Application with artificial intelligence (AI) techniques is considered for nuclear power plants (NPPs) that seem to be the last industry of the technology. The application includes accident diagnosis, automatic control, and decision support to reduce the operator’s burden. The most critical problem in their application is the lack of actual plant data to train and validate the AI algorithms. It is very difficult to collect the data from operating NPPs and even more to obtain the data about accidents in NPPs because those situations are very rare. For this reason, most of the studies on the AI applications to NPPs rely on the simulator that is software to mimic NPPs. However, it is highly uncertain that an AI algorithm that is trained by using a simulator can still work well for the actual NPP. This study suggests a Robust AI algorithm for diagnosing accidents in NPPs. The Robust AI is trained by the data collected in an environment (e.g., simulator) and can work under a similar but not exactly the same environment (e.g., actual NPP). Robust AI algorithm applies the Prototypical Network (PN), which is a kind of Meta-learning to extract major features from a few datasets and learn by these features. The PN learns a metric space in which classification can be performed by computing distances to prototype representations of each class. With the PN, the Robust AI algorithm extracts symptoms from the training data in the accident and uses these symptoms in the training of diagnosing accidents. The symptoms of accidents are almost identical between the simulator and the actual NPP, although the parametric values can be different. The suggested Robust AI algorithm is trained using a simulator and tested using another simulator of a different plant type, which is considered an actual plant. The experiment result shows that the Robust AI algorithm can properly diagnose accidents in different environments.
{"title":"Robust AI for Accident Diagnosis of Nuclear Power Plants Using Meta-Learning","authors":"Deail Lee, Heejae Lee, Jonghyun Kim","doi":"10.54941/ahfe1001442","DOIUrl":"https://doi.org/10.54941/ahfe1001442","url":null,"abstract":"Application with artificial intelligence (AI) techniques is considered for nuclear power plants (NPPs) that seem to be the last industry of the technology. The application includes accident diagnosis, automatic control, and decision support to reduce the operator’s burden. The most critical problem in their application is the lack of actual plant data to train and validate the AI algorithms. It is very difficult to collect the data from operating NPPs and even more to obtain the data about accidents in NPPs because those situations are very rare. For this reason, most of the studies on the AI applications to NPPs rely on the simulator that is software to mimic NPPs. However, it is highly uncertain that an AI algorithm that is trained by using a simulator can still work well for the actual NPP. This study suggests a Robust AI algorithm for diagnosing accidents in NPPs. The Robust AI is trained by the data collected in an environment (e.g., simulator) and can work under a similar but not exactly the same environment (e.g., actual NPP). Robust AI algorithm applies the Prototypical Network (PN), which is a kind of Meta-learning to extract major features from a few datasets and learn by these features. The PN learns a metric space in which classification can be performed by computing distances to prototype representations of each class. With the PN, the Robust AI algorithm extracts symptoms from the training data in the accident and uses these symptoms in the training of diagnosing accidents. The symptoms of accidents are almost identical between the simulator and the actual NPP, although the parametric values can be different. The suggested Robust AI algorithm is trained using a simulator and tested using another simulator of a different plant type, which is considered an actual plant. The experiment result shows that the Robust AI algorithm can properly diagnose accidents in different environments.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125515533","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}
With the rise of big data and artificial intelligence, intelligent design platforms with data technology-driven and application scenarios have gradually become a common focus of design academia and industry, during which various intelligent design platforms have emerged, bringing profound changes to the design paradigm. This paper firstly, by collecting and analyzing related literature and cases, we elaborate the concept of data-driven intelligent design and sort out the research and application status of intelligent design tools based on the design process; then we analyze the impact of intelligent design on the design paradigm from three perspectives: design process, design object and designer, combined with the application and development status of intelligent design tools. We found that the development and application of intelligent design tools have made considerable progress, but at this stage the design process still requires the participation of human designers, so human-machine collaborative intelligence will be one of the long-term issues in the development of intelligent design tools; secondly, the application and development of intelligent design tools, while empowering the design process, also poses new challenges to the functions of designers and the adaptation of human-machine relationships.
