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2023 Congress in Computer Science, Computer Engineering, and applied Computing, CSCE 2023

作者： Zhang, Chris Cheng Zhang, Kevin Ni, Rongdi Liu, Haoyu Shen, Jinhui Canada Youth Robotics Club Research and Development Department VancouverBC Canada The University of British Columbia Faculty of Applied Science VancouverBC Canada Jilin International Studies University Department of Human Resource Management Jilin Changchun China The University of Toronto Department of Mathematics TorontoON Canada School of Broadcasting and Hosting Arts Communication University of China Nanjing Jiangsu Nanjing China

ISBN: (纸本)9798350327595

This research paper explores the transformative impact of machine learning techniques in the field of computer vision. With the rapid evolution of machine learning algorithms, computer vision has witnessed remarkable advancements and diverse applications. This study dives into the intersection of machine learning and computer vision, presenting a comprehensive analysis of state-of-the-art algorithms and their real-world use cases. Notable machine learning algorithms are shown in the context of their applications in computer vision. The paper highlights one real-world example in particular: the use of machine learning techniques in long-term care for elderly patients using object recognition and pose estimation. Experimental results and shed light on algorithm performance and inherent limitations. The findings provide valuable insights into the untapped potential of machine learning in computer vision. © 2023 IEEE.

关键词： algorithms computer vision machine learning

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Using Fuzzy Mathematical Model in the Differential Diagnosis of Pancreatic Lesions Using Ultrasonography and Echographic Texture Analysis

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Critical Reviews in Biomedical Engineering 2024年第1期52卷 1-20页

作者： Korenevskiy, Nikolay A. Belozerov, Vladimir Anatolievich Al-Kasasbeh, Riad Taha Al-Smady, Moaath Musa Krutskikh, Vladislav Shalimova, Elena Al-Jundi, Mohammad Rodionova, Sofia N. Filist, Seregy Shaqadan, Ashraf Maksim, Ilyash Al-Habahbeh, Osama South-West State University Kursk Russia Endoscopy Department BMU "Kursk Regional Clinical Hospital" Kursk Russia Department of Mechatronics Engineering School of Engineering The University of Jordan Amman Jordan Jordan University Hospital Amman Jordan Radio Technical Fundamentals Department National Research University "MPEI" Moscow Russia Department of Endocrinology Eunice Kennedy Shriver National Institute of Child and Human Development National Institutes of Health BethesdaMD United States Department of Mechatronics Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan ITMO University Kronverksky St. Petersburg Russia

Malignant tumors of the pancreas are the fourth leading cause of cancer-related deaths. This is mainly because they are often diagnosed at a late stage. One of the challenges in diagnosing focal lesions in the pancreas is the difficulty in distinguishing them from other conditions due to the unique location and anatomy of the organ, as well as the similarity in their ultrasound characteristics. One of the most sensitive imaging modalities of the pancreas is endoscopic ultrasonography. However, clinicians recognize that EUS is a difficult and highly operator-dependent method, while its results are highly dependent on the experience of the investigator. Hybrid technologies based on artificial intelligence methods can improve the accuracy and objectify the results of endosonographic diagnostics. Endoscopic ultrasonogra-phy was performed on 272 patients with focal lesions of the pancreatobiliary zone, who had been treated in the surgical section of the Kursk Regional Clinical Hospital in 2014–2023. The study utilized an Olympus EVIS EXERA II video information endoscopic system, along with an EU-ME1 ultrasound unit equipped with GF UM160 and GF UC140P-AL5 echo endoscopes. Out of the focal formations in the pancreatobiliary zone, pancreatic cancer was detected in 109 patients, accounting for 40.1% of the cases. Additionally, 40 patients (14.7%) were diagnosed with local forms of chronic pan-creatitis. The reference sonograms displayed distinguishable focal pancreatic pathologies, leading to the development of hybrid fuzzy mathematical decision-making rules at the South-West State University in Kursk, Russian Federation. This research resulted in the creation of a fuzzy hybrid model for the differential diagnosis of chronic focal pancreatitis and pancreatic cancer. Endoscopic ultrasonography, combined with hybrid fuzzy logic methodology, has made it possible to create a model for differentiating between chronic focal pancreatitis and pancreatic ductal adenocarcinoma. Stati

