Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.
{"title":"Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting","authors":"K. Krishna, Rani Samal","doi":"10.4108/eetsis.5102","DOIUrl":"https://doi.org/10.4108/eetsis.5102","url":null,"abstract":"Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"88 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657663","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}
Lizet Doriela Mantari Mincami, Hilario Romero Giron, Edith Mariela Quispe Sanabria, Luis Alberto Poma Lago, Jose Francisco Via y Rada Vittes, Jessenia Vasquez Artica, Linda Flor Villa Ricapa
Introduction: Citizen science has generated large volumes of data contributed by citizens in the last decade. However, the lack of standardization in metadata threatens the interoperability and reuse of information.Objective: The objective was to develop a proposal for standards to document metadata in citizen science projects in order to improve interoperability and data reuse.Methods: A literature review was conducted that characterized the challenges in metadata documentation. Likewise, it analyzed previous experiences with standards such as Darwin Core and Dublin Core.Results: The review showed a high heterogeneity in the documentation, making interoperability difficult. The analyzes showed that standards facilitate the flow of information when they cover basic needs.Conclusions: It was concluded that standardizing metadata is essential to harness the potential of citizen science. The initial proposal, consisting of flexible norms focused on critical aspects, sought to establish bases for a collaborative debate considering the changing needs of this community.
{"title":"Development of Standards for Metadata Documentation in Citizen Science Projects","authors":"Lizet Doriela Mantari Mincami, Hilario Romero Giron, Edith Mariela Quispe Sanabria, Luis Alberto Poma Lago, Jose Francisco Via y Rada Vittes, Jessenia Vasquez Artica, Linda Flor Villa Ricapa","doi":"10.4108/eetsis.5704","DOIUrl":"https://doi.org/10.4108/eetsis.5704","url":null,"abstract":"Introduction: Citizen science has generated large volumes of data contributed by citizens in the last decade. However, the lack of standardization in metadata threatens the interoperability and reuse of information.Objective: The objective was to develop a proposal for standards to document metadata in citizen science projects in order to improve interoperability and data reuse.Methods: A literature review was conducted that characterized the challenges in metadata documentation. Likewise, it analyzed previous experiences with standards such as Darwin Core and Dublin Core.Results: The review showed a high heterogeneity in the documentation, making interoperability difficult. The analyzes showed that standards facilitate the flow of information when they cover basic needs.Conclusions: It was concluded that standardizing metadata is essential to harness the potential of citizen science. The initial proposal, consisting of flexible norms focused on critical aspects, sought to establish bases for a collaborative debate considering the changing needs of this community.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140660460","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}
Viraj Nishchal Shah, Deep Rahul Shah, M. Shetty, Deepa Krishnan, Vinayakumar Ravi, Swapnil Singh
INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets. METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.
{"title":"Investigation of Imbalanced Sentiment Analysis in Voice Data: A Comparative Study of Machine Learning Algorithms","authors":"Viraj Nishchal Shah, Deep Rahul Shah, M. Shetty, Deepa Krishnan, Vinayakumar Ravi, Swapnil Singh","doi":"10.4108/eetsis.4805","DOIUrl":"https://doi.org/10.4108/eetsis.4805","url":null,"abstract":" INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets. METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"19 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673393","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 cybersecurity field, identifying and dealing with threats from malicious websites (phishing, spam, and drive-by downloads, for example) is a major concern for the community. Consequently, the need for effective detection methods has become a necessity. Recent advances in Machine Learning (ML) have renewed interest in its application to a variety of cybersecurity challenges. When it comes to detecting phishing URLs, machine learning relies on specific attributes, such as lexical, host, and content based features. The main objective of our work is to propose, implement and evaluate a solution for identifying phishing URLs based on a combination of these feature sets. This paper focuses on using a new balanced dataset, extracting useful features from it, and selecting the optimal features using different feature selection techniques to build and conduct acomparative performance evaluation of four ML models (SVM, Decision Tree, Random Forest, and XGBoost). Results showed that the XGBoost model outperformed the others models, with an accuracy of 95.70% and a false negatives rate of 1.94%.