{"title":"A study on the current state of development of data-driven intelligent design and its impact on design paradigm","authors":"Huibin Zhao, Yuan Xiang","doi":"10.54941/ahfe1003285","DOIUrl":"https://doi.org/10.54941/ahfe1003285","url":null,"abstract":"With the rise of big data and artificial intelligence, intelligent design platforms with data technology-driven and application scenarios have gradually become a common focus of design academia and industry, during which various intelligent design platforms have emerged, bringing profound changes to the design paradigm. This paper firstly, by collecting and analyzing related literature and cases, we elaborate the concept of data-driven intelligent design and sort out the research and application status of intelligent design tools based on the design process; then we analyze the impact of intelligent design on the design paradigm from three perspectives: design process, design object and designer, combined with the application and development status of intelligent design tools. We found that the development and application of intelligent design tools have made considerable progress, but at this stage the design process still requires the participation of human designers, so human-machine collaborative intelligence will be one of the long-term issues in the development of intelligent design tools; secondly, the application and development of intelligent design tools, while empowering the design process, also poses new challenges to the functions of designers and the adaptation of human-machine relationships.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841796","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}
Alperen Sayar, Tuna Çakar, Tunahan Bozkan, Seyit Ertugrul, Fatma Gümüş
Speech has been accepted as one of the basic, efficient and powerful communication methods. At the beginning of the 20th century, electroacoustic analysis was used for determining emotions in psychology. In academics, Speech Emotion Recognition (SER) has become one of the most studied and investigated research areas. This research program aims to determine the emotional state of the speaker based on speech signals. Significant studies have been undertaken during the last two decades to identify emotions from speech by using machine learning. However, it is still a challenging task because emotions rotate from one to another and there are environmental factors which have significant effects on emotions. Furthermore, sound consists of numerous parameters and there are various anatomical characteristics to take into consideration. Determining an appropriate audio feature set for emotion recognition is still a critical decision point for an emotion recognition system. The demand for voice technology in both art and human – machine interaction systems has recently been increased. Our voice conveys both linguistic and paralinguistic messages in the course of speaking. The paralinguistic part, for example, rhythm and pitch, provides emotional cues to the speaker. The speech emotion recognition topic examines the question ‘How is it said?’ and an algorithm detects the emotional state of the speaker from an audio record. Although a considerable number of the studies have been conducted for selecting and extracting an optimal set of features, appropriate attributes for automatic emotion recognition from audio are still under research. The main aim of this study is obtaining the most distinctive emotional audio features. For this purpose, time- based features, frequency-based features and spectral shape-based features are used for comparing recognition accuracies. Besides these features, a pre-trained model is used for obtaining input for emotion recognition. Machine learning models are developed for classifying emotions with Support Vector Machine, Multi-Layer Perceptron and Convolutional Neural Network algorithms. Three emotional databases in English and German are combined and a larger database is obtained for training and testing the models. Emotions namely, Happy, Calm, Angry, Boredom, Disgust, Fear, Neutral, Sad and Surprised are classified with these models. When the classification results are examined, it is concluded that the pre- trained representations make the most successful predictions. The weighted accuracy ratio is 91% for both Convolutional Neural Network and Multilayer Perceptron algorithms while this ratio is 87% for the Support Vector Machine algorithm. A hybrid model is being developed which contains both a pre-trained model and spectral shaped based features. Speech contains silent and noisy sections which increase the computational complexity. Time performance is the other major factor which should be a great deal of careful considera
