Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.224238
Mostafa ElShabasy, Mohamed Abaza, Mohamed Fathy Abo Sree, Ahmed Fawzy
Nowadays, cooperative communication algorithms have been utilized in Wireless Sensor Network (WSN) to enhance the overall network performance. As is well known, a WSN needs to consider a number of important factors, such as the energy effectiveness and the longevity of the sensor nodes. The energy-aware cooperative medium access control (EC-MAC) protocol is a novel protocol proposed in this paper for usage in WSNs. EC-MAC protocol allows the source nodes to use the intermediate nodes as relays that can be used to transmit the source's data to the access point (AP). This paper demonstrates how the suggested relay selection method can be used by the EC-MAC protocol to choose the best relay node. After channel state information (CSI) has been calculated and acquired, the best relay should have the highest residual energy and the quickest transmission time. Then, by establishing suitable cooperative links, data transmission from a source node to an AP can be carried out. The effectiveness of the EC-MAC protocol in terms of system energy efficiency is examined in this study using the MATLAB simulation tool and compares the outcomes with other cooperative protocols like Modified Cooperative Access MAC Protocol (MCA-MAC) and Throughput and Energy aware Cooperative MAC Protocol (TEC-MAC) and the performance of WSNs employing the suggested EC-MAC protocol is examined in this research for both ideal and dynamic channel conditions. EC-MAC protocol achieved energy efficiency improvements of 20%, and 40% respectively, more than MCA-MAC and TEC-MAC protocols. The results indicated that EC-MAC protocol offers a higher level of energy efficiency for the WSN than other cooperative protocols currently in use.
{"title":"EC-MAC: Energy-Aware Cooperative MAC Protocol in Wireless Sensor Network","authors":"Mostafa ElShabasy, Mohamed Abaza, Mohamed Fathy Abo Sree, Ahmed Fawzy","doi":"10.37934/araset.45.1.224238","DOIUrl":"https://doi.org/10.37934/araset.45.1.224238","url":null,"abstract":"Nowadays, cooperative communication algorithms have been utilized in Wireless Sensor Network (WSN) to enhance the overall network performance. As is well known, a WSN needs to consider a number of important factors, such as the energy effectiveness and the longevity of the sensor nodes. The energy-aware cooperative medium access control (EC-MAC) protocol is a novel protocol proposed in this paper for usage in WSNs. EC-MAC protocol allows the source nodes to use the intermediate nodes as relays that can be used to transmit the source's data to the access point (AP). This paper demonstrates how the suggested relay selection method can be used by the EC-MAC protocol to choose the best relay node. After channel state information (CSI) has been calculated and acquired, the best relay should have the highest residual energy and the quickest transmission time. Then, by establishing suitable cooperative links, data transmission from a source node to an AP can be carried out. The effectiveness of the EC-MAC protocol in terms of system energy efficiency is examined in this study using the MATLAB simulation tool and compares the outcomes with other cooperative protocols like Modified Cooperative Access MAC Protocol (MCA-MAC) and Throughput and Energy aware Cooperative MAC Protocol (TEC-MAC) and the performance of WSNs employing the suggested EC-MAC protocol is examined in this research for both ideal and dynamic channel conditions. EC-MAC protocol achieved energy efficiency improvements of 20%, and 40% respectively, more than MCA-MAC and TEC-MAC protocols. The results indicated that EC-MAC protocol offers a higher level of energy efficiency for the WSN than other cooperative protocols currently in use.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"123 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.215223
Siti Rohani binti Mohd Nor, Nurul Syuhada Samsudin, Muhammad Asri bin Manap, Siti Mariam Norrulashikin
Over the last year, the COVID-19 epidemic has afflicted over 150 million individuals and killed over three million people globally. Various forecasting models attempted to estimate the temporal course of the COVID-19 pandemic during this time period in order to determine effectiveness of the government action in facing COVID-19 outbreak. In this study, Autoregressive Integrated Moving Average (ARIMA) models were used in order to forecast the COVID-19 mortality rates data in Malaysia. The accuracy of the ARIMA models is then evaluated by using Mean Absolute Error (MAE) and Root Mean Square Absolute Error (RMSE). The forecasting model with the lowest error is picked as the best. In this study, ARIMA (1,1,3) outperformed the ARIMA (1,1,2) and ARIMA (1,1,4) models since it has the lowest MAE and RMSE values. However, as compared to ARIMA (1,1,4), the study found that ARIMA (1,1,3) model is not adequate in terms of model fitting due to the errors were not normally distributed. Hence, ARIMA (1,1,4) model was chosen to make prediction of COVID-19 mortality rates. Accordingly, the findings through this study can be used as a preliminary study to predict the COVID-19 mortality rates and other future pandemic cases to mitigate risk of increasing cases.
