Search results for: Adult dataset

Authors: Muhammad Tahir Khalily

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Aim: The purpose of the present study was to examine the effectiveness of alpha-theta brainwave Neuro feedback treatment for the internal self-exploration of individuals with conversion disorder. Method: A Purposive sample technique was used and data collected from Kalsoom International Hospital Islamabad and Al-Malik Medical Center Rawalpindi. 123 participants were examined, Male child = 5, female child = 6, male adult = 64, female adult = 48. This study retrospectively examined the symptomatic changes in clients with a range of psychiatric disorders particularly 13 (Male = 3, Female = 10) conversion disorder clients who have received Neuro feedback treatment. Results: Our results indicated a significant improvement (P < .001) in the amelioration of our clients’ symptoms which have enabled them to bring their emotional memories into conscious awareness for the peaceful resolution without self-detachment. Conclusion: Majority of the clients improved their psychiatric symptoms including the symptoms of conversion disorder and was discharged from therapy with the accomplishment of therapeutic goals.

Keywords: EEG, neurofeedback, conversion, therapy

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Haonan Hu, Shuge Lei, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang

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This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement.

Keywords: medical imaging, ultrasound imaging, XAI, uncertainty measurement, trustworthy AI

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: Rebecca Johnson, Elizabeth Hoban

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Condom use among young men in Cambodia has declined between 2005 and 2014 which has public health implications such as increased risks of sexually transmitted infections, including HIV, and unplanned pregnancies. Conversations about sexual and reproductive health issues, including condom use, are not socially sanctioned in Cambodian society leaving young adults with limited knowledge of, and poor access to sexual and reproductive health services. Additionally, men play a dominant role in decision making regarding condom use within sexual partnerships. This study sought to fill a gap in knowledge by exploring young adult males’ attitudes, perceptions and behaviours regarding condom use. In February and March 2018, twenty young adult males, aged 18 to 24 years, were recruited from urban, peri urban and rural areas in Cambodia. The young adult males participated in a face-to-face semi structured interview that used an interview guide and photo elicitation method. The interview explored participants’ knowledge of sexual and reproductive health issues and efficacy, sexual behaviours, and use of condoms. Inductive thematic analysis was conducted and the following major themes emerged: understanding of reproduction, understanding of sexually transmitted infections, knowledge about condoms, condom use, access to condoms, and sexual behaviour. Participants’ knowledge of condoms and specific reasons for their use varied; most participants understood that condoms provide protection from sexually transmitted infections and prevent pregnancy. Stigma associated with condom access was consistently referred to as a problem and the main reason cited by young men for not using condoms during sexual intercourse. The perceived importance of condom use altered with partner type and relationship status, dependent upon the need for protection from sexually transmitted infections or pregnancy. Condoms were used for infection control in the context of multiple relationships, or as a contraceptive method for unmarried and some married couples. The majority of young men engaged in premarital sexual intercourse, of those men the many used condoms. The inconsistent use of condoms by young men in Cambodia is of public health concern because of the increased risk of sexually transmitted infections (including HIV), and unplanned pregnancy. Public health action is required in order to minimize long term health issues for individuals and the community. Health education is required to increase knowledge of condom use, sexually transmitted infections and HIV, and reduce the stigma associated with this topic. Sustainable health promotion programs are needed to increase ease of access to condoms for young people. Public health policy in Cambodia needs to be reviewed to improve sexual and reproductive health outcomes for young adults.

Keywords: Cambodia, condom use, sexual and reproductive health, young adult males

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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: Jin-Hee Kim

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The way that globalization distinguishes itself from the previous changes is scope and intensity of changes, which together affect many parts of a nation’s system. In this way, globalization has its relation with the concept of ‘internationalization’ in that a nation state formulates a set of strategies in many areas of its governance to actively react to it. In short, globalization is a ‘catalyst,’ and internationalization is a ‘response’. In this regard, the field of higher education is one of the representative cases that globalization has several consequences that change the terrain of national policy-making. Started and been dominated mainly by the Western world, it has now been expanded to the ‘late movers,’ such as Asia-Pacific countries. The case of internationalization of Korean higher education is, therefore, located in a unique place in this arena. Yet Korea still is one of the major countries of sending its students to the so-called, ‘first world.’ On the other hand, it has started its effort to recruit international students from the world to its higher education system. After new Millennium, particularly, internationalization of higher education has been launched in its full-scale and gradually been one of the important global policy agenda, striving in both ways by opening its turf to foreign educational service providers and recruiting prospective students from other countries. Particularly the latter, recruiting international students, has been highlighted under the government project named ‘Study Korea,’ launched in 2004. Not only global, but also local issues and motivations were based to launch this nationwide project. Bringing international students means various desirable economic outcomes such as reducing educational deficit as well as utilizing them in Korean industry after the completion of their study, to name a few. In addition, in a similar vein, Korea's higher education institutes have started to have a new comers of adult learners. When it comes to the questions regarding the quality and access of this new learning agency, the answer is quite tricky. This study will investigate the different dimension of education provision and learning process to empower diverse group regardless of nationality, race, class and gender in Korea. Listening to the voices of international students and adult learning as non-traditional participants in a changing Korean higher educational space not only benefit students themselves, but Korean stakeholders who should try to accommodate more comprehensive and fair educational provisions for more and more diversifying groups of learners.

