Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent de...Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent deployability, and flexibility offered by microservices, which is further enhanced by developments in cloud technology. This shift towards microservice architecture meets the modern business need for agility, facilitating rapid adaptability in a competitive landscape. Microservices offer an agile framework and, in many cases, can simplify the development process, though the implementation can vary and sometimes introduce complexities. Unlike monolithic systems, which can be cumbersome to modify, microservices enable quicker adjustments and faster deployment times, essential in today’s dynamic environment. This article delves into the essence of microservices and explores their growing prominence in the software industry.展开更多
The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information m...The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.展开更多
The rapid advancement of information technology has promoted the development of informatization in universities. The freshmen welcome information system of universities is the first important system to showcase the di...The rapid advancement of information technology has promoted the development of informatization in universities. The freshmen welcome information system of universities is the first important system to showcase the digital level of the university to new students. With the expansion of the enrollment scale of universities, improving the efficiency of welcoming work has become an urgent problem to be solved. This article analyzes the characteristics and existing problems of the welcoming work, combined with the main technical methods of information system construction, and based on the comprehensive situation of information systems in our university, proposes the construction goals and ideas of the welcoming information system, summarizes the construction process and operation results of the welcome system, and explores possible directions for future optimization.展开更多
Enterprise applications utilize relational databases and structured business processes, requiring slow and expensive conversion of inputs and outputs, from business documents such as invoices, purchase orders, and rec...Enterprise applications utilize relational databases and structured business processes, requiring slow and expensive conversion of inputs and outputs, from business documents such as invoices, purchase orders, and receipts, into known templates and schemas before processing. We propose a new LLM Agent-based intelligent data extraction, transformation, and load (IntelligentETL) pipeline that not only ingests PDFs and detects inputs within it but also addresses the extraction of structured and unstructured data by developing tools that most efficiently and securely deal with respective data types. We study the efficiency of our proposed pipeline and compare it with enterprise solutions that also utilize LLMs. We establish the supremacy in timely and accurate data extraction and transformation capabilities of our approach for analyzing the data from varied sources based on nested and/or interlinked input constraints.展开更多
Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and consider...Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.展开更多
A mechanical assembly is a composition of interrelated parts. Assembly data base stores the geometric models of indi-vidual parts, the spatial positions and orientations of the parts in the assembly, and the relations...A mechanical assembly is a composition of interrelated parts. Assembly data base stores the geometric models of indi-vidual parts, the spatial positions and orientations of the parts in the assembly, and the relationships between parts. An assembly of parts can be represented by its liaison which has a description of its relationships between the various parts in the assembly. The problem is to not only make the information available but also use the relevant information for making decisions, especially determination of the assembly sequence plan. The method described in this paper ex-tracts the feature based assembly information from CAD models of products and build up liaisons to facilitate assembly planning applications. The system works on the assumption that the designer explicitly defines joints and mating condi-tions. Further, a computer representation of mechanical assemblies in the form of liaisons is necessary in order to automate the generation of assembly plans. A novel method of extracting the assembly information and representing them in the form of liaisons is presented in this paper.展开更多
One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexi...One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.展开更多
The paper elaborates the features and superiority of applying multimedia technology to aquatic organisms in a Science and Life Technology course. The augmented reality application of multimedia technology in aquatic o...The paper elaborates the features and superiority of applying multimedia technology to aquatic organisms in a Science and Life Technology course. The augmented reality application of multimedia technology in aquatic organisms’ instruction has its own features, teaching information capacity, interactive user interface. The paper concludes the superiority of Unity and Vuforia AR instruction from five perspectives;meanwhile, the paper elaborates its disadvantages. An AR-based instruction attitude questionnaire was conducted at the end of the experiment to obtain learners’ perspectives of the system. Learners in AR approach demonstrate higher motivation and concentrate their attention on the learning tasks. Learners showed more positive attitude in willingness to use the AR-based instruction to improve learning interests. This study provided insights for better understanding the design, theory and practice of e-learning through augmented reality technology.展开更多
Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication...Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.展开更多
The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process i...The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.展开更多
The objective of this research is to propose a decision support system for avoiding flood on solar power plant site selection. Methodologically, the geographic information system (GIS) is used to determine the optimum...The objective of this research is to propose a decision support system for avoiding flood on solar power plant site selection. Methodologically, the geographic information system (GIS) is used to determine the optimum site for a solar power plant. It is intended to integrate the qualitative and quantitative variables based upon the adoption of the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. These methods are employed to unite the environmental aspects and social needs for electrical power systematically. Regarding a case study of the choice of a solar power plant site in Thailand, it demonstrates that the quantitative and qualitative criteria should be realized prior to analysis in the Fuzzy AHP-TOPSIS model. The fuzzy AHP is employed to determine the weights of qualitative and quantitative criteria that can affect the selection process. The adoption of the fuzzy AHP is aimed to model the linguistic unclear, ambiguous, and incomplete knowledge. Additionally, TOPSIS, which is a ranking multi-criteria decision making method, is employed to rank the alternative sites based upon overall efficiency. The contribution of this paper lies in the evolution of a new approach that is flexible and practical to the decision maker, in providing the guidelines for the solar power plant site choices under stakeholder needs: at the same time, the desirable functions are achieved, in avoiding flood, reducing cost, time and causing less environmental impact. The new approach is assessed in the empirical study during major flooding in Thailand during the fourth quarter of 2011 to 2012. The result analysis and sensitivity analysis are also presented.展开更多
An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, whic...An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.展开更多
Sensor plays an important role in robotics. Sensors are used to determine the current state of the system. Robotic applications demand sensors with high degrees of repeatability, precision, and reliability. Flex senso...Sensor plays an important role in robotics. Sensors are used to determine the current state of the system. Robotic applications demand sensors with high degrees of repeatability, precision, and reliability. Flex sensor is such a device, which accomplish the above task with great degree of accuracy. The pick and place operation of the robotics arm can be efficiently controlled using micro controller programming. This designed work is an educational based concept as robotic control is an exciting and high challenge research work in recent year.展开更多
Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across ...Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across many spheres of influence such as business, finance, medicine, etc. to enhance produces, causes, efficiency, etc. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep Learning Algorithms. This article used Convolutional Neural Networks (CNN) to classify scenes in the CIFAR-10 database, and detect emotions in the KDEF database. The proposed method converted the data to the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high frequency features, and processing the fused feature mapping resulted in an advance in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings revealed a substantial increase in accuracy.展开更多
With recent significant development in the portable device market, cloud computing is getting more and more utilized. Many sensitive data are stored in cloud central servers. To ensure privacy, these data are usually ...With recent significant development in the portable device market, cloud computing is getting more and more utilized. Many sensitive data are stored in cloud central servers. To ensure privacy, these data are usually encrypted before being uploaded—making file searching complicated. Although previous cloud computing searchable encryption schemes allow users to search encrypted data by keywords securely, these techniques only support exact keyword search and will fail if there are some spelling errors or if some morphological variants of words are used. In this paper, we provide the solution for fuzzy keyword search over encrypted cloud data. K-grams is used to produce fuzzy results. For security reasons, we use two separate servers that cannot communicate with each other. Our experiment result shows that our system is effective and scalable to handle large number of encrypted files.展开更多
Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of ob...Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.展开更多
