Search results for: activity learning

Authors: A. T. Omarova, G. N. Nakipova

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This article discusses the principles of object-oriented human capital development using the technology program. Also the article includes priorities of the strategy of industrial-innovative development of Kazakhstan in conditions of integration activity into the world community. The article shows the tasks of human resource management in the implementation of industrial and innovation development, particularities of Kazakhstan's theory of management staff, as well as due to the specificity of the Kazakhstan authorities. In the article, we have considered the factors which are affecting the people in the organization and also have considered mechanisms of HRM within organization in the conditions of innovative development in Kazakhstan.

Keywords: programming, management of human resources, innovation, investment, innovation process, HRD model, innovative development, integration, management, transformation, economic potential, competitiveness

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Authors: Maali-Amel Mersel, Lajos Fodor, Otto Horvath

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Photocatalytic H₂ production by H₂S decomposition is regarded to be an environmentally friendly process to produce carbon-free energy through direct solar energy conversion. For this purpose, sulphide-based materials, as photocatalysts, were widely used due to their excellent solar spectrum responses and high photocatalytic activity. The loading of proper co-catalysts that are based on cheap and earth-abundant materials on those semiconductors was shown to play an important role in the improvement of their efficiency. In this research, CdS-ZnS composite was studied because of its controllable band gap and excellent performance for H₂ evolution under visible light irradiation. The effects of the modification of this photocatalyst with different types of materials and the influence of the preparation parameters on its H₂ production activity were investigated. The CdS-ZnS composite with an enhanced photocatalytic activity for H₂ production was synthesized from ammine complexes. Two types of modification were used: compounds of Ni-group metals (NiS, PdS, and Pt) were applied as co-catalyst on the surface of CdS-ZnS semiconductor, while NiS, MnS, CoS, Ag₂S, and CuS were used as a dopant in the bulk of the catalyst. It was found that 0.1% of noble metals didn’t remarkably influence the photocatalytic activity, while the modification with 0.5% of NiS was shown to be more efficient in the bulk than on the surface. The modification with other types of metals results in a decrease of the rate of H₂ production, while the co-doping seems to be more promising. The preparation parameters (such as the amount of ammonia to form the ammine complexes, the order of the preparation steps together with the hydrothermal treatment) were also found to highly influence the rate of H₂ production. SEM, EDS and DRS analyses were made to reveal the structure of the most efficient photocatalysts. Moreover, the detection of the conduction band electron on the surface of the catalyst was also investigated. The excellent photoactivity of the CdS-ZnS catalysts with and without modification encourages further investigations to enhance the hydrogen generation by optimization of the reaction conditions.

Keywords: H₂S, photoactivity, photocatalytic H₂ production, CdS-ZnS

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Authors: Ibtissem El Ouar, Cornelia Braicu, Dalila Naimi, Alexendru Irimie, Ioana Berindan-Neagoe

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Helix aspersa, 'the garden snail' is a big land snail widely found in the Mediterranean countries, it is one of the most consumed species in the west of Algeria. It is commonly used in zootherapy to purify blood and to treat cardiovascular diseases and liver problems. The aim of our study is to investigate, the antitumor activity of an aqueous extract from Helix aspersa prepared by the traditional method on Hs578T; a triple negative breast cancer cell line. Firstly, the free radical scavenging activity of H. aspersa extract was assessed by measuring its capability for scavenging the radical 2,2-diphenyl-1-picrylhydrazyl (DPPH), as well as its ability to reduce ferric ion by the FRAP assay (ferric reducing ability). The cytotoxic effect of H. aspersa extract against Hs578T cells was evaluated by the MTT test (3-(4,5- dimethylthiazl-2-yl)-2,5- diphenyltetrazolium bromide)) while the mode of cell death induced by the extract has been determined by fluorescence microscopy using acredine orange/ethidium bromide (AO/EB) probe. The level of TNFα has also measured in cell medium by ELISA method. The results suggest that H. aspersa extract has an antioxidant activity, especially at high concentrations, it can reduce DPPH radical and ferric ion. The MTT test shows that H. aspersa extract has a great cytotoxic effect against breast cancer cells, the IC50 value correspond of the dilution 1% of the crude extract. Moreover, the AO/EB staining shows that TNFα induced necrosis is the main form of cell death induced by the extract. In conclusion, the present study may open new perspectives in the search for new natural anticancer drugs.

