Said Hamisi Said, Mussa Ally Dida, Efraim Michael Kosia, Ramadhani S. Sinde
The proliferation of counterfeit educational certificates is an ongoing issue around the world, including Tanzania. The effect of this malpractice is detrimental to the credibility of education. Traditional strategies to prevent fake certificates are abortive, calling for a more sophisticated approach. Blockchain technology has recently emerged as an ideal solution to this problem due to its inherent attributes that ensure disintermediation, immutability, tamper proof, anonymity, transparency, consensus, security, and trust. However, most existing blockchain-based solutions lack crucial functionalities that are pertinent to the Tanzanian education system. This study unveiled the challenges faced by the current verification system in Tanzania and proposed a blockchain-based conceptual model to address them. The proposed model is based on blockchain, smart contracts, and the Interplanetary File System (IPFS). Quantitative and qualitative methods were used to investigate certification problems in Tanzania and modeling techniques were used to construct the conceptual model. The findings showed that the main challenges of the current verification system emanate from manual procedures, unverifiable credentials, susceptibility of centralized storage systems, disintegrated verification systems, revocation problems, difficulties in communication, and high dependency on the issuers. These challenges undermine certificate verification, impose a significant setback in the fight against forgeries, and create loopholes. It was conceptually demonstrated that these issues can be resolved through the proposed blockchain-based solution.
{"title":"A Blockchain-based Conceptual Model to Address Educational Certificate Verification Challenges in Tanzania","authors":"Said Hamisi Said, Mussa Ally Dida, Efraim Michael Kosia, Ramadhani S. Sinde","doi":"10.48084/etasr.6170","DOIUrl":"https://doi.org/10.48084/etasr.6170","url":null,"abstract":"The proliferation of counterfeit educational certificates is an ongoing issue around the world, including Tanzania. The effect of this malpractice is detrimental to the credibility of education. Traditional strategies to prevent fake certificates are abortive, calling for a more sophisticated approach. Blockchain technology has recently emerged as an ideal solution to this problem due to its inherent attributes that ensure disintermediation, immutability, tamper proof, anonymity, transparency, consensus, security, and trust. However, most existing blockchain-based solutions lack crucial functionalities that are pertinent to the Tanzanian education system. This study unveiled the challenges faced by the current verification system in Tanzania and proposed a blockchain-based conceptual model to address them. The proposed model is based on blockchain, smart contracts, and the Interplanetary File System (IPFS). Quantitative and qualitative methods were used to investigate certification problems in Tanzania and modeling techniques were used to construct the conceptual model. The findings showed that the main challenges of the current verification system emanate from manual procedures, unverifiable credentials, susceptibility of centralized storage systems, disintegrated verification systems, revocation problems, difficulties in communication, and high dependency on the issuers. These challenges undermine certificate verification, impose a significant setback in the fight against forgeries, and create loopholes. It was conceptually demonstrated that these issues can be resolved through the proposed blockchain-based solution.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918145","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}
Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine learning, and deep learning-based algorithms. This study investigated a Transformer sequential-based approach to forecast the closing price for the next day. Ten sliding window timesteps were used to forecast next-day stock closing prices. This study aimed to investigate reliable techniques based on stock input features. The proposed Transformer-based method was compared with ARIMA, Long-Short Term Memory (LSTM), and Random Forest (RF) algorithms, showing its outstanding results on Yahoo Finance data, Facebook Intra data, and JPMorgan's Intra data. Each model was evaluated using Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).
{"title":"Evaluation of Stock Closing Prices using Transformer Learning","authors":"Tariq Saeed Mian","doi":"10.48084/etasr.6017","DOIUrl":"https://doi.org/10.48084/etasr.6017","url":null,"abstract":"Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine learning, and deep learning-based algorithms. This study investigated a Transformer sequential-based approach to forecast the closing price for the next day. Ten sliding window timesteps were used to forecast next-day stock closing prices. This study aimed to investigate reliable techniques based on stock input features. The proposed Transformer-based method was compared with ARIMA, Long-Short Term Memory (LSTM), and Random Forest (RF) algorithms, showing its outstanding results on Yahoo Finance data, Facebook Intra data, and JPMorgan's Intra data. Each model was evaluated using Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918146","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}
Mohanad Salih Farhan Al-Jadiri, Abdul Muttalib I. Said
This study investigated the effect of high-temperature fire flame on reinforced concrete columns coated with a layer of gypsum insulation. Six samples were cast and cured in a hot water bath at 67°C, covered on one side by 10 and 20 mm thick layers of gypsum plaster. The samples were exposed to a 900°C fire flame in a hydrocarbon fire furnace for one and two hours. The results showed that the gypsum plaster layer prevented a high-temperature rise within the core of the column. The differences between all gypsum-coated columns varied compared to those of the reference samples. The gypsum-coated columns had reduced axial displacements and no spalling and visible cracks on their faces. The improvement in the compressive strength of concrete will be discussed in a future paper. This study was carried out following ACI-318 and ASTM C1529.