This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introd...This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introduced by a quarter-car model. A novel mutation mechanism is employed in MPSO to enhance global search ability and increase convergence speed of basic PSO (BPSO) algorithm. MPSO optimization is used to find the optimum values of parameters by minimizing the sum of squares error. The performance of the MPSO is compared with other optimization methods including BPSO and Genetic Algorithm (GA) in offline parameter identification. The simulating results show that this algorithm not only has advantage of convergence property over BPSO and GA, but also can avoid the premature convergence problem effectively. The MPSO algorithm is also improved to detect and determine the variation of parameters. This novel algorithm is successfully applied for online parameter identification of suspension system.展开更多
The paper addresses a literature review of the technologies used in the transmission of measuring and logging data during well drilling. It presents a discussion about efficiency in data density transmission and relia...The paper addresses a literature review of the technologies used in the transmission of measuring and logging data during well drilling. It presents a discussion about efficiency in data density transmission and reliability, especially when it comes to software and automated tools. Initially, this paper analyzes the principle of the telemetry systems, considering the mud pulse telemetry, acoustic telemetry, electromagnetic telemetry and wired drill pipe telemetry. They were detailed highlighting information about functionality, data transmission and its linkage to supporting software. Focus is also given to details of the main advantages and disadvantages of each technology considering the influences of lithology, drilling fluid and formation fluids in the reliability and capacity of data transmission.展开更多
As online trade and interactions on the internet are on the rise, a key issue is how to use simple and effective evaluation methods to accomplish trust decision-making for customers. It is well known that subjective t...As online trade and interactions on the internet are on the rise, a key issue is how to use simple and effective evaluation methods to accomplish trust decision-making for customers. It is well known that subjective trust holds uncertainty like randomness and fuzziness. However, existing approaches which are commonly based on probability or fuzzy set theory can not attach enough importance to uncertainty. To remedy this problem, a new quantifiable subjective trust evaluation approach is proposed based on the cloud model. Subjective trust is modeled with cloud model in the evaluation approach, and expected value and hyper-entropy of the subjective cloud is used to evaluate the reputation of trust objects. Our experimental data shows that the method can effectively support subjective trust decisions and provide a helpful exploitation for subjective trust evaluation.展开更多
Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed...Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability.展开更多
文摘Microservices have revolutionized traditional software architecture. While monolithic designs continue to be common, particularly in legacy applications, there is a growing trend towards the modularity, independent deployability, and flexibility offered by microservices, which is further enhanced by developments in cloud technology. This shift towards microservice architecture meets the modern business need for agility, facilitating rapid adaptability in a competitive landscape. Microservices offer an agile framework and, in many cases, can simplify the development process, though the implementation can vary and sometimes introduce complexities. Unlike monolithic systems, which can be cumbersome to modify, microservices enable quicker adjustments and faster deployment times, essential in today’s dynamic environment. This article delves into the essence of microservices and explores their growing prominence in the software industry.
文摘The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.
文摘The rapid advancement of information technology has promoted the development of informatization in universities. The freshmen welcome information system of universities is the first important system to showcase the digital level of the university to new students. With the expansion of the enrollment scale of universities, improving the efficiency of welcoming work has become an urgent problem to be solved. This article analyzes the characteristics and existing problems of the welcoming work, combined with the main technical methods of information system construction, and based on the comprehensive situation of information systems in our university, proposes the construction goals and ideas of the welcoming information system, summarizes the construction process and operation results of the welcome system, and explores possible directions for future optimization.
文摘Enterprise applications utilize relational databases and structured business processes, requiring slow and expensive conversion of inputs and outputs, from business documents such as invoices, purchase orders, and receipts, into known templates and schemas before processing. We propose a new LLM Agent-based intelligent data extraction, transformation, and load (IntelligentETL) pipeline that not only ingests PDFs and detects inputs within it but also addresses the extraction of structured and unstructured data by developing tools that most efficiently and securely deal with respective data types. We study the efficiency of our proposed pipeline and compare it with enterprise solutions that also utilize LLMs. We establish the supremacy in timely and accurate data extraction and transformation capabilities of our approach for analyzing the data from varied sources based on nested and/or interlinked input constraints.
文摘Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.
