Demand of bricks is increasing day by day by the requirement of new structures. Due to manufacturing of bricks, over-burnt bricks are also made. Generally over-burnt bricks are the waste materials which is generally used for dumping purpose and sometime it is thrown in useful land that create social and environmental problems. Therefore, this material should be properly treated, recycled and used in concrete as coarse aggregate. In this research work, six (06) batches were made with 1:2:4 mix and 0.5 water cement ratio. Each batch consists of five (05) samples. Six (06) batches were made with 0%, 20%, 40%, 60%, 80% and 100% replacement of natural coarse aggregates with over-burnt bricks aggregates. The outcome of study reveals that the gradation of both the aggregates shows same pattern and the trend of curve was almost similar, with minor difference in range values over a sieve. The water absorption of over-burnt bricks aggregates was more than the water absorption of natural coarse aggregates and the specific gravity of over-burnt bricks aggregates was less than the specific gravity of natural coarse aggregates. The result of slump test shows that there was continuous decrease in workability of concrete mix, as replacement of over-burnt bricks aggregates increased. 30 concrete cubes of (6” x 6” x 6”) size were prepared and cured for 28 days followed by compressive strength. The result shows that the concrete derived from over-burnt bricks aggregates attained lower compressive strength than the regular concrete. However, the values obtained from over-burnt bricks aggregates are still acceptable, especially for reasonable levels of the replacement ratio up-to 60%. This concrete can be used in new constructions, but it is proposed to be initially utilized in low load areas.
{"title":"Effects of Over-burnt Bricks on Gradation, Water Absorption and Specific Gravity of Aggregates, Workability and Compressive Strength of Concrete","authors":"Ajay Kumar, Jansher Khan, Rameez Ali Bangwar","doi":"10.56532/mjsat.v4i2.215","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.215","url":null,"abstract":"Demand of bricks is increasing day by day by the requirement of new structures. Due to manufacturing of bricks, over-burnt bricks are also made. Generally over-burnt bricks are the waste materials which is generally used for dumping purpose and sometime it is thrown in useful land that create social and environmental problems. Therefore, this material should be properly treated, recycled and used in concrete as coarse aggregate. In this research work, six (06) batches were made with 1:2:4 mix and 0.5 water cement ratio. Each batch consists of five (05) samples. Six (06) batches were made with 0%, 20%, 40%, 60%, 80% and 100% replacement of natural coarse aggregates with over-burnt bricks aggregates. The outcome of study reveals that the gradation of both the aggregates shows same pattern and the trend of curve was almost similar, with minor difference in range values over a sieve. The water absorption of over-burnt bricks aggregates was more than the water absorption of natural coarse aggregates and the specific gravity of over-burnt bricks aggregates was less than the specific gravity of natural coarse aggregates. The result of slump test shows that there was continuous decrease in workability of concrete mix, as replacement of over-burnt bricks aggregates increased. 30 concrete cubes of (6” x 6” x 6”) size were prepared and cured for 28 days followed by compressive strength. The result shows that the concrete derived from over-burnt bricks aggregates attained lower compressive strength than the regular concrete. However, the values obtained from over-burnt bricks aggregates are still acceptable, especially for reasonable levels of the replacement ratio up-to 60%. This concrete can be used in new constructions, but it is proposed to be initially utilized in low load areas.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700651","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}
Silvia Ganesan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan
The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.
{"title":"IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions","authors":"Silvia Ganesan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan","doi":"10.56532/mjsat.v4i2.293","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.293","url":null,"abstract":"The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729170","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}
Swathi Manoharan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan
Greenhouses have long been important in the advancement of agricultural operations because they provide regulated settings for optimal plant growth. With the introduction of real-time monitoring and automation capabilities, the Internet of Things (IoT) integration into greenhouse systems represents a revolutionary change. This abstract delves into the wider field of greenhouse technology, highlighting the role that IoT plays in improving agricultural in controlled environments. Conventional greenhouses provide plants with a protected environment, but they might not be as accurate or flexible. Intelligent control of environmental conditions is made possible by the introduction of IoT-enabled greenhouses, which utilize data exchange protocols, actuators, and sensors that are networked. The project aims to elevate traditional greenhouse models by integrating Node-RED and MQTT technologies. Transitioning from a Blynk-based prototype showcases the system's versatility. Other key components, including NodeMCU, sensors for real-time data, and LED lighting, collaborate to redefine controlled environment agriculture. The Raspberry Pi serves as a central hub, facilitating seamless communication through Node-RED and MQTT. This advanced greenhouse system harmonizes cutting-edge technologies, showcasing a commitment to sophistication and adaptability in agricultural practices.
