Pub Date : 2024-07-30DOI: 10.30574/ijsra.2024.12.2.1294
Naldo Janius, Siti Khatijah Binti Jemat, Mohammad Aniq Bin Amdan
This qualitative study explores how different parenting styles influence the academic performance of secondary school students in Kota Belud, Sabah Malaysia. Through in-depth interviews with teachers, this research investigates the nuances of authoritative, authoritarian, permissive and neglectful parenting styles. Thematic analysis revealed that an authoritative parenting style, which balances responsiveness and demandingness, promotes better academic outcomes by increasing motivation and self-discipline. In contrast, an authoritarian and neglectful parenting style often results in lower academic achievement due to increased stress and lack of support. This study underscores the important role of positive and supportive parenting in enhancing students' educational experiences and success.
{"title":"Parenting style on academic performance among secondary students at Kota Belud, Sabah","authors":"Naldo Janius, Siti Khatijah Binti Jemat, Mohammad Aniq Bin Amdan","doi":"10.30574/ijsra.2024.12.2.1294","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1294","url":null,"abstract":"This qualitative study explores how different parenting styles influence the academic performance of secondary school students in Kota Belud, Sabah Malaysia. Through in-depth interviews with teachers, this research investigates the nuances of authoritative, authoritarian, permissive and neglectful parenting styles. Thematic analysis revealed that an authoritative parenting style, which balances responsiveness and demandingness, promotes better academic outcomes by increasing motivation and self-discipline. In contrast, an authoritarian and neglectful parenting style often results in lower academic achievement due to increased stress and lack of support. This study underscores the important role of positive and supportive parenting in enhancing students' educational experiences and success.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"7 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796177","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/ijsra.2024.12.2.1355
Niyazmetov Mansur Ruzmatovich, Ostonokulov Azamat Abdukarimovich
This paper explores the methods used for recognizing and recording revenues and expenses in public sector entities in Uzbekistan. It delves into the current difficulties faced and contrasts these practices with global norms, suggesting a detailed plan to improve financial governance. The proposed measures involve the implementation of contemporary accounting frameworks, the use of advanced technology, the revision of existing rules, and the promotion of global partnerships.
{"title":"Approaches to improving revenue and expense management in Uzbekistan's public sector","authors":"Niyazmetov Mansur Ruzmatovich, Ostonokulov Azamat Abdukarimovich","doi":"10.30574/ijsra.2024.12.2.1355","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1355","url":null,"abstract":"This paper explores the methods used for recognizing and recording revenues and expenses in public sector entities in Uzbekistan. It delves into the current difficulties faced and contrasts these practices with global norms, suggesting a detailed plan to improve financial governance. The proposed measures involve the implementation of contemporary accounting frameworks, the use of advanced technology, the revision of existing rules, and the promotion of global partnerships.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"7 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796181","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/ijsra.2024.12.2.1315
Blessie Pradeeka Gudelly, Komal Kumar Bollepogu Raja
Fluorescent imaging in Drosophila species is indispensable for investigating dynamic biological processes and visualizing gene expression patterns with cellular precision. This technique leverages the transparency of Drosophila larvae and pupae, combined with advanced microscopy, to enable real-time observation of developmental events such as morphogenesis and organogenesis. Genetically encoded fluorescent proteins and dyes allow specific labeling of cells and proteins, facilitating detailed studies of spatial and temporal dynamics within intact tissues. Techniques like confocal and two-photon microscopy provide high resolution and depth penetration, essential for 3D reconstruction and quantitative analysis of complex biological structures. Fluorescent imaging in Drosophila supports disease modeling, drug screening, and therapeutic exploration, bridging insights from basic biology to potential clinical applications. It is therefore necessary to develop imaging techniques and protocols that accurately capture and profile gene expression patterns in a wide range of Drosophila tissues. In this study, we present a detailed protocol for preparing and imaging transgenic Drosophila abdomen, which will enable researchers investigate gene expression patterns underlying fundamental biological processes in the abdomen.