{"title":"Modelling and Forecasting the COVID-19 Mortality Rates in Malaysia by using ARIMA Model","authors":"Siti Rohani binti Mohd Nor, Nurul Syuhada Samsudin, Muhammad Asri bin Manap, Siti Mariam Norrulashikin","doi":"10.37934/araset.45.1.215223","DOIUrl":"https://doi.org/10.37934/araset.45.1.215223","url":null,"abstract":"Over the last year, the COVID-19 epidemic has afflicted over 150 million individuals and killed over three million people globally. Various forecasting models attempted to estimate the temporal course of the COVID-19 pandemic during this time period in order to determine effectiveness of the government action in facing COVID-19 outbreak. In this study, Autoregressive Integrated Moving Average (ARIMA) models were used in order to forecast the COVID-19 mortality rates data in Malaysia. The accuracy of the ARIMA models is then evaluated by using Mean Absolute Error (MAE) and Root Mean Square Absolute Error (RMSE). The forecasting model with the lowest error is picked as the best. In this study, ARIMA (1,1,3) outperformed the ARIMA (1,1,2) and ARIMA (1,1,4) models since it has the lowest MAE and RMSE values. However, as compared to ARIMA (1,1,4), the study found that ARIMA (1,1,3) model is not adequate in terms of model fitting due to the errors were not normally distributed. Hence, ARIMA (1,1,4) model was chosen to make prediction of COVID-19 mortality rates. Accordingly, the findings through this study can be used as a preliminary study to predict the COVID-19 mortality rates and other future pandemic cases to mitigate risk of increasing cases.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"125 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.154167
Norsuzila Yaacob, Nur Syaza Zainali, Amirul Asraf Abdul Rahman, Azita Laily Yusof, Murizah Kassim, Ahmad Shazri Nazif Salehudin
Water quality is an assessment of how appropriate water is for a certain use or purpose, taking into consideration various physical, chemical, and biological factors that can affect its suitability. These factors can include pH, turbidity, dissolved oxygen, temperature, and the presence of pollutants or pathogens. The outdated method has been used by scientists and researchers to monitor the quality of water from the sources. The objective of this project is to create an efficient Internet of Things (IoT) system that can various sensors to continuously monitor water quality. The system is implemented using Arduino as the microcontroller, and sensors. A real-time monitoring system that is IoT-based was done to improve the examination process of the water sample. The system device is containing a NodeMCU ESP8266 microcontroller, pH, temperature, and turbidity sensors and uses the Blynk application. The system experiment results show that the device can show different readings based on the variety of water samples from different water bodies.
{"title":"Design of Water Quality Monitoring System Based on Internet of Things Technology","authors":"Norsuzila Yaacob, Nur Syaza Zainali, Amirul Asraf Abdul Rahman, Azita Laily Yusof, Murizah Kassim, Ahmad Shazri Nazif Salehudin","doi":"10.37934/araset.45.1.154167","DOIUrl":"https://doi.org/10.37934/araset.45.1.154167","url":null,"abstract":"Water quality is an assessment of how appropriate water is for a certain use or purpose, taking into consideration various physical, chemical, and biological factors that can affect its suitability. These factors can include pH, turbidity, dissolved oxygen, temperature, and the presence of pollutants or pathogens. The outdated method has been used by scientists and researchers to monitor the quality of water from the sources. The objective of this project is to create an efficient Internet of Things (IoT) system that can various sensors to continuously monitor water quality. The system is implemented using Arduino as the microcontroller, and sensors. A real-time monitoring system that is IoT-based was done to improve the examination process of the water sample. The system device is containing a NodeMCU ESP8266 microcontroller, pH, temperature, and turbidity sensors and uses the Blynk application. The system experiment results show that the device can show different readings based on the variety of water samples from different water bodies.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"106 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.90107
Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, Shahrul Azman Bakar, Haryati Abidin, Shaiful Jahari Hashim, Mohd Na’aim Samad, Nik Norasma Che’ya, Mohd Roshdi Hassan, Hwee San Lim, Rosni Abdullah, Yusri Yusup, Syahidah Akmal Muhammad, Teh Sin Yin, Mohamed Barakat A. Gibril