Keywords: breast cancer, Helix aspersa, Hs578t cell line, necrosis

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Ngong Eugene Ekia

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Education is the foundation for the success of any society as its impact transcends across all sectors, including economics, politics, and social welfare. It is through education that individuals acquire the necessary knowledge and skills to succeed in life and contribute meaningfully to society. However, the world is changing rapidly, and it is vital for education systems to adapt to these changes to remain relevant. In this paper, we will discuss the current trends and challenges in education and management and propose solutions that can enable individuals to thrive in an ever-evolving world.

Keywords: access to education, effective teaching and learning, strong management practices, and empowering and personal development

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Authors: Rawaz A. Ahmed, Katherine Huddersman

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High-lipid Fats, Oils and Grease (FOGs) from wastewater are underutilized despite their potential for conversion into valuable fuels; this work describes a surface-functionalized fibrous Polyacrylonitrile (PAN) mesh as a novel heterogeneous acid catalyst for the conversion of free fatty acids (FFAs), via a catalytic esterification process into biodiesel. The esterification of stearic acid (SA) with methanol was studied over an acidified PAN solid acid catalyst. Disappearance of the carboxylic acid (C=O) peak of the stearic acid at 1696 cm-1 in the FT-IR spectrum with the associated appearance of the ester (C=O) peak at 1739 cm-1 confirmed the production of the methyl stearate. This was further supported by 1H NMR spectra with the appearance of the ester (-CH₂OCOR) at 3.60-3.70 ppm. Quantitate analysis by GC-FID showed the catalyst has excellent activity with >95 % yield of methyl stearate (MS) at 90 ◦C after 3 h and a molar ratio of methanol to SA of 35:1. To date, to our best knowledge, there is no research in the literature on the esterification reaction for biodiesel production using a modified PAN mesh as a catalyst. It is noteworthy that this acidified PAN mesh catalyst showed comparable activity to conventional Brönsted acids, namely H₂SO₄ and p-TSA, as well as exhibiting higher activity than various other heterogeneous catalysts such as zeolites, ion-exchange resins and acid clay.

Keywords: fats oil and greases (FOGs), free fatty acid, esterification reaction, methyl ester, PAN

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Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

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Authors: M. E. Abalaka, O. A. Falusi, M. Galadima, D. Damisa

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The antimicrobial activity of aqueous, ethanolic and methanolic extracts of Chromolaena odorata (Siam weed) was evaluated against four wound isolates: Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Klebsiella pneumoniae at the concentrations of 200mg/ml, 100mg/ml, 50mg/ml and 25mg/ml respectively. S. aureus and E. coli showed high susceptibility to the various extracts than the other test isolates. The aqueous extract showed activity against Staphylococcus aureus with a mean diameter of zone of inhibition of 16 ± 3.00 at concentration of 200mg/ml and as low as 8 ± 0.00 at concentration of 25mg/ml; E. coli showed susceptibility with a mean diameter of zone of inhibition of 18 ± 2.00 and 10 ± 0.00 at a concentration of 200mg/ml and 25mg/ml respectively. Pseudomonas aeruginosa and Klebsiella pneumoniae were resistant to the aqueous extract. Methanol extract showed activity against Staphylococcus aureus with a mean diameter of zone of inhibition at 28 ± 4.00 and 12 ± 2.30 at a concentration of 200mg/ml and 25mg/ml respectively; while E. coli was susceptible with mean diameter of zone of inhibition of 18 ± 2.00 and as low as 12 ± 0.00 at a concentration of 200mg/ml and 50mg/ml respectively, Pseudomonas aeruginosa showed considerable susceptibility with mean diameter of zone of inhibition of 13 ± 1.00 and 12 ± 0.00 at a concentration of 200mg/ml and 100mg/ml respectively. The ethanol extract showed activity against S. aureus with a mean diameter zone of inhibition of 15 ± 2.00 and 9 ± 0.00 at a concentration of 200mg/ml and 25mg/ml respectively: E. coli showed susceptibility with a mean diameter zone of inhibition of 20 ± 4.00 and 13 ± 2.00 at a concentration of 200mg/ml and 25mg/ml respectively. Pseudomonas aeruginosa showed considerable susceptibility with a mean diameter zone of inhibition of 13 ± 1.00 and 9 ± 0.00 at a concentration of 200mg/ml and 100mg/ml respectively. The results above indicate the efficacy and potency of the crude extracts of Chromolaena odorata leaf on the tested wound isolates.