{"title":"Reinforced Concrete Columns Insulated by Different Gypsum Layers Exposed to 900°C One Side Fire Flame","authors":"Mohanad Salih Farhan Al-Jadiri, Abdul Muttalib I. Said","doi":"10.48084/etasr.6083","DOIUrl":"https://doi.org/10.48084/etasr.6083","url":null,"abstract":"This study investigated the effect of high-temperature fire flame on reinforced concrete columns coated with a layer of gypsum insulation. Six samples were cast and cured in a hot water bath at 67°C, covered on one side by 10 and 20 mm thick layers of gypsum plaster. The samples were exposed to a 900°C fire flame in a hydrocarbon fire furnace for one and two hours. The results showed that the gypsum plaster layer prevented a high-temperature rise within the core of the column. The differences between all gypsum-coated columns varied compared to those of the reference samples. The gypsum-coated columns had reduced axial displacements and no spalling and visible cracks on their faces. The improvement in the compressive strength of concrete will be discussed in a future paper. This study was carried out following ACI-318 and ASTM C1529.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918301","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}
A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes.
{"title":"A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)","authors":"Sultan Alotaibi","doi":"10.48084/etasr.6106","DOIUrl":"https://doi.org/10.48084/etasr.6106","url":null,"abstract":"A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918440","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}
This study aims to investigate experimentally the flexural behavior of lightweight Self-Compacted Concrete (SCC) beams made by Expanded Polystyrene (EPS) concrete and reinforced with rebars and steel fibers. To achieve the aims of this study, seven simply supported EPS lightweight fiber-reinforced concrete beams were fabricated and tested up to failure to study the effects of EPS content and the volume fraction of the steel fibers on their flexural behavior. The tested specimens were divided into two groups with one additional reference beam to be cast without using EPS or steel fibers. In the first group, three lightweight specimens were constructed using 25% EPS beads and were reinforced with 0%, 0.75%, and 1.5% steel fiber volume fractions. The second group is similar to the first group but was fabricated using 50% EPS beads. The test results showed that the mechanical properties of the hardened concrete were significantly reduced due to polystyrene EPS beads with some enhancement when steel fibers were added to the concrete mix. The flexure strength of EPS-LWT concrete beams was significantly reduced due to the polystyrene EPS beads. Furthermore, the results revealed remarkable enhancement in the flexure strength of the tested beams due to the steel fiber reinforcement.
