Diabetic retinopathy (DR) is a significant complication arising from diabetes, affecting the eyes and potentially causing vision loss if not identified and addressed promptly. Over the years, there has been a significant advancement in the field of DR detection, primarily driven by advancements in imaging techniques and machine learning algorithms. This review paper presents a comprehensive overview of different techniques and advancements in the detection of diabetic retinopathy using deep learning and several neural network architectures. The comparative study of the existing datasets for the DR detection with the benefits, challenges and possible solutions for each dataset is also provided. The paper discusses the methods, preprocessing, implementation platforms and results of various implementation of CNN architectures like Deep CNN, CNN with Transfer Learning, Capsule Networks and DNN. The objective of this paper is to furnish researchers and clinicians with a thorough understanding of the present status of diabetic retinopathy detection, highlight the strengths and limitations of existing approaches, and identify future research directions in this vital area of healthcare.
{"title":"A Comprehensive Review on Diabetic Retinopathy Detection Techniques using Neural Network Architectures","authors":"Sheetal J. Nagar, Nikhil Gondaliya","doi":"10.52783/jes.5309","DOIUrl":"https://doi.org/10.52783/jes.5309","url":null,"abstract":"Diabetic retinopathy (DR) is a significant complication arising from diabetes, affecting the eyes and potentially causing vision loss if not identified and addressed promptly. Over the years, there has been a significant advancement in the field of DR detection, primarily driven by advancements in imaging techniques and machine learning algorithms. This review paper presents a comprehensive overview of different techniques and advancements in the detection of diabetic retinopathy using deep learning and several neural network architectures. The comparative study of the existing datasets for the DR detection with the benefits, challenges and possible solutions for each dataset is also provided. The paper discusses the methods, preprocessing, implementation platforms and results of various implementation of CNN architectures like Deep CNN, CNN with Transfer Learning, Capsule Networks and DNN. The objective of this paper is to furnish researchers and clinicians with a thorough understanding of the present status of diabetic retinopathy detection, highlight the strengths and limitations of existing approaches, and identify future research directions in this vital area of healthcare. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835473","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}
Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient. regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we carried out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.
{"title":"Machine Learning Method for Forecasting Wind Power Using Continuous Wind Speed Data","authors":"Ankita Sinha, R. Ranjan, Sanjeet Kumar, Abhishek Kumar, Shashi Raj, Reena Kumari","doi":"10.52783/jes.5322","DOIUrl":"https://doi.org/10.52783/jes.5322","url":null,"abstract":"Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient. regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we carried out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835551","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}
With the escalating concerns worldwide regarding climate change and environmental sustainability, there is an increasing focus on emissions and ecological footprint reduction in supply chain operations in the USA. This study explored the application of predictive analytics and machine learning in the supply chain management domain for reducing carbon emissions and granting sustainable operations. For the present research paper, Walmart organization provided all the supply chain activity data used in this research study, it consisted of comprehensive data on their industrial activity levels, production outputs, energy consumption, types of fuels used, geographical data, and weather conditions. Three Machine learning algorithms were trained and tested, notably, Random Forest, XG-Boost, and the Bagging algorithm. Based on all the metrics, Random Forest was the best classifier because of its excellent generalization, high measure of precision and recall, and high AUC. As per the results, the random forest algorithm was the most accurate in its predictions of all the models evaluated. Implementing the random forest benefits businesses in America with high accuracy and robustness, flexibility, scalability, risk management, and Mitigation. As regards the US economy, deploying the Random Forest can benefit the government in the following ways: reducing carbon footprint, attracting foreign investment, and enhancing competitive advantage.