关键词： Textures

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Method for Determining the Body’s Level of Protection According to Oxidant Status in Assessing the Influence of Industrial Risk Factors on Health

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Critical Reviews in Biomedical Engineering 2023年第2期51卷 1-17页

作者： Korenevskiy, Nikolay A. Al-Kasasbeh, Riad Taha Al-Kasasbeh, Etab Al-Smady, Moaath Musa Aikeyeva, Altyn A. Al-Jundi, Mohammad Rodionova, Sofia N. Al-Habahbeh, Osama M. Filist, Sergey Alshamasin, Mahdi Salman Maksim, Ilyash South-West State University Kursk Russia Department of Mechatronics Engineering School of Engineering The University of Jordan Amman Jordan Al-Karak College Al-Balqa Applied University Jordan Jordan University Hospital Amman Jordan Eurasian National University Nur-Sultan Kazakhstan Department of Endocrinology Eunice Kennedy Shriver National Institute of Child and Human Development National Institutes of Health BethesdaMD United States Department of Mechatronics Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan ITMO University Kronverksky St. Petersburg Russia

This work aims at improving the quality of health assessments, specifically under the influence of occupational risk factors. For this purpose, additional informative indicators are utilized in prognostic and diagnostic models. The models are used to characterize the level of body protection based on oxidative status. A quantitative method is proposed to assess the body’s level of protection by means of the levels of lipid peroxidation and antioxidant activity, which characterize the body’s oxidative status. A mechanism is developed for integrating the proposed method into prognostic and diagnostic decision rules. The developed rules are in the form of mathematical models used to synthesize hybrid fuzzy decision rules, which are then used to quantify the level of body protection (LBP) against external risk factors, based on the use of protection level functions in terms of lipid peroxidation and antioxidant activity. A mechanism for embedding LBP into predictive and diagnostic decision rules has been proposed. The proposed method is used to predict the occurrence and development of coronary heart disease in railroad locomotive drivers. It was found that to improve the predicting and diagnosing of diseases caused by external pathogenic factors, quantitative assessments of LBP, determined by oxidative status, can be implemented. It has been established that the use of the protection level indicator in predictive decision rules makes it possible to increase the efficiency of the prediction while simultaneously increasing its accuracy. © 2023 by Begell House, Inc.

关键词： Forecasting

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Palmyra and coconut haustorium: A comprehensive review on nutritional composition, bioactive potential, value-added products and its health benefits

Food Chemistry Advances

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Food Chemistry Advances 2024年 5卷

作者： E, Raja Rajeshwari P S, Sathanya S, Vignesh N, Baskaran Department of Academics and Human Resource Development National Institute of Food Technology Entrepreneurship and Management – Thanjavur (NIFTEM-T) Tamil Nadu 613005 India

The various parts of the palmyra and coconut trees which are widely distributed in the southern parts of the Indian subcontinent have been studied and utilized for many years. These contain a white and spongy edible component in them which is found during the germination of the seeds called haustorium. The palmyra and coconut trees each have different sections with special medical properties. Recently, the haustorium of both coconut and palmyra trees has been gaining increased attention from researchers due to its nutritional properties. Certain studies on the haustoria have revealed the presence of various compounds that are beneficial for human health. The bioactive compounds found in the previous studies include phenols, flavonoids, terpenoids, pyrimidine groups and certain vitamins which act as antioxidant, anti-inflammatory, anti-ulcer, anti-diabetic, anti-cancer, and anti-mutagenic agents. This review focuses on comparing the nutritional composition, bioactive compounds, sugar profile fatty acid profile, amino acid profile, minerals, and vitamins of both palmyra and coconut haustorium. This review also reports on the value addition of the haustoria as usually they are consumed in their raw form. These value-added products not only enhance the economic viability of these underutilized resources but also contribute to extending their shelf life, making them more accessible in the market. Also, this review emphasizes the haustorium's potential defence against negative consequences. Therefore, this review paper helps in understanding the palmyra and coconut haustorium and its potential health benefits. © 2024 The Author(s)

