Artificial intelligence (AI) has had a profound impact on various industries, and one prominent domain where its influence is evident is in chatbot technology. Chatbots, computer programs designed to simulate human conversation, have evolved significantly through the advancements in AI, becoming more sophisticated and intelligent. This research paper aims to explore the current state of AI-powered chatbot technology, focusing on the latest advancements and leading innovations. The study delves into the application of natural language processing (NLP) algorithms, machine learning models, and deep learning techniques in chatbot development to gain insights into their capabilities and limitations. The research also highlights leading innovations in AI-powered chatbot technology, such as virtual assistants and voice-enabled chatbots. These conversational agents have transformed various industries, providing innovative solutions to virtual reference services and customer-company interactions. The study delves into the contextual understanding and personalized responses that chatbots can provide, offering tailored interactions to meet users' specific needs and preferences. Furthermore, the integration of other technologies, including speech recognition and sentiment analysis, enhances chatbot capabilities, improving user satisfaction and engagement. However, while AI-powered chatbots have enhanced user experiences, customer satisfaction, and efficiency in industries like customer support and service, they also raise potential ethical and privacy concerns. Medical chatbots, in particular, pose legal and ethical challenges that require careful management and the development of appropriate ethical frameworks. Understanding the advancements, innovations, and impact of AI on chatbot technology is essential for recognizing the potential benefits and challenges these systems present. By addressing ethical and privacy concerns, chatbots can responsibly shape the future of human-computer interactions, further contributing to the broader understanding of AI's role in transforming industries and enhancing user experiences.
{"title":"The Impact of Artificial Intelligence on Chatbot Technology: A Study on the Current Advancements and Leading Innovations","authors":"Farhan Aslam","doi":"10.47672/ejt.1561","DOIUrl":"https://doi.org/10.47672/ejt.1561","url":null,"abstract":"Artificial intelligence (AI) has had a profound impact on various industries, and one prominent domain where its influence is evident is in chatbot technology. Chatbots, computer programs designed to simulate human conversation, have evolved significantly through the advancements in AI, becoming more sophisticated and intelligent. This research paper aims to explore the current state of AI-powered chatbot technology, focusing on the latest advancements and leading innovations. The study delves into the application of natural language processing (NLP) algorithms, machine learning models, and deep learning techniques in chatbot development to gain insights into their capabilities and limitations. The research also highlights leading innovations in AI-powered chatbot technology, such as virtual assistants and voice-enabled chatbots. These conversational agents have transformed various industries, providing innovative solutions to virtual reference services and customer-company interactions. The study delves into the contextual understanding and personalized responses that chatbots can provide, offering tailored interactions to meet users' specific needs and preferences. Furthermore, the integration of other technologies, including speech recognition and sentiment analysis, enhances chatbot capabilities, improving user satisfaction and engagement. However, while AI-powered chatbots have enhanced user experiences, customer satisfaction, and efficiency in industries like customer support and service, they also raise potential ethical and privacy concerns. Medical chatbots, in particular, pose legal and ethical challenges that require careful management and the development of appropriate ethical frameworks. Understanding the advancements, innovations, and impact of AI on chatbot technology is essential for recognizing the potential benefits and challenges these systems present. By addressing ethical and privacy concerns, chatbots can responsibly shape the future of human-computer interactions, further contributing to the broader understanding of AI's role in transforming industries and enhancing user experiences.","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135064852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: Fish farming refers to the farming of aquatic organisms such as fish. It involves cultivating freshwater and saltwater populations under controlled conditions. The purpose of this research is to provide a solution to the fish farmer by developing an application that would be easy to monitor water quality during the fish farming process. This will help the fish farmers to intervene timely, and therefore increase their production. The design and implementation of IoT Monitoring Systems in Fish farming helps to observe the farming system remotely by using different sensors for the water parameters. The research focused on developing a system for real time monitoring of culture tank water quality as a proof of concept and testing the basic functionalities of the system. Methodology: The research adopted the Rapid application methodology, which was deemed best due to its iterative approach to applications development as it also delivers systems faster at a lower cost in time-constrained projects. This methodology was suitable for our research given the time constraints in developing the application. Secondary data was used to determine the water quality aspects that require monitoring. Interviewing the operators of the culture tank also provided information on what should be incorporated in the model. by developing an application that would be easy to monitor water quality during the fish farming process. The application dashboard is the graphical user interface that the users shall use to interact with application components. Findings: This research proposes a solution, which is a real-time culture tank (hatchery) water quality-monitoring model, which utilizes a web application that shall be adopted by the staff of the University of Rwanda Fish Farming and Research Station and farmers. The model utilizes the IoT concept, which enables information gathering about water quality through the corresponding sensors. The status of the water quality aspects shall then be relayed on a real-time basis through a cloud platform. The farmer can then act based on the information provided, or the model can act on the farmer's behalf based on predetermined actions. The model's data can be extracted and analysed in a variety of different ways. Recommendations: This research contributed in developing a technological solution for real time monitoring water quality aspects of culture tank (hatchery) that can be adopted by fish farmers in Rwasave Fish Farming and Research Station by providing them with real time data whenever they are within or away from the culture tank (hatchery) site. This helps to eliminate or minimize the risk of losing fish and wastages due late interventions.it was validated by supervisors of the project.