{"title":"Predictive Analytics and Machine Learning Applications in the USA for Sustainable Supply Chain Operations and Carbon Footprint Reduction","authors":"Md Rokibul Hasan, Md zahidul Islam, Mahfuz Alam, Md Sumsuzoha, Md Rokibul Hasan","doi":"10.52783/jes.5138","DOIUrl":"https://doi.org/10.52783/jes.5138","url":null,"abstract":"With the escalating concerns worldwide regarding climate change and environmental sustainability, there is an increasing focus on emissions and ecological footprint reduction in supply chain operations in the USA. This study explored the application of predictive analytics and machine learning in the supply chain management domain for reducing carbon emissions and granting sustainable operations. For the present research paper, Walmart organization provided all the supply chain activity data used in this research study, it consisted of comprehensive data on their industrial activity levels, production outputs, energy consumption, types of fuels used, geographical data, and weather conditions. Three Machine learning algorithms were trained and tested, notably, Random Forest, XG-Boost, and the Bagging algorithm. Based on all the metrics, Random Forest was the best classifier because of its excellent generalization, high measure of precision and recall, and high AUC. As per the results, the random forest algorithm was the most accurate in its predictions of all the models evaluated. Implementing the random forest benefits businesses in America with high accuracy and robustness, flexibility, scalability, risk management, and Mitigation. As regards the US economy, deploying the Random Forest can benefit the government in the following ways: reducing carbon footprint, attracting foreign investment, and enhancing competitive advantage. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835292","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}
Visual object tracking（VOT）is a key topic in computer vision tasks. It serves as an essential component of various advanced problems in the field, such as motion analysis, event detection, and activity understanding. VOT finds extensive applications, including human-computer interaction in video, video surveillance, and autonomous driving. Due to the rapid development of deep neural networks（DNNs）, VOT has achieved unprecedented progress. However, the lack of interpretability in DNNs has introduced certain security risks, notably backdoor attacks. A neural network backdoor attack involves an attacker injecting hidden backdoors into the network, making the compromised model behave normally with regular inputs but produce predetermined outputs when specific conditions set by the attacker are met. Existing triggers for VOT backdoor attacks are poorly concealed. We leverage the sensitivity of DNNs to small perturbations to generate pixel-level indistinguishable perturbations in the frequency domain, thus proposing an invisible backdoor attack. This method ensures both effectiveness and concealment. Additionally, we employ a differential evolution（DE） algorithm to optimize trigger generation, thereby reducing the attacker's required capabilities. We have validated the effectiveness of the attack across various datasets and models.
视觉物体跟踪（VOT）是计算机视觉任务中的一个关键主题。它是该领域各种高级问题（如运动分析、事件检测和活动理解）的重要组成部分。VOT 应用广泛，包括视频中的人机交互、视频监控和自动驾驶。由于深度神经网络（DNN）的快速发展，VOT 取得了前所未有的进步。然而，由于深度神经网络缺乏可解释性，因此带来了一定的安全风险，特别是后门攻击。神经网络后门攻击是指攻击者在网络中注入隐藏的后门，使被攻击的模型在正常输入的情况下表现正常，但在满足攻击者设定的特定条件时产生预定的输出。现有的 VOT 后门攻击触发器隐蔽性很差。我们利用 DNN 对微小扰动的敏感性，在频域生成像素级的不可分扰动，从而提出了一种隐形后门攻击。这种方法同时确保了有效性和隐蔽性。此外，我们还采用了微分进化（DE）算法来优化触发器的生成，从而降低攻击者所需的能力。我们在各种数据集和模型中验证了这种攻击的有效性。
{"title":"Frequency Domain Backdoor Attacks for Visual Object Tracking","authors":"Jiahao Luo","doi":"10.52783/jes.5089","DOIUrl":"https://doi.org/10.52783/jes.5089","url":null,"abstract":"Visual object tracking（VOT）is a key topic in computer vision tasks. It serves as an essential component of various advanced problems in the field, such as motion analysis, event detection, and activity understanding. VOT finds extensive applications, including human-computer interaction in video, video surveillance, and autonomous driving. Due to the rapid development of deep neural networks（DNNs）, VOT has achieved unprecedented progress. However, the lack of interpretability in DNNs has introduced certain security risks, notably backdoor attacks. A neural network backdoor attack involves an attacker injecting hidden backdoors into the network, making the compromised model behave normally with regular inputs but produce predetermined outputs when specific conditions set by the attacker are met. Existing triggers for VOT backdoor attacks are poorly concealed. We leverage the sensitivity of DNNs to small perturbations to generate pixel-level indistinguishable perturbations in the frequency domain, thus proposing an invisible backdoor attack. This method ensures both effectiveness and concealment. Additionally, we employ a differential evolution（DE） algorithm to optimize trigger generation, thereby reducing the attacker's required capabilities. We have validated the effectiveness of the attack across various datasets and models.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661318","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}