Due to environmental threats and weather uncertainty concerns, oil palm yield prediction is crucial for sustaining crop production. This can be achieved through machine learning and utilising remotely sensed data to predict crop yield. However, the comparative studies on remotely sensed data in adopting the machine learning models are still limited due to the data accessibility. Therefore, we compare and evaluate the prediction accuracy between different satellites, namely MODIS and Landsat-7, using machine learning algorithms and the topology of deep neural networks. Random forest and stacking outperformed linear regression, ridge regression, and lasso regression for both Landsat-7 NDVI (R2= 0.78–0.80; RMSE=1.00- 1.26 tonnes per hectare; MAE=0.77- 0.79 tonnes per hectares; MAPE=0.03-0.04 tonnes per hectare) and MODIS NDVI (R2= 0.60–0.65 tonnes per hectares; RMSE= 2.72–2.81 tonne per hectares; MAE= 1.42-1.55, MAPE= 1.01- 1.02 tonnes per hectares). The Landsat-7 NDVI revealed that neural networks with a deeper network topology (R2= 0.85; RMSE= 1.42 tonnes per hectare; MAE=0.57 tonnes per hectares; MAPE=0.06 tonnes per hectare) outperformed neural networks with a baseline and broader network topologies in terms of performance. In contrast, MODIS-NDVI revealed that the neural network with a wider network topology had the highest overall prediction accuracy and the lowest prediction error (R2= 0.75; RMSE= 2.81 tonnes per hectare; MAE=2.27 tonnes per tonnes; MAPE= 0.13). Because of its higher spatial resolution in comparison to MODIS, landsat-7 NDVI used in neural networks with a deep network topology provided the best model performance. Although the use of NDVI as a single input factor may cause uncertainty in some extents, it is an efficient and reliable method for improving yield estimation with the use of medium-resolution satellites, which has important implications for early warning towards the reduction in yield production.
{"title":"Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data","authors":"Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, Shahrul Azman Bakar, Haryati Abidin, Shaiful Jahari Hashim, Mohd Na’aim Samad, Nik Norasma Che’ya, Mohd Roshdi Hassan, Hwee San Lim, Rosni Abdullah, Yusri Yusup, Syahidah Akmal Muhammad, Teh Sin Yin, Mohamed Barakat A. Gibril","doi":"10.37934/araset.45.1.90107","DOIUrl":"https://doi.org/10.37934/araset.45.1.90107","url":null,"abstract":"Due to environmental threats and weather uncertainty concerns, oil palm yield prediction is crucial for sustaining crop production. This can be achieved through machine learning and utilising remotely sensed data to predict crop yield. However, the comparative studies on remotely sensed data in adopting the machine learning models are still limited due to the data accessibility. Therefore, we compare and evaluate the prediction accuracy between different satellites, namely MODIS and Landsat-7, using machine learning algorithms and the topology of deep neural networks. Random forest and stacking outperformed linear regression, ridge regression, and lasso regression for both Landsat-7 NDVI (R2= 0.78–0.80; RMSE=1.00- 1.26 tonnes per hectare; MAE=0.77- 0.79 tonnes per hectares; MAPE=0.03-0.04 tonnes per hectare) and MODIS NDVI (R2= 0.60–0.65 tonnes per hectares; RMSE= 2.72–2.81 tonne per hectares; MAE= 1.42-1.55, MAPE= 1.01- 1.02 tonnes per hectares). The Landsat-7 NDVI revealed that neural networks with a deeper network topology (R2= 0.85; RMSE= 1.42 tonnes per hectare; MAE=0.57 tonnes per hectares; MAPE=0.06 tonnes per hectare) outperformed neural networks with a baseline and broader network topologies in terms of performance. In contrast, MODIS-NDVI revealed that the neural network with a wider network topology had the highest overall prediction accuracy and the lowest prediction error (R2= 0.75; RMSE= 2.81 tonnes per hectare; MAE=2.27 tonnes per tonnes; MAPE= 0.13). Because of its higher spatial resolution in comparison to MODIS, landsat-7 NDVI used in neural networks with a deep network topology provided the best model performance. Although the use of NDVI as a single input factor may cause uncertainty in some extents, it is an efficient and reliable method for improving yield estimation with the use of medium-resolution satellites, which has important implications for early warning towards the reduction in yield production.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"120 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126687","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}
Neuroscientific evidence suggests that weight gain may be associated with changes in brain lobes' volume and function, as well as impulsive behaviour related to eating. However, it remains unclear whether impulsivity behaviour in overweight subjects is linked to abnormal activity in the resting state. To address this question, we propose a novel method to assess the relationship between different levels of body mass index (BMI) and neural activity of the prefrontal cortex (PFC) using electroencephalography (EEG) resting state data. EEG signals recorded during open-eye resting state from 36 subjects were divided into two groups based on BMI: overweight and normal weight subjects. We applied wavelet transform technique to compute the power for decomposed EEG bands and extracted coherence maps to assess the functional connectivity of the PFC. The one-way analysis of variance (ANOVA) was employed to assess the difference in EEG variables between the study groups. The results show a significant increase in the power of the sub-Theta band (4.49-5.34) Hz in overweight subjects compared to normal weight subjects (p-value = 0.001), as well as dysfunctional connectivity between left-right prefrontal sites in the overweight group with decreasing coherence function. These outcomes suggest that the specific PFC-EEG signals observed in overweight individuals are consistent with EEG patterns seen in other impulsivity-related diseases. Therefore, our findings reveal a specific EEG pattern in overweight adults that could be potentially utilized in developing neurotherapy-based treatment methods for overweight management.