{"title":"IOT Monitoring Systems in Fish Farming Case Study:” University of Rwanda Fish Farming and Research Station (Ur-FFRs)”","authors":"Ineza Yves, Gasana Madson, Irankunda Innocent, H. Claude, Niyonsaba Maximillien, Bitegetsimana Gedeon","doi":"10.47672/ejt.1559","DOIUrl":"https://doi.org/10.47672/ejt.1559","url":null,"abstract":"Purpose: Fish farming refers to the farming of aquatic organisms such as fish. It involves cultivating freshwater and saltwater populations under controlled conditions. The purpose of this research is to provide a solution to the fish farmer by developing an application that would be easy to monitor water quality during the fish farming process. This will help the fish farmers to intervene timely, and therefore increase their production. The design and implementation of IoT Monitoring Systems in Fish farming helps to observe the farming system remotely by using different sensors for the water parameters. The research focused on developing a system for real time monitoring of culture tank water quality as a proof of concept and testing the basic functionalities of the system. \u0000Methodology: The research adopted the Rapid application methodology, which was deemed best due to its iterative approach to applications development as it also delivers systems faster at a lower cost in time-constrained projects. This methodology was suitable for our research given the time constraints in developing the application. Secondary data was used to determine the water quality aspects that require monitoring. Interviewing the operators of the culture tank also provided information on what should be incorporated in the model. by developing an application that would be easy to monitor water quality during the fish farming process. The application dashboard is the graphical user interface that the users shall use to interact with application components. \u0000Findings: This research proposes a solution, which is a real-time culture tank (hatchery) water quality-monitoring model, which utilizes a web application that shall be adopted by the staff of the University of Rwanda Fish Farming and Research Station and farmers. The model utilizes the IoT concept, which enables information gathering about water quality through the corresponding sensors. The status of the water quality aspects shall then be relayed on a real-time basis through a cloud platform. The farmer can then act based on the information provided, or the model can act on the farmer's behalf based on predetermined actions. The model's data can be extracted and analysed in a variety of different ways. \u0000Recommendations: This research contributed in developing a technological solution for real time monitoring water quality aspects of culture tank (hatchery) that can be adopted by fish farmers in Rwasave Fish Farming and Research Station by providing them with real time data whenever they are within or away from the culture tank (hatchery) site. This helps to eliminate or minimize the risk of losing fish and wastages due late interventions.it was validated by supervisors of the project.","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"5 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87374868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: The article "Network Automation in the Contemporary Economy" explores the concepts and methods of effective network management. The application stack, Jinja template engine, automation architecture, Nornir inventory management, application flow, logging, debugging, and live code testing are just a few of the subjects it covers. Network administrators are more important as digital technologies evolve quickly in order to maintain a safe and dependable network connection. The purpose of this article is to give network administrators the information and abilities they need to successfully traverse the intricacies of network management. It starts by going through the application stack and explaining the roles and relationships between each layer. The Ninja template engine is then described, along with an explanation of how its potent grammar makes network configuration management simple. Methodology: The research design employed in this study is a combination of qualitative and quantitative approaches. It involved an extensive literature review to gather existing knowledge on network automation and management practices. Additionally, empirical data was collected through surveys and interviews with network administrators to understand their experiences, challenges, and perspectives on network automation. Findings: The study found that network automation offers numerous benefits, including increased efficiency, reduced human errors, and enhanced network security. The application stack was identified as a critical component of network architecture, and its proper management can significantly impact network performance. The Jinja template engine proved to be an effective tool for simplifying network configuration tasks and promoting standardization across the network infrastructure. Recommendations: To policymakers, we recommend investing in training programs and resources to equip network administrators with the necessary skills to implement and manage network automation effectively. Developing clear guidelines and standards for network automation can also help organizations adopt automation practices seamlessly. Theory: The study was informed by the "Network Automation Theory," which posits that automating network management tasks can streamline operations, enhance reliability, and free up human resources for more strategic initiatives. The theory suggests that proper implementation of automation tools and frameworks can lead to a more agile and resilient network infrastructure. The validation of the theory was achieved through empirical data collected from network administrators and their experiences with network automation. The findings aligned with the propositions of the theory, confirming that network automation indeed brings significant benefits to organizations. Policy: For policymakers, we propose the formulation of a comprehensive policy framework that encourages the adoption of network automation technologies. The policy should f