{"title":"Structural Performance of Lightweight Fiber Reinforced Polystyrene Aggregate Self-Compacted Concrete Beams","authors":"Rafaa Mahmood Abbas, Rawah Khalid Rakaa","doi":"10.48084/etasr.6217","DOIUrl":"https://doi.org/10.48084/etasr.6217","url":null,"abstract":"This study aims to investigate experimentally the flexural behavior of lightweight Self-Compacted Concrete (SCC) beams made by Expanded Polystyrene (EPS) concrete and reinforced with rebars and steel fibers. To achieve the aims of this study, seven simply supported EPS lightweight fiber-reinforced concrete beams were fabricated and tested up to failure to study the effects of EPS content and the volume fraction of the steel fibers on their flexural behavior. The tested specimens were divided into two groups with one additional reference beam to be cast without using EPS or steel fibers. In the first group, three lightweight specimens were constructed using 25% EPS beads and were reinforced with 0%, 0.75%, and 1.5% steel fiber volume fractions. The second group is similar to the first group but was fabricated using 50% EPS beads. The test results showed that the mechanical properties of the hardened concrete were significantly reduced due to polystyrene EPS beads with some enhancement when steel fibers were added to the concrete mix. The flexure strength of EPS-LWT concrete beams was significantly reduced due to the polystyrene EPS beads. Furthermore, the results revealed remarkable enhancement in the flexure strength of the tested beams due to the steel fiber reinforcement.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918449","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}
Tuan Anh Bui, Duc-Do Le, Duc-Toan Tran, Manh-Toan Nguyen, Van-Thuc Tran, Ngoc-Tam Bui
Preventing surface damage is crucial for optimal machine performance, with lubricants and additives playing a vital role in achieving this objective. This study specifically focuses on evaluating the influence of fly-ash additives on the wear resistance of machine components when incorporated into lubricant oil. The experiments were conducted following ASTM standard operating conditions, utilizing the four-ball wear test to measure the scratch width and weight loss of balls using different lubricant oil formulations, including 0, 0.1%, 0.5%, 0.75%, and 1% additive. The findings demonstrate that the inclusion of 0.5% fly ash additive in the lubricant oil results in a significant reduction in both scratch width and weight loss of the balls. However, it should be noted that higher additive ratios may lead to increased scratch width and weight loss due to the agglomeration of the fly ash particles on the sliding surfaces. To achieve optimal effectiveness in reducing friction and wear, it is recommended to carefully control the content of fly ash within an appropriate range. Furthermore, this study highlights the width of scratches on balls as a reliable indicator for assessing the anti-wear properties of oils. The insights gained from this research offer valuable guidance to manufacturers in the selection of suitable anti-wear oils for specific applications. Further investigations could explore the impact of different lubricants and additive ratios to identify the most appropriate lubrication parameters. Overall, this study contributes to a better understanding of the effects of fly ash additives on the performance of lubricant oil and provides practical guidance for optimizing lubrication strategies in diverse industrial contexts.
{"title":"Analyzing the Impact of Fly Ash Additive Ratio on Lubricant Properties","authors":"Tuan Anh Bui, Duc-Do Le, Duc-Toan Tran, Manh-Toan Nguyen, Van-Thuc Tran, Ngoc-Tam Bui","doi":"10.48084/etasr.6114","DOIUrl":"https://doi.org/10.48084/etasr.6114","url":null,"abstract":"Preventing surface damage is crucial for optimal machine performance, with lubricants and additives playing a vital role in achieving this objective. This study specifically focuses on evaluating the influence of fly-ash additives on the wear resistance of machine components when incorporated into lubricant oil. The experiments were conducted following ASTM standard operating conditions, utilizing the four-ball wear test to measure the scratch width and weight loss of balls using different lubricant oil formulations, including 0, 0.1%, 0.5%, 0.75%, and 1% additive. The findings demonstrate that the inclusion of 0.5% fly ash additive in the lubricant oil results in a significant reduction in both scratch width and weight loss of the balls. However, it should be noted that higher additive ratios may lead to increased scratch width and weight loss due to the agglomeration of the fly ash particles on the sliding surfaces. To achieve optimal effectiveness in reducing friction and wear, it is recommended to carefully control the content of fly ash within an appropriate range. Furthermore, this study highlights the width of scratches on balls as a reliable indicator for assessing the anti-wear properties of oils. The insights gained from this research offer valuable guidance to manufacturers in the selection of suitable anti-wear oils for specific applications. Further investigations could explore the impact of different lubricants and additive ratios to identify the most appropriate lubrication parameters. Overall, this study contributes to a better understanding of the effects of fly ash additives on the performance of lubricant oil and provides practical guidance for optimizing lubrication strategies in diverse industrial contexts.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918456","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}
Huda Alamoudi, Nahla Aljojo, Asmaa Munshi, Abdullah Alghoson, Ameen Banjar, Araek Tashkandi, Anas Al-Tirawi, Iqbal Alsaleh
Recently, Sentiment Analysis (SA) has become a crucial area of research as it enables us to gauge people's opinions from various sources such as student evaluations, social media posts, product reviews, etc. This paper aims to create an Arabic dataset derived from student satisfaction surveys conducted at the University of Jeddah regarding their subjects and instructors. In addition, this study presents an evaluation of classical machine learning models such as Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest classifier for Arabic SA, whereas the results are compared using various metrics. Furthermore, AraBERT was used for the pre-trained transformer to improve the performance, achieving an accuracy of 78%. The paper fills the lack of SA research in the education domain in the Arabic language.