Keywords: education equity, access, globalisation, international students, adult learning, learning support

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Authors: Renata Roma, Christine Tardif-Williams

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Engaging in daily walks with a dog (f.e. Canis lupus familiaris) during the COVID-19 pandemic may be linked to feelings of greater social-connectedness and global self-worth, and lower stress after controlling for mental health issues, lack of physical contact with others, and other stressors associated with the current pandemic. Therefore, maintaining a routine of dog walking might mitigate the effects of stressors experienced during the pandemic and promote well-being. However, many dog owners do not walk their dogs for many reasons, which are related to the owner’s and the dog’s personalities. Note that the consistency of certain personality characteristics among dogs demonstrates that it is possible to accurately measure different dimensions of personality in both dogs and their human counterparts. In addition, behavioural ratings (e.g., the dog personality questionnaire - DPQ) are reliable tools to assess the dog’s personality. Clarifying the relevance of personality factors in the context of young adult-dog relationships can shed light on interactional aspects that can potentially foster protective behaviours and promote well-being among young adults during the pandemic. This study examines if and how nine combinations of dog- and young adult-related personality characteristics (e.g., neuroticism-fearfulness) can amplify the influence of personality factors in the context of dog walking during the COVID-19 pandemic. Responses to an online large-scale survey among 440 (389 females; 47 males; 4 nonbinaries, Mage=20.7, SD= 2.13 range=17-25) young adults living with a dog in Canada were analyzed using structural equation modeling (SEM). As extraversion, conscientiousness, and neuroticism, measured through the five-factor model (FFM) inventory, are related to maintaining a routine of physical activities, these dimensions were selected for this analysis. Following an approach successfully adopted in the field of dog-human interactions, the FFM was used as the organizing framework to measure and compare the human’s and the dog’s personality in the context of dog walking. The dog-related personality dimensions activity/excitability, responsiveness to training, and fearful were correlated dimensions captured through DPQ and were added to the analysis. Two questions were used to assess dog walking. The actor-partner interdependence model (APIM) was used to check if the young adult’s responses about the dog were biased; no significant bias was observed. Activity/excitability and responsiveness to training in dogs were greatly associated with dog walking. For young adults, high scores in conscientiousness and extraversion predicted more walks with the dog. Conversely, higher scores in neuroticism predicted less engagement in dog walking. For participants high in conscientiousness, the dog’s responsiveness to training (standardized=0.14, p=0.02) and the dog’s activity/excitability (standardized=0.15, p=0.00) levels moderated dog walking behaviours by promoting more daily walks. These results suggest that some combinations in young adult and dog personality characteristics are associated with greater synergy in the young adult-dog dyad that might amplify the impact of personality factors on young adults’ dog-walking routines. These results can inform programs designed to promote the mental and physical health of young adults during the Covid-19 pandemic by highlighting the impact of synergy and reciprocity in personality characteristics between young adults and dogs.

Keywords: Covid-19 pandemic, dog walking, personality, structural equation modeling, well-being

