Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0276
Sugata Sen, Santosh Nandi
The well accepted multi-dimensional measures have failed to properly project the vulnerability of human-beings towards poverty. Some of the reasons behind this inability may be the failure of the existing measures to consider the graduality within the concept of poverty and the disparities within the household in wealth distribution. So, this work wants to develop a measure to estimate the vulnerability of households in becoming poor through incorporating the intra-household disparities through the factors which suffer from graduality. The decomposition of the grade of vulnerability on the causal factors is also under the purview of this work. To that respect the idea of fuzzy logic and decomposition through artificial intelligence has been used to develop a mathematical framework. So, the idea of Shapley Value Decomposition method has been used extensively. This decomposition is implemented here with the help of Shapley Machine Learning. This decomposition will help the planners to locate the role of different dimensions behind the vulnerability of human beings to become poor more efficiently.
{"title":"Decomposition of intra-household disparity sensitive fuzzy multi-dimensional poverty index: A study of vulnerability through Shapley machine learning","authors":"Sugata Sen, Santosh Nandi","doi":"10.30574/wjaets.2024.12.2.0276","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0276","url":null,"abstract":"The well accepted multi-dimensional measures have failed to properly project the vulnerability of human-beings towards poverty. Some of the reasons behind this inability may be the failure of the existing measures to consider the graduality within the concept of poverty and the disparities within the household in wealth distribution. So, this work wants to develop a measure to estimate the vulnerability of households in becoming poor through incorporating the intra-household disparities through the factors which suffer from graduality. The decomposition of the grade of vulnerability on the causal factors is also under the purview of this work. To that respect the idea of fuzzy logic and decomposition through artificial intelligence has been used to develop a mathematical framework. So, the idea of Shapley Value Decomposition method has been used extensively. This decomposition is implemented here with the help of Shapley Machine Learning. This decomposition will help the planners to locate the role of different dimensions behind the vulnerability of human beings to become poor more efficiently.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"3 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0261
Wei Wang
This paper explores the integration of Transformer architectures into human pose estimation, a critical task in computer vision that involves detecting human figures and predicting their poses by identifying body joint positions. With applications ranging from enhancing interactive gaming experiences to advancing biomechanical analyses, human pose estimation demands high accuracy and flexibility, particularly in dynamic and partially occluded scenes. This study hypothesizes that Transformers, renowned for their ability to manage long-range dependencies and focus on relevant data parts through self-attention mechanisms, can significantly outperform existing deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We introduce the PoseTransformer, a hybrid model that combines the precise feature extraction capabilities of CNNs with the global contextual awareness of Transformers, aiming to set new standards for accuracy and adaptability in pose estimation tasks. The model's effectiveness is demonstrated through rigorous testing on benchmark datasets, showing substantial improvements over traditional approaches, especially in complex scenarios.
{"title":"Advancing human pose estimation with transformer models: An experimental approach","authors":"Wei Wang","doi":"10.30574/wjaets.2024.12.2.0261","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0261","url":null,"abstract":"This paper explores the integration of Transformer architectures into human pose estimation, a critical task in computer vision that involves detecting human figures and predicting their poses by identifying body joint positions. With applications ranging from enhancing interactive gaming experiences to advancing biomechanical analyses, human pose estimation demands high accuracy and flexibility, particularly in dynamic and partially occluded scenes. This study hypothesizes that Transformers, renowned for their ability to manage long-range dependencies and focus on relevant data parts through self-attention mechanisms, can significantly outperform existing deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We introduce the PoseTransformer, a hybrid model that combines the precise feature extraction capabilities of CNNs with the global contextual awareness of Transformers, aiming to set new standards for accuracy and adaptability in pose estimation tasks. The model's effectiveness is demonstrated through rigorous testing on benchmark datasets, showing substantial improvements over traditional approaches, especially in complex scenarios.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.
{"title":"Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments","authors":"Halima Oluwabunmi, Halima Oluwabunmi Bello, Adebimpe Bolatito, Maxwell Nana Ameyaw","doi":"10.30574/wjaets.2024.12.2.0266","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0266","url":null,"abstract":"Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"6 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0280
Victor Ugonna Akpulonu, Agbese Echo Agbese, Chijioke Emmanuel Obizue, Aernan Nater, Nasiru Abdulsalam, Ikegbo Stanely Ogochukwu, Murtala Aminu-Baba, Adoyi Helen Ene
The rapid advancement of electronic device technologies has led to the creation of intelligent systems aimed at enhancing various aspects of human life. One of the most significant of these advancements is the Internet of Things (IoT), which has revolutionized monitoring, controlling, and security features across numerous applications. In agriculture, IoT-based systems are increasingly crucial for optimizing greenhouse conditions, essential for efficient crop cultivation. This research focuses on the design and construction of an Arduino-based greenhouse monitoring system utilizing IoT technology. The system automates the monitoring and regulation of key environmental parameters such as temperature, humidity, light, sodium, potassium, phosphorus and soil moisture, using sensors and actuators managed by the microcontroller. Prototyping methods was adopted. The integration of IoT enables real-time data collection and remote control, significantly reducing manual labor and enhancing crop yield. Additionally, the system incorporates dual power sources, utilizing both grid and solar energy to ensure uninterrupted operation. The lettuce crop yield increase by 20% which makes the system a better alternative to other. The implementation of this automated system showcases the potential of IoT in creating smarter, more sustainable agricultural practices.