关键词： Bioactive compounds Coconut haustorium Nutritional composition Palmyra haustorium Phytochemical properties Value-added products

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Nexus Between Employee Engagement and Job Performance: A Study of MSMEs in Ghana During COVID-19: The Moderating Roles of Job Demand and Job resources

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Jindal Journal of Business Research 2023年第1期13卷 30 - 56页

作者： Daniel Nkansah Raymond Gyimah Daniel Agyemfour-Agyemang Sarpong James Kwasi Annan Department of Human Resource and Organisational Development KNUST School of Business Kwame Nkrumah University of Science and Technology Kumasi Ghana Faculty of Business and Management Studies Koforidua Technical University Koforidua Ghana

Micro, small, and medium-sized enterprises (MSMEs) across the globe have been the major victims of the COVID-19 pandemic, which has plunged the business world into a crisis. However, employee engagement (EE) has been labeled as an essential nutrient that organizations need in order to survive in these turbulent times. This study was to investigate the effect of EE on employee performance during the COVID-19 pandemic and how job demands and job resources moderate this relationship. A sample size of 395 respondents were selected from a variety of MSMEs via a convenience sampling method. The study used a survey questionnaire approach as the main method for collecting primary data. The result of the correlation analysis showed that there was a significant relationship between EE and employee performance. It was revealed that job demands and job resources moderated the relationship between EE and employee performance. Finally, the result showed that job demands had an insignificant effect on EE, but job resources had a significant effect on EE. Thus, it can be concluded that, in the face of COVID-19, EE is a significant predictor of EP in the MSME sector.

关键词： Employee engagement job demand MSMEs employee performance job resources Ghana

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Dependence of Carboxylate Anions on Physicochemical Properties of Tributyloctylphosphonium-Based Ionic Liquids

Dependence of Carboxylate Anions on Physicochemical Properti...

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24th Symposium on Molten Salts and Ionic Liquids: In Memory of Yasuhiko Ito, MSIL 2024, held at the PRiME 2024 Meeting

作者： Akamatsu, H. Kubo, S. Kikuchi, S. Tsuchida, Y. Tsunashima, K. Hotta, H. Yokobiki, A. Funasako, Y. Okuno, Y. Hamada, A. Yamada, H. Tani, A. Graduate School of Human Development and Environment Kobe University Hyogo Kobe657-8501 Japan Department of Applied Chemistry and Biochemistry National Institute of Technology Wakayama College Wakayama Gobo644-0023 Japan Department of Integrated Engineering Chemistry and Biochemistry Course National Institute of Technology Yonago College Tottori Yonago683-8502 Japan Development Division Wakayama Plant Komatsuya Corporation Wakayama649-1202 Japan Department of Chemical Engineering National Institute of Technology Nara College Yamatokoriyama Nara639-1080 Japan

ISBN: (纸本)9781623326630

A new group of tributyloctylphosphonium-based ionic liquids containing several carboxylate anions was designed, prepared and physicochemically characterized. All phosphonium carboxylates obtained in this study became viscous liquids at room temperature. The formate-based ionic liquid exhibited the highest density and number density due to the smallest anion size. All ionic liquids exhibited typical ionic conduction behavior, since the viscosity decreased and the conductivity increased with increasing temperature. It was also found that the viscosity tended to decrease with increasing the carbon numbers of the alkyl chains of lower carboxylate anions. Compared to the propionate-based ionic liquids, lactate-based ionic liquids exhibited higher viscosity. The phosphonium ionic liquids based on carboxylate anions showed higher thermal stability than nitrogen-based counterpart ionic liquids. ©The Electrochemical Society.