{"title":"Emotion Recognition from Speech via the Use of Different Audio Features, Machine Learning and Deep Learning Algorithms","authors":"Alperen Sayar, Tuna Çakar, Tunahan Bozkan, Seyit Ertugrul, Fatma Gümüş","doi":"10.54941/ahfe1003279","DOIUrl":"https://doi.org/10.54941/ahfe1003279","url":null,"abstract":"Speech has been accepted as one of the basic, efficient and powerful communication methods. At the beginning of the 20th century, electroacoustic analysis was used for determining emotions in psychology. In academics, Speech Emotion Recognition (SER) has become one of the most studied and investigated research areas. This research program aims to determine the emotional state of the speaker based on speech signals. Significant studies have been undertaken during the last two decades to identify emotions from speech by using machine learning. However, it is still a challenging task because emotions rotate from one to another and there are environmental factors which have significant effects on emotions. Furthermore, sound consists of numerous parameters and there are various anatomical characteristics to take into consideration. Determining an appropriate audio feature set for emotion recognition is still a critical decision point for an emotion recognition system. The demand for voice technology in both art and human – machine interaction systems has recently been increased. Our voice conveys both linguistic and paralinguistic messages in the course of speaking. The paralinguistic part, for example, rhythm and pitch, provides emotional cues to the speaker. The speech emotion recognition topic examines the question ‘How is it said?’ and an algorithm detects the emotional state of the speaker from an audio record. Although a considerable number of the studies have been conducted for selecting and extracting an optimal set of features, appropriate attributes for automatic emotion recognition from audio are still under research. The main aim of this study is obtaining the most distinctive emotional audio features. For this purpose, time- based features, frequency-based features and spectral shape-based features are used for comparing recognition accuracies. Besides these features, a pre-trained model is used for obtaining input for emotion recognition. Machine learning models are developed for classifying emotions with Support Vector Machine, Multi-Layer Perceptron and Convolutional Neural Network algorithms. Three emotional databases in English and German are combined and a larger database is obtained for training and testing the models. Emotions namely, Happy, Calm, Angry, Boredom, Disgust, Fear, Neutral, Sad and Surprised are classified with these models. When the classification results are examined, it is concluded that the pre- trained representations make the most successful predictions. The weighted accuracy ratio is 91% for both Convolutional Neural Network and Multilayer Perceptron algorithms while this ratio is 87% for the Support Vector Machine algorithm. A hybrid model is being developed which contains both a pre-trained model and spectral shaped based features. Speech contains silent and noisy sections which increase the computational complexity. Time performance is the other major factor which should be a great deal of careful considera","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046039","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}
Yuetian Chen, Bowen Shi, Peiru Liu, Ruohua Li, Mei Si
Visual storytelling is an art form that has been utilized for centuries to communicate stories, convey messages, and evoke emotions. The images and text must be used in harmony to create a compelling narrative experience. With the rise of text-to-image generation models such as Stable Diffusion, it is becoming more promising to investigate methods of automatically creating illustrations for stories. However, these diffusion models are usually developed to generate a single image, resulting in a lack of consistency be- tween figures and objects across different illustrations of the same story, which is especially important in stories with human characters.This work introduces a novel technique for creating consistent human figures in visual stories. This is achieved in two steps. The first step is to collect human portraits with various identifying characteristics, such as gender and age, that describe the character. The second step is to use this collection to train DreamBooth to generate a unique token ID for each type of character. These IDs can then be used to replace the names of the story characters in the image-generation process. By combining these two steps, we can create controlled human figures for various visual storytelling contexts.