{"title":"Network Automation","authors":"Tayyab Muhammad, M. Munir","doi":"10.47672/ejt.1547","DOIUrl":"https://doi.org/10.47672/ejt.1547","url":null,"abstract":"Purpose: The article \"Network Automation in the Contemporary Economy\" explores the concepts and methods of effective network management. The application stack, Jinja template engine, automation architecture, Nornir inventory management, application flow, logging, debugging, and live code testing are just a few of the subjects it covers. Network administrators are more important as digital technologies evolve quickly in order to maintain a safe and dependable network connection. The purpose of this article is to give network administrators the information and abilities they need to successfully traverse the intricacies of network management. It starts by going through the application stack and explaining the roles and relationships between each layer. The Ninja template engine is then described, along with an explanation of how its potent grammar makes network configuration management simple. \u0000Methodology: The research design employed in this study is a combination of qualitative and quantitative approaches. It involved an extensive literature review to gather existing knowledge on network automation and management practices. Additionally, empirical data was collected through surveys and interviews with network administrators to understand their experiences, challenges, and perspectives on network automation. \u0000Findings: The study found that network automation offers numerous benefits, including increased efficiency, reduced human errors, and enhanced network security. The application stack was identified as a critical component of network architecture, and its proper management can significantly impact network performance. The Jinja template engine proved to be an effective tool for simplifying network configuration tasks and promoting standardization across the network infrastructure. \u0000Recommendations: To policymakers, we recommend investing in training programs and resources to equip network administrators with the necessary skills to implement and manage network automation effectively. Developing clear guidelines and standards for network automation can also help organizations adopt automation practices seamlessly. \u0000Theory: The study was informed by the \"Network Automation Theory,\" which posits that automating network management tasks can streamline operations, enhance reliability, and free up human resources for more strategic initiatives. The theory suggests that proper implementation of automation tools and frameworks can lead to a more agile and resilient network infrastructure. The validation of the theory was achieved through empirical data collected from network administrators and their experiences with network automation. The findings aligned with the propositions of the theory, confirming that network automation indeed brings significant benefits to organizations. \u0000Policy: For policymakers, we propose the formulation of a comprehensive policy framework that encourages the adoption of network automation technologies. The policy should f","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"13 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75816903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study is aimed at characterising and understanding the mechanical and microstructural behaviour of natural and calcined kaolin clay for geopolymer applications. The clay samples obtained from Kaduna State, Northern area of Nigeria, were calcined within the temperature range of 700 and 900oC and are represented by MK7, MK8 and MK9. The raw kaolin and metakaolin were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF) and scanning electron microscopy (SEM). To produce geopolymer binders, the precursors were reacted with a 1:1 combination of sodium hydroxide and sodium silicate solution and a precursor to activator ratio of 1.2 was suitable for preparation. The compressive strength of the samples was determined at three testing age of 7, 14 and 28 days. From the results obtained, the kaolin-based geopolymer generally had a long setting time and testing was impossible at the 7-day testing age. The compressive strength of other samples however, increased with increase in the calcination temperature. The values obtained at the maximum testing age of 28 days for all samples fall between 5.1 -14.9 MPa. The scanning electron microscope morphology of the produced binder, show the presence of air trapped in the paste which may have reduced the strength value. Vibrating samples before setting can improve the performance of the geopolymer binders by removing most of the trapped air. Geopolymers produced from this study based on strength obtained, can be used in less critical areas of the construction industry.