Bhavesh D. Patel, Gautam V. Bhatt, Harsh K. Vaghela, Nitin H. Adroja, Roshani N Maheshwari
This paper compares the control strategies of Quasi z source inverter for wind power generation. The generator in the conventional wind energy conversion system uses kinetic energy from the wind to produce electrical energy. Owing to wind fluctuations, the generator's output is connected to the load via a rectifier and inverter to keep the voltage at the load side constant. The 2-stage conversion phenomenon has its limitation of being expensive along with possessing lower efficiency. Z source inverters offer a novel conversion approach and can utilized to alleviate the limitations. However, they come with certain downsides as well, such as unequal input current, high inrush current, and high voltage stress. The quasi-Z source inverter (QZSI), a single-stage power converter based on the Z source inverter topology, can overcome it. It performs this by using an impedance network that couples with the source and the inverter to provide a voltage boost for the wind power generating system. In this paper, a comparative study of different control strategies of quasi-z source inverter is performed for a wind power system to find out one efficient strategy.
本文比较了风力发电准 z 源逆变器的控制策略。传统风能转换系统中的发电机利用风的动能产生电能。由于风力波动，发电机的输出通过整流器和逆变器连接到负载，以保持负载端电压恒定。两级转换现象有其局限性，即成本高、效率低。Z 源逆变器提供了一种新颖的转换方法，可用于缓解上述限制。不过，它们也有一些缺点，如输入电流不均、浪涌电流大和电压应力高。准 Z 源逆变器 (QZSI) 是一种基于 Z 源逆变器拓扑结构的单级功率转换器，可以克服这些问题。它通过使用一个与源和逆变器耦合的阻抗网络，为风力发电系统提供升压。本文针对风力发电系统，对准 Z 源逆变器的不同控制策略进行了比较研究，以找出一种有效的策略。
{"title":"Comparison of Control Strategies of Quasi Z-Source Inverter for Wind Power Generation","authors":"Bhavesh D. Patel, Gautam V. Bhatt, Harsh K. Vaghela, Nitin H. Adroja, Roshani N Maheshwari","doi":"10.52783/jes.5251","DOIUrl":"https://doi.org/10.52783/jes.5251","url":null,"abstract":"This paper compares the control strategies of Quasi z source inverter for wind power generation. The generator in the conventional wind energy conversion system uses kinetic energy from the wind to produce electrical energy. Owing to wind fluctuations, the generator's output is connected to the load via a rectifier and inverter to keep the voltage at the load side constant. The 2-stage conversion phenomenon has its limitation of being expensive along with possessing lower efficiency. Z source inverters offer a novel conversion approach and can utilized to alleviate the limitations. However, they come with certain downsides as well, such as unequal input current, high inrush current, and high voltage stress. The quasi-Z source inverter (QZSI), a single-stage power converter based on the Z source inverter topology, can overcome it. It performs this by using an impedance network that couples with the source and the inverter to provide a voltage boost for the wind power generating system. In this paper, a comparative study of different control strategies of quasi-z source inverter is performed for a wind power system to find out one efficient strategy. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835396","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}
For necessary action to be taken in a timely and economical way, accurate service-life forecast of buildings is essential. But the oversimplified assumptions of the traditional prediction models result in approximations that are not correct. The capacity of “machine learning” to overcome the shortcomings of traditional future models is reviewed in this research. This can be attributed to its capacity to represent the intricate physical and chemical dynamics of the degradation mechanism. The study also summarizes other studies that suggested “machine learning” may be used to support the assessment of reinforced concrete building durability. Comprehensive discussion is also held regarding the benefits of using machine learning to evaluate the service life and durability of “reinforced concrete” buildings. It is becoming easier to apply “machine learning for durability and service-life” evaluation thanks to the growing trend of wireless sensors gathering an increasing amount of in-service data. In light of the most recent developments and the state of the art in this particular field, the presentation ends by suggesting future directions.
