires practitioners to learn continuously, fostering empathy and understanding. Cultural humility enhances palliative care, ensuring it resonates genuinely across cultural backgrounds and enriches patient-provider interactions.

Keywords: cultural competency, cultural diversity, cultural humility, palliative care, self-awareness

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Authors: Osatuyi Kehinde Micheal

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There is much research that has contributed to an improved understanding of the future of food security, especially during the COVID-19 pandemic. A pathway was developed by using a local community kitchen in Muizenberg in western cape province, cape town, south Africa, a case study to map out the future of food security in times of crisis. This kitchen aims to provide nutritious, affordable, plant-based meals to our community. It is also a place of diverse learning, sharing, empowering the volunteers, and growth to support the local economy and future resilience by sustaining our community kitchen for the community. This document contains an overview of the story of the community kitchen on how we create self-sustainability as a new pathway development to sustain the community and reduce Zero hunger in the regional food system. This paper describes the key elements of how we respond to covid-19 pandemic by sharing food parcels and creating 13 soup kitchens across the community to tackle the immediate response to covid-19 pandemic and agricultural systems by growing home food gardening in different homes, also having a consciousness Dry goods store to reduce Zero waste and a local currency as an innovation to reduce food crisis. Insights gained from our article and outreach and their value in how we create adaptation, transformation, and sustainability as a new development pathway to solve any future problem crisis in the food security system in our society.

Keywords: sustainability, food security, community development, adapatation, transformation

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Authors: Penn P. Wu, Jeannie Jo

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Blockchain technology has rapidly gained popularity in industry. This paper attempts to assist academia to answer four questions. First, should community colleges begin offering education to nurture blockchain-literate students for the job market? Second, what are the appropriate topical areas to cover? Third, should it be an individual course? And forth, should it be a technical or management course? This paper starts with identifying the knowledge domains of blockchain technology and the topical areas each domain has, and continues with placing them in appropriate academic territories (Computer Sciences vs. Business) and subjects (programming, management, marketing, and laws), and then develops an evaluation model to determine the appropriate topical area for community colleges to teach. The evaluation is based on seven factors: maturity of technology, impacts on management, real-world applications, subject classification, knowledge prerequisites, textbook readiness, and recommended pedagogies. The evaluation results point to an interesting direction that offering an introductory course is an ideal option to guide students through the learning journey of what blockchain is and how it applies to business. Such an introductory course does not need to engage students in the discussions of mathematics and sciences that make blockchain technologies possible. While it is inevitable to brief technical topics to help students build a solid knowledge foundation of blockchain technologies, community colleges should avoid offering students a course centered on the discussion of developing blockchain applications.

Keywords: blockchain, pedagogies, blockchain technologies, blockchain course, blockchain pedagogies

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: George Onomah

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Background: The integration of information communication technology (ICT) in Mathematics education faces notable challenges, which this study aimed to dissect and understand. Objectives: The primary goal was to assess the internal and external factors affecting the adoption of ICT by in-service Mathematics teachers. Internal factors examined included teachers' pedagogical beliefs, prior teaching experience, attitudes towards computers, and proficiency with technology. External factors included the availability of technological resources, the level of ICT training received, the sufficiency of allocated time for technology use, and the institutional culture within educational environments. Methods: A descriptive survey design was employed to methodically investigate these factors. Data collection was carried out using a five-point Likert scale questionnaire, administered to a carefully selected sample of 100 in-service Mathematics teachers through a combination of purposive and convenience sampling techniques. Findings: Results from multiple regression analysis revealed a significant underutilization of ICT in Mathematics teaching, highlighting a pronounced deficiency in current classroom practices. Recommendations: The findings suggest an urgent need for educational department heads to implement regular and comprehensive ICT training programs aimed at enhancing teachers' technological capabilities and promoting the integration of ICT in Mathematics teaching methodologies.