{"title":"Protocol for dissection of Drosophila abdomens for fluorescent imaging","authors":"Blessie Pradeeka Gudelly, Komal Kumar Bollepogu Raja","doi":"10.30574/ijsra.2024.12.2.1315","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1315","url":null,"abstract":"Fluorescent imaging in Drosophila species is indispensable for investigating dynamic biological processes and visualizing gene expression patterns with cellular precision. This technique leverages the transparency of Drosophila larvae and pupae, combined with advanced microscopy, to enable real-time observation of developmental events such as morphogenesis and organogenesis. Genetically encoded fluorescent proteins and dyes allow specific labeling of cells and proteins, facilitating detailed studies of spatial and temporal dynamics within intact tissues. Techniques like confocal and two-photon microscopy provide high resolution and depth penetration, essential for 3D reconstruction and quantitative analysis of complex biological structures. Fluorescent imaging in Drosophila supports disease modeling, drug screening, and therapeutic exploration, bridging insights from basic biology to potential clinical applications. It is therefore necessary to develop imaging techniques and protocols that accurately capture and profile gene expression patterns in a wide range of Drosophila tissues. In this study, we present a detailed protocol for preparing and imaging transgenic Drosophila abdomen, which will enable researchers investigate gene expression patterns underlying fundamental biological processes in the abdomen.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"10 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796275","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}
In today's global economy, managing supply chains sustainably is crucial for businesses wanting to reduce their environmental impact while still making a profit. This article reviews how big data analytics can help achieve these sustainability goals. By using big data, companies can improve their supply chain practices, reduce carbon emissions, and implement more sustainable business strategies. Big data provides detailed insights and better levels of control over various supply chain activities, making it a vital tool for driving sustainability. Big data isn't just about internal operations; it also uncovers supplier practices, letting businesses assess their supply chains' environmental impact. Predictive maintenance, driven by big data, plays a powerful role here. It keeps operations running smooth by monitoring equipment health and foreseeing issues before they cause downtime. This proactive approach not only ensures machinery runs efficiently but also lowers energy consumption and emissions associated with breakdowns. Big data also plays a crucial role in optimizing transportation, where it analyzes traffic flow, weather data, and fuel efficiency to design smarter delivery routes. This approach cuts down on fuel consumption and emissions, making logistics more eco-friendly. Energy efficiency is also a priority; big data tracks energy usage across facilities, uncovering areas where consumption can be reduced. This not only lowers energy bills but also decreases greenhouse gas emissions. Big data goes beyond just making things greener; it also helps businesses save money! Here's how: by using big data to predict exactly how much of a product people will buy, companies can avoid making more than they need. This means less waste and less sitting around in warehouses, which is good for the environment and good for the company's bottom line. In short, big data is a win-win for both the planet and your wallet. Case studies from top industry players like Walmart, Nestlé and Maersk illustrate how big data improves sustainable supply chain management (SSCM) with tangible benefits. Yet, integrating big data has its own challenge: ensuring data accuracy, addressing privacy issues, and recruiting skilled personnel are key hurdles. Looking ahead, trends in SSCM—such as AI, machine learning, blockchain, and IoT advancements—hold promise for enhanced insights and predictive capabilities, shaping the future of sustainable supply chains.