{"title":"Assessing the Relationship Between Body Mass Index and Neural Activity of Prefrontal Cortex in Overweight Adults Using EEG-Resting State Data: A Wavelet Transform Analysis","authors":"Mohammed Isam Al-Hiyali, Asnor Juraiza Ishak, Maged Saleh Saeed Al-Quraishi, Sarmad Nozad Mahmood","doi":"10.37934/araset.45.1.137153","DOIUrl":"https://doi.org/10.37934/araset.45.1.137153","url":null,"abstract":"Neuroscientific evidence suggests that weight gain may be associated with changes in brain lobes' volume and function, as well as impulsive behaviour related to eating. However, it remains unclear whether impulsivity behaviour in overweight subjects is linked to abnormal activity in the resting state. To address this question, we propose a novel method to assess the relationship between different levels of body mass index (BMI) and neural activity of the prefrontal cortex (PFC) using electroencephalography (EEG) resting state data. EEG signals recorded during open-eye resting state from 36 subjects were divided into two groups based on BMI: overweight and normal weight subjects. We applied wavelet transform technique to compute the power for decomposed EEG bands and extracted coherence maps to assess the functional connectivity of the PFC. The one-way analysis of variance (ANOVA) was employed to assess the difference in EEG variables between the study groups. The results show a significant increase in the power of the sub-Theta band (4.49-5.34) Hz in overweight subjects compared to normal weight subjects (p-value = 0.001), as well as dysfunctional connectivity between left-right prefrontal sites in the overweight group with decreasing coherence function. These outcomes suggest that the specific PFC-EEG signals observed in overweight individuals are consistent with EEG patterns seen in other impulsivity-related diseases. Therefore, our findings reveal a specific EEG pattern in overweight adults that could be potentially utilized in developing neurotherapy-based treatment methods for overweight management.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.129136
Nooradelena Mohd Ruslim, Yuhani Yusof, Mohd Sham Mohamad, Mohd Firdaus Abdul-Wahab, Faisal
A splicing system is a formal characterization of the ability to generate certain enzymatic activities acting on deoxyribonucleic acid (DNA) molecules. In this paper, the results from Laun’s experiment are used in characterizing the type of splicing languages. In the experiments, two initial strings are involved with different features on the selected restriction enzymes. Case I and Case II discussed in this paper show that the splicing languages obtained from these experiments are in adult and limit languages. Nevertheless, the result obtained in this paper is more precise in showing the type of splicing languages which is beyond adult and limit languages when presented via a directed splicing graph. The features of the restriction enzyme that affect the formation of active persistent language are investigated based on the results proposed by Yusof.
