Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.
{"title":"Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm","authors":"Nur Hamid, Willy Dharmawan, Hidetaka Nambo","doi":"10.3390/asi6060109","DOIUrl":"https://doi.org/10.3390/asi6060109","url":null,"abstract":"Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"76 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134901572","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}
Emmanuel Karapidakis, Marios Nikologiannis, Marini Markaki, Ariadni Kikaki, Sofia Yfanti
The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union’s (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the same climate goals. In order to achieve these goals, ensuring a consistent supply and the effective use of clean energy is pursued, as it has a significant impact on the sustainable development and growth of the country. As the Greek tourism sector is one of the most energy-consuming of the national economy and a major contributor to the country’s GDP, opportunities are presented for innovation and investment in sustainable practices. Such investments must focus on buildings and facilities, where the energy consumption is concentrated. One of the most popular holiday destinations in Greece is the island of Crete. Visitation patterns are seasonal, which means during the summer months, Crete is exceptionally popular and more demanding energy-wise. One of the highest energy-demanding types of tourism-based businesses is the hospitality industry. Energy demands in hotels are driven by factors such as heating, cooling, lighting, and hot water. Thus, such activities require thermal and electrical energy to function. Electrical energy is one of the most essential forms of energy for hotels, as it powers a wide range of critical systems and services throughout the establishment. Therefore, the hotels are highly susceptible to fluctuations in energy prices which can significantly impact the operational costs of hotels. This paper presents an analysis of the annual consumption for the year of 2022 of five hotels located in Crete. An algorithm is also implemented which strives to minimize the capital expenditure (CAPEX), while ensuring a sufficient percentage of self-sufficiency.
{"title":"Assessment of Batteries’ Contribution for Optimal Self-Sufficiency in Large Building Complexes","authors":"Emmanuel Karapidakis, Marios Nikologiannis, Marini Markaki, Ariadni Kikaki, Sofia Yfanti","doi":"10.3390/asi6060107","DOIUrl":"https://doi.org/10.3390/asi6060107","url":null,"abstract":"The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union’s (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the same climate goals. In order to achieve these goals, ensuring a consistent supply and the effective use of clean energy is pursued, as it has a significant impact on the sustainable development and growth of the country. As the Greek tourism sector is one of the most energy-consuming of the national economy and a major contributor to the country’s GDP, opportunities are presented for innovation and investment in sustainable practices. Such investments must focus on buildings and facilities, where the energy consumption is concentrated. One of the most popular holiday destinations in Greece is the island of Crete. Visitation patterns are seasonal, which means during the summer months, Crete is exceptionally popular and more demanding energy-wise. One of the highest energy-demanding types of tourism-based businesses is the hospitality industry. Energy demands in hotels are driven by factors such as heating, cooling, lighting, and hot water. Thus, such activities require thermal and electrical energy to function. Electrical energy is one of the most essential forms of energy for hotels, as it powers a wide range of critical systems and services throughout the establishment. Therefore, the hotels are highly susceptible to fluctuations in energy prices which can significantly impact the operational costs of hotels. This paper presents an analysis of the annual consumption for the year of 2022 of five hotels located in Crete. An algorithm is also implemented which strives to minimize the capital expenditure (CAPEX), while ensuring a sufficient percentage of self-sufficiency.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"25 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953968","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 employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.
{"title":"Simulating the Software Development Lifecycle: The Waterfall Model","authors":"Antonios Saravanos, Matthew X. Curinga","doi":"10.3390/asi6060108","DOIUrl":"https://doi.org/10.3390/asi6060108","url":null,"abstract":"This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"78 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134900740","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}
Alamir Labib Awad, Saleh Mesbah Elkaffas, Mohammed Waleed Fakhr
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.