{"title":"Characterisation and Performance of Nigerian Kaolin and Metakaolin in Geopolymer Synthesis","authors":"M. Enoh, Ekanem Agbonko, M. Ededet","doi":"10.47672/ejt.1542","DOIUrl":"https://doi.org/10.47672/ejt.1542","url":null,"abstract":"This study is aimed at characterising and understanding the mechanical and microstructural behaviour of natural and calcined kaolin clay for geopolymer applications. The clay samples obtained from Kaduna State, Northern area of Nigeria, were calcined within the temperature range of 700 and 900oC and are represented by MK7, MK8 and MK9. The raw kaolin and metakaolin were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF) and scanning electron microscopy (SEM). To produce geopolymer binders, the precursors were reacted with a 1:1 combination of sodium hydroxide and sodium silicate solution and a precursor to activator ratio of 1.2 was suitable for preparation. The compressive strength of the samples was determined at three testing age of 7, 14 and 28 days. From the results obtained, the kaolin-based geopolymer generally had a long setting time and testing was impossible at the 7-day testing age. The compressive strength of other samples however, increased with increase in the calcination temperature. The values obtained at the maximum testing age of 28 days for all samples fall between 5.1 -14.9 MPa. The scanning electron microscope morphology of the produced binder, show the presence of air trapped in the paste which may have reduced the strength value. Vibrating samples before setting can improve the performance of the geopolymer binders by removing most of the trapped air. Geopolymers produced from this study based on strength obtained, can be used in less critical areas of the construction industry. \u0000 ","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"77 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83879081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: Solar energy is necessary for our global ecosystem. In certain times and parts of the world, the heat generated from the energy of the Sun becomes uncomfortable for us and we need to use additional energy to cool down the generated heat from the Sun. This additional energy usage is detrimental to our environment due to generation of air pollution and use of our natural energy resources. The purpose of this paper is to investigate prevention of undesired generation of heat by the Sun using reflective coating materials. Methodology: In this study, using literature review, Ga doped Zinc Oxide (GZO) is theoretically investigated as a visible light transparent and infrared reflective coating material for the Plexiglas canopy of a solar car. The effect of doping concentration and film thickness is reviewed and discussed. Indium Tin Oxide (ITO) is also studied, using various literature reviews, as an infrared reflective coating material that is transparent in the visible light region. Finally, a spectrophotometer was used to examine the optical properties of a Plexiglas, slide glass, and ITO coated glass, and the results of UV and visible light transmittance measurement of the three materials is presented and discussed. Findings: GZO and ITO films both have high transparency in the visible region of the solar spectrum and high reflectivity to IR radiation starting from 1500 nm to higher wavelengths. None of these films completely reflect the IR radiation with wavelengths in the range of 700 nm to 1500 nm which contribute to generation of heat most. Many factors such as doping concentration and thickness of the films affect reflectivity of the films due to their effect on shifting the plasma frequency. Increasing doping concentration and thickness of the GZO films until a certain threshold improves IR reflectivity of GZO films, but it was not possible to precisely formulate these factors within this study. Reducing the particle size in nanoparticle ITO film improves transmittance of the film in the visible light region and its reflectance in the IR region. The measurement results showed that Plexiglas and glass have a high transparency to visible and near IR radiation. Recommendations: Further investigation and experimentation is recommended to fully understand and formulate doping concentration and film thickness of GZO and ITO to achieve optimal IR reflectivity within the 700 to 1500 nm wavelength region while maintaining visible light transparency. In regards to ITO coating, reduced nanoparticle size is recommended to improve the desired visible and IR transparency and reflectivity of the film. Although GZO and ITO coatings’ doping concentration and thickness to achieve IR reflectivity within the 700 to 1500 nm wavelengths is not well understood in this study, to reduce heat generation they are still recommended as a coating material due to their reflectivity to IR with wavelengths of above 1500 nm which also play a role in generation of h