拼接系统是对产生作用于脱氧核糖核酸（DNA）分子的某些酶活动的能力的正式表征。本文利用劳恩的实验结果来描述拼接语言的类型。在实验中，有两个初始字符串与所选限制性酶的不同特征有关。本文讨论的案例 I 和案例 II 表明，从这些实验中获得的拼接语言属于成人语言和限制语言。尽管如此，本文的结果通过有向拼接图更精确地显示了超出成语和极限语言的拼接语言类型。本文以尤索夫提出的结果为基础，研究了影响活跃持久语言形成的限制酶特征。
{"title":"Characterize Type of Splicing Languages via Directed Splicing Graph","authors":"Nooradelena Mohd Ruslim, Yuhani Yusof, Mohd Sham Mohamad, Mohd Firdaus Abdul-Wahab, Faisal","doi":"10.37934/araset.45.1.129136","DOIUrl":"https://doi.org/10.37934/araset.45.1.129136","url":null,"abstract":"A splicing system is a formal characterization of the ability to generate certain enzymatic activities acting on deoxyribonucleic acid (DNA) molecules. In this paper, the results from Laun’s experiment are used in characterizing the type of splicing languages. In the experiments, two initial strings are involved with different features on the selected restriction enzymes. Case I and Case II discussed in this paper show that the splicing languages obtained from these experiments are in adult and limit languages. Nevertheless, the result obtained in this paper is more precise in showing the type of splicing languages which is beyond adult and limit languages when presented via a directed splicing graph. The features of the restriction enzyme that affect the formation of active persistent language are investigated based on the results proposed by Yusof.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"123 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.2839
Aeizaal Azman Abdul Wahab, Nur Qamarina Muhammad Adnan, Syed Sahal Nazli Alhady, Wan Amir Fuad Wajdi Othman
OFDM has become popular method to be applied in data communication as it has high data rate. Selective Mapping OFDM (SLM OFDM) is introduced to over-come the OFDM’s disadvantage which is high PAPR. SLM OFDM offers PAPR reduction by selecting OFDM waveform with the lowest PAPR among many copies of waveform candidates, U. As the world become more digitalized and demanding, Green OFDM shows up with more candidates, U^2/4, without making computation more complex. More candidates will promote more choices with lower PAPR values compare to the SLM OFDM. In the recent years, re-searchers have come out with a new improved Green OFDM version 2 with more waveform candidates to be chosen, U^2. The improved Green OFDM ver-sion 2 scheme will produce lower PAPR values compare to the SLM OFDM and the original Green OFDM. Thus, technologies with higher data transmission are able to be created.
{"title":"An Improved PAPR Reduction Scheme using Green OFDM","authors":"Aeizaal Azman Abdul Wahab, Nur Qamarina Muhammad Adnan, Syed Sahal Nazli Alhady, Wan Amir Fuad Wajdi Othman","doi":"10.37934/araset.45.1.2839","DOIUrl":"https://doi.org/10.37934/araset.45.1.2839","url":null,"abstract":"OFDM has become popular method to be applied in data communication as it has high data rate. Selective Mapping OFDM (SLM OFDM) is introduced to over-come the OFDM’s disadvantage which is high PAPR. SLM OFDM offers PAPR reduction by selecting OFDM waveform with the lowest PAPR among many copies of waveform candidates, U. As the world become more digitalized and demanding, Green OFDM shows up with more candidates, U^2/4, without making computation more complex. More candidates will promote more choices with lower PAPR values compare to the SLM OFDM. In the recent years, re-searchers have come out with a new improved Green OFDM version 2 with more waveform candidates to be chosen, U^2. The improved Green OFDM ver-sion 2 scheme will produce lower PAPR values compare to the SLM OFDM and the original Green OFDM. Thus, technologies with higher data transmission are able to be created.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"124 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.108116
Noratira Abd Samad, Muhammad Fikri Hasmori, Nor Haslinda Abas, Farzaneh Moayedi, Mustafa Klufallah
Fall-from-height accidents are a significant cause of injuries and fatalities in construction industries. The occurrence of this accident may have been caused by any number of contributing factors. In addition, various preventive measures have been proposed to prevent this accident from occurring. To capture the true condition of a fall from height accident, the causes of the accident, the effects of the accident, and the preventive actions should be rendered out. Therefore, it is possible to analyse these accidents in order to determine their fundamental causes and implement effective preventative measures. This article presents a literature-based framework for analysing fall-from-height accidents. A literature review was conducted to examine existing studies and identify similar themes and patterns on fall from height accidents in the construction industry to illustrate the framework. Where the framework outlines the key factors that contribute to the accident. The framework comprises four main components: (1) fall from height accident, (2) causes of accident, (3) effect of accident, and (4) development of safety preventive measures. Along with the literature review that was carried out, an identification and outlining of the factors and subfactors of causes of accidents, effects of accidents, and preventative safety measures was taken out. The proposed framework provides a structured approach for analysing fall-from-height accidents, which can help organizations to identify the underlying causes of such accidents and implement appropriate measures to prevent them. The framework is flexible and can be adapted to suit the needs of other industries and organizations. The paper also discusses the future research directions.