Keywords: antibacterial, Chromolaena odorata, leaf extracts, test isolates

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Authors: Deepa Kapoor, Rajshree Singh

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This paper appreciates the emergence and growing importance as a new production factor makes the development of technologies, methodologies and strategies for measurement, creation, and diffusion into one of the main priorities of the organizations in the knowledge society. There are many models for creation and management of knowledge and diverse and varied perspectives for study, analysis, and understanding. In this article, we will conduct a theoretical approach to the type of models for the creation and management of knowledge; we will discuss some of them and see some of the difficulties and the key factors that determine the success of the processes for the creation and management of knowledge.

Keywords: knowledge creation, knowledge management, organizational development, organization learning

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Authors: Maha Wiss, Wael Khreich

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Animals, objects, foods, plants, and other non-human terms are commonly used as a source of metaphors to describe females in formal and slang language. Comparing women to non-human items not only reflects cultural views that might conceptualize women as subordinates or in a lower position than humans, yet it conveys this degradation to the listeners. Moreover, the dehumanizing representation of females in the language normalizes the derogation and even encourages sexism and aggressiveness against women. Although dehumanization has been a popular research topic for decades, according to our knowledge, no studies have linked women's dehumanizing language to the machine learning field. Therefore, we introduce our research work as one of the first attempts to create a tool for the automated detection of the dehumanizing depiction of females in English texts. We also present the first labeled dataset on the charted topic, which is used for training supervised machine learning algorithms to build an accurate classification model. The importance of this work is that it accomplishes the first step toward mitigating dehumanizing language against females.

Keywords: gender bias, machine learning, NLP, women dehumanization

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

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Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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Authors: Linnea Stenliden

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The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors by Latour. The learning conditions are found to be distinguished by broad complexity characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation

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Authors: Ashit Syngle, Inderjit Verma, Pawan Krishan

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Aim: Endothelial progenitor cells (EPCs) are unique populations which have reparative potential in overcoming the endothelial damage and reducing cardiovascular risk. Patients with ankylosing spondylitis (AS) have increased risk of cardiovascular morbidity and mortality. The aim of this study was to investigate the endothelial progenitor cell population in AS patients and its potential relationships with disease variables. Methods: Endothelial progenitor cells were measured in peripheral blood samples from 20 AS and 20 healthy controls by flow cytometry on the basis of CD34 and CD133 expression. Disease activity was evaluated by using Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). Functional ability was monitored by using Bath Ankylosing Spondylitis Functional Index (BASFI). Results: EPCs were depleted in AS patients as compared to the healthy controls (CD34+/CD133+: 0.027 ± 0.010 % vs. 0.044 ± 0.011 %, p<0.001). EPCs depletion were significantly associated with disease duration (r=-0.52, p=0.01) and BASDAI (r=-0.45, p=0.04). Conclusion: This is the first study to demonstrate endothelial progenitor cells depletion in AS patients. EPCs depletion inversely correlates with disease duration and disease activity, suggesting the pivotal role of inflammation in depletion of EPCs. EPC would possibly also serve as a therapeutic target for preventing cardiovascular disease in AS.

Keywords: ankylosing spondylitis, endothelial progenitor cells, inflammation, vascular damage

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: Anik Barua, Rabiul Hossain, Labonno Barua, Rashadul Hossain, Nurul Absar