Keywords: ICT, Mathematics, integration, barriers

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Authors: Julie Chuah Suan Choo

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Due to the continuous change in workforce and the demands of the global workplace, many employers had lamented that the majority of university graduates were not prepared in the key areas of employment such as critical thinking, writing, self-direction and global knowledge which are most needed for the purposes of promotion. Further, critical thinking skills are deemed as integral parts of transformational pedagogy which aims at having a more informed society. To add to this, literature teaching has recently been advocated for enhancing students’ critical thinking and reasoning. Thus this study explored the effects of incorporating a few strategies in teaching literature, namely a Shakespeare play, into a course design to enhance these skills. An experiment involving a pretest and posttest using the California Critical Thinking Skills Test (CCTST) were administered on 80 first-year students enrolled in the Bachelor of Arts programme who were randomly assigned into the control group and experimental group. For the next 12 weeks, the experimental group was given intervention which included guided in-class discussion with Socratic questioning skills, learning log to detect their weaknesses in logical reasoning; presentations and quizzes. The results of CCTST which included paired T-test using SPSS version 22 indicated significant differences between the two groups. Findings have significant implications on the course design as well as pedagogical practice in using literature to enhance students’ critical thinking skills.

Keywords: literature teaching, critical thinking, California critical thinking skills test (CCTST), course design

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Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Betelhem M. Negede, Maarten Voors, Hugo De Groote, Bart Minten

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Farmers in Ethiopia produce most of their own food during one agricultural season per year. Therefore, they need to use on-farm storage technologies to bridge the lean season and benefit from price arbitrage. Maize stored using traditional storage bags offer no protection from insects and molds, leading to high storage losses. In Ethiopia access to and use of modern storage technologies are still limited, restraining farmers to benefit from local maize price fluctuations. We used a randomized controlled trial among 871 maize farmers to evaluate the impacts of Purdue Improved Crop Storage (PICS) bags, also known as hermetic bags, on storage losses, and especially on behavioral changes with respect to consumption, marketing, and income among maize farmers in Ethiopia. This study builds upon the limited previous experimental research that has tried to understand farmers’ grain storage and post-harvest losses and identify mechanisms behind the persistence of these challenges. Our main hypothesis is that access to PICS bags allows farmers to increase production, storage and maize income. Also delay the length of maize storage, reduce maize post-harvest losses and improve their food security. Our results show that even though farmers received only three PICS bags that represent 10percent of their total maize stored, they delay their length of maize storage for sales by two weeks. However, we find no treatment effect on maize income, suggesting that the arbitrage of two weeks is too small. Also, we do not find any reduction in storage losses due to farmers’ reaction by selling early and by using cheap and readily available but potentially harmful storage chemicals. Looking at the heterogeneity treatment effects between the treatment variable and highland and lowland villages, we find a decrease in the percentage of maize stored by 4 percent in the highland villages. This confirms that location specific factors, such as agro-ecology and proximity to markets are important factors that influence whether and how much of the harvest a farmer stores. These findings highlight the benefits of hermetic storage bags, by allowing farmers to make inter-temporal arbitrage and by reducing potential health risks from storage chemicals. The main policy recommendation that emanates from our study is that postharvest losses reduction throughout the whole value chain is an important pathway to food and income security in Sub-Saharan Africa (SSA). However, future storage loss interventions with hermetic storage technologies should take into account the agro-ecology of the study area and quantify storage losses beyond farmers self-reported losses, such as the count and weigh method. Finally, studies on hermetic storage technologies indicate positive impacts on post-harvest losses and in improving food security, but the adoption and use of these technologies is currently still low in SSA. Therefore, future works on the scaling up of hermetic bags, should consider reasons why farmers only use PICS bags to store grains for consumption, which is usually related to a safety-first approach or due to lack of incentives (higher price from maize not treated with chemicals), and no grain quality check.

Keywords: arbitrage, PICS hermetic bags, post-harvest storage loss, RCT

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Authors: Shahla Ali

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In the context of promoting green infrastructure development, new opportunities are emerging to re-examine sustainable development practices. This paper presents an initial exploration of the development of community-investor dispute prevention and facilitation mechanisms in the context of the Belt and Road Initiative (BRI) spanning Asia, Africa, and Europe. Given the widescale impact of China’s multi-jurisdictional development initiative, learning how to coordinate with local communities is vital to realizing inclusive and sustainable growth. In the 20 years since the development of the first multilateral community-investor dispute resolution mechanism developed by the International Finance Centre/World Bank, much has been learned about public facilitation, community engagement, and dispute prevention during the early stages of major infrastructure development programs. This paper will explore initial findings as they relate to initiatives underway along the BRI within the Asian Infrastructure Investment Bank and the Asian Development Bank. Given the borderless nature of sustainability concerns, insights from diverse regions are critical to deepening insights into best practices. Drawing on a case-based methodology, this paper will explore the achievements, challenges, and lessons learned in community-investor dispute prevention and resolution for major infrastructure projects in the greater China region.