{"title":"Stock Market Prediction Using Deep Reinforcement Learning","authors":"Alamir Labib Awad, Saleh Mesbah Elkaffas, Mohammed Waleed Fakhr","doi":"10.3390/asi6060106","DOIUrl":"https://doi.org/10.3390/asi6060106","url":null,"abstract":"Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"112 49","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137609","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 this paper, an alternative design approach to the construction of bridge crane systems is analyzed with respect to the potentials and challenges of use based on two possible construction methods. Compared to conventional crane bridges, which are manufactured as a single part, the innovation of the approach relates to designing the crane bridge in segments and assembling it from standardized individual components, which are small in dimension, to form a plug-in structure. These are then prestressed by means of a tensile member to establish the load-bearing capacity. The motivation of the alternative design concept arises from a challenging manufacturing and costly transportation of common crane bridges. Here, the different design approaches are first presented as to how a segmental crane bridge can be constructed and which function the involved components fulfill. In this context, the novel construction method also gives rise to new constraints that are not covered by the common design standards and are therefore discussed. The paper concludes with a comparative study to identify advantages and disadvantages of both concepts regarding defined criteria with the aim of determining design improvements and elaborates the potentials and challenges of the approach with respect to a future industrial implementation. Moreover, these findings additionally form the basis for further research work in this area.
{"title":"Application of Segmented and Prestressed Supporting Structures in Bridge Crane Systems: Potentials and Challenges","authors":"Jan Oellerich, Keno Jann Büscher","doi":"10.3390/asi6060105","DOIUrl":"https://doi.org/10.3390/asi6060105","url":null,"abstract":"In this paper, an alternative design approach to the construction of bridge crane systems is analyzed with respect to the potentials and challenges of use based on two possible construction methods. Compared to conventional crane bridges, which are manufactured as a single part, the innovation of the approach relates to designing the crane bridge in segments and assembling it from standardized individual components, which are small in dimension, to form a plug-in structure. These are then prestressed by means of a tensile member to establish the load-bearing capacity. The motivation of the alternative design concept arises from a challenging manufacturing and costly transportation of common crane bridges. Here, the different design approaches are first presented as to how a segmental crane bridge can be constructed and which function the involved components fulfill. In this context, the novel construction method also gives rise to new constraints that are not covered by the common design standards and are therefore discussed. The paper concludes with a comparative study to identify advantages and disadvantages of both concepts regarding defined criteria with the aim of determining design improvements and elaborates the potentials and challenges of the approach with respect to a future industrial implementation. Moreover, these findings additionally form the basis for further research work in this area.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241309","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}
It is a well-known fact that air approaches the eye region of the rotating impeller of a centrifugal fan with shock-less entry conditions in an ideal scenario. The flow in this region is associated with induced swirl losses, leading to cumulative performance losses. Proper flow guidance in the vicinity of the eye region is essential to minimize possible flow losses. The flow guiding structure may be in the form of a projection or extrusion connected to the rotating impeller of the turbo machines and is generally named a hub. These attachments enhance the overall flow augmentation of the turbo machines in terms of static pressure improvement by reducing a significant amount of inlet turning losses. This article attempts to highlight the efficacy of hubs of various shapes and sizes on the pressure rise of the centrifugal fan using Computational Fluid Dynamics (CFD). Simulation results revealed that the optimized hub configuration yields about an 8.4% higher head coefficient and 8.6% higher relative theoretical efficiency than that obtained for the hub-less base configuration. This improvement in these paraments therefore also commemorates the global progress in energy efficiency as per the UN’s Sustainable Development Goals, SDG 7 in particular. Simultaneously, in the Artificial Neural Network (ANN), a Multi-Layer Perceptron (MLP) model is used to forecast the performance of a centrifugal fan with an optimized hub design. The results predicted by the ANN model are found to be in close agreement with the optimized hub shape’s numerical results.