{"title":"Future Prospects and Recent Advancements in Machine Learning for Assessing the Service Life and Durability of Reinforced Concrete Buildings","authors":"Reena Kumari, Neha Rani, Rashmi Rani, Chandan Kumar, Vijeta Bachan","doi":"10.52783/jes.5463","DOIUrl":"https://doi.org/10.52783/jes.5463","url":null,"abstract":"For necessary action to be taken in a timely and economical way, accurate service-life forecast of buildings is essential. But the oversimplified assumptions of the traditional prediction models result in approximations that are not correct. The capacity of “machine learning” to overcome the shortcomings of traditional future models is reviewed in this research. This can be attributed to its capacity to represent the intricate physical and chemical dynamics of the degradation mechanism. The study also summarizes other studies that suggested “machine learning” may be used to support the assessment of reinforced concrete building durability. Comprehensive discussion is also held regarding the benefits of using machine learning to evaluate the service life and durability of “reinforced concrete” buildings. It is becoming easier to apply “machine learning for durability and service-life” evaluation thanks to the growing trend of wireless sensors gathering an increasing amount of in-service data. In light of the most recent developments and the state of the art in this particular field, the presentation ends by suggesting future directions.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835454","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}
Dr. Dineshkumar Bhagwandas, Vaghela, Mr. Sachinkumar, H. Makwana, Mr. Haresh, D. Chande, Mr. Priyam Mehta
Social media platforms and micro blogging websites can be used as a potential source for gathering opinions and sentiments from the public on a variety of topics, such as the present state of affairs in nations that have experienced conflict. Twitter, in example, offers a variety of text tweets that might link to feelings across time and geography. Using Textblob and Vader as a lexicon method, this research paper performs sentiment analysis over a dataset containing tweets regarding the situation before and after Russia invades Ukraine. It also performs standard machine learning over the dataset. This machine learning model categorizes opinions about Russia's invasion of Ukraine according to sentiments. The current study examines different machine learning algorithms and focuses on the Doc2Vec feature extraction approach utilizing Chi2 (χ2) as a feature selection. The objective of this research is to use Twitter to get people's opinions about the war. The current study helps news media organizations analyze public opinion, particularly that of Russia and Ukraine, about the conflict and draw attention to upcoming difficulties.