Keywords: law and development, dispute prevention, sustainable development, mitigation

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Authors: Su Yen Chen

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ChatGPT has shown both strengths and limitations in clinical, educational, and research settings, raising important concerns about accuracy, transparency, and ethical use. Despite an improved understanding of user acceptance and satisfaction, there is still a gap in how general AI perceptions translate into practical applications within healthcare. This study focuses on examining the perceptions of ChatGPT's impact among 266 healthcare majors in Taiwan, exploring its implications for their career development, as well as its utility in clinical practice, medical education, and research. By employing a structured survey with precisely defined subscales, this research aims to probe the breadth of ChatGPT's applications within healthcare, assessing both the perceived benefits and the challenges it presents. Additionally, to further enhance the comprehensiveness of our methodology, we have incorporated qualitative data collection methods, which provide complementary insights to the quantitative findings. The findings from the survey reveal that perceptions and usage of ChatGPT among healthcare majors vary significantly, influenced by factors such as its perceived utility, risk, novelty, and trustworthiness. Graduate students and those who perceive ChatGPT as more beneficial and less risky are particularly inclined to use it more frequently. This increased usage is closely linked to significant impacts on personal career development. Furthermore, ChatGPT's perceived usefulness and novelty contribute to its broader impact within the healthcare domain, suggesting that both innovation and practical utility are key drivers of acceptance and perceived effectiveness in professional healthcare settings. Trust emerges as an important factor, especially in clinical settings where the stakes are high. The trust that healthcare professionals place in ChatGPT significantly affects its integration into clinical practice and influences outcomes in medical education and research. The reliability and practical value of ChatGPT are thus critical for its successful adoption in these areas. However, an interesting paradox arises with regard to the ease of use. While making ChatGPT more user-friendly is generally seen as beneficial, it also raises concerns among users who have lower levels of trust and perceive higher risks associated with its use. This complex interplay between ease of use and safety concerns necessitates a careful balance, highlighting the need for robust security measures and clear, transparent communication about how AI systems work and their limitations. The study suggests several strategic approaches to enhance the adoption and integration of AI in healthcare. These include targeted training programs for healthcare professionals to increase familiarity with AI technologies, reduce perceived risks, and build trust. Ensuring transparency and conducting rigorous testing are also vital to foster trust and reliability. Moreover, comprehensive policy frameworks are needed to guide the implementation of AI technologies, ensuring high standards of patient safety, privacy, and ethical use. These measures are crucial for fostering broader acceptance of AI in healthcare, as the study contributes to enriching the discourse on AI's role by detailing how various factors affect its adoption and impact.

Keywords: ChatGPT, healthcare, survey study, IT adoption, behaviour, applcation, concerns

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Authors: N. Rambabu, H. Gururaj, Reynold Washington, Oommen George