{"title":"Arabic Sentiment Analysis for Student Evaluation using Machine Learning and the AraBERT Transformer","authors":"Huda Alamoudi, Nahla Aljojo, Asmaa Munshi, Abdullah Alghoson, Ameen Banjar, Araek Tashkandi, Anas Al-Tirawi, Iqbal Alsaleh","doi":"10.48084/etasr.6347","DOIUrl":"https://doi.org/10.48084/etasr.6347","url":null,"abstract":"Recently, Sentiment Analysis (SA) has become a crucial area of research as it enables us to gauge people's opinions from various sources such as student evaluations, social media posts, product reviews, etc. This paper aims to create an Arabic dataset derived from student satisfaction surveys conducted at the University of Jeddah regarding their subjects and instructors. In addition, this study presents an evaluation of classical machine learning models such as Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest classifier for Arabic SA, whereas the results are compared using various metrics. Furthermore, AraBERT was used for the pre-trained transformer to improve the performance, achieving an accuracy of 78%. The paper fills the lack of SA research in the education domain in the Arabic language.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918477","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}
Faizah Alshammari, Nahla Aljojo, Araek Tashkandi, Abdullah Alghoson, Ameen Banjar, Nidhal K. El Abbadi
Riyadh is the most populous city in Saudi Arabia, with a population of over five million people. The governmental and economic centers of Saudi Arabia are located in the city. Due to the fact that the metropolitan region that surrounds Riyadh is continuously growing and expanding, appropriate planning is essential. To be able to formulate efficient plans, one needs access to trustworthy facts and information. Failing to have a clear picture of the future renders planning inefficient. Along with a hybrid time-series prediction of the expansion of the wider Riyadh metropolitan area, an urban growth forecasting model was constructed for the Riyadh region as part of this study. This model was used to make projections about the city's future population. This prediction was conducted with the application of Linear Regression (LR), Seasonal Auto-Regressive Integrated Moving Average (SARIMAX), and Auto-Regressive Integrated Moving Average (ARIMA). The dataset for this study consisted of satellite images of the region surrounding Riyadh that were acquired between 1992 and 2022. Mean Absolute Percentage Error (MAPE) was applied to measure the performance of the proposed hybrid models. The calculated MAPE vales are 2.0% for SARIMAX, 12% for LR, and 22% for ARIMA. As a consequence, the hybrid model's forecast for the future of the region suggests that the projections made regarding the expansion are keeping pace.
{"title":"A Hybrid Time-Series Prediction of the Greater Riyadh's Metropolitan Area Expansion","authors":"Faizah Alshammari, Nahla Aljojo, Araek Tashkandi, Abdullah Alghoson, Ameen Banjar, Nidhal K. El Abbadi","doi":"10.48084/etasr.6350","DOIUrl":"https://doi.org/10.48084/etasr.6350","url":null,"abstract":"Riyadh is the most populous city in Saudi Arabia, with a population of over five million people. The governmental and economic centers of Saudi Arabia are located in the city. Due to the fact that the metropolitan region that surrounds Riyadh is continuously growing and expanding, appropriate planning is essential. To be able to formulate efficient plans, one needs access to trustworthy facts and information. Failing to have a clear picture of the future renders planning inefficient. Along with a hybrid time-series prediction of the expansion of the wider Riyadh metropolitan area, an urban growth forecasting model was constructed for the Riyadh region as part of this study. This model was used to make projections about the city's future population. This prediction was conducted with the application of Linear Regression (LR), Seasonal Auto-Regressive Integrated Moving Average (SARIMAX), and Auto-Regressive Integrated Moving Average (ARIMA). The dataset for this study consisted of satellite images of the region surrounding Riyadh that were acquired between 1992 and 2022. Mean Absolute Percentage Error (MAPE) was applied to measure the performance of the proposed hybrid models. The calculated MAPE vales are 2.0% for SARIMAX, 12% for LR, and 22% for ARIMA. As a consequence, the hybrid model's forecast for the future of the region suggests that the projections made regarding the expansion are keeping pace.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The impact of disasters on the population and environment is an important research area. Multiple criteria need to be analyzed while making policy decisions in order to control the effect of a disaster. Researchers have used many variants of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) method for prioritizing the alternatives. Additionally, the detrimental effects of disasters have compelled stakeholders to proactively prepare by strengthening crucial key elements of an Early Warning System (EWS) so that timely alerts can be produced. In this paper, a Disaster Information Provider (DIP) framework is proposed, which employs a TOPSIS variant to bolster weak elements of a people-centric EWS. Governments may utilize delivered rankings to strengthen the weak elements of the EWS in an affected area. Extensive experimentation proves the usability of the DIP framework for strengthening EWS.