{"title":"Design and construction of Arduino based greenhouse monitoring system using IoT","authors":"Victor Ugonna Akpulonu, Agbese Echo Agbese, Chijioke Emmanuel Obizue, Aernan Nater, Nasiru Abdulsalam, Ikegbo Stanely Ogochukwu, Murtala Aminu-Baba, Adoyi Helen Ene","doi":"10.30574/wjaets.2024.12.2.0280","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0280","url":null,"abstract":"The rapid advancement of electronic device technologies has led to the creation of intelligent systems aimed at enhancing various aspects of human life. One of the most significant of these advancements is the Internet of Things (IoT), which has revolutionized monitoring, controlling, and security features across numerous applications. In agriculture, IoT-based systems are increasingly crucial for optimizing greenhouse conditions, essential for efficient crop cultivation. This research focuses on the design and construction of an Arduino-based greenhouse monitoring system utilizing IoT technology. The system automates the monitoring and regulation of key environmental parameters such as temperature, humidity, light, sodium, potassium, phosphorus and soil moisture, using sensors and actuators managed by the microcontroller. Prototyping methods was adopted. The integration of IoT enables real-time data collection and remote control, significantly reducing manual labor and enhancing crop yield. Additionally, the system incorporates dual power sources, utilizing both grid and solar energy to ensure uninterrupted operation. The lettuce crop yield increase by 20% which makes the system a better alternative to other. The implementation of this automated system showcases the potential of IoT in creating smarter, more sustainable agricultural practices.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"1 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research presents a comprehensive analysis of the trading patterns of gold and silver, focusing on their roles as safe-haven assets and their value retention during economic downturns. The study addresses the lack of holistic approaches in the existing literature by integrating data cleaning, descriptive statistics, trend analysis, volatility assessment, and ARIMA modelling to predict future trading values and inform investment strategies. By leveraging these methodologies, the research aims to provide detailed insights into these precious metals' historical and future performance. The findings are expected to aid investors in making informed decisions balancing risk and potential returns.
{"title":"Comprehensive analysis of gold and silver trading patterns and future projections","authors":"Subir Gupta, Biprajit Biswas, Dipankar Roy, Joyita Ghosh, Kamaluddin Mandal, Abhik Choudhary","doi":"10.30574/wjaets.2024.12.2.0282","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0282","url":null,"abstract":"This research presents a comprehensive analysis of the trading patterns of gold and silver, focusing on their roles as safe-haven assets and their value retention during economic downturns. The study addresses the lack of holistic approaches in the existing literature by integrating data cleaning, descriptive statistics, trend analysis, volatility assessment, and ARIMA modelling to predict future trading values and inform investment strategies. By leveraging these methodologies, the research aims to provide detailed insights into these precious metals' historical and future performance. The findings are expected to aid investors in making informed decisions balancing risk and potential returns.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0297
Sunday Akinwamide, Rashidat Idris-Tajudeen, Titilope Helen Akin-Olayemi
The COVID-19 pandemic has had a profound impact on global health, necessitating the development of predictive models to manage and mitigate its effects. Early diagnosis is crucial for preventing the progression of diseases that can significantly endanger human life. This study explores the application of supervised machine learning techniques to predict Post-COVID-19 outcomes, including long-term health complications and recovery trajectories. In this study, we utilized 10 advanced supervised machine learning algorithms, including both stand-alone models (Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, and Gaussian Naive Bayes) and ensemble learning techniques (Bagging Decision Tree Ensemble, Boosting Decision Tree Ensemble, Voting Ensemble, and Stacked Generalization – Stacking Ensemble). These models were applied to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. The performance of each model was evaluated using an 80:20 train-test split as well as 5, 10, 15, 20, and 25-fold cross-validation. Evaluation metrics included accuracy, precision, recall, F1-score, and the confusion matrix. The results indicate that the Decision Tree algorithm outperformed the other models, achieving an accuracy of 98.81%, a precision of 1.00, a recall of 0.98, and an F1-score of 0.99. Our results indicate that machine learning models can effectively predict Post-COVID-19 conditions, providing valuable insights for healthcare providers and policymakers.