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Motivation: Under the USAID Strengthening Health Outcomes through Private Sector (SHOPS-TB) initiative, Karnataka Health Promotion Trust (KHPT) with technical support of Abt associates is implementing a TB prevention and care model in Karnataka State, South India. KHPT is the interface agency between the public and private sectors, and providers and the target community facilitating early TB case detection and enhancing treatment compliance through private health care providers (pHCP) engagement in RNTCP. The project coverage is 0.84 million urban poor from 663 slums in 12 districts of Karnataka. Problem Statement: India with the highest burden of global TB (26%) and two million cases annually, accounts for approximately one fifth of the global incidence. WHO estimates 300,000 people die from TB annually in India. India expanded the coverage of Directly Observed Treatment, Short-course chemotherapy (DOTS) to the entire country as early as 2006. However, the performance of RNTCP has not been uniform across states. While the national annual new smear-positive (NSP) case notification rate is 53, it is much lower at 47 in Karnataka. A third of TB patients in India reside in urban slums. Approach: Under SHOPS, KHPT actively engages with communities through key opinion leaders and community structures. Interpersonal communication, by Outreach workers through house-to-house visits and at aggregation points, is the primary method used for communication about TB and its management and to increase demand for sputum examination and DOTS. pHCP are mapped, trained and mentored by KHPT. ORWs also provide patient and family counseling on TB treatment, side effects and adherence, screen close contacts of index patients especially children under 6 years of age and screen co-morbidities including HIV, diabetes and malnutrition and risk factors including alcoholism, tobacco use, occupational hazards making appropriate accompanied or documented referrals. A treatment ‘buddy’ system for the patients involving close friends or family members, ICT-based support, DOTS Prerana (inspiration) groups of TB patients, family members and community, DOTS Mitra (friend) helpline services are also used for care and support services. Results: The intervention educated 39988 slum dwellers, referred 1731 chest symptomatics, tested 1061 patients and initiated 248 patients on anti-TB treatment within three months of intervention through continuous community engagement. Conclusions: The intervention’s potential to increase access to preferred health care providers, reduce patient and health system delays in diagnosis and initiation of treatment, improve health seeking behaviour and enhance compliance of pHCPs to standard treatment protocols is being monitored. Initial results are promising.

Keywords: DOTS, KHPT, health outcomes, public and private sector

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Hazel James P. Agngarayngay, Arnold R. Elepaño

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Mango is the third most important export fruit in the Philippines. Despite the expanding mango trade in world market, problems on postharvest losses caused by pests and diseases are still prevalent. Many disease control and pest disinfestation methods have been studied and adopted. Heat treatment is necessary to eliminate pests and diseases to be able to pass the quarantine requirements of importing countries. During heat treatments, temperature and time are critical because fruits can easily be damaged by over-exposure to heat. Modeling the process enables researchers and engineers to study the behaviour of temperature distribution within the fruit over time. Understanding physical processes through modeling and simulation also saves time and resources because of reduced experimentation. This research aimed to simulate the heat transfer mechanism and predict the temperature distribution in ‘Carabao' mangoes during hot water treatment (HWT) and extended hot water treatment (EHWT). The simulation was performed in ANSYS CFD Software, using ANSYS CFX Solver. The simulation process involved model creation, mesh generation, defining the physics of the model, solving the problem, and visualizing the results. Boundary conditions consisted of the convective heat transfer coefficient and a constant free stream temperature. The three-dimensional energy equation for transient conditions was numerically solved to obtain heat flux and transient temperature values. The solver utilized finite volume method of discretization. To validate the simulation, actual data were obtained through experiment. The goodness of fit was evaluated using mean temperature difference (MTD). Also, t-test was used to detect significant differences between the data sets. Results showed that the simulations were able to estimate temperatures accurately with MTD of 0.50 and 0.69 °C for the HWT and EHWT, respectively. This indicates good agreement between the simulated and actual temperature values. The data included in the analysis were taken at different locations of probe punctures within the fruit. Moreover, t-tests showed no significant differences between the two data sets. Maximum heat fluxes obtained at the beginning of the treatments were 394.15 and 262.77 J.s-1 for HWT and EHWT, respectively. These values decreased abruptly at the first 10 seconds and gradual decrease was observed thereafter. Data on heat flux is necessary in the design of heaters. If underestimated, the heating component of a certain machine will not be able to provide enough heat required by certain operations. Otherwise, over-estimation will result in wasting of energy and resources. This study demonstrated that the simulation was able to estimate temperatures accurately. Thus, it can be used to evaluate the influence of various treatment conditions on the temperature-time history in mangoes. When combined with information on insect mortality and quality degradation kinetics, it could predict the efficacy of a particular treatment and guide appropriate selection of treatment conditions. The effect of various parameters on heat transfer rates, such as the boundary and initial conditions as well as the thermal properties of the material, can be systematically studied without performing experiments. Furthermore, the use of ANSYS software in modeling and simulation can be explored in modeling various systems and processes.