{"title":"Harnessing big data for Sustainable Supply Chain Management (SSCM): Strategies to reduce carbon footprint","authors":"Uchechukwu Christopher Anozie, Oyinlola Esther Obafunsho, Rebecca Olubunmi Toromade, Gbenga Adewumi","doi":"10.30574/ijsra.2024.12.2.1344","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1344","url":null,"abstract":"In today's global economy, managing supply chains sustainably is crucial for businesses wanting to reduce their environmental impact while still making a profit. This article reviews how big data analytics can help achieve these sustainability goals. By using big data, companies can improve their supply chain practices, reduce carbon emissions, and implement more sustainable business strategies. Big data provides detailed insights and better levels of control over various supply chain activities, making it a vital tool for driving sustainability. Big data isn't just about internal operations; it also uncovers supplier practices, letting businesses assess their supply chains' environmental impact. Predictive maintenance, driven by big data, plays a powerful role here. It keeps operations running smooth by monitoring equipment health and foreseeing issues before they cause downtime. This proactive approach not only ensures machinery runs efficiently but also lowers energy consumption and emissions associated with breakdowns. Big data also plays a crucial role in optimizing transportation, where it analyzes traffic flow, weather data, and fuel efficiency to design smarter delivery routes. This approach cuts down on fuel consumption and emissions, making logistics more eco-friendly. Energy efficiency is also a priority; big data tracks energy usage across facilities, uncovering areas where consumption can be reduced. This not only lowers energy bills but also decreases greenhouse gas emissions. Big data goes beyond just making things greener; it also helps businesses save money! Here's how: by using big data to predict exactly how much of a product people will buy, companies can avoid making more than they need. This means less waste and less sitting around in warehouses, which is good for the environment and good for the company's bottom line. In short, big data is a win-win for both the planet and your wallet. Case studies from top industry players like Walmart, Nestlé and Maersk illustrate how big data improves sustainable supply chain management (SSCM) with tangible benefits. Yet, integrating big data has its own challenge: ensuring data accuracy, addressing privacy issues, and recruiting skilled personnel are key hurdles. Looking ahead, trends in SSCM—such as AI, machine learning, blockchain, and IoT advancements—hold promise for enhanced insights and predictive capabilities, shaping the future of sustainable supply chains.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796304","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/ijsra.2024.12.2.1252
Mark Ivan Mapa, Dominee Kyle M. Ibarlin, Edwin R. Arboleda
The continuous grow of demand for effective and efficient wireless communication systems also demands the continuous innovation in antenna designs. Along with this growth of demand, Multiple Input Multiple Output (MIMO) antenna was derived. This literature review discusses core principles of MIMO antenna, contrasting it with the traditional Single-Input Single Output (SISO) antenna and explores recent design techniques employed on MIMO antenna that could impact and change the future antenna technology and wireless communication. Furthermore, this includes application of MIMO antenna in wireless communication including compact configurations and multi-band operation. This paper also acknowledges the challenges associated in operating with MIMO antenna such as complexity and cost. This review offers a comprehensive overview of MIMO antenna, emphasizing its fundamental operation, design techniques, and its role in improving the wireless communication.
对高效无线通信系统的需求不断增长，也要求天线设计不断创新。随着需求的增长，多输入多输出（MIMO）天线应运而生。这篇文献综述讨论了多输入多输出天线的核心原理，将其与传统的单输入单输出（SISO）天线进行了对比，并探讨了多输入多输出天线采用的最新设计技术，这些技术可能会影响和改变未来的天线技术和无线通信。此外，这还包括 MIMO 天线在无线通信中的应用，包括紧凑型配置和多频段操作。本文还探讨了与 MIMO 天线操作相关的挑战，如复杂性和成本。本综述全面概述了 MIMO 天线，强调了其基本操作、设计技术及其在改善无线通信方面的作用。
{"title":"Optimizing antenna performance: A review of multiple-input multiple output (MIMO) antenna design techniques","authors":"Mark Ivan Mapa, Dominee Kyle M. Ibarlin, Edwin R. Arboleda","doi":"10.30574/ijsra.2024.12.2.1252","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1252","url":null,"abstract":"The continuous grow of demand for effective and efficient wireless communication systems also demands the continuous innovation in antenna designs. Along with this growth of demand, Multiple Input Multiple Output (MIMO) antenna was derived. This literature review discusses core principles of MIMO antenna, contrasting it with the traditional Single-Input Single Output (SISO) antenna and explores recent design techniques employed on MIMO antenna that could impact and change the future antenna technology and wireless communication. Furthermore, this includes application of MIMO antenna in wireless communication including compact configurations and multi-band operation. This paper also acknowledges the challenges associated in operating with MIMO antenna such as complexity and cost. This review offers a comprehensive overview of MIMO antenna, emphasizing its fundamental operation, design techniques, and its role in improving the wireless communication.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"11 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795576","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/ijsra.2024.12.2.1329
Susan Maestro, Puja Rana
This review paper investigates the factors that influence how organizations adopt Artificial Intelligence (AI). It focuses on technological, organizational, human, and external aspects, analyzing the drivers and obstacles to AI integration. Key frameworks such as the Technology Acceptance Model (TAM), Diffusion of Innovations (DOI) Theory, and the Technology-Organization-Environment (TOE) framework are used to understand these dynamics. The paper addresses challenges like technical difficulties and ethical issues, alongside the benefits AI can provide, such as improved decision-making and increased efficiency. It also looks at emerging trends like explainable AI and offers guidance for organizations to use AI technologies effectively. This analysis aims to contribute to scholarly discussions and offer actionable insights, assisting organizations in overcoming the complexities of AI adoption and leveraging its transformative effects.