{"title":"A Literature-Based Framework for Analysing Fall-From-Height Accidents and Safety Preventive Measures in the Construction Industry","authors":"Noratira Abd Samad, Muhammad Fikri Hasmori, Nor Haslinda Abas, Farzaneh Moayedi, Mustafa Klufallah","doi":"10.37934/araset.45.1.108116","DOIUrl":"https://doi.org/10.37934/araset.45.1.108116","url":null,"abstract":"Fall-from-height accidents are a significant cause of injuries and fatalities in construction industries. The occurrence of this accident may have been caused by any number of contributing factors. In addition, various preventive measures have been proposed to prevent this accident from occurring. To capture the true condition of a fall from height accident, the causes of the accident, the effects of the accident, and the preventive actions should be rendered out. Therefore, it is possible to analyse these accidents in order to determine their fundamental causes and implement effective preventative measures. This article presents a literature-based framework for analysing fall-from-height accidents. A literature review was conducted to examine existing studies and identify similar themes and patterns on fall from height accidents in the construction industry to illustrate the framework. Where the framework outlines the key factors that contribute to the accident. The framework comprises four main components: (1) fall from height accident, (2) causes of accident, (3) effect of accident, and (4) development of safety preventive measures. Along with the literature review that was carried out, an identification and outlining of the factors and subfactors of causes of accidents, effects of accidents, and preventative safety measures was taken out. The proposed framework provides a structured approach for analysing fall-from-height accidents, which can help organizations to identify the underlying causes of such accidents and implement appropriate measures to prevent them. The framework is flexible and can be adapted to suit the needs of other industries and organizations. The paper also discusses the future research directions.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"105 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.239248
Bhuvana R, Hemalatha R.J.
Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.
{"title":"Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)","authors":"Bhuvana R, Hemalatha R.J.","doi":"10.37934/araset.45.1.239248","DOIUrl":"https://doi.org/10.37934/araset.45.1.239248","url":null,"abstract":"Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short \"train\" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"119 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.37934/araset.45.1.1727
Dayang Siti Norhafiza Abang Ahmad, Fazleen Abdul Fatah, Abdul Rahman Saili, Jamayah Saili, Nur Masriyah Hamzah, Rumaizah Che Md Nor, Zubaidah Omar
Applications of smart farming have been introduced as a way out of various production issues in the agriculture sector, especially during the occurrence of COVID-19. Several technical studies have also been done to develop the modules that meet the operational requirements in Malaysia and determine their benefits and impacts on farmers. Despite the availability of smart farming technologies and their benefits to farmers’ productivity and profitability, adoption of smart farming among Malaysian farmers, especially in rural areas, remains a challenge. Therefore, a qualitative study among farmers in Sarawak and Sabah was conducted to determine the challenges that arise with the adoption of smart farming technologies. The results highlighted that farmer faced challenges in regard to the high startup cost of technology, lack of expertise and knowledge on technologies, connectivity and access in rural areas, farm size, and governmental support. In practice, this study also discussed the challenges of adopting smart farming mentioned by participants and some possible solutions for future attention.
{"title":"Exploration of the Challenges in Adopting Smart Farming Among Smallholder Farmers: A Qualitative Study","authors":"Dayang Siti Norhafiza Abang Ahmad, Fazleen Abdul Fatah, Abdul Rahman Saili, Jamayah Saili, Nur Masriyah Hamzah, Rumaizah Che Md Nor, Zubaidah Omar","doi":"10.37934/araset.45.1.1727","DOIUrl":"https://doi.org/10.37934/araset.45.1.1727","url":null,"abstract":"Applications of smart farming have been introduced as a way out of various production issues in the agriculture sector, especially during the occurrence of COVID-19. Several technical studies have also been done to develop the modules that meet the operational requirements in Malaysia and determine their benefits and impacts on farmers. Despite the availability of smart farming technologies and their benefits to farmers’ productivity and profitability, adoption of smart farming among Malaysian farmers, especially in rural areas, remains a challenge. Therefore, a qualitative study among farmers in Sarawak and Sabah was conducted to determine the challenges that arise with the adoption of smart farming technologies. The results highlighted that farmer faced challenges in regard to the high startup cost of technology, lack of expertise and knowledge on technologies, connectivity and access in rural areas, farm size, and governmental support. In practice, this study also discussed the challenges of adopting smart farming mentioned by participants and some possible solutions for future attention.","PeriodicalId":430114,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"124 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126252","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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