{"title":"Automated Visual Story Synthesis with Character Trait Control","authors":"Yuetian Chen, Bowen Shi, Peiru Liu, Ruohua Li, Mei Si","doi":"10.54941/ahfe1003275","DOIUrl":"https://doi.org/10.54941/ahfe1003275","url":null,"abstract":"Visual storytelling is an art form that has been utilized for centuries to communicate stories, convey messages, and evoke emotions. The images and text must be used in harmony to create a compelling narrative experience. With the rise of text-to-image generation models such as Stable Diffusion, it is becoming more promising to investigate methods of automatically creating illustrations for stories. However, these diffusion models are usually developed to generate a single image, resulting in a lack of consistency be- tween figures and objects across different illustrations of the same story, which is especially important in stories with human characters.This work introduces a novel technique for creating consistent human figures in visual stories. This is achieved in two steps. The first step is to collect human portraits with various identifying characteristics, such as gender and age, that describe the character. The second step is to use this collection to train DreamBooth to generate a unique token ID for each type of character. These IDs can then be used to replace the names of the story characters in the image-generation process. By combining these two steps, we can create controlled human figures for various visual storytelling contexts.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125379327","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}
Learning, as well as the importance of a high-performance computer is significantly emerging. In parallel computing, we denote the concept of interconnection between the single memory and multiple processors as multi-processor. In a similar context, multi-computing signifies the connection of memory-loaded processors through the communication link. The relationship between the performance of multi-computing and the processor’s linkage structure is extremely proximate. Let the connection structure of the processor be an interconnection network. The interconnection network can be modeled through a classical graph consisting of node and edge. In this regard, a multi-computing processor is expressed as a node, communication link as an edge. When categorizing the suggested interconnection network through the criteria of the number of nodes, it can be classified as follows: Mesh class type consisted of the n×k nodes (Torus, Toroidal mesh, Diagonal mesh, Honeycomb mesh), Hypercube class type with 2^n nodes (Hypercube, Folded hypercube, Twisted cube, de Breijin), and Star graph class type (Star graph, Bubblesort star, Alternating group, Macro-star, Transposition) with n! nodes. The mesh type structure is a planar graph that is widely being utilized in the domains such as VLSI circuit design and base station installing (covering) problems in a mobile communication network. Mesh class types are comparatively easier to design and could potentially be implemented in algorithmic domains in a practical manner. Therefore, it is considered as a classical measure that is extensively preferred when designing a parallel computing network system. This study suggests the novel mesh structure De3 with the degree of three and designs an optimal routing algorithm as well as a parallel route algorithm (병렬경로알고리즘) based on the diameter analysis. The address of the node in the De3 graph is expressed with n-bit binary digits, and the edge is noted with the operator %. We built the interval function (구간 함수) that computes the locational property of the corresponding nodes to derive an optimal routing path from node u to node v among the De3 graph structure. We represent the optimal routing algorithm based on the interval function, calculating and validating the diameter of the De3 graph. Furthermore, we propose the algorithm that establishes the node disjoint parallel path which addresses a non-overlap path from node u to v. The outcome of this study proposes a novel interconnection network structure that is applicable in the routing algorithm optimization by limiting the communication links to three while the number of nodes These results implicate the viable operation among the high-performance edge computing system in a cost-efficient and effective manner.
{"title":"Three-degree graph and design of an optimal routing algorithm","authors":"BoOck Seong, Jimin Ahn, Myeongjun Son, H. Lee","doi":"10.54941/ahfe1001466","DOIUrl":"https://doi.org/10.54941/ahfe1001466","url":null,"abstract":"Learning, as well as the importance of a high-performance computer is significantly emerging. In parallel computing, we denote the concept of interconnection between the single memory and multiple processors as multi-processor. In a similar context, multi-computing signifies the connection of memory-loaded processors through the communication link. The relationship between the performance of multi-computing and the processor’s linkage structure is extremely proximate. Let the connection structure of the processor be an interconnection network. The interconnection network can be modeled through a classical graph consisting of node and edge. In this regard, a multi-computing processor is expressed as a node, communication link as an edge. When categorizing the suggested interconnection network through the criteria of the number of nodes, it can be classified as follows: Mesh class type consisted of the n×k nodes (Torus, Toroidal mesh, Diagonal mesh, Honeycomb mesh), Hypercube class type with 2^n nodes (Hypercube, Folded hypercube, Twisted cube, de Breijin), and Star graph class type (Star graph, Bubblesort star, Alternating group, Macro-star, Transposition) with n! nodes. The mesh type structure is a planar graph that is widely being utilized in the domains such as VLSI circuit design and base station installing (covering) problems in a mobile communication network. Mesh