{"title":"Numerical Investigations and Artificial Neural Network-Based Performance Prediction of a Centrifugal Fan Having Innovative Hub Geometry Designs","authors":"Madhwesh Nagaraj, Kota Vasudeva Karanth","doi":"10.3390/asi6060104","DOIUrl":"https://doi.org/10.3390/asi6060104","url":null,"abstract":"It is a well-known fact that air approaches the eye region of the rotating impeller of a centrifugal fan with shock-less entry conditions in an ideal scenario. The flow in this region is associated with induced swirl losses, leading to cumulative performance losses. Proper flow guidance in the vicinity of the eye region is essential to minimize possible flow losses. The flow guiding structure may be in the form of a projection or extrusion connected to the rotating impeller of the turbo machines and is generally named a hub. These attachments enhance the overall flow augmentation of the turbo machines in terms of static pressure improvement by reducing a significant amount of inlet turning losses. This article attempts to highlight the efficacy of hubs of various shapes and sizes on the pressure rise of the centrifugal fan using Computational Fluid Dynamics (CFD). Simulation results revealed that the optimized hub configuration yields about an 8.4% higher head coefficient and 8.6% higher relative theoretical efficiency than that obtained for the hub-less base configuration. This improvement in these paraments therefore also commemorates the global progress in energy efficiency as per the UN’s Sustainable Development Goals, SDG 7 in particular. Simultaneously, in the Artificial Neural Network (ANN), a Multi-Layer Perceptron (MLP) model is used to forecast the performance of a centrifugal fan with an optimized hub design. The results predicted by the ANN model are found to be in close agreement with the optimized hub shape’s numerical results.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135589526","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}
Azamat Mukhatov, Tuan-Anh Le, Ton Duc Do, Tri T. Pham
This study examines the characteristics of various scanning trajectories or patterns under the influence of scanning parameters in order to develop a theory to define their corresponding image resolutions. The lack of an accurate estimation of pixel size for a specified set of scanning parameters and their connection is a key challenge with existing scanning methods. Thus, this research aimed to propose a novel approach to estimate the pixel size of different scanning techniques. The findings showed that there is a link between pixel size and a frequency ratio NP, which is the ratio of two waveform frequencies that regulates the density of the scanning pattern. A theory has been developed in this study to explain the relationship between scanning parameters and scanning density or pixel size, which was not previously considered. This unique theory permitted the a priori estimate of the image resolution using a particular set of scanning parameters, including the scan time, frequencies, frequency ratio, and their amplitudes. This paper presents a novel and systematic approach for estimating the pixel size of various scanning trajectories, offering the user additional flexibility in adjusting the scanning time or frequency to achieve the desired resolution. Our findings also reveal that in order to achieve a high-quality image with high signal-to-noise and low error, the scanning trajectory must be able to generate a fairly uniform or regular pattern with a small pixel size.
{"title":"Universal Behavior of the Image Resolution for Different Scanning Trajectories","authors":"Azamat Mukhatov, Tuan-Anh Le, Ton Duc Do, Tri T. Pham","doi":"10.3390/asi6060103","DOIUrl":"https://doi.org/10.3390/asi6060103","url":null,"abstract":"This study examines the characteristics of various scanning trajectories or patterns under the influence of scanning parameters in order to develop a theory to define their corresponding image resolutions. The lack of an accurate estimation of pixel size for a specified set of scanning parameters and their connection is a key challenge with existing scanning methods. Thus, this research aimed to propose a novel approach to estimate the pixel size of different scanning techniques. The findings showed that there is a link between pixel size and a frequency ratio NP, which is the ratio of two waveform frequencies that regulates the density of the scanning pattern. A theory has been developed in this study to explain the relationship between scanning parameters and scanning density or pixel size, which was not previously considered. This unique theory permitted the a priori estimate of the image resolution using a particular set of scanning parameters, including the scan time, frequencies, frequency ratio, and their amplitudes. This paper presents a novel and systematic approach for estimating the pixel size of various scanning trajectories, offering the user additional flexibility in adjusting the scanning time or frequency to achieve the desired resolution. Our findings also reveal that in order to achieve a high-quality image with high signal-to-noise and low error, the scanning trajectory must be able to generate a fairly uniform or regular pattern with a small pixel size.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"20 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935296","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}
Lamyae El Youbi El Idrissi, Ismail Akharraz, Abdelaziz Ahaitouf
Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE).