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Authors: Eric Bang

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Interactions between receptors and ligands are crucial for many essential biological processes, including neurotransmission and metabolism. Ligand-receptor analyses that examine cell behavior and interactions often utilize cell type-specific RNA expressions from single-cell RNA sequencing (scRNA-seq) data. Using CellPhoneDB, a public repository consisting of ligands, receptors, and ligand-receptor interactions, the cell-cell interactions were explored in a specific scRNA-seq dataset from kidney tissue and portrayed the results with dot plots and heat maps. Depending on the type of cell, each ligand-receptor pair was aligned with the interacting cell type and calculated the positori probabilities of these associations, with corresponding P values reflecting average expression values between the triads and their significance. Using single-cell data (sample kidney cell references), genes in the dataset were cross-referenced with ones in the existing CellPhoneDB dataset. For example, a gene such as Pleiotrophin (PTN) present in the single-cell data also needed to be present in the CellPhoneDB dataset. Using the single-cell transcriptomics data via slide-seq and reference data, the CellPhoneDB program defines cell types and plots them in different formats, with the two main ones being dot plots and heat map plots. The dot plot displays derived measures of the cell to cell interaction scores and p values. For the dot plot, each row shows a ligand-receptor pair, and each column shows the two interacting cell types. CellPhoneDB defines interactions and interaction levels from the gene expression level, so since the p-value is on a -log10 scale, the larger dots represent more significant interactions. By performing an interaction analysis, a significant interaction was discovered for myeloid and T-cell ligand-receptor pairs, including those between Secreted Phosphoprotein 1 (SPP1) and Fibronectin 1 (FN1), which is consistent with previous findings. It was proposed that an effective protocol would involve a filtration step where cell types would be filtered out, depending on which ligand-receptor pair is activated in that part of the tissue, as well as the incorporation of the CellPhoneDB data in a streamlined workflow pipeline. The filtration step would be in the form of a Python script that expedites the manual process necessary for dataset filtration. Being in Python allows it to be integrated with the CellPhoneDB dataset for future workflow analysis. The manual process involves filtering cell types based on what ligand/receptor pair is activated in kidney cells. One limitation of this would be the fact that some pairings are activated in multiple cells at a time, so the manual manipulation of the data is reflected prior to analysis. Using the filtration script, accurate sorting is incorporated into the CellPhoneDB database rather than waiting until the output is produced and then subsequently applying spatial data. It was envisioned that this would reveal wherein the cell various ligands and receptors are interacting with different cell types, allowing for easier identification of which cells are being impacted and why, for the purpose of disease treatment. The hope is this new computational method utilizing spatially explicit ligand-receptor association data can be used to uncover previously unknown specific interactions within kidney tissue.

Keywords: bioinformatics, Ligands, kidney tissue, receptors, spatial transcriptome

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: Raed Alnajjar, Mohd Zakree, Ahmad Nazri

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In this study, we apply Discrete Group Search Optimizer (DGSO) for solving Traveling Salesman Problem (TSP). The DGSO is a nature inspired optimization algorithm that imitates the animal behavior, especially animal searching behavior. The proposed DGSO uses a vector representation and some discrete operators, such as destruction, construction, differential evolution, swap and insert. The TSP is a well-known hard combinatorial optimization problem, which seeks to find the shortest path among numbers of cities. The performance of the proposed DGSO is evaluated and tested on benchmark instances which listed in LIBTSP dataset. The experimental results show that the performance of the proposed DGSO is comparable with the other methods in the state of the art for some instances. The results show that DGSO outperform Ant Colony System (ACS) in some instances whilst outperform other metaheuristic in most instances. In addition to that, the new results obtained a number of optimal solutions and some best known results. DGSO was able to obtain feasible and good quality solution across all dataset.

Keywords: discrete group search optimizer (DGSO); Travelling salesman problem (TSP); Variable neighborhood search(VNS)

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Khairul Abid Awang

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Happiness and life satisfaction are the vital components in assessing the well-being of the societies and their social progress. These components varies and changes within communities and across time. Thus, due to low level of happiness and life satisfaction, it might affect mental health status psychologically and generally disrupt the health pattern of the individual. Hence, this present study aims to identify the happiness determinants, assessing the happiness and life satisfaction level, as well as examine the relation between happiness among Kampung Jaya Bakti adult community. Quantitative and cross-sectional study with total of 100 observations of 18 years old and above was participated in this study. The study was done in a semi-rural village which is geocentric towards river that branched from South East Asia Sea. Several happiness and life satisfaction instruments was used, inclusive of Individual Determinants of Happiness Scale (IDoHS) that had been developed in this study, in order to identify the determinants of happiness in the area based on the listed determinants. The listed determinants in IDoHS are: i) Daily activities. ii) Adaptation. iii) Goals. iv) Life Events. v) Living condition. vi) Self-confidence. vii) Personality traits. viii) Religion. The results revealed that 98% of the respondents agreed that the listed determinants in IDoHS are statistically significant in determining the happiness. The happiness level revealed that 71 percent (%) of the respondents are ‘very happy’, followed by 26% (‘moderately happy’) and 3% (‘neutral’), while life satisfaction level revealed that 70% of the respondents are ‘very highly satisfied’, followed by 29% (‘highly satisfied’) and 1% (‘moderately satisfied’). Pearson Correlation (Pearson’s r) shows that; happiness determinants listed in IDoHS are affecting happiness level (Pearson’s r: .514, p < 0.01) and life satisfaction level (Pearson’s r: .504, p < 0.01) respectively, as well as happiness that are statistically significant in affecting life satisfaction (Pearson’s r: .653, p < 0.01; Pearson’s r; .546, p < 0.01). It is concluded that the listed determinants in IDoHS are the determinants that affecting happiness and it is also noted that happiness are affecting life satisfaction and vice versa.