class types are comparatively easier to design and could potentially be implemented in algorithmic domains in a practical manner. Therefore, it is considered as a classical measure that is extensively preferred when designing a parallel computing network system. This study suggests the novel mesh structure De3 with the degree of three and designs an optimal routing algorithm as well as a parallel route algorithm (병렬경로알고리즘) based on the diameter analysis. The address of the node in the De3 graph is expressed with n-bit binary digits, and the edge is noted with the operator %. We built the interval function (구간 함수) that computes the locational property of the corresponding nodes to derive an optimal routing path from node u to node v among the De3 graph structure. We represent the optimal routing algorithm based on the interval function, calculating and validating the diameter of the De3 graph. Furthermore, we propose the algorithm that establishes the node disjoint parallel path which addresses a non-overlap path from node u to v. The outcome of this study proposes a novel interconnection network structure that is applicable in the routing algorithm optimization by limiting the communication links to three while the number of nodes These results implicate the viable operation among the high-performance edge computing system in a cost-efficient and effective manner.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130943081","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}
Gian Luca Liehner, Alexander Hick, Hannah Biermann, P. Brauner, M. Ziefle
Over the last couple of years, artificial intelligence (AI)—namely machine learning algorithms—has rapidly entered our daily lives. Applications can be found in medicine, law, finance, production, education, mobility, and entertainment. To achieve this, a large amount of research has been undertaken, to optimize algorithms that by learning from data are able to process natural language, recognize objects through computer vision, interact with their environment with the help of robotics, or take autonomous decisions without the help of human input. With that, AI is acquiring core human capabilities raising the question of the impact of AI use on our society and its individuals. To form a basis for addressing those questions, it is crucial to investigate the public perception of artificial intelligence. This area of research is however often overlooked as with the fast development of AI technologies demands and wishes of individuals are often neglected. To counteract this, our study focuses on the public's perception, attitudes, and trust towards artificial intelligence. To that end, we followed a two-step research approach. We first conducted semi-structured interviews which laid the foundation for an online questionnaire. Building upon the interviews, we designed an online questionnaire (N=124) in which in addition to user diversity factors such as belief in a dangerous world, sensitivity to threat, and technology optimism, we asked respondents to rate prejudices, myths, risks, and chances about AI. Our results show that in general respondents view AI as a tool that can act independently, adapt, and help them in their daily lives. With that being said, respondents also indicate that they are not able to understand the underlying mechanisms of AI, and with this doubt, the maturity of the technology, leading to privacy concerns, fear of misuse, and security issues. While respondents are willing to use AI nevertheless, they are less willing to place their trust in the technology. From a user diversity point of view, we found, that both trust and use intention are correlated to the belief in a dangerous world and technology optimism. In summary, our research shows that while respondents are willing to use AI in their everyday lives, still some concerns remain that can impact their trust in the technology. Further research should explore the mediation of concerns to include them in a responsible development process that ensures a positive impact of AI on individuals' lives and our society.
{"title":"Perceptions, attitudes and trust toward artificial intelligence — An assessment of the public opinion","authors":"Gian Luca Liehner, Alexander Hick, Hannah Biermann, P. Brauner, M. Ziefle","doi":"10.54941/ahfe1003271","DOIUrl":"https://doi.org/10.54941/ahfe1003271","url":null,"abstract":"Over the last couple of years, artificial intelligence (AI)—namely machine learning algorithms—has rapidly entered our daily lives. Applications can be found in medicine, law, finance, production, education, mobility, and entertainment. To achieve this, a large amount of research has been undertaken, to optimize algorithms that by learning from data are able to process natural language, recognize objects through computer vision, interact with their environment with the help of robotics, or take autonomous decisions without the help of human input. With that, AI is acquiring core human capabilities raising the question of the impact of AI use on our society and its individuals. To form a basis for addressing those questions, it is crucial to investigate the public perception of artificial intelligence. This area of research is however often overlooked as with the fast development of AI technologies demands and wishes of individuals are often neglected. To counteract this, our study focuses on the public's perception, attitudes, and trust towards artificial intelligence. To that end, we followed a two-step research approach. We first conducted semi-structured interviews which laid the foundation for an online questionnaire. Building upon the interviews, we designed an online questionnaire (N=124) in which in addition to user diversity factors such as belief in a dangerous world, sensitivity to threat, and technology optimism, we asked respondents to rate prejudices, myths, risks, and chances about AI. Our results show that in general respondents view AI as a tool that can act