关键词： Carboxylation

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Does smallholder farmers’ awareness of Sustainable development Goal 2 improve household food security in the Northern Region of Ghana?

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Agriculture and Food Security 2021年第1期10卷 9页

作者： Mabe, Franklin Nantui Mumuni, Eliasu Sulemana, Nashiru Department of Agricultural and Resource Economics Faculty of Agribusiness and Applied Economics University for Development Studies Tamale Ghana Department of Communication and Innovation Technology Faculty of Communication and Cultural Studies University for Development Studies Tamale Ghana Department of Agricultural Extension Rural Development and Gender Studies Faculty of Agribusiness and Applied Economics University for Development Studies Tamale Ghana

Background: Sustainable development Goal 2 aims at ending hunger, achieving food security, improving nutrition and promoting sustainable agriculture. Whilst some smallholder farmers are aware of this goal, others are not. The question that arises is whether or not awareness translates into food security. Therefore, this study assessed whether or not smallholder farmers’ awareness of Sustainable development Goal 2 improves household food security in the Northern Region of Ghana. Methods: The study used cross-sectional primary data collected from two districts and two municipalities in the region. An endogenous switching regression treatment effects model with ordered outcome was used to estimate the effects of smallholder farmers’ awareness of Sustainable development Goal 2 on household food insecurity level. Results: The age of household head, distance of households to the regional capital, membership of farmer-based organizations, access to e-extension, education, and ownership of radio are the key drivers of farmers’ awareness of Sustainable development Goal 2. The results from the endogenous switching regression treatment effects model with ordered outcome showed that households who are aware of the second goal are more food secure than their counterparts. Conclusions: It is therefore prudent for stakeholders promoting and championing Sustainable development Goals to educate farmers on goal 2 as their awareness of the goal is critical to achieving food security. © 2021, The Author(s).

关键词： Awareness Food Insecurity Experience Scale and Sustainable development Goal 2 Smallholder farmers

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YouTuber’s Video as Cross-Cultural Learning resource for Chinese-as-Foreign-Language Learners – Perspective of Big ‘C’ and Small ‘c’ Culture 5th

YouTuber’s Video as Cross-Cultural Learning Resource for Ch...

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5th International Conference on Innovative Technologies and Learning, ICITL 2022

作者： Kao, I.-Lin Chang, Chi-Cheng Yen, Wan-Hsuan Master’s and Doctor’s Program in Teaching Chinese as a Second Language National Chengchi University Taipei City Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei City Taiwan

ISBN: (纸本)9783031152726

Due to the influence of the global pandemic, online and distance learning has replaced traditional classroom and face-to-face interaction so students can continue learning. Internet mediated self-directed learning also climbed to a new height and foreign language learning from YouTube video is a very popular learning method for many. Furthermore, YouTube videos can supplement cross-cultural knowledge for foreign language (FL) learners, which is a critical yet hard to develop ability for FL teachers. However, unlike for FL textbooks, the cross-cultural elements have not been examined thoroughly for YouTube videos. Therefore, this study analyzed the most subscribed and watched Japanese Chinese-as-FL YouTuber, "Lee Sisters ch", which is also the official representative of HSK (Hanyu Shuiping Kaoshi) test. We selected 30 most watched videos and analyzed their contents according to Chastain’s Big ‘C’ small ‘c’ culture framework. The result showed 86.67% of the most popular 30 videos covered topics about culture: 23.33% of the videos were related to big ‘C’ culture and 63.33% were about small ‘c’ culture. Similar to earlier researches on FL textbooks, "Literature" was the main theme of big ‘C’. On the other hand, "Everyday living" was the most frequent small ‘c’ topic, which is a unique phenomenon for YouTube FL channels. Furthermore, not only did the portion of big ‘C’ and small ‘c’ cultures match the suggestions of FL scholars, the language used in all videos were also mainly in Japanese that fit the needs of Japanese CFL learners. It is concluded YouTube video may be suitable to learn cross-cultural knowledge for FL learner, especially for Japanese students. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Legalities in Metaverse – Ethereum Fraud Detection