{"title":"Investigation of IR Reflective Coating for Plexiglas Canopy","authors":"Sara Perjalian","doi":"10.47672/ejt.1532","DOIUrl":"https://doi.org/10.47672/ejt.1532","url":null,"abstract":"Purpose: Solar energy is necessary for our global ecosystem. In certain times and parts of the world, the heat generated from the energy of the Sun becomes uncomfortable for us and we need to use additional energy to cool down the generated heat from the Sun. This additional energy usage is detrimental to our environment due to generation of air pollution and use of our natural energy resources. The purpose of this paper is to investigate prevention of undesired generation of heat by the Sun using reflective coating materials. \u0000Methodology: In this study, using literature review, Ga doped Zinc Oxide (GZO) is theoretically investigated as a visible light transparent and infrared reflective coating material for the Plexiglas canopy of a solar car. The effect of doping concentration and film thickness is reviewed and discussed. Indium Tin Oxide (ITO) is also studied, using various literature reviews, as an infrared reflective coating material that is transparent in the visible light region. Finally, a spectrophotometer was used to examine the optical properties of a Plexiglas, slide glass, and ITO coated glass, and the results of UV and visible light transmittance measurement of the three materials is presented and discussed. \u0000Findings: GZO and ITO films both have high transparency in the visible region of the solar spectrum and high reflectivity to IR radiation starting from 1500 nm to higher wavelengths. None of these films completely reflect the IR radiation with wavelengths in the range of 700 nm to 1500 nm which contribute to generation of heat most. Many factors such as doping concentration and thickness of the films affect reflectivity of the films due to their effect on shifting the plasma frequency. Increasing doping concentration and thickness of the GZO films until a certain threshold improves IR reflectivity of GZO films, but it was not possible to precisely formulate these factors within this study. Reducing the particle size in nanoparticle ITO film improves transmittance of the film in the visible light region and its reflectance in the IR region. The measurement results showed that Plexiglas and glass have a high transparency to visible and near IR radiation. \u0000Recommendations: Further investigation and experimentation is recommended to fully understand and formulate doping concentration and film thickness of GZO and ITO to achieve optimal IR reflectivity within the 700 to 1500 nm wavelength region while maintaining visible light transparency. In regards to ITO coating, reduced nanoparticle size is recommended to improve the desired visible and IR transparency and reflectivity of the film. Although GZO and ITO coatings’ doping concentration and thickness to achieve IR reflectivity within the 700 to 1500 nm wavelengths is not well understood in this study, to reduce heat generation they are still recommended as a coating material due to their reflectivity to IR with wavelengths of above 1500 nm which also play a role in generation of h","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"66 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86889750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fahad Ahmed, A. Fareed, Atiya Khan, Danish Q Siddiqui, Javeria Akhter, D. Khan
Purpose: Every day, new technologies and appealing devices are released that are aimed at a large portion of our population, particularly youth, and students. These devices get young people addicted; the addiction is to frequently utilize or grow depending on the gadget. People spend a lot of time on it, and it can induce psychological and neurological disturbances, which can lead to significant problems. This study aims to determine the association between the usage of electronic gadgets on students’ physical health and cognitive skills. As COVID-19 indicated extensive use of technology, the main outcome of this study is to intervene in the impact of electronic gadgets either positive or negative on student life. Methodology: Over 01 year (Feb 21 to Jan 22), this Quantitative- Analytical Cross-sectional study was conducted at Indus University of Health and science, Korangi Crossing, Karachi. Students of three academic departments of the university participated in the study i.e., Indus College of Allied Health Sciences, Indus College of Physical Therapy, and College of Nursing. Sample size of 170 with the margin of error as 4.6% and 95% confidence level. Both male and female age group between (16 to 35) active user of social media and electronic gadgets was enrolled in study from any discipline (nursing, allied health, biosciences, medical technology etc.). Any person with mental and physical disorder previously diagnosed or under any psychological treatment or medication was not included in the study Findings: Recommendations: If the study is repeated with a larger and more representative sample (which was not possible due to COVID Pandemic) significant influence or otherwise, of time spent on electronic gadgets on physical health could also be detected.