{"title":"Variables Impacting the AI Adoption in Organizations","authors":"Susan Maestro, Puja Rana","doi":"10.30574/ijsra.2024.12.2.1329","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1329","url":null,"abstract":"This review paper investigates the factors that influence how organizations adopt Artificial Intelligence (AI). It focuses on technological, organizational, human, and external aspects, analyzing the drivers and obstacles to AI integration. Key frameworks such as the Technology Acceptance Model (TAM), Diffusion of Innovations (DOI) Theory, and the Technology-Organization-Environment (TOE) framework are used to understand these dynamics. The paper addresses challenges like technical difficulties and ethical issues, alongside the benefits AI can provide, such as improved decision-making and increased efficiency. It also looks at emerging trends like explainable AI and offers guidance for organizations to use AI technologies effectively. This analysis aims to contribute to scholarly discussions and offer actionable insights, assisting organizations in overcoming the complexities of AI adoption and leveraging its transformative effects.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"10 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795591","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/ijsra.2024.12.2.1194
Asdi Durahim, Abd. Rahman Pakaya, Meyko Panigoro
This study aims to determine The effect of motivation on student learning achievement, The effect of learning discipline on student learning achievement, The effect of family environment on student learning achievement, and The effect of motivation, discipline, and family environment simultaneously on student learning achievement at sma negeri 1 tapa, bone bolango regency in the 2022/2023 academic year. The research design used is quantitative, with a population of 300 students and a sample size of 75. Data were collected via documentation, observation, and questionnaire methods. Data analysis was conducted using multiple regression analysis. The results showed that; There was a significant influence of learning motivation on student achievement with a t-count value of 3.894 > t-table 1.666 and a Sig. of 0.000 < α 0.05. There is a significant influence of learning discipline on student achievement with a t-count value of 3.546 > t-table 1.666 and a Sig. 0.001 <α 0.05. There is a significant influence of the family environment on student achievement with a t-count of 2.925 > t-table of 1.666 and a Sig. 0.005 < α 0.05, and There is a significant influence of learning motivation, study discipline, and family environment together on student achievement at SMA Negeri I Tapa, Bone Bolango Regency with the results of the analysis of the value of F-count 30.572 > F-table 3.124 with a value Sig. 0.000 <α 0.05. The adjusted determination coefficient value is 0.564, meaning that 56.40% of learning achievement is influenced by learning motivation, learning discipline, and family environment while the remaining 43.60% is influenced by other factors.
本研究旨在确定学习动机对学生学习成绩的影响、学习纪律对学生学习成绩的影响、家庭环境对学生学习成绩的影响，以及学习动机、学习纪律和家庭环境同时对 2022/2023 学年骨博兰戈县 sma negeri 1 tapa 学生学习成绩的影响。本研究采用定量研究设计，研究对象为 300 名学生，样本容量为 75 个。数据通过文献、观察和问卷调查等方法收集。数据分析采用多元回归分析法。结果表明：学习动机对学生成绩有显著影响，t 计数值为 3.894 > t 表 1.666，Sig.为 0.000 < α 0.05。学习纪律对学生成绩有明显影响，t 计数值为 3.546 > t 表 1.666，Sig.0.001 t 表 1.666，Sig.0.005 < α 0.05，学习动机、学习纪律和家庭环境共同对 Bone Bolango 郡 SMA Negeri I Tapa 学生的学习成绩有显著影响，分析结果为 F 数 30.572 > F 表 3.124，Sig.0.000 <α 0.05.调整后的决定系数值为 0.564，这意味着 56.40%的学习成绩受学习动机、学习纪律和家庭环境的影响，其余 43.60%受其他因素的影响。
{"title":"The effect of motivation, learning discipline, and family environment on the learning achievement of students in public high school 1 tapa","authors":"Asdi Durahim, Abd. Rahman Pakaya, Meyko Panigoro","doi":"10.30574/ijsra.2024.12.2.1194","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1194","url":null,"abstract":"This study aims to determine The effect of motivation on student learning achievement, The effect of learning discipline on student learning achievement, The effect of family environment on student learning achievement, and The effect of motivation, discipline, and family environment simultaneously on student learning achievement at sma negeri 1 tapa, bone bolango regency in the 2022/2023 academic year. The research design used is quantitative, with a population of 300 students and a sample size of 75. Data were collected via documentation, observation, and questionnaire methods. Data analysis was conducted using multiple regression analysis. The results showed that; There was a significant influence of learning motivation on student achievement with a t-count value of 3.894 > t-table 1.666 and a Sig. of 0.000 < α 0.05. There is a significant influence of learning discipline on student achievement with a t-count value of 3.546 > t-table 1.666 and a Sig. 0.001 <α 0.05. There is a significant influence of the family environment on student achievement with a t-count of 2.925 > t-table of 1.666 and a Sig. 0.005 < α 0.05, and There is a significant influence of learning motivation, study discipline, and family environment together on student achievement at SMA Negeri I Tapa, Bone Bolango Regency with the results of the analysis of the value of F-count 30.572 > F-table 3.124 with a value Sig. 0.000 <α 0.05. The adjusted determination coefficient value is 0.564, meaning that 56.40% of learning achievement is influenced by learning motivation, learning discipline, and family environment while the remaining 43.60% is influenced by other factors.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"9 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795795","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}