{"title":"Prediction of post-covid-19 using supervised machine learning techniques","authors":"Sunday Akinwamide, Rashidat Idris-Tajudeen, Titilope Helen Akin-Olayemi","doi":"10.30574/wjaets.2024.12.2.0297","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0297","url":null,"abstract":"The COVID-19 pandemic has had a profound impact on global health, necessitating the development of predictive models to manage and mitigate its effects. Early diagnosis is crucial for preventing the progression of diseases that can significantly endanger human life. This study explores the application of supervised machine learning techniques to predict Post-COVID-19 outcomes, including long-term health complications and recovery trajectories. In this study, we utilized 10 advanced supervised machine learning algorithms, including both stand-alone models (Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, and Gaussian Naive Bayes) and ensemble learning techniques (Bagging Decision Tree Ensemble, Boosting Decision Tree Ensemble, Voting Ensemble, and Stacked Generalization – Stacking Ensemble). These models were applied to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. The performance of each model was evaluated using an 80:20 train-test split as well as 5, 10, 15, 20, and 25-fold cross-validation. Evaluation metrics included accuracy, precision, recall, F1-score, and the confusion matrix. The results indicate that the Decision Tree algorithm outperformed the other models, achieving an accuracy of 98.81%, a precision of 1.00, a recall of 0.98, and an F1-score of 0.99. Our results indicate that machine learning models can effectively predict Post-COVID-19 conditions, providing valuable insights for healthcare providers and policymakers.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0275
Rethemiotaki Irene
The purpose of this paper is to compare the effectiveness of using flipped teaching against traditional teaching, with active learning techniques, in physics and mathematics courses. The study was conducted on 100 middle and high school students who were first subjected to traditional teaching (control group) and then subjected to flipped teaching (experimental group). To check the effectiveness of the two methods, the students were submitted to an assessment test in the lesson taught with the two methods. To test the existence of a statistically significant difference between the performance of the two groups, the independent sample t-test and ANOVA test were used for continuous variables. In addition, multiple logistic regression analysis with Odds Ratios was used to predict student achievement depending on the teaching method used. The results of the study showed that there is no statistically significant difference in the average performance of students with the two teaching methods.
本文旨在比较在物理和数学课程中使用翻转教学和传统教学以及主动学习技术的效果。研究对象是 100 名初高中学生，他们先接受传统教学（对照组），然后接受翻转教学（实验组）。为了检验两种方法的效果，学生们在使用两种方法教授的课程中接受了评估测试。为检验两组学生的表现是否存在显著的统计学差异，对连续变量采用了独立样本 t 检验和方差分析检验。此外，研究还使用了多重逻辑回归分析（Odds Ratios）来预测不同教学方法下的学生成绩。研究结果表明，采用两种教学方法的学生的平均成绩在统计学上没有显著差异。
{"title":"The study of an innovative Eéducationnel practice in Greek students: The flipped learning","authors":"Rethemiotaki Irene","doi":"10.30574/wjaets.2024.12.2.0275","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0275","url":null,"abstract":"The purpose of this paper is to compare the effectiveness of using flipped teaching against traditional teaching, with active learning techniques, in physics and mathematics courses. The study was conducted on 100 middle and high school students who were first subjected to traditional teaching (control group) and then subjected to flipped teaching (experimental group). To check the effectiveness of the two methods, the students were submitted to an assessment test in the lesson taught with the two methods. To test the existence of a statistically significant difference between the performance of the two groups, the independent sample t-test and ANOVA test were used for continuous variables. In addition, multiple logistic regression analysis with Odds Ratios was used to predict student achievement depending on the teaching method used. The results of the study showed that there is no statistically significant difference in the average performance of students with the two teaching methods.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0296
Kola Samuel, Gomina, Kola Samuel Gomina, Oshuwa Emily Gomina, Linda Egbubine, Chinonyerem Anyanaso, Adebusuyi Samuel Ilesanmi, Gift Maureen, Obunukwu
Food security and poverty among households are intricately linked, each influencing the other in multifaceted ways. Food security, characterized by sufficient, safe, and nutritious food availability, access, utilization, and stability, is fundamental for overall well-being and a human right. In households experiencing food insecurity, chronic hunger and malnutrition are prevalent, impairing productivity and health, perpetuating a cycle of poverty. Conversely, poverty limits households' ability to access diverse, nutritious diets and essential agricultural resources, exacerbated by inadequate infrastructure. This vulnerability to economic shocks and food price fluctuations deepens food insecurity and poverty. Addressing food security is critical for poverty alleviation, as adequate nutrition supports health, productivity, and economic stability. This study aims to project food security and poverty levels among households, proposing measures to mitigate and eliminate these challenges. By examining current statuses and identifying contributing factors, the research seeks to inform targeted interventions enhancing food security and reducing poverty. Methodologies include secondary data analysis from the Central Bank of Nigeria's publications and other authoritative sources, ensuring robust findings to guide policy and practice. The findings confirm significant relationships between poverty, unemployment, and agricultural output in Nigeria, underscoring the interconnected socio-economic dynamics shaping sustainable development. These insights prompt recommendations for governments in the United States and Africa to invest in sustainable agriculture, strengthen social safety nets, promote economic inclusivity, and foster international cooperation to enhance food security and alleviate poverty globally.