{"title":"Alarming Increase in Electronic Gadget Usage among Students during a Layer of the Global Pandemic","authors":"Fahad Ahmed, A. Fareed, Atiya Khan, Danish Q Siddiqui, Javeria Akhter, D. Khan","doi":"10.47672/ejt.1533","DOIUrl":"https://doi.org/10.47672/ejt.1533","url":null,"abstract":"Purpose: Every day, new technologies and appealing devices are released that are aimed at a large portion of our population, particularly youth, and students. These devices get young people addicted; the addiction is to frequently utilize or grow depending on the gadget. People spend a lot of time on it, and it can induce psychological and neurological disturbances, which can lead to significant problems. This study aims to determine the association between the usage of electronic gadgets on students’ physical health and cognitive skills. As COVID-19 indicated extensive use of technology, the main outcome of this study is to intervene in the impact of electronic gadgets either positive or negative on student life. \u0000Methodology: Over 01 year (Feb 21 to Jan 22), this Quantitative- Analytical Cross-sectional study was conducted at Indus University of Health and science, Korangi Crossing, Karachi. Students of three academic departments of the university participated in the study i.e., Indus College of Allied Health Sciences, Indus College of Physical Therapy, and College of Nursing. Sample size of 170 with the margin of error as 4.6% and 95% confidence level. Both male and female age group between (16 to 35) active user of social media and electronic gadgets was enrolled in study from any discipline (nursing, allied health, biosciences, medical technology etc.). Any person with mental and physical disorder previously diagnosed or under any psychological treatment or medication was not included in the study \u0000Findings: \u0000Recommendations: If the study is repeated with a larger and more representative sample (which was not possible due to COVID Pandemic) significant influence or otherwise, of time spent on electronic gadgets on physical health could also be detected. \u0000 \u0000 \u0000 \u0000 ","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88937331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: Unpredictable and rapid change in weather patterns has great impact on agricultural activities, especially for precision agriculture that results in worsened water resources availability, decreased soil fertility, use of pesticide as well as decreased yield productivity. In attempt to alleviate these challenges, this study aims at developing a real-time weather and farm field data driven Artificial Intelligence (AI) and Internet of Things (IoT) system that analyze, manage and schedule irrigation and fertigation as well as enabling farmers to interact with their farms via Smart phone or PCs to optimize energy and water resources. Methodology: The system employs weather condition monitoring sensors such as atmospheric pressure, air temperature, air humidity and wind speed for collecting real-time farm field data and uses Fuzzy Inference System (FIS) to predict rainfall rate at farm area for 24 hours period. The system also gathers field data such as soil moisture content and soil nutrient content and uses the Machine Learning (ML) algorithms to predict the time for irrigation and fertigation. By combining weather and farm field data, the system schedules the irrigation and fertigation activity. In addition, the mobile application is developed for the farmers to interact, control and monitor the farming activities and the data is presented to the farmers in both graphical and numerical formats. Findings: The system prototype deployed and tested in the two hectors Maize farm proved that 55% of water, 51% of energy and 20% of fertilizer were saved as well as increases in 20% of Maize yield production compared to previous season. Recommendations: Since the current irrigation and fertigation practices are based on predetermined time of the day and threshold values for automatic irrigation, this solution introduced the new concept of real-time and short-term weather forecasting that enables farmers to balance the irrigation period and weather pattern for water and fertilizer resources optimization.