{"title":"Personalized E-Learning Recommender System Based on Autoencoders","authors":"Lamyae El Youbi El Idrissi, Ismail Akharraz, Abdelaziz Ahaitouf","doi":"10.3390/asi6060102","DOIUrl":"https://doi.org/10.3390/asi6060102","url":null,"abstract":"Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE).","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"109 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136234172","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 journal retracts the article “Express Data Processing on FPGA: Network Interface Cards for Streamlined Software Inspection for Packet Processing” [...]
该期刊撤回了文章“FPGA上的快速数据处理:用于分组处理的流线型软件检测的网络接口卡”[…]
{"title":"RETRACTED: Pradeep et al. Express Data Processing on FPGA: Network Interface Cards for Streamlined Software Inspection for Packet Processing. Appl. Syst. Innov. 2023, 6, 9","authors":"Sunkari Pradeep, Yogesh Kumar Sharma, Chaman Verma, Gutha Sreeram, Panugati Hanumantha Rao","doi":"10.3390/asi6060101","DOIUrl":"https://doi.org/10.3390/asi6060101","url":null,"abstract":"The journal retracts the article “Express Data Processing on FPGA: Network Interface Cards for Streamlined Software Inspection for Packet Processing” [...]","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263160","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}
Kamal Chapagain, Samundra Gurung, Pisut Kulthanavit, Somsak Kittipiyakul
Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor in optimizing power generation and consumption, saving energy resources, and determining energy prices. However, integrating energy mix scenarios, including solar and wind power, which are highly nonlinear and seasonal, into an existing grid increases the uncertainty of generation, creating additional challenges for precise forecasting. To tackle such challenges, state-of-the-art methods and algorithms have been implemented in the literature. Artificial Intelligence (AI)-based deep learning models can effectively handle the information of long time-series data. Based on patterns identified in datasets, various scenarios can be developed. In this paper, several models were constructed and tested using deep AI networks in two different scenarios: Scenario1 used data for weekdays, excluding holidays, while Scenario2 used the data without exclusion. To find the optimal configuration, the models were trained and tested within a large space of alternative hyperparameters. We used an Artificial Neural Network (ANN)-based Feedforward Neural Network (FNN) to show the minimum prediction error for Scenario1 and a Recurrent Neural Network (RNN)-based Gated Recurrent Network (GRU) to show the minimum prediction error for Scenario2. From our results, it can be concluded that the weekday dataset in Scenario1 prepared by excluding weekends and holidays provides better forecasting accuracy compared to the holistic dataset approach used in Scenario2. However, Scenario2 is necessary for predicting the demand on weekends and holidays.
{"title":"Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data","authors":"Kamal Chapagain, Samundra Gurung, Pisut Kulthanavit, Somsak Kittipiyakul","doi":"10.3390/asi6060100","DOIUrl":"https://doi.org/10.3390/asi6060100","url":null,"abstract":"Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor in optimizing power generation and consumption, saving energy resources, and determining energy prices. However, integrating energy mix scenarios, including solar and wind power, which are highly nonlinear and seasonal, into an existing grid increases the uncertainty of generation, creating additional challenges for precise forecasting. To tackle such challenges, state-of-the-art methods and algorithms have been implemented in the literature. Artificial Intelligence (AI)-based deep learning models can effectively handle the information of long time-series data. Based on patterns identified in datasets, various scenarios can be developed. In this paper, several models were constructed and tested using deep AI networks in two different scenarios: Scenario1 used data for weekdays, excluding holidays, while Scenario2 used the data without exclusion. To find the optimal configuration, the models were trained and tested within a large space of alternative hyperparameters. We used an Artificial Neural Network (ANN)-based Feedforward Neural Network (FNN) to show the minimum prediction error for Scenario1 and a Recurrent Neural Network (RNN)-based Gated Recurrent Network (GRU) to show the minimum prediction error for Scenario2. From our results, it can be concluded that the weekday dataset in Scenario1 prepared by excluding weekends and holidays provides better forecasting accuracy compared to the holistic dataset approach used in Scenario2. However, Scenario2 is necessary for predicting the demand on weekends and holidays.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"154 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262882","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：