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Background: Globally, the burden of cancer is increasing consistently. Modern cancer therapies include lots of toxicity in the non-targeted organs reducing the life expectancy of the patients. Hence, scientists are trying to seek noble compounds from natural sources to treat cancer. Objectives: The objectives of the present study are to evaluate the phytochemicals, in vitro antioxidants, and in vivo and in silico anticancer study of various solvent fractions of Tinospora cordifolia (Willd.). Methodology: In this experiment, standard quantitative and qualitative assay methods were used to analyze the phytochemicals. The antioxidant activity was measured using the DPPH and ABTS scavenging methods. The in vivo antitumor activity is evaluated against Ehrlich ascites carcinoma (EAC) cell bearing in Swiss albino mice. In-silico ADME/T and molecular docking study were performed to assess the potential of stated phytochemicals against Transcription Factor STAT3b/DNA Complex of adenocarcinoma. Findings: Phytochemical screening confirmed the presence of flavonoids, alkaloids, glycosides, tannins, and carbohydrates. A significant amount of phenolic (20.19±0.3 mg/g GAE) and flavonoids (9.46±0.18 mg/g GAE) were found in methanolic extract in quantitative screening. Tinospora cordifolia methanolic extract showed promising DPPH and ABTS scavenging activity with the IC50 value of 1222.99 µg/mL and 1534.34 µg/mL, respectively, which was concentration dependent. In vivo anticancer activity in EAC cell-bearing mice showed significant (P < 0.05) percent inhibition of cell growth (60.12±1.22) was found at the highest dose compared with standard drug 5-Fluorouracil (81.18±1.28). Forty-two phytochemicals exhibit notable pharmacokinetics properties and passed drug-likeness screening tests in silico. In molecular docking study, (25S)-3Beta-acetoxy-5-alpha-22-beta-spirost-9(11)-en-12-beta-ol showed docking score (-8.5 kJ/mol) with significant non-bonding interactions with target enzyme. Conclusions: The results were found to be significant and confirmed that the methanolic extract of Tinospora cordifolia has remarkable antitumor activity with antioxidant potential. The Tinospora cordifolia methanolic extract may be considered a potent anticancer agent for advanced research.

Keywords: anticancer, antioxidant, Tinospora cordifolia, EAC cell

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Authors: Mamani Acurio Alex, Aguilar Alonso Igor

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The lack of interest and the lack of motivation are related. The lack of both in school generates serious problems such as school dropout or a low level of learning. Augmented reality has been very useful in different areas, and in this research, it was implemented in the area of education. Information necessary for the correct development of this mobile application with augmented reality was searched using six different research repositories. It was concluded that the application must be immersive, attractive, and fun for students, and the necessary technologies for its construction were defined.

Keywords: augmented reality, Vuforia, school interest, learning

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Authors: Dagim Ali Hussen, Adnan A. Bekhit, Ariaya Hymete

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In this study, several pyridine derivatives were synthesized and evaluated for their in vitro antimicrobial activity against gram-positive bacteria (S. aureus and B. Cereus), gram-negative bacteria (P. aeruginosa and E. coli) and fungus (C. albican and A niger). The intermediate chalcone derivative 2a,b was synthesized by condensation of pyrazole aldehydes 1a,b with acetophenone in alcoholic KOH. Cyclization of 2a,b with ethyl cyanoacetate ad ammonium acetate resulted in pyridine carbonitrile derivatives 3a,b. Furthermore, condensation of pyridine-4-carboxaldeyhe with different amino-derivatives gave rise to pyridine derivatives 5a,b, 6a,b. The oxadiazole derivative 7a was prepared by cyclization of 6a with acetic anhydride. Characterization of the synthesized compound was performed using IR, 1H NMR, 13C NMR spectra and elemental microanalyses. The antimicrobial results revealed that compounds 5a, 6b and 7a exhibited half fold antibacterial activity compared to ampicillin, against B. cereus. On the other hand, compound 3b showed an equivalent activity compared to miconazole against candida albican (CANDAL 03) and to clotrimazole against the clinical isolate candida albican 6647. Moreover, this compound 3b was further tested for its acute toxicity profile. The results showed that oral LD50 is more that 300 mg/kg and parentral LD50 is more than 100 mg/kg. Compound 3b is a good candidate for antifungal agent with good toxicity profile, and deserves more chemical derivatization and clinical study.

Keywords: antifungal, antimicrobial, Candida albican, pyridine

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Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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Authors: Yuh-Jen Wu, Chun-Min Lin

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Homophonic words are common in Mandarin Chinese which belongs to the tonal language family. Using homophonic cues to study foreign languages is one of the learning techniques of mnemonics that can aid the retention and retrieval of information in the human memory. When learning difficult foreign words, some learners transpose them with words in a language they are familiar with to build an association and strengthen working memory. These phonological clues are beneficial means for novice language learners. In the classroom, if mnemonic skills are used at the appropriate time in the instructional sequence, it may achieve their maximum effectiveness. For Chinese-speaking students, proper use of Chinese-English homophonic word pairs may help them learn difficult vocabulary. In this study, a database program is developed by employing Visual Basic. The database contains two corpora, one with Chinese lexical items and the other with English ones. The Chinese corpus contains 59,053 Chinese words that were collected by a web crawler. The pronunciations of this group of words are compared with words in an English corpus based on WordNet, a lexical database for the English language. Words in both databases with similar pronunciation chunks and batches are detected. A total of approximately 1,000 Chinese lexical items are located in the preliminary comparison. These homophonic word pairs can serve as a valuable tool to assist Chinese-speaking students in learning and memorizing new English vocabulary.