independently, adapt, and help them in their daily lives. With that being said, respondents also indicate that they are not able to understand the underlying mechanisms of AI, and with this doubt, the maturity of the technology, leading to privacy concerns, fear of misuse, and security issues. While respondents are willing to use AI nevertheless, they are less willing to place their trust in the technology. From a user diversity point of view, we found, that both trust and use intention are correlated to the belief in a dangerous world and technology optimism. In summary, our research shows that while respondents are willing to use AI in their everyday lives, still some concerns remain that can impact their trust in the technology. Further research should explore the mediation of concerns to include them in a responsible development process that ensures a positive impact of AI on individuals' lives and our society.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123656305","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 growth of the global population together with several unpredicted crises such as political, health, and financial, create an environment of uncertainty in which social innovations can be developed to offer stability in people’s lives and create new business development opportunities for the benefit of the economy and the society. One of the undoubted rights of every human being is access to affordable medical treatment. However, the costs and time needed for research and development on new or specialized drugs are not often covered by governmental budgets and initiatives that could make such medicines accessible to all who needed them. Private companies invest tremendous amounts and expect returns on their investments. This gap, between the availability of a drug and its accessibility, created the social need for a generic drug market and the inspiration for advanced innovations to serve it. Research indicates that the price of brand-name drugs can drop up to 80% after the commercialization of a new generic which has the same action and can potentially replace them. The global generic drug market worth is expected to increase from $311.8 billion in 2021 to $442.3 billion in 2026. Excipients represent a market value of $4 billion, accounting for 0.5% of the total pharmaceutical market. The global market of AI was estimated at 43.1 billion in 2020 and is predicted to reach $228.3 billion by 2026 with a 32.7 % CAGR. On the other hand, the revenues of the AI Health market are projected to grow from $6.9 billion in 2021 to $67.4 billion in 2027 reaching $120.2 billion by 2028 with a CAGR of 45.3%.The choice of excipients in drug development is a critical and time-consuming process. Currently, excipients are chosen based on the route of administration, physicochemical characteristics, place of action, and the type of release of the active ingredient. The process involves many quality control tests on the drug such as fragility, dissolution, disintegration, dosage uniformity, and stability, which are repeated when the excipient changes. This laborious and time-consuming process considers a massive number of existing excipients categorized into different functional groups used for different purposes.This paper addresses this challenge and introduces an approach to resolve it using Artificial Intelligence for social innovation in the formulation development industry. Specifically, the paper presents an Expert system (ES) based software architecture to facilitate assess and utilize drug-excipient relationship data scattered in various forms of documentation and/or scientific literature. The inference engine of the ES operates with rule base and case-based reasoning powered by Machine Reading Comprehension (MRC) and Natural Language Processing (NLP) technologies that populate and enrich the knowledge base. The MRC and NLP technologies interpret existing drug formulations and propose potential new drug formulations, based on its physicochemical character
{"title":"Machine Reading Comprehension and Expert System technologies for social innovation in the drug excipient selection process","authors":"E. Markopoulos, Chrystalla Protopapa","doi":"10.54941/ahfe1003273","DOIUrl":"https://doi.org/10.54941/ahfe1003273","url":null,"abstract":"The growth of the global population together with several unpredicted crises such as political, health, and financial, create an environment of uncertainty in which social innovations can be developed to offer stability in people’s lives and create new business development opportunities for the benefit of the economy and the society. One of the undoubted rights of every human being is access to affordable medical treatment. However, the costs and time needed for research and development on new or specialized drugs are not often covered by governmental budgets and initiatives that could make such medicines accessible to all who needed them. Private companies invest tremendous amounts and expect returns on their investments. This gap, between the availability of a drug and its accessibility, created the social need for a generic drug market and the inspiration for advanced innovations to serve it. Research indicates that the price of brand-name drugs can drop up to 80% after the commercialization of a new generic which has the same action and can potentially replace them. The global generic drug market worth is expected to increase from $311.8 billion in 2021 to $442.3 billion in 2026. Excipients represent a market value of $4 billion, accounting for 0.5% of the total pharmaceutical market. The global market of AI was estimated at 43.1 billion in 2020 and is predicted to reach $228.3 billion by 2026 with a 32.7 % CAGR. On the other hand, the revenues of the AI Health market are projected to grow from $6.9 billion in 2021 to $67.4 billion in 2027 reaching $120.2 billion by 2028 with a CAGR of 45.3%.The choice of excipients in drug development is a critical and time-consuming