Keywords: heat transfer, heat treatment, mango, modeling and simulation

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Authors: Tony Wilson

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This paper sets out a practices perspective or practices theory, which has become pervasive from business to sociological studies. In doing so, it locates the perspective historically (in the work of the philosopher Heidegger) and provides a contemporary illustration of its application to communication, consumer and cultural studies as central to this conference theme. The structured account of practices (as articulated in eight ‘axioms’) presented towards the conclusion of this paper is an initial statement - planned to encourage further detailed qualitative and systematic research in areas of interest to the conference. Practice theories of equipped and situated construction of participatory meaning (as in media and marketing consuming) are frequently characterized as lacking common ground, or core principles. This paper explores whether by retracing a journey to earlier philosophical underwriting, a shared territory promoting new research can be located as current philosophical hermeneutics. Moreover, through returning to hermeneutic first principles, the paper shows that a series of spatio-temporal metaphors become available - appropriate to analyzing communication as a process across disciplines in which it is considered. Thus one can argue, for instance, that media users engage (enter) digital text from their diverse ‘horizons of expectation’, in a productive enlarging ‘fusion’ of horizons of understanding, thereby ‘projecting’ a new narrative, integrated in a ‘hermeneutic circle’ of meaning. A politics of communication studies may contest a horizon of understanding - so engaging in critical ‘distancing’. Marketing’s consumers can occupy particular places on a horizon of understanding. Media users pass over borders of changing, revised perspectives. Practices research can now not only be discerned in multiple disciplines but equally crosses disciplines. The ubiquitous practice of media use by managers and visitors in a shopping mall - the mediatization of malls - responds to investigating not just with media study expertise, but from an interpretive marketing perspective. How have mediated identities of person or place been changed? Emphasizing understanding of entities in a material environment as ‘equipment’, practices theory enables the quantitative correlation of use and demographic variable as ‘Zeug Score’. Human behavior is fundamentally habitual - shaped by its tacit assumptions - occasionally interrupted by reflection. Practices theory acknowledges such action to be minimally monitored yet nonetheless considers it as constructing narrative. Thus presented in research, ‘storied’ behavior can then be seen to be (in)formed and shaped from a shifting hierarchy of ‘horizons’ or of perspectives - from habituated to reflective - rather than a single seamless narrative. Taking a communication practices perspective here avoids conflating tacit, transformative and theoretical understanding in research. In short, a historically grounded and unifying statement of contemporary practices theory will enhance its potential as a tool in communication, consumer and cultural research, landscaping interpretative horizons of human behaviour through exploring widely the culturally (in)formed narratives equipping and incorporated (reflectively, unreflectively) in people’s everyday lives.

Keywords: communication, consumer, cultural practices, hermeneutics

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Daniel Rowe, Christopher R. Vogel, Richard H. J. Willden

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The University of Oxford’s recirculating water flume is a combined wave and current test tank with a 1 m depth, 1.1 m width, and 10 m long working section, and is capable of flow speeds up to 1 ms−1 . This study documents the hydrodynamic characteristics of the facility in preparation for experimental testing of horizontal axis tidal stream turbine models. The turbine to be tested has a rotor diameter of 0.6 m and is a modified version of one of two model-scale turbines tested in previous experimental campaigns. An Acoustic Doppler Velocimeter (ADV) was used to measure the flow at high temporal resolution at various locations throughout the flume, enabling the spatial uniformity and turbulence flow parameters to be investigated. The mean velocity profiles exhibited high levels of spatial uniformity at the design speed of the flume, 0.6 ms−1 , with variations in the three-dimensional velocity components on the order of ±1% at the 95% confidence level, along with a modest streamwise acceleration through the measurement domain, a target 5 m working section of the flume. A high degree of uniformity was also apparent for the turbulence intensity, with values ranging between 1-2% across the intended swept area of the turbine rotor. The integral scales of turbulence exhibited a far higher degree of variation throughout the water column, particularly in the streamwise and vertical scales. This behaviour is believed to be due to the high signal noise content leading to decorrelation in the sampling records. To achieve more realistic levels of vertical velocity shear in the flume, a simple procedure to practically generate target vertical shear profiles in open-channel flows is described. Here, the authors arranged a series of non-uniformly spaced parallel bars placed across the width of the flume and normal to the onset flow. By adjusting the resistance grading across the height of the working section, the downstream profiles could be modified accordingly, characterised by changes in the velocity profile power law exponent, 1/n. Considering the significant temporal variation in a tidal channel, the choice of the exponent denominator, n = 6 and n = 9, effectively provides an achievable range around the much-cited value of n = 7 observed at many tidal sites. The resulting flow profiles, which we intend to use in future turbine tests, have been characterised in detail. The results indicate non-uniform vertical shear across the survey area and reveal substantial corner flows, arising from the differential shear between the target vertical and cross-stream shear profiles throughout the measurement domain. In vertically sheared flow, the rotor-equivalent turbulence intensity ranges between 3.0-3.8% throughout the measurement domain for both bar arrangements, while the streamwise integral length scale grows from a characteristic dimension on the order of the bar width, similar to the flow downstream of a turbulence-generating grid. The experimental tests are well-defined and repeatable and serve as a reference for other researchers who wish to undertake similar investigations.