Keywords: Chinese, corpus, English, homophonic words, vocabulary

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Authors: Ali Kazemi

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

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

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Authors: Ya-Ting Carolyn Yang, Ping-Han Cheng, Shi-Hui Gilbert Chang, Terry Yuan-Fang Chen, Chih-Chieh Li

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Nanotechnology is widely applied in various areas so professionals in the related fields have to know more than nano knowledge. In the study, we focus on adopting ICT-assisted PBL in college general education to foster professionals who possess multiple abilities. The research adopted a pretest and posttest quasi-experimental design. The control group received traditional instruction, and the experimental group received ICT-assisted PBL instruction. Descriptive statistics will be used to describe the means, standard deviations, and adjusted means for the tests between the two groups. Next, analysis of covariance (ANCOVA) will be used to compare the final results of the two research groups after 6 weeks of instruction. Statistics gathered in the end of the research can be used to make contrasts. Therefore, we will see how different teaching strategies can improve students’ understanding about nanotechnology and learning skills.

Keywords: nanotechnology, science education, project-based learning, information and communication technology

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Authors: Maira C. M. Fonseca, Maria Aparecida N. Sediyama, Rosana Goncalves R. das Dores, Sanzio Mollica Vidigal, Alberto C. P. Dias

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Among Brazilian plant diversity, many species are used as food and considered minor crops (non-conventional plant foods) (NCPF). Arrowroot (Maranta arundinacea) is a NCPF from which starch is extracted from rhizome do not have gluten. Thus, arrowroot flower starch can be consumed by celiac people. Additional, some medicinal and functional proprieties are assigned to arrowroot leaves which currently are underutilized. In Brazil, it’s cultivated mainly by small scale farmers and there is no specific recommendation for fertilization. This work aimed to determinate the best fertilization for rhizome production and to verify its influence in phenolic composition and antioxidant activity of leaf extracts. Two arrowroot varieties, “Common” and “Seta”, were cultivated in organic system at state of Minas Gerais, Brazil, using cattle manure with three levels of nitrogen (N) (0, 300 and 900 kg N ha-1). The experiment design was in randomized block with four replicates. The highest production of rhizomes in both varieties, “Common” (38198.24 kg ha-1) and “Seta” (43567.71 kg ha-1), were obtained with the use of 300 kg N ha-1. With this fertilization, the total aerial part, petiole and leaf production in the varieties were respectively: “Common” (190.312 kg ha-1; 159.312 kg ha-1; 31.100 kg ha-1) and “Seta” (207.656 kg ha-1; 180.539 kg ha-1; 27.062 kg ha-1). Methanolic leaf extracts were analysed by HPLC-DAD. The major phenolic compounds found were caffeioylquinic acids, p-coumaric derivatives and flavonoids. In general, the production of these compounds significantly decreases with the increase levels of nitrogen (900 kg N ha-1). With 300 kg N ha-1 the phenolic production was similar to control. The antioxidant activity was evaluated using DPPH method and was detected around 60% of radical scavenging when 0.1 mg/mL of plant extracts were used. We concluded that fertilization with 300 kg N ha-1 increased arrowroot rhizome production, maintaining phenolic compounds yield at leaves.

Keywords: antioxidant activity, non-conventional plants, organic fertilization, phenolic compounds