Keywords: adult community, determinants, happiness, life satisfaction, subjective well-being

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Authors: Shelin Jin, Jin Huang, Shiqin Cao, Qiuzhen Jia, Bo Zhang, Zhenyu Sun

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Stripe rust and leaf rust, caused by Puccinia striiformis f.sp. tritici and Puccinia recondita f.sp. tritici are two of the most damaging diseases of wheat in China. In recent years, leaf rust has migrated to some wheat growing areas previously suitable for stripe rust, resulting in a mixture of the two diseases occurring in the same area and at the same time, and seriously damage wheat production in China, Gansu Province. The most effective method of prevention those two diseases are through the use of resistant cultivars. However, many studies have only carried out of resistance of wheat varieties for a single disease; resistance to both diseases is unknown. In order to definite the resistance composition of wheat varieties to these two diseases, 715 wheat varieties/lines from 8 breeding units in Gansu province were collected to test for the resistance to stripe rust and leaf rust at seedling stage in greenhouse and at adult plant stage in field in 2016-2018, respectively. Spore suspensions with the fresh mixture races of CYR32, CYR33, and CYR34 of Puccinia striiformis f.sp. tritici and mixture races of THTP, THTT, TKTT, and THTS of Puccinia recondita f.sp. tritici were used for inoculation separately. The result shows that only 4.74% of the varieties/lines show comprehensive resistance to strip rust and leaf rust at all growth stages, and there are 34 wheat varieties/lines including Tianxuan 67, 2006-1-4-1-4-2-7-2-3-10, 03-139-1-2-2-1-2-1, Qingnong 21, Lenghan 5, 04-203-1-1-1 and so on. In seedling stage, the frequencies of resistant varieties/lines to wheat strip rust and leaf rust were 56.64% and 30.23%. While the materials were susceptible to these diseases were 43.36% and 69.77%. 71 varieties/lines were resistant to those two diseases, accounted for 9.93%. 10 varieties/lines, accounted for 1.4%, were highly resistant (including immune/near immune) to those two diseases. In adult-plant stage, the frequencies of resistant varieties/lines to wheat strip rust and leaf rust were 76.53% and 36.11%. While the materials were susceptible to these diseases were 23.47% and 63.89%. 137 varieties/lines were resistant to those two diseases, accounted for 19.16%. 59 varieties/lines, accounted for 8.25%, were highly resistant (including immune/near immune) to those two diseases. Overall, the 715 varieties /lines had high resistance to wheat strip rust, but poor resistance to leaf rust. This study found out some resistant materials which had better comprehensive resistance to leaf rust and strip rust, also pointed out the resistance characteristics of 715 varieties/lines to those two diseases at the seedling stage and adult-plant stage, which will be of great guiding significance in wheat resistance breeding and comprehensive control those two diseases in China, Gansu Province in the future.

Keywords: Puccinia striiformis f.sp. tritici, Puccinia recondita f.sp. tritici, resistance of variety, wheat

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Nasiruddin Khan, Nasser M. Al-Daghri, Dara A. Al-Disi, Asim Al-Fadda, Mohamed Al-Seif, Gyanendra Tripathi, A. L. Harte, Philip G. Mcternan