{"title":"IoT-enabled Greenhouse Systems: Optimizing Plant Growth and Efficiency","authors":"Swathi Manoharan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan","doi":"10.56532/mjsat.v4i2.294","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.294","url":null,"abstract":"Greenhouses have long been important in the advancement of agricultural operations because they provide regulated settings for optimal plant growth. With the introduction of real-time monitoring and automation capabilities, the Internet of Things (IoT) integration into greenhouse systems represents a revolutionary change. This abstract delves into the wider field of greenhouse technology, highlighting the role that IoT plays in improving agricultural in controlled environments. Conventional greenhouses provide plants with a protected environment, but they might not be as accurate or flexible. Intelligent control of environmental conditions is made possible by the introduction of IoT-enabled greenhouses, which utilize data exchange protocols, actuators, and sensors that are networked. The project aims to elevate traditional greenhouse models by integrating Node-RED and MQTT technologies. Transitioning from a Blynk-based prototype showcases the system's versatility. Other key components, including NodeMCU, sensors for real-time data, and LED lighting, collaborate to redefine controlled environment agriculture. The Raspberry Pi serves as a central hub, facilitating seamless communication through Node-RED and MQTT. This advanced greenhouse system harmonizes cutting-edge technologies, showcasing a commitment to sophistication and adaptability in agricultural practices.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732037","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}
Anne Dashini Kannan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan
The growing interest in the fish farming industry is driven by the depletion of natural fish stocks in the market. However, intensive aquaculture systems, which involve raising fish in artificial tanks and cages, can lead to challenges such as low-quality fish and increased mortality rates, depending on the species being cultivated. To address these issues and maximize yield, this paper proposes a fish quality monitoring system with automatic correction. The system focuses on monitoring and maintaining critical water quality parameters essential for fish growth, including temperature, water level, and pH level. The system comprises an Arduino connected to sensors and a web-based application for data collection and monitoring. Correction devices such as an aquarium heater, a valve, and a water pump are integrated into the system to maintain these parameters at optimal levels for fish development. To assess the system's efficiency and reliability, two fish monitoring setups were compared: one using the proposed controlled system and the other using a traditional setup. Results indicate that the controlled system increased efficiency, reduced stress on fish farmers, decreased fish mortality rates, and improved product quality compared to the traditional setup.
{"title":"Improving Fish Quality and Yield: An Automated Monitoring System for Intensive Aquaculture","authors":"Anne Dashini Kannan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan","doi":"10.56532/mjsat.v4i2.296","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.296","url":null,"abstract":"The growing interest in the fish farming industry is driven by the depletion of natural fish stocks in the market. However, intensive aquaculture systems, which involve raising fish in artificial tanks and cages, can lead to challenges such as low-quality fish and increased mortality rates, depending on the species being cultivated. To address these issues and maximize yield, this paper proposes a fish quality monitoring system with automatic correction. The system focuses on monitoring and maintaining critical water quality parameters essential for fish growth, including temperature, water level, and pH level. The system comprises an Arduino connected to sensors and a web-based application for data collection and monitoring. Correction devices such as an aquarium heater, a valve, and a water pump are integrated into the system to maintain these parameters at optimal levels for fish development. To assess the system's efficiency and reliability, two fish monitoring setups were compared: one using the proposed controlled system and the other using a traditional setup. Results indicate that the controlled system increased efficiency, reduced stress on fish farmers, decreased fish mortality rates, and improved product quality compared to the traditional setup.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734259","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}
Krishna AL Sannasy Rao, Chong Peng Lean, Ng Poh Kiat, Feng Yuan Kong, M. Reyasudin, Basir Khan, Daniel Ismail, Chen Li
Artificial intelligence and machine learning are essential for the development of IR4.0 due to their ability to analyse vast amounts of data, automate processes, and drive innovation across various sectors. These technologies enable intelligent decision-making, predictive analytics, and automation, leading to increased efficiency, productivity, and competitiveness in the digital age. In IR4.0, AI and ML power smart systems and connected devices, transforming industries. They facilitate the integration of digital, physical, and biological systems, enabling the creation of personalized medicine and medical diagnosis smart manufacturing, self-autonomous driving vehicles, smart cities, and smart home. Hence, this review aims to address the contribution of AI and ML in the development of medical diagnosis, smart manufacturing, smart cars, smart cities, and smart homes as well as to highlight the existing challenges faced by AI and ML in these fields. This review also showcases the relevant prospects of AI and ML applications in the fields mentioned.