process. Currently, excipients are chosen based on the route of administration, physicochemical characteristics, place of action, and the type of release of the active ingredient. The process involves many quality control tests on the drug such as fragility, dissolution, disintegration, dosage uniformity, and stability, which are repeated when the excipient changes. This laborious and time-consuming process considers a massive number of existing excipients categorized into different functional groups used for different purposes.This paper addresses this challenge and introduces an approach to resolve it using Artificial Intelligence for social innovation in the formulation development industry. Specifically, the paper presents an Expert system (ES) based software architecture to facilitate assess and utilize drug-excipient relationship data scattered in various forms of documentation and/or scientific literature. The inference engine of the ES operates with rule base and case-based reasoning powered by Machine Reading Comprehension (MRC) and Natural Language Processing (NLP) technologies that populate and enrich the knowledge base. The MRC and NLP technologies interpret existing drug formulations and propose potential new drug formulations, based on its physicochemical character","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126183824","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}
Today the web generates a large amount of data, the same ones that come from social networks, online platforms, communities, cloud computing, etc., but one type of data has not been recognized for its relevance and that is data from Learning Management Systems like Moodle in the educational context. Considering this context, this research will apply some Artificial Intelligence methods and techniques such as the TSA methodology, Text mining, and Sentiment Analysis to assess the data about the opinion of the students, converting them into stable information structures that allow their reflection and analysis. The work carried out focuses on determining the level of user satisfaction, in this case, the students, of the virtual learning platforms. The results obtained show that applying Artificial Intelligence allows obtaining relevant information that helps to undertake improvement actions by authorities and managers in the educational context based on the opinion of the students, detecting important problems in online learning during these times of COVID-19 we are just past.
{"title":"Application of Educational Context Data using Artificial Intelligence Methods","authors":"Myriam Peñafiel, Maria Vásquez, Diego Vásquez","doi":"10.54941/ahfe1003283","DOIUrl":"https://doi.org/10.54941/ahfe1003283","url":null,"abstract":"Today the web generates a large amount of data, the same ones that come from social networks, online platforms, communities, cloud computing, etc., but one type of data has not been recognized for its relevance and that is data from Learning Management Systems like Moodle in the educational context. Considering this context, this research will apply some Artificial Intelligence methods and techniques such as the TSA methodology, Text mining, and Sentiment Analysis to assess the data about the opinion of the students, converting them into stable information structures that allow their reflection and analysis. The work carried out focuses on determining the level of user satisfaction, in this case, the students, of the virtual learning platforms. The results obtained show that applying Artificial Intelligence allows obtaining relevant information that helps to undertake improvement actions by authorities and managers in the educational context based on the opinion of the students, detecting important problems in online learning during these times of COVID-19 we are just past.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122900441","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 order to explore innovative interaction methods from the technical level of AI, and further improve the use experience of interactive products, the author proposes the interactive design of AI urban modern life products. The author takes the artificial intelligence technology as the center, and applies the technical means to the product interaction design. After investigation and analysis of the technical means of its application, it summarizes how artificial intelligence drives the development of product interaction design. In addition, it further analyzes the application thinking and performance in the whole design process in combination with specific design cases. The results show that: The people aged 25-30 and 35-49 are undoubtedly the main consumers and users because of their economic foundation and health awareness, the main buyers are men, but women pay more attention to them, it can be seen that women have a strong degree of health awareness and sense of responsibility for their families. According to Maslow's needs theory, human needs are divided into five aspects: Physiological needs, security needs, social needs, respect needs and self-realization needs. At present, water purification products only reach the level of safety requirements, because of the design concept and technical limitations of traditional water purification products, the upgrading of products is slow, it is not comprehensive to simply emphasize the research and development of water purification technology. In the era of consumption upgrading, many water purification products ignore the social needs, respect needs and higher needs of consumers in the competitive environment, that is, the human-computer interaction mode, emotional experience of products and the sense of achievement of product use. The author puts forward the redefinition of multi-dimensional product design concepts such as traditional product interaction design methods, interactive interfaces and information architecture, and envisages the future development direction.