{"title":"Twitter Based Sentiment Analysis of Russia-Ukraine War Using Machine Learning","authors":"Dr. Dineshkumar Bhagwandas, Vaghela, Mr. Sachinkumar, H. Makwana, Mr. Haresh, D. Chande, Mr. Priyam Mehta","doi":"10.52783/jes.5255","DOIUrl":"https://doi.org/10.52783/jes.5255","url":null,"abstract":"Social media platforms and micro blogging websites can be used as a potential source for gathering opinions and sentiments from the public on a variety of topics, such as the present state of affairs in nations that have experienced conflict. Twitter, in example, offers a variety of text tweets that might link to feelings across time and geography. Using Textblob and Vader as a lexicon method, this research paper performs sentiment analysis over a dataset containing tweets regarding the situation before and after Russia invades Ukraine. It also performs standard machine learning over the dataset. This machine learning model categorizes opinions about Russia's invasion of Ukraine according to sentiments. The current study examines different machine learning algorithms and focuses on the Doc2Vec feature extraction approach utilizing Chi2 (χ2) as a feature selection. The objective of this research is to use Twitter to get people's opinions about the war. The current study helps news media organizations analyze public opinion, particularly that of Russia and Ukraine, about the conflict and draw attention to upcoming difficulties. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835175","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}
Nitin H. Adroja, †. NasreenbanuNazirbhaiMansoori, Dhaval Yogeshbhai, ‡. Raval
Pollution free renewable energy sources are key solutions for increasing power demand. Unpredictable nature of power electronic based renewable energy sources impacts negatively on grid voltage and frequency profile. Energy storage is one of the solutions to overcome fluctuating power generation from renewable energy sources. Shorter life span of energy storage makes it costly. Grid forming control is another solution to overcome fluctuating power generation from renewable energy sources. Grid forming converters can regulate voltage and frequency of existing grid by regulating output active and reactive power. Grid forming converter can also form the grid for the remote area where the loads are isolated from utility grid. To regulate output active power grid forming converter also require energy storage when utilized with renewable energy sources. High Voltage Direct Current (HVDC) with one of the converters operating under grid forming mode can supply large isolated load. Renewable energy sources which is operating under grid following mode can also be integrated with High Voltage Direct Current (HVDC) with one of the converters operating under grid forming mode. In this research work, capability of grid forming converter based HVDC in varying load condition has been verified. To understand the effect of integration of renewable energy with grid forming based HVDC, Doubly Fed Induction Generation (DFIG) based wind turbine has been integrated. To validate the capability of grid forming converter based HVDC modelling has been done in Simulink/MATLAB. Results show that Grid forming converter based HVDC system is capable to fulfil the load demand in varying load/generation condition.
{"title":"Enhancing Renewable Energy Integration with Grid-Forming Converter-Based HVDC Systems: Modelling and Validation","authors":"Nitin H. Adroja, †. NasreenbanuNazirbhaiMansoori, Dhaval Yogeshbhai, ‡. Raval","doi":"10.52783/jes.5259","DOIUrl":"https://doi.org/10.52783/jes.5259","url":null,"abstract":"Pollution free renewable energy sources are key solutions for increasing power demand. Unpredictable nature of power electronic based renewable energy sources impacts negatively on grid voltage and frequency profile. Energy storage is one of the solutions to overcome fluctuating power generation from renewable energy sources. Shorter life span of energy storage makes it costly. Grid forming control is another solution to overcome fluctuating power generation from renewable energy sources. Grid forming converters can regulate voltage and frequency of existing grid by regulating output active and reactive power. Grid forming converter can also form the grid \u0000for the remote area where the loads are isolated from utility grid. To regulate output active power grid forming converter also require energy storage when utilized with renewable energy sources. High Voltage Direct Current (HVDC) with one of the converters operating under grid forming mode can supply large isolated load. Renewable energy sources which is operating under grid following mode can also be integrated with High Voltage Direct Current (HVDC) with one of the converters operating under grid forming mode. In this research work, capability of grid forming converter based HVDC in varying load condition has been verified. To understand the effect of integration of renewable energy with grid forming based HVDC, Doubly Fed Induction Generation (DFIG) based wind turbine has been integrated. To validate the capability of grid forming converter based HVDC modelling has been done in Simulink/MATLAB. Results show that Grid forming converter based HVDC system is capable to fulfil the load demand in varying load/generation condition. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835735","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 integration of Artificial Intelligence (AI) has been reshaping healthcare globally. However, the AI adoption in Jordan is met with cautious progress. AI has shown substantial potential to enhance healthcare services and foster Emotional Intelligence (EI), especially in advanced economies. Despite its proven effectiveness elsewhere, the Jordanian populace is reluctant to adopt AI in the healthcare sector, with predictions for hospitalizations, medical consultations, and treatment recommendations being sluggish to gain acceptance. This study investigates the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model. While UTAUT typically considers performance expectancy, effort expectancy, social influence, and facilitating conditions as key determinants of technology acceptance, this study argues that emotional intelligence, including self-regulated, self-awareness, motivation, empathy, and social skills, should be integrated as direct determinants of behavioural intention. In this study, a quantitative approach has been employed, whereby questionnaires were delivered through email and messaging apps to evaluate the impact of emotional intelligence on Jordanians’ willingness to adopt AI technology in the healthcare sector. The findings suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct, particularly in developing countries like Jordan, where user models for AI adoption are less explored. The implications of the study extend to healthcare planners and developers in Jordan, providing insights into factors, which influence the successful adoption of AI technologies among diverse user groups. This study has provided valuable recommendations for developers of AI-based healthcare systems, enabling them to align their assistance with the perceptions and behaviours of Middle Eastern users. By doing so, they can foster increased acceptance of AI-based healthcare systems in the Middle East and other developing regions to improve healthcare services.