人工智能和机器学习对 IR4.0 的发展至关重要，因为它们能够分析海量数据、实现流程自动化并推动各行各业的创新。这些技术可实现智能决策、预测分析和自动化，从而在数字时代提高效率、生产力和竞争力。在 IR4.0 中，人工智能和 ML 为智能系统和互联设备提供动力，改变着各行各业。它们促进了数字、物理和生物系统的整合，使个性化医疗和医疗诊断、智能制造、自动驾驶汽车、智能城市和智能家居成为可能。因此，本综述旨在探讨人工智能和 ML 在医疗诊断、智能制造、智能汽车、智能城市和智能家居发展中的贡献，并强调人工智能和 ML 在这些领域面临的现有挑战。本综述还展示了人工智能和 ML 在上述领域的相关应用前景。
{"title":"AI and ML in IR4.0: A Short Review of Applications and Challenges","authors":"Krishna AL Sannasy Rao, Chong Peng Lean, Ng Poh Kiat, Feng Yuan Kong, M. Reyasudin, Basir Khan, Daniel Ismail, Chen Li","doi":"10.56532/mjsat.v4i2.291","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.291","url":null,"abstract":"Artificial intelligence and machine learning are essential for the development of IR4.0 due to their ability to analyse vast amounts of data, automate processes, and drive innovation across various sectors. These technologies enable intelligent decision-making, predictive analytics, and automation, leading to increased efficiency, productivity, and competitiveness in the digital age. In IR4.0, AI and ML power smart systems and connected devices, transforming industries. They facilitate the integration of digital, physical, and biological systems, enabling the creation of personalized medicine and medical diagnosis smart manufacturing, self-autonomous driving vehicles, smart cities, and smart home. Hence, this review aims to address the contribution of AI and ML in the development of medical diagnosis, smart manufacturing, smart cars, smart cities, and smart homes as well as to highlight the existing challenges faced by AI and ML in these fields. This review also showcases the relevant prospects of AI and ML applications in the fields mentioned.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359312","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}
Gophinath Krishnan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan
Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.
{"title":"A Raspberry Pi-Powered IoT Smart Farming System for Efficient Water Irrigation and Crop Monitoring","authors":"Gophinath Krishnan, Chong Peng Lean, Chen Li, Kong Feng Yuan, Ng Poh Kiat, M. Reyasudin, Basir Khan","doi":"10.56532/mjsat.v4i2.295","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.295","url":null,"abstract":"Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359475","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}
Krishna AL Sannasy Rao, Chong Peng Lean, Kong Feng, Yuan, Ng Poh Kiat, Chen Li, M. Reyasudin, Basir Khan, Daniel Ismail
By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.
{"title":"Transformative Applications of IoT in Diverse Industries: A Mini Review","authors":"Krishna AL Sannasy Rao, Chong Peng Lean, Kong Feng, Yuan, Ng Poh Kiat, Chen Li, M. Reyasudin, Basir Khan, Daniel Ismail","doi":"10.56532/mjsat.v4i2.292","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.292","url":null,"abstract":"By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358376","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}
Akinul Islam Jony, Anika Tahsin Rithin, Siam Ibne Edrish
This Various text summarization methods, such as extractive, abstractive, and human abstraction concepts have been compared in terms of performance, each with its specialties and limitations. This research analyses comparisons among the methods and some of their techniques used in text summarization. Our initial contribution is to suggest a thorough overview of the methods. The research methodology aims to compare text summarization methods through a systematic literature review to understand the topic and select appropriate methods. The search method involves keyword-based and citation-based techniques using academic search engines. The comparison of methods will consider various evaluation criteria such as document structure, content importance, quantitative approach, qualitative approach, dependency on machine learning, sentence generation, central concept identification, human involvement, representation in mathematics, and historical approaches. The methods would be evaluated based on these criteria to provide an objective and comprehensive comparison. No method consistently produces accurate text summaries. The best course of action will depend on the particulars and constraints of the current work because each method has both positive and negative aspects. The two primary methods for text summarization were discovered to be extractive and abstractive. This comparison study analysed various text summary and revealing each method's positive attributes and drawbacks. By giving a comprehensive overview of the main two methods, this comparative analysis advances the subject of text summarizing.