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For the last few decades, the prevalence of type 2 diabetes mellitus (T2DM) in the Kingdom of Saudi Arabia (KSA) is increasing alarmingly high and is unprecedented at 31.6 %. Preventive measures should be taken to curb down the increasing incidence. In this prospective, 3-month study, we aimed to determine whether dietary modification program would confer favorable affects among overweight and obese adult Saudi women with or without T2DM. A total of 92 Saudi women [18 healthy controls, 24 overweight subjects and 50 overweight or obese patients with early onset T2DM were included in this prospective study. Baseline anthropometrics and fasting blood samples were taken at baseline and after 3 months. Fasting blood sugar and lipid profile were measured routinely. A 500 Kcal deficit energy diet less than their daily recommended dietary allowances were prescribed to all participants. After 3 months of follow-up visit, significant improvements were observed in both the overweight and DMT2 group as compared to baseline with decreased mean BMI [Overweight Group 28.54±1.49 versus 27.95±2.25, p<0.05; DMT2 group 35.24±7.67 versus 35.04±8.07, p<0.05] and hip circumference [Overweight group 109.67±5.01 versus 108.07±4.07, p<0.05; DMT2 group 112.3±13.43 versus 109.21±12.71, p<0.01]. Moreover, in the overweight group, baseline HDL-cholesterol was significantly associated with protein intake and inversely associated with carbohydrate intake in controls. In the DMT2 group, carbohydrate intake at baseline was significantly associated with BMI. A 3-month 500kcal/day deficit dietary modification alone is probably effective among adult overweight or obese Saudi females without or with T2DM. Longer prospective studies are to determine whether the dietary intervention alone can reduce progression of T2DM among high-risk adult Arabs.

Keywords: diet, lipid, obesity, T2DM

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Authors: Zan Chen, D. Kwok

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The SkillsFuture Singapore’s mission to develop a responsive and forward-looking Training and Adult Education (TAE) and workforce development system is undergirded by how successful TAE providers are in their business performance and strategies that strengthen their operational efficiency and processes. Therefore, understanding the factors that drive the business performance of TAE providers is critical to the success of SkillsFuture Singapore’s initiatives. This study aims to investigate how business strategy, work autonomy, work intensity and professional development support impact the business performance of private TAE providers. Specifically, the three research questions are: (1) Are there significant relationships between the above-mentioned four factors and TAE providers’ business performance?; (2) Are there significant differences on the four factors between low and high TAE providers’ business performance groups?; and (3) To what extent and in what manner do the four factors predict TAE providers’ business performance? This was part of the first national study on organizations and professionals working in the Training and Adult Education (TAE) sector. Data from 265 private TAE providers where respondents were Chief Executive Officers representatives from the Senior Management were analyzed. The results showed that business strategy (the extent that the organization leads the way in terms of developing new products and services; uses up-to-date learning technologies; customizes its products and services to the client’s needs), work autonomy (the extent that the staff personally have an influence on how hard they work; deciding what tasks they are to do; deciding how they are to do the tasks, and deciding the quality standards to which they work) and professional development support (both monetary and non-monetary support and incentives) had positive and significant relationships with business performance. However, no significant relationship is found between work intensity and business performance. A business strategy, work autonomy and professional development support were significantly higher in the high business performance group compared to the low-performance group among the TAE providers. Results of hierarchical regression analyses controlling for the size of the TAE providers showed significant impacts of business strategy, work autonomy and professional development support on TAE providers’ business performance. Overall, the model accounted for 27% of the variance in TAE providers’ business performance. This study provides policymakers with insights into improving existing policies, designing new initiatives and implementing targeting interventions to support TAE providers. The findings also have implications on how the TAE providers could better formulate their organizational strategies and business models. Finally, limitations of study, along with directions for future research will be discussed in the paper.

Keywords: adult education, business performance, business strategy, training, work autonomy

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

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Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

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Authors: Boudjenah-Haroun Saliha, Isselnane Souad, Nouani Abdelwahab, Baaissa Babelhadj, Mati Abderrahmane

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Algeria is experiencing significant development of the dairy sector, where consumption of milk and milk products increased by 2.7 million tons in 2008 to 4,400,000 tons in 2013, and cheese production has reached 1640 tons in the year 2014 with average consumption of 0.7 kg/person/year. Although rennet is still the most used coagulating enzyme in cheese, its production has been growing worldwide shortage. This shortage is primarily due to a growing increase in the production and consumption of cheese, and the inability to increase in parallel the production of rennet. This shortage has caused very large fluctuations in its price). To overcome these obstacles, much research has been undertaken to find effective and competitive substitutes used industrially. For this, the selection of a local production of rennet substitute is desirable. It would allow a permanent supply with limited dependence on imports and price fluctuations. Investigations conducted by our research team showed that extracts coagulants from the stomachs of older camels are characterized by a coagulating power than those from younger camels. The objective of this work is to study the possibility of substituting commercial rennet coagulant by gastric enzymes from adult camels for coagulation bovine milk. Excerpts from the raw camel coagulants obtained are characterized through their teneures proteins and clotting and proteolytic activities. Milk clotting conditions by the action of these extracts were optimized. Milk clotting time all treated with enzyme preparations and under different conditions was calculated. Bovine rennet has been used for comparison. The results show that crude extracts from gastric adult camel can be good substituting bovine rennet.