{"title":"Exploring Emotional Intelligence in Jordan’s Artificial Intelligence (AI) Healthcare Adoption: A UTAUT Framework","authors":"Mahmoud Mohammad Ahmad Ibrahim","doi":"10.52783/jes.5143","DOIUrl":"https://doi.org/10.52783/jes.5143","url":null,"abstract":"The integration of Artificial Intelligence (AI) has been reshaping healthcare globally. However, the AI adoption in Jordan is met with cautious progress. AI has shown substantial potential to enhance healthcare services and foster Emotional Intelligence (EI), especially in advanced economies. Despite its proven effectiveness elsewhere, the Jordanian populace is reluctant to adopt AI in the healthcare sector, with predictions for hospitalizations, medical consultations, and treatment recommendations being sluggish to gain acceptance. This study investigates the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model. While UTAUT typically considers performance expectancy, effort expectancy, social influence, and facilitating conditions as key determinants of technology acceptance, this study argues that emotional intelligence, including self-regulated, self-awareness, motivation, empathy, and social skills, should be integrated as direct determinants of behavioural intention. In this study, a quantitative approach has been employed, whereby questionnaires were delivered through email and messaging apps to evaluate the impact of emotional intelligence on Jordanians’ willingness to adopt AI technology in the healthcare sector. The findings suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct, particularly in developing countries like Jordan, where user models for AI adoption are less explored. The implications of the study extend to healthcare planners and developers in Jordan, providing insights into factors, which influence the successful adoption of AI technologies among diverse user groups. This study has provided valuable recommendations for developers of AI-based healthcare systems, enabling them to align their assistance with the perceptions and behaviours of Middle Eastern users. By doing so, they can foster increased acceptance of AI-based healthcare systems in the Middle East and other developing regions to improve healthcare services. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662443","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}
M. MakwanaVinod, R. Pravin, Chandrala Monir, Patel Dharmendra, Jaradi Pritesh
Conventional solar still have poor efficiency and low distillate output. Climate parameter play important role in efficiency of solar still. Many investigators have investigated the effect of climate parameter to improve the performance of solar still. This review paper evaluates the effect of several climate parameters like wind velocity, ambient temperature, location and vapour pressure. Review was to be done to minimize adverse effect of climate parameter to improve the performance solar still. From this review, it is found that productivity of still increase with increasing wind speed but performance of still little bit decrease with higher wind velocity approximately more than 9 m/s. There is direct relationship between the solar radiation and ambient temperature. The daily productivity increased as ambient temperature increased and directly promotional to the solar radiation. The productivity remains intact during the variation in vapour pressure of surrounding air on solar still. Further, it is found that at low latitude station in India, yearly total radiation and seasonally radiation are approximately equal irrespective of E-W or N-S orientation for double slop single basin solar still. At high latitude, the east-west orientation receives more radiation than the south-north orientation, taking the year as a whole, while there is no effect of orientation in case of lower latitude for double slope single basin solar still. The single slope solar still single basin facing south collects greater amount of solar radiation as compared to the dual slope single basin solar still at lower and higher latitude locations. Solar still would be kept south facing for northern latitude and north facing for southern latitude.
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