Keywords: acoustic doppler Velocimeter, experimental hydrodynamics, open-channel flow, shear profiles, tidal stream turbines

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Authors: Chunyang Miao, Bingxin Li, Shanglong Ning, Christopher J. B. Ford

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While it is challenging to fabricate electronic devices close to atomic dimensions in conventional top-down lithography, molecular electronics is promising to help maintain the exponential increase in component densities via using molecular building blocks to fabricate electronic components from the bottom up. It offers smaller, faster, and more energy-efficient electronic and photonic systems. A self-assembled monolayer (SAM) of molecules is a layer of molecules that self-assembles on a substrate. They are mechanically flexible, optically transparent, low-cost, and easy to fabricate. A large-area multi-layer structure has been designed and investigated by the team, where a SAM of designed molecules is sandwiched between graphene and gold electrodes. Each molecule can act as a quantum dot, with all molecules conducting in parallel. When a source-drain bias is applied, significant current flows only if a molecular orbital (HOMO or LUMO) lies within the source-drain energy window. If electrons tunnel sequentially on and off the molecule, the charge on the molecule is well-defined and the finite charging energy causes Coulomb blockade of transport until the molecular orbital comes within the energy window. This produces ‘Coulomb diamonds’ in the conductance vs source-drain and gate voltages. For different tunnel barriers at either end of the molecule, it is harder for electrons to tunnel out of the dot than in (or vice versa), resulting in the accumulation of two or more charges and a ‘Coulomb staircase’ in the current vs voltage. This nanostructure exhibits highly reproducible Coulomb-staircase patterns, together with additional oscillations, which are believed to be attributed to molecular vibrations. Molecules are more isolated than semiconductor dots, and so have a discrete phonon spectrum. When tunnelling into or out of a molecule, one or more vibronic states can be excited in the molecule, providing additional transport channels and resulting in additional peaks in the conductance. For useful molecular electronic devices, achieving the optimum orbital alignment of molecules to the Fermi energy in the leads is essential. To explore it, a drop of ionic liquid is employed on top of the graphene to establish an electric field at the graphene, which screens poorly, gating the molecules underneath. Results for various molecules with different alignments of Fermi energy to HOMO have shown highly reproducible Coulomb-diamond patterns, which agree reasonably with DFT calculations. In summary, this large-area SAM molecular junction is a promising candidate for future electronic circuits. (1) The small size (1-10nm) of the molecules and good flexibility of the SAM lead to the scalable assembly of ultra-high densities of functional molecules, with advantages in cost, efficiency, and power dissipation. (2) The contacting technique using graphene enables mass fabrication. (3) Its well-observed Coulomb blockade behaviour, narrow molecular resonances, and well-resolved vibronic states offer good tuneability for various functionalities, such as switches, thermoelectric generators, and memristors, etc.

Keywords: molecular electronics, Coulomb blokade, electron-phonon coupling, self-assembled monolayer

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Authors: Huicong Jia, Donghua Pan

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As a key component of disaster reduction management, disaster emergency relief and reconstruction is an important process. Based on disaster system theory, this study analyzed the Jiangsu tornado from the formation mechanism of disasters, through to the economic losses, loss of life, and social infrastructure losses along the tornado disaster chain. The study then assessed the emergency relief and reconstruction efforts, based on an analytic hierarchy process method. The results were as follows: (1) An unstable weather system was the root cause of the tornado. The potentially hazardous local environment, acting in concert with the terrain and the river network, was able to gather energy from the unstable atmosphere. The wind belt passed through a densely populated district, with vulnerable infrastructure and other hazard-prone elements, which led to an accumulative disaster situation and the triggering of a catastrophe. (2) The tornado was accompanied by a hailstorm, which is an important triggering factor for a tornado catastrophe chain reaction. (3) The evaluation index (EI) of the emergency relief and reconstruction effect for the ‘‘6.23’’ tornado disaster in Yancheng was 91.5. Compared to other relief work in areas affected by disasters of the same magnitude, there was a more successful response than has previously been experienced. The results provide new insights for studies of disaster systems and the recovery measures in response to tornado catastrophe in China.