Keywords: Algeria, camel, cheese, coagulation, gastric extracts, milk

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: Issam M Hajjaji, Adel Tajoury, Salah R Benhamid

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The MENA region has the highest prevalence of diabetes in the world. Various risk scores were developed, not all appropriate locally. The objective of this study is to apply the FINDRISC Score to adult Libyan males to determine its significance, sensitivity, specificity and Positive Predictive Values as an initial screening tool for type 2 diabetes, and suggest a cut-off point. Methods: 600 subjects answered the questionnaire at their place of work, and their waist, weight, height & BP were measured. Thereafter, after excluding those with known diabetes, an Oral Glucose Tolerance Test was done. Results: 414 subjects aged 19-78 completed the questionnaire and tests. 35 (8.4%) had impaired glucose tolerance (IGT) and 13 (3.1%) had diabetes (DM). The AUC-ROC for IGT was 0.614 (95% CI: 0.527-0.701), for DM 0.810 (95% CI: 0.709-0.911) and for both 0.689 (95% CI: 0.609-0.769). The Positive Predictive Value for a cut-off score of 5 were 15.5%, 11.7% & 5.7% for both conditions combined, prediabetes & diabetes respectively. The equivalent values for a cut-off score of 8 were 16.1%, 9.0% & 7.7%. The Negative Predictive Values were uniformly above 90%. Conclusions & Recommendations: The FINDRISC Score had a low predictive value for dysglycaemia in this sample and performed at a level of significance for IGT that is similar to other MENA countries, but did better for DM. A larger sample that included women is suggested, with a view of adjusting the Score to suit the local population.

Keywords: diabetes, FINDRISK, Libya, prediabetes

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Authors: Sinarwati Mohamad Suhaili, Naomie Salim, Mohamad Nazim Jambli

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Sequence-to-sequence (seq2seq) models augmented with attention mechanisms are playing an increasingly important role in automated customer service. These models, which are able to recognize complex relationships between input and output sequences, are crucial for optimizing chatbot responses. Central to these mechanisms are neural attention weights that determine the focus of the model during sequence generation. Despite their widespread use, there remains a gap in the comparative analysis of different attention weighting functions within seq2seq models, particularly in the domain of chatbots using the Customer Support Twitter (CST) dataset. This study addresses this gap by evaluating four distinct attention-scoring functions—dot, multiplicative/general, additive, and an extended multiplicative function with a tanh activation parameter — in neural generative seq2seq models. Utilizing the CST dataset, these models were trained and evaluated over 10 epochs with the AdamW optimizer. Evaluation criteria included validation loss and BLEU scores implemented under both greedy and beam search strategies with a beam size of k=3. Results indicate that the model with the tanh-augmented multiplicative function significantly outperforms its counterparts, achieving the lowest validation loss (1.136484) and the highest BLEU scores (0.438926 under greedy search, 0.443000 under beam search, k=3). These results emphasize the crucial influence of selecting an appropriate attention-scoring function in improving the performance of seq2seq models for chatbots. Particularly, the model that integrates tanh activation proves to be a promising approach to improve the quality of chatbots in the customer support context.

Keywords: attention weight, chatbot, encoder-decoder, neural generative attention, score function, sequence-to-sequence

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

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The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

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Authors: Prakash Dhakal

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Hepatic portal venous gas (HPVG) is diagnosed via computed tomography due to unusual imaging features. HPVG, when linked with pneumatosis intestinalis, has a high mortality rate and requires urgent intervention. We present a case of a 26-year-old young adult with superior mesenteric artery thrombosis who presented with severe abdominal pain. He had a history of COVID vaccination (First dose of COVISHILED) 15 days back. On imaging, HPVG and pneumatosis intestinalis were seen owing to the urgent intervention of the patient. The reliable interpretation of the imaging findings along with quick intervention led to a favorable outcome in our case. Herein we present a thorough review of the patient with a history of COVID-19 vaccination with superior mesenteric artery thrombosis leading to bowel ischemia and hepatic portal venous gas. The patient underwent subtotal small bowel resection.

Keywords: COVID-19 vaccination, SMA thrombosis, portal venoius gas, pneumatosis intestinalis

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

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Authors: Mikiyas Adugna

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Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

Procedia PDF Downloads 74