Legalities in Metaverse – Ethereum Fraud Detection

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International Conference on Science technology Engineering and Management (ICONSTEM)

作者： Manoj Hudnurkar Kartik Singh Suhas Ambekar Geeta Sahu Harshil Yecho Symbiosis Centre for Management & Human Resource Development (SCMHRD) Symbiosis International (Deemed University) Pune India Department of Information Technology and Data Science Vidyalankar School of Information Technology (VSIT) (University of Mumbai) Mumbai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The spread of the Metaverse has created new moral and legal challenges, especially when it comes to protecting against fraud. This study explores the legal complexities surrounding Ethereum-specific metaverse transactions and provides guidance on how to identify and address fraudulent activity. The focus of this investigation is the field of fraud detection in the Ethereum environment. Current methods of detecting fraud, such as Blockchain analysis and machine learning-based algorithms, are carefully examined. Next, a new approach to fraud detection is put out, which is predicated on a collection of seven machine learning algorithms: K-Nearest Neighbours (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, KNN, XGBoost (XGB), and Artificial Neural Network (ANN). The results of this study are carefully outlined, demonstrating the accuracy, recall, F1 score, and precision that each of the previously listed algorithms demonstrated. Moreover, this article lays out possible directions for further research, including the incorporation of group approaches and the investigation of creating characteristics to strengthen fraud detection abilities. This academic paper provides a significant and novel insight into the identification of fraudulent activity in Ethereum transactions, highlighting the potential benefits of using machine learning algorithms for this kind of discernment.

关键词： Support vector machines Machine learning algorithms Accuracy Metaverse Artificial neural networks Fraud Decision trees

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Predicting Delays for Truck Delivery Logistics: An Application of AI and ML

Predicting Delays for Truck Delivery Logistics: An Applicati...

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International Conference on Science technology Engineering and Management (ICONSTEM)

作者： Manoj Hudnurkar Kuriakose M. Renji Suhas Ambekar Geeta Sahu Kavitha Mary Joseph Symbiosis Centre for Management & Human Resource Development (SCMHRD) Symbiosis International (Deemed University) Pune India Department of Information Technology and Data Science Vidyalankar School of Information Technology (VSIT) (University of Mumbai) Mumbai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Supply chain management has a lot of logistical challenges, which can significantly affect the efficacy and efficiency of logistics operations as a whole. Machine learning techniques are increasingly popular because they can evaluate vast data volumes and produce precise predictions. Logistics management must include the forecast of delays in the supply chain. It entails anticipating delays in logistics operations, which can assist logistics managers in effectively planning and allocating resources. This paper summarizes the logistics delay prediction in truck delivery systems and identifies some opportunities and problems. The article also suggests a framework for predicting logistics delays based on machine learning methods and assesses the framework's effectiveness using actual data. Thus, the research uses machine learning and AI approaches to anticipate logistics delays. The study gathered information on previous logistical operations and determined the major causes of delays. Then, models for predicting logistical delays were used using machine learning techniques, such as Artificial Neural Networks (ANN), decision trees, random forests, and support vector machines. Our findings demonstrate that these methods have an accuracy rate of up to 97.6 % in predicting logistics delays. Also, we discovered that the Adaboost classifier model performed better than the other models in accuracy and precision. Our study offers insightful information on the application of machine learning methods for anticipating logistics delays, which can assist supply chain managers and logistics firms in better organizing and manage their operations to cut down on delays and increase overall effectiveness.

关键词： Costs Accuracy Machine learning algorithms Supply chains Machine learning Predictive models Real-time systems

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