{"title":"AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources","authors":"N. Pierre, Ishimwe Viviane Ishimwe Viviane, Uwimana Lambert, Ishimwe Viviane, Irakora Shadrack, Bakunzi Erneste, Nshimyumuremyi Schadrack, Ntawukuriryayo Alexis, Karanguza Francois, Habiyaremye Theogene","doi":"10.47672/ejt.1496","DOIUrl":"https://doi.org/10.47672/ejt.1496","url":null,"abstract":"Purpose: Unpredictable and rapid change in weather patterns has great impact on agricultural activities, especially for precision agriculture that results in worsened water resources availability, decreased soil fertility, use of pesticide as well as decreased yield productivity. In attempt to alleviate these challenges, this study aims at developing a real-time weather and farm field data driven Artificial Intelligence (AI) and Internet of Things (IoT) system that analyze, manage and schedule irrigation and fertigation as well as enabling farmers to interact with their farms via Smart phone or PCs to optimize energy and water resources. \u0000Methodology: The system employs weather condition monitoring sensors such as atmospheric pressure, air temperature, air humidity and wind speed for collecting real-time farm field data and uses Fuzzy Inference System (FIS) to predict rainfall rate at farm area for 24 hours period. The system also gathers field data such as soil moisture content and soil nutrient content and uses the Machine Learning (ML) algorithms to predict the time for irrigation and fertigation. By combining weather and farm field data, the system schedules the irrigation and fertigation activity. In addition, the mobile application is developed for the farmers to interact, control and monitor the farming activities and the data is presented to the farmers in both graphical and numerical formats. \u0000Findings: The system prototype deployed and tested in the two hectors Maize farm proved that 55% of water, 51% of energy and 20% of fertilizer were saved as well as increases in 20% of Maize yield production compared to previous season. \u0000Recommendations: Since the current irrigation and fertigation practices are based on predetermined time of the day and threshold values for automatic irrigation, this solution introduced the new concept of real-time and short-term weather forecasting that enables farmers to balance the irrigation period and weather pattern for water and fertilizer resources optimization. \u0000 ","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"19 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82389651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: The purpose of this study is to address the limited use of transfer learning techniques in radio frequency machine learning and to propose a customized taxonomy for radio frequency applications. The aim is to enable performance gains, improved generalization, and cost-effective training data solutions in this specific domain.
Methodology: The research design employed in this study involves a comprehensive review of existing literature on transfer learning in radio frequency machine learning. The researchers collected relevant papers from reputable sources and analyzed them to identify patterns, trends, and insights. The method of data collection primarily relied on examining and synthesizing existing literature. Data analysis involved identifying key findings and developing a customized taxonomy for radio frequency applications.
Findings: The study's findings highlight the limited utilization of transfer learning techniques in radio frequency machine learning. While transfer learning has shown significant performance improvements in computer vision and natural language processing, its potential in the wireless communications domain has yet to be fully explored. The customized taxonomy proposed in this study provides a consistent framework for analyzing and comparing existing and future efforts in this field.
Recommendations: Based on the findings, the study recommends further research and experimentation to explore the potential of transfer learning techniques in radio frequency machine learning. This includes investigating performance gains, improving generalization capabilities, and addressing concerns related to training data costs. Additionally, collaborations between researchers and practitioners in the field are encouraged to facilitate knowledge exchange and foster innovation. Practice: To practitioners in the field of radio frequency machine learning, this study emphasizes the potential benefits of incorporating transfer learning techniques. It encourages practitioners to explore the application of transfer learning in their specific domain, leveraging prior knowledge to enhance performance and address training data challenges. It also highlights the importance of staying informed about the latest developments and collaborating with experts in the field. Policy: To policy makers, the study underscores the need for supportive policies that promote research and development in radio frequency machine learning. It recommends creating an environment that fosters innovation, encourages collaborations between academia and industry, and provides resources and incentives for further exploration of transfer learning techniques. Policy makers should consider the potential impact of transfer learning on the wireless communications industry and support initiatives that enhance its adoption and implementation.
{"title":"Transfer Learning in Natural Language Processing (NLP)","authors":"Jasmin Bharadiya","doi":"10.47672/ejt.1490","DOIUrl":"https://doi.org/10.47672/ejt.1490","url":null,"abstract":"Purpose: The purpose of this study is to address the limited use of transfer learning techniques in radio frequency machine learning and to propose a customized taxonomy for radio frequency applications. The aim is to enable performance gains, improved generalization, and cost-effective training data solutions in this specific domain.