{"title":"Combining Lexical, Host, and Content-based features for Phishing Websites detection using Machine Learning Models","authors":"Samiya Hamadouche, Ouadjih Boudraa, Mohamed Gasmi","doi":"10.4108/eetsis.4421","DOIUrl":"https://doi.org/10.4108/eetsis.4421","url":null,"abstract":"In cybersecurity field, identifying and dealing with threats from malicious websites (phishing, spam, and drive-by downloads, for example) is a major concern for the community. Consequently, the need for effective detection methods has become a necessity. Recent advances in Machine Learning (ML) have renewed interest in its application to a variety of cybersecurity challenges. When it comes to detecting phishing URLs, machine learning relies on specific attributes, such as lexical, host, and content based features. The main objective of our work is to propose, implement and evaluate a solution for identifying phishing URLs based on a combination of these feature sets. This paper focuses on using a new balanced dataset, extracting useful features from it, and selecting the optimal features using different feature selection techniques to build and conduct acomparative performance evaluation of four ML models (SVM, Decision Tree, Random Forest, and XGBoost). Results showed that the XGBoost model outperformed the others models, with an accuracy of 95.70% and a false negatives rate of 1.94%.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691130","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}
Venkata Naga, Rani Bandaru, M. Sumalatha, Shaik Mohammad Rafee, Kantheti Prasadraju, M. S. Lakshmi
INTRODUCTION: The foundation of cybersecurity is privacy, standardization, and interoperability—all of which are essential for compatibility, system integration, and the protection of user data. In order to better understand the complex interrelationships among privacy, standards, and interoperability in cybersecurity, this article explains their definitions, significance, difficulties, and advantages. OBJECTIVES: The purpose of this article is to examine the relationship between privacy, standards, and interoperability in cybersecurity, with a focus on how these factors might improve cybersecurity policy and protect user privacy. METHODS: This paper thoroughly examines privacy, standards, and interoperability in cybersecurity using methods from social network analysis. It combines current concepts and literature to reveal the complex processes at work. RESULTS: The results highlight how important interoperability and standardization are to bolstering cybersecurity defences and preserving user privacy. Effective communication and cooperation across a variety of technologies are facilitated by adherence to standards and compatible systems. CONCLUSION: Strong cybersecurity plans must prioritize interoperability and standardization. These steps strengthen resilience and promote coordinated incident response, which is especially important for industries like healthcare that depend on defined procedures to maintain operational security.
{"title":"Enhancing Privacy Measures in Healthcare within Cyber-Physical Systems through Cryptographic Solutions","authors":"Venkata Naga, Rani Bandaru, M. Sumalatha, Shaik Mohammad Rafee, Kantheti Prasadraju, M. S. Lakshmi","doi":"10.4108/eetsis.5732","DOIUrl":"https://doi.org/10.4108/eetsis.5732","url":null,"abstract":"INTRODUCTION: The foundation of cybersecurity is privacy, standardization, and interoperability—all of which are essential for compatibility, system integration, and the protection of user data. In order to better understand the complex interrelationships among privacy, standards, and interoperability in cybersecurity, this article explains their definitions, significance, difficulties, and advantages. \u0000OBJECTIVES: The purpose of this article is to examine the relationship between privacy, standards, and interoperability in cybersecurity, with a focus on how these factors might improve cybersecurity policy and protect user privacy. \u0000METHODS: This paper thoroughly examines privacy, standards, and interoperability in cybersecurity using methods from social network analysis. It combines current concepts and literature to reveal the complex processes at work. \u0000RESULTS: The results highlight how important interoperability and standardization are to bolstering cybersecurity defences and preserving user privacy. Effective communication and cooperation across a variety of technologies are facilitated by adherence to standards and compatible systems. \u0000CONCLUSION: Strong cybersecurity plans must prioritize interoperability and standardization. These steps strengthen resilience and promote coordinated incident response, which is especially important for industries like healthcare that depend on defined procedures to maintain operational security.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"13 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712738","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 major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.
{"title":"The Digital Transformation of College English Classroom: Application of Artificial Intelligence and Data Science","authors":"Yanling Li","doi":"10.4108/eetsis.5636","DOIUrl":"https://doi.org/10.4108/eetsis.5636","url":null,"abstract":"A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"1988 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718965","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}
INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends. OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment. METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance. RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.