The realm of financial risk management is undergoing a seismic shift, driven by the transformative power of big data analytics. Financial institutions are now leveraging vast datasets not just as historical records but as powerful tools to revolutionize risk management practices. This paper explores how big data enhances predictive modeling, real-time risk assessment, and addresses associated challenges and future directions. Big data facilitates predictive modeling by analyzing diverse datasets, including traditional financial data, consumer behavior, and social media sentiment. This allows financial institutions to predict future performance and identify risks from external factors like political instability. Real-time risk assessment is another significant benefit, allowing continuous monitoring and dynamic adjustments. Financial institutions can now detect potential fraud in real-time and monitor social media for market sentiment shifts, enabling proactive risk mitigation. However, the integration of big data is challenging, while big data offers immense potential, challenges exist. Scattered data across systems hinders a complete risk picture, so integration into a unified platform is crucial. Additionally, robust security measures are paramount to safeguard sensitive information and build customer trust, as data privacy is a top concern in the big data era. Big data's future in financial risk management shines bright. Machine learning and AI will boost predictive models and real-time risk assessment, with AI constantly learning and refining strategies. Integrating alternative data like IoT and social media sentiment unlocks deeper risk insights. While big data revolutionizes risk management, overcoming data silos and security challenges is key. As technology advances, the future promises continuous innovation for a more secure financial landscape
{"title":"The impact of big data analytics on financial risk management","authors":"Omolara Patricia Olaiya, Agwubuo Chigozie Cynthia, Sarah Onyeche Usoro, Omotoyosi Qazeem Obani, Kenneth Chukwujekwu Nwafor, Olajumoke Oluwagbemisola Ajayi","doi":"10.30574/ijsra.2024.12.2.1313","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1313","url":null,"abstract":"The realm of financial risk management is undergoing a seismic shift, driven by the transformative power of big data analytics. Financial institutions are now leveraging vast datasets not just as historical records but as powerful tools to revolutionize risk management practices. This paper explores how big data enhances predictive modeling, real-time risk assessment, and addresses associated challenges and future directions. Big data facilitates predictive modeling by analyzing diverse datasets, including traditional financial data, consumer behavior, and social media sentiment. This allows financial institutions to predict future performance and identify risks from external factors like political instability. Real-time risk assessment is another significant benefit, allowing continuous monitoring and dynamic adjustments. Financial institutions can now detect potential fraud in real-time and monitor social media for market sentiment shifts, enabling proactive risk mitigation. However, the integration of big data is challenging, while big data offers immense potential, challenges exist. Scattered data across systems hinders a complete risk picture, so integration into a unified platform is crucial. Additionally, robust security measures are paramount to safeguard sensitive information and build customer trust, as data privacy is a top concern in the big data era. Big data's future in financial risk management shines bright. Machine learning and AI will boost predictive models and real-time risk assessment, with AI constantly learning and refining strategies. Integrating alternative data like IoT and social media sentiment unlocks deeper risk insights. While big data revolutionizes risk management, overcoming data silos and security challenges is key. As technology advances, the future promises continuous innovation for a more secure financial landscape","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795920","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/ijsra.2024.12.2.1270
Bao The Pham, Ho Thanh Thuy, Pham The Bao, Dieu Le