{"title":"Interactive design of water purification products based on modern urban life","authors":"Sijia Wang","doi":"10.54941/ahfe1003288","DOIUrl":"https://doi.org/10.54941/ahfe1003288","url":null,"abstract":"In order to explore innovative interaction methods from the technical level of AI, and further improve the use experience of interactive products, the author proposes the interactive design of AI urban modern life products. The author takes the artificial intelligence technology as the center, and applies the technical means to the product interaction design. After investigation and analysis of the technical means of its application, it summarizes how artificial intelligence drives the development of product interaction design. In addition, it further analyzes the application thinking and performance in the whole design process in combination with specific design cases. The results show that: The people aged 25-30 and 35-49 are undoubtedly the main consumers and users because of their economic foundation and health awareness, the main buyers are men, but women pay more attention to them, it can be seen that women have a strong degree of health awareness and sense of responsibility for their families. According to Maslow's needs theory, human needs are divided into five aspects: Physiological needs, security needs, social needs, respect needs and self-realization needs. At present, water purification products only reach the level of safety requirements, because of the design concept and technical limitations of traditional water purification products, the upgrading of products is slow, it is not comprehensive to simply emphasize the research and development of water purification technology. In the era of consumption upgrading, many water purification products ignore the social needs, respect needs and higher needs of consumers in the competitive environment, that is, the human-computer interaction mode, emotional experience of products and the sense of achievement of product use. The author puts forward the redefinition of multi-dimensional product design concepts such as traditional product interaction design methods, interactive interfaces and information architecture, and envisages the future development direction.","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132479862","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 article presents the gathering of works related to the usage of virtual assistants into the 4.0 industry in order to stablish the parameters and essential characteristics to define the creation of a ‘chatbot’ virtual assistant. This device should be applicable to a process of 4 tanks which are interconnected with a robust multivariable PID control with the aim of controlling and supervising this process using a mobile messaging application from a smartphone by sending key words in text messages which will be interpreted by the chatbot and this will be capable of acting depending on the message it receives; it can be either a consultation of the status of the process and the tanks which will be answered with a text message with the required information, or a command which will make it work starting or stopping the process. This system is proposed as a solution in the case of long-distance supervision and control during different processes. With this, an option to optimize the execution of actions such as security, speed, reliability of data, and resource maximization can be implemented, which leads to a better general performance of an industry
{"title":"Development of a virtual assistant chatbot based on Artificial Intelligence to control and supervise a process of 4 tanks which are interconnected","authors":"Sandro González-González, L. Serpa-Andrade","doi":"10.54941/ahfe1001464","DOIUrl":"https://doi.org/10.54941/ahfe1001464","url":null,"abstract":"This article presents the gathering of works related to the usage of virtual assistants into the 4.0 industry in order to stablish the parameters and essential characteristics to define the creation of a ‘chatbot’ virtual assistant. This device should be applicable to a process of 4 tanks which are interconnected with a robust multivariable PID control with the aim of controlling and supervising this process using a mobile messaging application from a smartphone by sending key words in text messages which will be interpreted by the chatbot and this will be capable of acting depending on the message it receives; it can be either a consultation of the status of the process and the tanks which will be answered with a text message with the required information, or a command which will make it work starting or stopping the process. This system is proposed as a solution in the case of long-distance supervision and control during different processes. With this, an option to optimize the execution of actions such as security, speed, reliability of data, and resource maximization can be implemented, which leads to a better general performance of an industry","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125396940","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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