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Authors: Boudjelal Amel

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Arisarum vulgare, a member of the Araceae family, is an herbaceous perennial widely distributed in the Mediterranean region. A. vulgare is recognized for its medicinal properties and holds significant traditional importance in Algeria for the treatment of various human ailments, including pain, infections, inflammation, digestive disorders, skin problems, eczema, cancer, wounds, burns and gynecological diseases. Despite its extensive traditional use, scientific exploration of A. vulgare remains limited. The study aims to investigate for the first time the therapeutic potential of A. vulgare ethanolic extract obtained by ultrasound-assisted extraction. The chemical composition of the extract was determined by LC-MS/MS analysis. For in vitro phytopharmacological evaluation, several assays, including DPPH, ABTS, FRAP and reducing power, were employed to evaluate the antioxidant activity. The antibacterial activity was assessed againt Escherichia coli, Salmonella typhimurium, Staphylococus aureus, Enterococcus feacium by disk diffusion and microdilution methods. The possible inhibitory activity of ethanolic extract was analyzed against the cholinesterases enzymes (AChE and BChE). The DNA protection activity of A. vulgare ethanolic extract was estimated using the agarose gel electrophoresis method. The capacities of the extract to protect plasmid DNA (pBR322) from the oxidizing effects of H2O2 and UV treatment were evaluated by their DNA-breaking forms. The in vivo wound healing potential of a traditional ointment containing 5% of A. vulgare ethanolic extract was also investigated. The LC-MS/MS profiling of the extract revealed the presence of various bioactive compounds, including naringenin, chlorogenic, vanillic, cafeic, coumaric acids, trans-cinnamic and trans ferrulic acids. The plant extract presented considerable antioxidant potential, being the most active for Reducing power (0,07326±0.001 mg/ml) and DPPH (0.14±0.004 mg/ml). The extract showed the highest inhibition zone diameter against Enterococcus feacium (36±0.1 mm). The ethanolic extract of A. vulgare suppressed the growth of Staphylococus aureus, Escherichia coli and Salmonella typhimurium according to the MIC values. The extract of the plant significantly inhibited both AChE and BChE enzymes. DNA protection activity of the A. vulgare extract was determined as 90.41% for form I and 51.92% for form II. The in vivo experiments showed that 5% ethanolic extract ointment accelerated the wound healing process. The topical application of the traditional formulation enhanced wound closure (95,36±0,6 %) and improved histological parameters in the treated group compared to the control groups. The promising biological properties of Arisarum vulgare revealed that the plant could be appraised as a potential origin of bioactive molecules having multifunctional medicinal uses.

Keywords: arisarum vulgare, LC-MS/MS, antioxidant activity, antimicrobial activity, cholinesterases enzymes inhibition, dna-damage activity, in vivo wound healing

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Authors: Robert Jesiolowski, Monique Jesiolowski

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Today more than ever before, we have access to new technologies which provide unforeseen opportunities for educators to pursue in online education. It starts with an idea, but that needs to be coupled with the right team of experts willing to take big risks and put in the hard work to build something different. An instructional design team was empowered to reimagine an Introduction to Sociology university course as a Game-Based Learning (GBL) experience utilizing cutting edge Virtual Reality (VR) technology. The result was a collaborative process that resulted in a type of learning based in Game theory, Method of Loci, and VR Immersion Simulations to promote deeper retention of core concepts. The team deconstructed the way that university courses operated, in order to rebuild the educational process in a whole learner-centric manner. In addition to a review of the build process, this paper will explore the results of in-course surveys completed by student participants.

Keywords: higher education, innovation, virtual reality, game-based learning, loci method

Procedia PDF Downloads 69

Authors: S. Lekhavat, T. Kajsongkram, S. Sang-han

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Dried ginger was extracted and investigated the effect of ethanol concentration and enzyme pre-treatment on its bioactive compounds in solvent extraction process. Sliced fresh gingers were dried by oven dryer at 70 °C for 24 hours and ground to powder using grinder which their size were controlled by passing through a 20-mesh sieve. In enzyme pre-treatment process, ginger powder was sprayed with 1 % (w/w) cellulase and then was incubated at 45 °C for 2 hours following by extraction process using ethanol at concentration of 0, 20, 40, 60 and 80 % (v/v), respectively. The ratio of ginger powder and ethanol are 1:9 and extracting conditions were controlled at 80 °C for 2 hours. Bioactive compounds extracted from ginger, either enzyme-treated or non enzyme-treated samples, such as total phenolic content (TPC), 6-Gingerol (6 G), 6-Shogaols (6 S) and antioxidant activity (IC50 using DPPH assay), were examined. Regardless of enzyme treatment, the results showed that 60 % ethanol provided the highest TPC (20.36 GAE mg /g. dried ginger), 6G (0.77%), 6S (0.036 %) and the lowest IC50 (625 μg/ml) compared to other ratios of ethanol. Considering the effect of enzyme on bioactive compounds and antioxidant activity, it was found that enzyme-treated sample has more 6G (0.17-0.77 %) and 6S (0.020-0.036 %) than non enzyme-treated samples (0.13-0.77 % 6G, 0.015-0.036 % 6S). However, the results showed that non enzyme-treated extracts provided higher TPC (6.76-20.36 GAE mg /g. dried ginger) and Lowest IC50 (625-1494 μg/ml ) than enzyme-treated extracts (TPC 5.36-17.50 GAE mg /g. dried ginger, IC50 793-2146 μg/ml).