{"title":"A Comparative Study and Analysis of Text Summarization Methods","authors":"Akinul Islam Jony, Anika Tahsin Rithin, Siam Ibne Edrish","doi":"10.56532/mjsat.v4i2.231","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.231","url":null,"abstract":"This Various text summarization methods, such as extractive, abstractive, and human abstraction concepts have been compared in terms of performance, each with its specialties and limitations. This research analyses comparisons among the methods and some of their techniques used in text summarization. Our initial contribution is to suggest a thorough overview of the methods. The research methodology aims to compare text summarization methods through a systematic literature review to understand the topic and select appropriate methods. The search method involves keyword-based and citation-based techniques using academic search engines. The comparison of methods will consider various evaluation criteria such as document structure, content importance, quantitative approach, qualitative approach, dependency on machine learning, sentence generation, central concept identification, human involvement, representation in mathematics, and historical approaches. The methods would be evaluated based on these criteria to provide an objective and comprehensive comparison. No method consistently produces accurate text summaries. The best course of action will depend on the particulars and constraints of the current work because each method has both positive and negative aspects. The two primary methods for text summarization were discovered to be extractive and abstractive. This comparison study analysed various text summary and revealing each method's positive attributes and drawbacks. By giving a comprehensive overview of the main two methods, this comparative analysis advances the subject of text summarizing.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140390470","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}
Nurul Azarima, Mohd Ali, N. Tukiran, Raihanah Roslan
Oil authentication has been widely discussed in recent years. One of the issues is the usage of gutter oil. This happened in China where many of the street foods were prepared using oils from sewage, gutters, and restaurant fryers. Other concerning issues including the adulteration of high-quality edible oils with cheaper oils and fresh palm oil with recycled cooking oil are common problems related to oil fraud. This may provoke the safety and the rights of public consumers. Hence, advanced, efficient, and rapid technology such as Fourier Transform Infrared Spectroscopy (FTIR) is needed to overcome the limitations of other technologies such as differential scanning calorimetry (DSC), gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) in analysing edible oils’ quality parameters, authentication, safety, stability and in foods related to oils. This review discusses the uses of FTIR in the analysis of edible oils and their authentication.
{"title":"Authenticating Edible Oils Using Fourier Transform Infrared Spectroscopy: A Review","authors":"Nurul Azarima, Mohd Ali, N. Tukiran, Raihanah Roslan","doi":"10.56532/mjsat.v4i2.237","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.237","url":null,"abstract":"Oil authentication has been widely discussed in recent years. One of the issues is the usage of gutter oil. This happened in China where many of the street foods were prepared using oils from sewage, gutters, and restaurant fryers. Other concerning issues including the adulteration of high-quality edible oils with cheaper oils and fresh palm oil with recycled cooking oil are common problems related to oil fraud. This may provoke the safety and the rights of public consumers. Hence, advanced, efficient, and rapid technology such as Fourier Transform Infrared Spectroscopy (FTIR) is needed to overcome the limitations of other technologies such as differential scanning calorimetry (DSC), gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) in analysing edible oils’ quality parameters, authentication, safety, stability and in foods related to oils. This review discusses the uses of FTIR in the analysis of edible oils and their authentication.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140390789","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}
Breast cancer is a highly common and life-threatening disease that affects people worldwide. Early and accurate diagnosis of breast cancer can enhance patients' prognosis and survival rate. This paper conducts a comparative examination of the Wisconsin Breast Cancer Diagnostic (WBCD) dataset by employing four distinct deep learning models: Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The collection consists of 569 examples of Fine Needle Aspirate (FNA) photographs of breast cancers, with each case containing thirty parameters that define the features of the cell nuclei. By doing a comparative analysis of the advantages and disadvantages of the models, we will evaluate them based on their accuracy, precision, recall, and F1-score. Based on our research, CNN achieves the best level of accuracy at 98.25%, which is followed by GRU at 97.37%, FNN at 96.49%, and LSTM at 95.61%. It is determined that CNN is the most suitable model for this task and that deep learning models are valuable and encouraging tools for diagnosing breast cancer.
{"title":"Deep Learning Paradigms for Breast Cancer Diagnosis: A Comparative Study on Wisconsin Diagnostic Dataset","authors":"Akinul Islam Jony, Arjun Kumar Bose Arnob","doi":"10.56532/mjsat.v4i2.245","DOIUrl":"https://doi.org/10.56532/mjsat.v4i2.245","url":null,"abstract":"Breast cancer is a highly common and life-threatening disease that affects people worldwide. Early and accurate diagnosis of breast cancer can enhance patients' prognosis and survival rate. This paper conducts a comparative examination of the Wisconsin Breast Cancer Diagnostic (WBCD) dataset by employing four distinct deep learning models: Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The collection consists of 569 examples of Fine Needle Aspirate (FNA) photographs of breast cancers, with each case containing thirty parameters that define the features of the cell nuclei. By doing a comparative analysis of the advantages and disadvantages of the models, we will evaluate them based on their accuracy, precision, recall, and F1-score. Based on our research, CNN achieves the best level of accuracy at 98.25%, which is followed by GRU at 97.37%, FNN at 96.49%, and LSTM at 95.61%. It is determined that CNN is the most suitable model for this task and that deep learning models are valuable and encouraging tools for diagnosing breast cancer.","PeriodicalId":496585,"journal":{"name":"Malaysian Journal of Science and Advanced Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140389646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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