{"title":"Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization","authors":"Ramanpreet Kaur, Divya Anand, Upinder Kaur, Sahil Verma","doi":"10.4108/eetsis.5716","DOIUrl":"https://doi.org/10.4108/eetsis.5716","url":null,"abstract":"INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends. \u0000OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment. \u0000METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance. \u0000RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment \u0000CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"249 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140720112","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 access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.
{"title":"Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations","authors":"Yuanfang Wei, Li Song","doi":"10.4108/eetsis.5671","DOIUrl":"https://doi.org/10.4108/eetsis.5671","url":null,"abstract":"The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"258 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719852","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 realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.
{"title":"Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning","authors":"Priya Mishra, Balaji Boopal, Naveen Mishra","doi":"10.4108/eetsis.5693","DOIUrl":"https://doi.org/10.4108/eetsis.5693","url":null,"abstract":"In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"111 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140724520","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 paper presents a novel approach to enhance the success rate and accuracy of speaker recognition and identification systems. The methodology involves employing data augmentation techniques to enrich a small dataset with audio recordings from five speakers, covering both male and female voices. Python programming language is utilized for data processing, and a convolutional autoencoder is chosen as the model. Spectrograms are used to convert speech signals into images, serving as input for training the autoencoder. The developed speaker recognition system is compared against traditional systems relying on the MFCC feature extraction technique. In addition to addressing the challenges of a small dataset, the paper explores the impact of a "mismatch condition" by using different time durations of the audio signal during both training and testing phases. Through experiments involving various activation and loss functions, the optimal pair for the small dataset is identified, resulting in a high success rate of 92.4% in matched conditions. Traditionally, Mel-Frequency Cepstral Coefficients (MFCC) have been widely used for this purpose. However, the COVID-19 pandemic has drawn attention to the virus's impact on the human body, particularly on areas relevant to speech, such as the chest, throat, vocal cords, and related regions. COVID-19 symptoms, such as coughing, breathing difficulties, and throat swelling, raise questions about the influence of the virus on MFCC, pitch, jitter, and shimmer features. Therefore, this research aims to investigate and understand the potential effects of COVID-19 on these crucial features, contributing valuable insights to the development of robust speaker recognition systems.
{"title":"Exploring the Impact of Mismatch Conditions, Noisy Backgrounds, and Speaker Health on Convolutional Autoencoder-Based Speaker Recognition System with Limited Dataset","authors":"Arundhati Niwatkar, Y. Kanse, Ajay Kumar Kushwaha","doi":"10.4108/eetsis.5697","DOIUrl":"https://doi.org/10.4108/eetsis.5697","url":null,"abstract":"This paper presents a novel approach to enhance the success rate and accuracy of speaker recognition and identification systems. The methodology involves employing data augmentation techniques to enrich a small dataset with audio recordings from five speakers, covering both male and female voices. Python programming language is utilized for data processing, and a convolutional autoencoder is chosen as the model. Spectrograms are used to convert speech signals into images, serving as input for training the autoencoder. The developed speaker recognition system is compared against traditional systems relying on the MFCC feature extraction technique. In addition to addressing the challenges of a small dataset, the paper explores the impact of a \"mismatch condition\" by using different time durations of the audio signal during both training and testing phases. Through experiments involving various activation and loss functions, the optimal pair for the small dataset is identified, resulting in a high success rate of 92.4% in matched conditions. Traditionally, Mel-Frequency Cepstral Coefficients (MFCC) have been widely used for this purpose. However, the COVID-19 pandemic has drawn attention to the virus's impact on the human body, particularly on areas relevant to speech, such as the chest, throat, vocal cords, and related regions. COVID-19 symptoms, such as coughing, breathing difficulties, and throat swelling, raise questions about the influence of the virus on MFCC, pitch, jitter, and shimmer features. Therefore, this research aims to investigate and understand the potential effects of COVID-19 on these crucial features, contributing valuable insights to the development of robust speaker recognition systems.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"56 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140721753","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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