Keywords: antioxidant activity, enzyme, extraction, ginger

Procedia PDF Downloads 239

Authors: Karl Eddie T. Malabanan

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Due to the quick transmission and public health risk of coronavirus disease, schools and universities have shifted to distant learning. Teachers everywhere were forced to shift gears instantly in order to react to the needs of students and families using synchronous and asynchronous virtual teaching. This study aims to explore the lived experiences of physical education teachers who are currently experiencing remote learning in teaching during the time of the COVID-19 pandemic. Specifically, the challenges that the physical education teachers encounter during remote learning and teaching. The participants include 12 physical education teachers who have taught in higher education institutions for at least five years. The researcher utilized qualitative research; specifically, the researcher used Consensual Qualitative Research (CQR). The results of this study showed that there are five categories for the Lived Experiences of Physical Education Teachers with thirty-one subcategories. This study revealed that physical education teachers experienced very challenging situations during the time of the pandemic. It also found that students had challenges in the abrupt transition from traditional to virtual learning classes, but it also showed that students are tenacious and willing to face any adversity. The researcher also finds that teachers are mentally drained during this time. Furthermore, one of the main focuses for the teachers should be on improving their well-being. And lastly, to cope with the challenges, teachers employ socializing to relieve tension and anxiety.

Keywords: lived experiences, consensual qualitative research, pandemic, education

Procedia PDF Downloads 77

Authors: He Lingsong, Wang Junfeng, Tan Qiong, Xu Jiang

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The paper describes efforts on reconstruction methods of engineering testing and measurement technology course by applying new techniques and applications. Firstly, flipped classroom was introduced. In-class time was used for in-depth discussions and interactions while theory concept teaching was done by self-study course outside of class. Secondly, two hands-on practices of technique applications, including the program design of MATLAB Signal Analysis and the measurement application of Arduino sensor, have been covered in class. Class was transformed from an instructor-centered teaching process into an active student-centered learning process, consisting of the pre-class massive open online course (MOOC), in-class discussion and after-class practice. The third is to change sole written homework to the research-oriented application practice assignments, so as to enhance the breadth and depth of the course.

Keywords: testing and measurement, flipped classroom, MOOC, research-oriented learning, practice-oriented learning

Procedia PDF Downloads 132

Authors: Edgardo V. Gubatanga Jr., Mark Joshua Salvacion

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Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps.

Keywords: aerial LiDAR, colorization, deep learning, intensity images

Procedia PDF Downloads 146

Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

Procedia PDF Downloads 524

Authors: Pirjo Kaakinen, Helvi Kyngäs, Danica Železnik

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People live longer than earlier and healthy lifestyle is one of the important issue to prevent functional inability and chronic disease. Aim of this study was describe the lifestyle changes of Carintia region’s people. The study was cross-sectional study. Data were collected by questionnaire in two period, first May 2013 (N=63) and on March 2014 (N=77) at the University of College of ‘Health Sciences Slovenj Gradec’. The study was part of project ‘Choose a healthy lifestyle - an investment for the future’. The questionnaire included self-estimated questions about physical activity, nutrition, grocery habit, smoking habit and alcohol use. Participants were measured clinical parameters such as blood pressure, blood glucose, BMI. Most of participants were women (61%) and they were over 60 years old (64%). Participants have a high BMI (75%) and elevated blood pressure (64%). However, almost all participants (89%) had normal blood glucose level. Slightly more than half of participants (54%) have normal cholesterol level. Participants (83%) eat breakfast in the morning and most of them have three or four daily meals. Fruit and vegetable consumption decrease during this study. Half of participants (51%) drank a litre of water per day and popular beverage was coffee or tea. To drink carbonate beverages was not so popular over 60 year’s old participants than younger one. There was less smokers in 2014 than 2013. Intensity of daily physical activity increased during healthy lifestyle project. The most popular form of physical activity was hiking. This study showed that the project can be effective in the Carintia region’s people lifestyle changes even it was short time. People did changes in their daily life and there were a positive influence physical activity, meal frequency, fruit, vegetable and alcohol consumption. In the future is needed the follow up study to get out longitudinal lifestyle changes.

Keywords: adults, healthy lifestyle, health education, quality of life

Procedia PDF Downloads 252