{"title":"A Novel Framework to Strengthen Early Warning Systems","authors":"Harita Ahuja, Sunita Narang, Rakhi Saxena","doi":"10.48084/etasr.6289","DOIUrl":"https://doi.org/10.48084/etasr.6289","url":null,"abstract":"The impact of disasters on the population and environment is an important research area. Multiple criteria need to be analyzed while making policy decisions in order to control the effect of a disaster. Researchers have used many variants of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) method for prioritizing the alternatives. Additionally, the detrimental effects of disasters have compelled stakeholders to proactively prepare by strengthening crucial key elements of an Early Warning System (EWS) so that timely alerts can be produced. In this paper, a Disaster Information Provider (DIP) framework is proposed, which employs a TOPSIS variant to bolster weak elements of a people-centric EWS. Governments may utilize delivered rankings to strengthen the weak elements of the EWS in an affected area. Extensive experimentation proves the usability of the DIP framework for strengthening EWS.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the era of data analytics, the use of web analytics tools is widely spread among Small and Medium Enterprises (SMEs), especially in the e-commerce sector, to analyze web usage data and measure website performance due to the several features it provides. Most web analytics studies were conducted based on marketing rather than a managerial point of view. Therefore, there is a necessity to study the impact from a managerial perspective by exploring the impact on financial and non-financial performance and the decision-making process. This study assessed the impact of web analytics tools on the overall performance of SMEs, targeting Saudi SMEs and considering the lack of web analytics studies in this sector in Saudi Arabia. The sample involved eight Saudi SMEs who participated by answering interview questions, which were analyzed using thematic analysis. The findings demonstrated four main themes: the current web analytics tools used by Saudi SMEs, the motivations behind their use, and their impact on SMEs' performance and decision-making. The results showed that these tools positively affect the overall performance of SMEs. Google Analytics (GA) was the tool most used by the sample. Seven motives were discovered that encourage SMEs to use web analytics tools. This study also addressed the common Key Performance Indicators (KPIs) used by the participants. Concerning decision-making, it was found that web analytics tools affected and supported SMEs' decisions. Furthermore, this study evaluated the role of Zid, the Saudi e-commerce platform for data analytics and performance measurement.
{"title":"The Impact of Web Analytics Tools on the Performance of Small and Medium Enterprises","authors":"Abrar M. Almatrafi, Zahyah H. Alharbi","doi":"10.48084/etasr.6261","DOIUrl":"https://doi.org/10.48084/etasr.6261","url":null,"abstract":"In the era of data analytics, the use of web analytics tools is widely spread among Small and Medium Enterprises (SMEs), especially in the e-commerce sector, to analyze web usage data and measure website performance due to the several features it provides. Most web analytics studies were conducted based on marketing rather than a managerial point of view. Therefore, there is a necessity to study the impact from a managerial perspective by exploring the impact on financial and non-financial performance and the decision-making process. This study assessed the impact of web analytics tools on the overall performance of SMEs, targeting Saudi SMEs and considering the lack of web analytics studies in this sector in Saudi Arabia. The sample involved eight Saudi SMEs who participated by answering interview questions, which were analyzed using thematic analysis. The findings demonstrated four main themes: the current web analytics tools used by Saudi SMEs, the motivations behind their use, and their impact on SMEs' performance and decision-making. The results showed that these tools positively affect the overall performance of SMEs. Google Analytics (GA) was the tool most used by the sample. Seven motives were discovered that encourage SMEs to use web analytics tools. This study also addressed the common Key Performance Indicators (KPIs) used by the participants. Concerning decision-making, it was found that web analytics tools affected and supported SMEs' decisions. Furthermore, this study evaluated the role of Zid, the Saudi e-commerce platform for data analytics and performance measurement.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917997","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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