Recently, e-commerce has become a vital component of our purchasing habits. Central to this evolution is the recommendation system, an advanced algorithm designed to personalize the shopping experience and significantly boost consumer demand. With its diverse and ever-changing inventory, the fashion industry benefits immensely from these algorithms, making it a fascinating case study for understanding the broader impacts of technology on consumerism. Traditional fashion recommendation systems are fundamentally based on item compatibility, but keeping up with trends is also essential. To address this, we propose a two-stage system: fashion detection and outfit suggestions based on the identified items. Users receive images of Key Opinion Leaders (KOLs) or Influencers wearing similar outfits. These recommendations ensure item compatibility, offer diverse styles, and remain fashionable. At the outset, we experimented with YOLOv8 to select the best version. Next, we implemented fashion image retrieval based on feature extraction using two pre-trained networks. To enhance reliability, we developed a voting and ranking algorithm. Our experiments, conducted on a self-collected dataset, evaluated the system’s effectiveness in detecting fashion objects and the efficiency of content-based image retrieval
{"title":"Building framework recommendation system for trendy fashion e-commerce based on deep learning with Top-K","authors":"Bao The Pham, Ho Thanh Thuy, Pham The Bao, Dieu Le","doi":"10.30574/ijsra.2024.12.2.1270","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1270","url":null,"abstract":"Recently, e-commerce has become a vital component of our purchasing habits. Central to this evolution is the recommendation system, an advanced algorithm designed to personalize the shopping experience and significantly boost consumer demand. With its diverse and ever-changing inventory, the fashion industry benefits immensely from these algorithms, making it a fascinating case study for understanding the broader impacts of technology on consumerism. Traditional fashion recommendation systems are fundamentally based on item compatibility, but keeping up with trends is also essential. To address this, we propose a two-stage system: fashion detection and outfit suggestions based on the identified items. Users receive images of Key Opinion Leaders (KOLs) or Influencers wearing similar outfits. These recommendations ensure item compatibility, offer diverse styles, and remain fashionable. At the outset, we experimented with YOLOv8 to select the best version. Next, we implemented fashion image retrieval based on feature extraction using two pre-trained networks. To enhance reliability, we developed a voting and ranking algorithm. Our experiments, conducted on a self-collected dataset, evaluated the system’s effectiveness in detecting fashion objects and the efficiency of content-based image retrieval","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795990","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/ijsra.2024.12.2.1319
Nan Zhang
Pension insurance, as one of the basic social security, is directly related to the personal interests of university staff, and is widely concerned by the staff. In the institutional merging pension insurance background, the insurance system changes to the college personnel management work has brought great impact and challenges. This paper discusses the challenges faced by the personnel management of universities in the context of the reform of pension insurance for institutions and puts forward corresponding optimization strategies to enhance the effectiveness of personnel management, thereby safeguarding the legitimate rights and interests of teaching staff and promoting the benign development of universities.
{"title":"A few thoughts on the transition period of pension insurance reform on the personnel management work of universities in China","authors":"Nan Zhang","doi":"10.30574/ijsra.2024.12.2.1319","DOIUrl":"https://doi.org/10.30574/ijsra.2024.12.2.1319","url":null,"abstract":"Pension insurance, as one of the basic social security, is directly related to the personal interests of university staff, and is widely concerned by the staff. In the institutional merging pension insurance background, the insurance system changes to the college personnel management work has brought great impact and challenges. This paper discusses the challenges faced by the personnel management of universities in the context of the reform of pension insurance for institutions and puts forward corresponding optimization strategies to enhance the effectiveness of personnel management, thereby safeguarding the legitimate rights and interests of teaching staff and promoting the benign development of universities.","PeriodicalId":14366,"journal":{"name":"International Journal of Science and Research Archive","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":"141796157","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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