Keywords: China, disaster system, emergency relief, tornado catastrophe

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: Ruwan Gunawardane

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This paper attempts to emphasize the benefits of conducting an intensive course in English for university entrants. In the Sri Lankan university context, an intensive course in English is usually conducted amidst various obstacles. In the 1970s and 1980s, undergraduates had intensive programmes in English for two to three months. Towards the end of the 1990s, a programme called General English Language Training (GELT) was conducted for the new students, and it was done outside universities before they entered their respective universities. Later it was not conducted, and that also resulted in students’ poor performance in English at university. However, having understood its importance, an eight week long intensive course in English was conducted for the new intake of the Faculty of Science, University of Ruhuna. As the findings show, the students heavily benefited from the programme. More importantly, they had the opportunity to refresh their knowledge of English gained at school and private institutions while gaining new knowledge. Another advantage was that they had plenty of time to enjoy learning English since the learners had adequate opportunities to carry out communicative tasks and the course was not exam-oriented, which reduced their fear of making mistakes in English considerably. The data was collected through an open-ended questionnaire given to 60 students, and their oral feedback was also taken into consideration. In addition, a focus group interview with 6 teachers was also conducted to get an idea about their experience and perception. The data were qualitatively analyzed. The findings suggest that an intensive programme in English undoubtedly lays a good foundation for the students’ academic career at university.

Keywords: intensive course, English, teachers, undergraduates, experience, perception

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Authors: Mahfoodh Saleh Al Sabbagh, Venkoba Rao

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There is plenty of research, both in academic and consultancy circles, about the importance and benefits of employee engagement and customer engagement and how it gives organization an opportunity to reduce variability and improve performance. Customer engagement is directly related to the engagement level of the organization's employees. It is therefore important to measure both. This research drawing from the work of Human Sigma by Fleming and Asplund, attempts to assess engagement level of customer and employees - the human systems of business - in an educational setup. Student is important to an educational institute and is a customer to be served efficiently and effectively. Considering student as customer and faculty as employees serving them, in–depth interviews were conducted to analyze the relationship between faculty and student engagement in two leading colleges in Oman, one from private sector and another from public sector. The study relied mainly on secondary data sources to understand the concept of engagement. However, the search of secondary sources was extensive to compensate the limited primary data. The results indicate that high faculty engagement is likely to lead to high student engagement. Engaged students were excited about learning, loved the feeling of they being cared as a person by their faculty and advocated the organization to other. The interaction truly represents an opportunity to build emotional connection to the organization. This study could be of interest to organizations interest in building and maintaining engagement with employees and customers.

Keywords: customer engagement, consumer psychology, strategy, educational institutes

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Authors: Abhijeet Ostawal, Parmjit Lall

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The run times for a large strategic model that we were managing had become too long leading to delays in project delivery, increased costs and loss in productivity. Software developers are continuously working towards developing more efficient tools by changing their algorithms and processes. The issue faced by our team was how do you apply the latest technologies on validated existing models which are based on much older versions of software that do not have the latest software capabilities. The multi-model transport model that we had could only be run in sequential assignment order. Recent upgrades to the software now allowed the assignment to be run in parallel, a concept called parallelization. Parallelization is a Python script working only within the latest version of the software. A full model transfer to the latest version was not possible due to time, budget and the potential changes in trip assignment. This article is to show the method to adapt and update the Python script in such a way that it can be used in older software versions by calling the latest version and then recalling the old version for assignment model without affecting the results. Through a process of trial-and-error run time savings of up to 30-40% have been achieved. Assignment results were maintained within the older version and through this learning process we’ve applied this methodology to other even older versions of the software resulting in huge time savings, more productivity and efficiency for both client and consultant.

Keywords: model run time, demand model, parallelisation, python scripting

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

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Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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