{"title":"Analyzing the intersection between food security and poverty status among households","authors":"Kola Samuel, Gomina, Kola Samuel Gomina, Oshuwa Emily Gomina, Linda Egbubine, Chinonyerem Anyanaso, Adebusuyi Samuel Ilesanmi, Gift Maureen, Obunukwu","doi":"10.30574/wjaets.2024.12.2.0296","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0296","url":null,"abstract":"Food security and poverty among households are intricately linked, each influencing the other in multifaceted ways. Food security, characterized by sufficient, safe, and nutritious food availability, access, utilization, and stability, is fundamental for overall well-being and a human right. In households experiencing food insecurity, chronic hunger and malnutrition are prevalent, impairing productivity and health, perpetuating a cycle of poverty. Conversely, poverty limits households' ability to access diverse, nutritious diets and essential agricultural resources, exacerbated by inadequate infrastructure. This vulnerability to economic shocks and food price fluctuations deepens food insecurity and poverty. Addressing food security is critical for poverty alleviation, as adequate nutrition supports health, productivity, and economic stability. This study aims to project food security and poverty levels among households, proposing measures to mitigate and eliminate these challenges. By examining current statuses and identifying contributing factors, the research seeks to inform targeted interventions enhancing food security and reducing poverty. Methodologies include secondary data analysis from the Central Bank of Nigeria's publications and other authoritative sources, ensuring robust findings to guide policy and practice. The findings confirm significant relationships between poverty, unemployment, and agricultural output in Nigeria, underscoring the interconnected socio-economic dynamics shaping sustainable development. These insights prompt recommendations for governments in the United States and Africa to invest in sustainable agriculture, strengthen social safety nets, promote economic inclusivity, and foster international cooperation to enhance food security and alleviate poverty globally.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"1 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.
{"title":"Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection","authors":"Halima Oluwabunmi, Halima Oluwabunmi Bello, Adebimpe Bolatito, Maxwell Nana Ameyaw","doi":"10.30574/wjaets.2024.12.2.0265","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0265","url":null,"abstract":"High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"7 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.30574/wjaets.2024.12.2.0253
Onyango Laban, Oliver Owin, Natuhwera Pius
Integrating Artificial Intelligence (AI) in supply chain management (SCM) signifies a significant advancement with profound implications for modern businesses, including those in Uganda. This research paper critically examines the challenges and opportunities associated with this integration, using Uganda as a case study. A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country. Additionally, it explores AI's diverse opportunities in optimizing supply chain processes for Ugandan businesses, including demand forecasting, inventory management, and logistics optimization within Uganda's unique operational landscape. Furthermore, the paper discusses the potential impact of AI integration on various stakeholders within Uganda's supply chain ecosystem, including suppliers, manufacturers, distributors, and customers. By synthesizing insights from academic research and industry practices in Uganda, this paper provides valuable insights for Ugandan businesses aiming to leverage AI technologies in their SCM strategies. Ultimately, this research contributes to a deeper understanding of the complexities of integrating AI in SCM within the Ugandan context and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.
{"title":"Integration of Artificial Intelligence in supply chain management: challenges and opportunities in Uganda","authors":"Onyango Laban, Oliver Owin, Natuhwera Pius","doi":"10.30574/wjaets.2024.12.2.0253","DOIUrl":"https://doi.org/10.30574/wjaets.2024.12.2.0253","url":null,"abstract":"Integrating Artificial Intelligence (AI) in supply chain management (SCM) signifies a significant advancement with profound implications for modern businesses, including those in Uganda. This research paper critically examines the challenges and opportunities associated with this integration, using Uganda as a case study. A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country. Additionally, it explores AI's diverse opportunities in optimizing supply chain processes for Ugandan businesses, including demand forecasting, inventory management, and logistics optimization within Uganda's unique operational landscape. Furthermore, the paper discusses the potential impact of AI integration on various stakeholders within Uganda's supply chain ecosystem, including suppliers, manufacturers, distributors, and customers. By synthesizing insights from academic research and industry practices in Uganda, this paper provides valuable insights for Ugandan businesses aiming to leverage AI technologies in their SCM strategies. Ultimately, this research contributes to a deeper understanding of the complexities of integrating AI in SCM within the Ugandan context and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.","PeriodicalId":275182,"journal":{"name":"World Journal of Advanced Engineering Technology and Sciences","volume":"1 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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