 Methodology: The research design employed in this study involves a comprehensive review of existing literature on transfer learning in radio frequency machine learning. The researchers collected relevant papers from reputable sources and analyzed them to identify patterns, trends, and insights. The method of data collection primarily relied on examining and synthesizing existing literature. Data analysis involved identifying key findings and developing a customized taxonomy for radio frequency applications.
 Findings: The study's findings highlight the limited utilization of transfer learning techniques in radio frequency machine learning. While transfer learning has shown significant performance improvements in computer vision and natural language processing, its potential in the wireless communications domain has yet to be fully explored. The customized taxonomy proposed in this study provides a consistent framework for analyzing and comparing existing and future efforts in this field.
 Recommendations: Based on the findings, the study recommends further research and experimentation to explore the potential of transfer learning techniques in radio frequency machine learning. This includes investigating performance gains, improving generalization capabilities, and addressing concerns related to training data costs. Additionally, collaborations between researchers and practitioners in the field are encouraged to facilitate knowledge exchange and foster innovation. Practice: To practitioners in the field of radio frequency machine learning, this study emphasizes the potential benefits of incorporating transfer learning techniques. It encourages practitioners to explore the application of transfer learning in their specific domain, leveraging prior knowledge to enhance performance and address training data challenges. It also highlights the importance of staying informed about the latest developments and collaborating with experts in the field. Policy: To policy makers, the study underscores the need for supportive policies that promote research and development in radio frequency machine learning. It recommends creating an environment that fosters innovation, encourages collaborations between academia and industry, and provides resources and incentives for further exploration of transfer learning techniques. Policy makers should consider the potential impact of transfer learning on the wireless communications industry and support initiatives that enhance its adoption and implementation.
","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135703230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts. Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions. Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research. Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges.
目的:本研究的目的是研究企业在整合和有效利用人工智能(AI)技术方面面临的挑战。它旨在全面了解人工智能技术如何产生商业价值以及它们提供的预期收益。该研究还试图确定人工智能采用和使用的促进因素和抑制因素，探索组织环境中不同类型的人工智能使用，并分析其一级和二级影响。方法:本研究采用综合文献综述研究设计。研究人员在Scopus和Web of Science等数据库中使用预定义的标准进行了系统的搜索。搜索产生了21篇相关论文，为本研究进行了分析和综合。数据收集方法依赖于对现有文献的检查。数据分析包括从选定的论文中确定关键主题、趋势和见解。研究者通过定性分析提取相关发现，并综合信息得出有意义的结论。研究结果:该研究揭示了一些关于人工智能在企业中的整合和使用的见解。这表明，组织正在努力理解人工智能技术如何产生价值，以及如何有效地将其纳入其运营中。缺乏对人工智能及其价值产生过程的全面了解被认为是一个主要障碍。此外，该研究还强调了人工智能采用和使用的促进因素和抑制因素。它确定了组织环境中各种类型的人工智能应用程序，并探讨了它们对业务运营的影响。调查结果揭示了企业在利用人工智能技术方面面临的挑战，并提出了进一步研究的领域。给从业者的建议:该研究强调了获取有关人工智能技术及其潜在价值产生过程的全面知识的重要性。致政策制定者:该研究强调需要支持性政策和法规来促进人工智能的采用。它建议创造一个促进人工智能研究和开发的有利环境。理论和验证:该研究可能受到与人工智能采用、组织变革或创新相关的现有理论的影响。实践:对于从业者来说，该研究强调了理解人工智能技术的价值和潜力的重要性。政策:对于政策制定者来说，该研究强调了促进人工智能采用和应对相关挑战的政策框架的必要性。
{"title":"The Impact of Artificial Intelligence on Business Processes","authors":"J. Bharadiya","doi":"10.47672/ejt.1488","DOIUrl":"https://doi.org/10.47672/ejt.1488","url":null,"abstract":"Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts. \u0000Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions. \u0000Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research. \u0000Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges. \u0000 ","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"41 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82216091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance.
{"title":"Machine Learning in Cybersecurity: Techniques and Challenges","authors":"J. Bharadiya","doi":"10.47672/ejt.1486","DOIUrl":"https://doi.org/10.47672/ejt.1486","url":null,"abstract":"In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance. \u0000 ","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"22 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79225077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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