文摘A mechanical assembly is a composition of interrelated parts. Assembly data base stores the geometric models of indi-vidual parts, the spatial positions and orientations of the parts in the assembly, and the relationships between parts. An assembly of parts can be represented by its liaison which has a description of its relationships between the various parts in the assembly. The problem is to not only make the information available but also use the relevant information for making decisions, especially determination of the assembly sequence plan. The method described in this paper ex-tracts the feature based assembly information from CAD models of products and build up liaisons to facilitate assembly planning applications. The system works on the assumption that the designer explicitly defines joints and mating condi-tions. Further, a computer representation of mechanical assemblies in the form of liaisons is necessary in order to automate the generation of assembly plans. A novel method of extracting the assembly information and representing them in the form of liaisons is presented in this paper.
文摘One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.
文摘The paper elaborates the features and superiority of applying multimedia technology to aquatic organisms in a Science and Life Technology course. The augmented reality application of multimedia technology in aquatic organisms’ instruction has its own features, teaching information capacity, interactive user interface. The paper concludes the superiority of Unity and Vuforia AR instruction from five perspectives;meanwhile, the paper elaborates its disadvantages. An AR-based instruction attitude questionnaire was conducted at the end of the experiment to obtain learners’ perspectives of the system. Learners in AR approach demonstrate higher motivation and concentrate their attention on the learning tasks. Learners showed more positive attitude in willingness to use the AR-based instruction to improve learning interests. This study provided insights for better understanding the design, theory and practice of e-learning through augmented reality technology.
文摘Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.
文摘The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.
文摘The objective of this research is to propose a decision support system for avoiding flood on solar power plant site selection. Methodologically, the geographic information system (GIS) is used to determine the optimum site for a solar power plant. It is intended to integrate the qualitative and quantitative variables based upon the adoption of the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. These methods are employed to unite the environmental aspects and social needs for electrical power systematically. Regarding a case study of the choice of a solar power plant site in Thailand, it demonstrates that the quantitative and qualitative criteria should be realized prior to analysis in the Fuzzy AHP-TOPSIS model. The fuzzy AHP is employed to determine the weights of qualitative and quantitative criteria that can affect the selection process. The adoption of the fuzzy AHP is aimed to model the linguistic unclear, ambiguous, and incomplete knowledge. Additionally, TOPSIS, which is a ranking multi-criteria decision making method, is employed to rank the alternative sites based upon overall efficiency. The contribution of this paper lies in the evolution of a new approach that is flexible and practical to the decision maker, in providing the guidelines for the solar power plant site choices under stakeholder needs: at the same time, the desirable functions are achieved, in avoiding flood, reducing cost, time and causing less environmental impact. The new approach is assessed in the empirical study during major flooding in Thailand during the fourth quarter of 2011 to 2012. The result analysis and sensitivity analysis are also presented.
文摘An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.
文摘Sensor plays an important role in robotics. Sensors are used to determine the current state of the system. Robotic applications demand sensors with high degrees of repeatability, precision, and reliability. Flex sensor is such a device, which accomplish the above task with great degree of accuracy. The pick and place operation of the robotics arm can be efficiently controlled using micro controller programming. This designed work is an educational based concept as robotic control is an exciting and high challenge research work in recent year.
文摘Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across many spheres of influence such as business, finance, medicine, etc. to enhance produces, causes, efficiency, etc. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep Learning Algorithms. This article used Convolutional Neural Networks (CNN) to classify scenes in the CIFAR-10 database, and detect emotions in the KDEF database. The proposed method converted the data to the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high frequency features, and processing the fused feature mapping resulted in an advance in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings revealed a substantial increase in accuracy.
文摘With recent significant development in the portable device market, cloud computing is getting more and more utilized. Many sensitive data are stored in cloud central servers. To ensure privacy, these data are usually encrypted before being uploaded—making file searching complicated. Although previous cloud computing searchable encryption schemes allow users to search encrypted data by keywords securely, these techniques only support exact keyword search and will fail if there are some spelling errors or if some morphological variants of words are used. In this paper, we provide the solution for fuzzy keyword search over encrypted cloud data. K-grams is used to produce fuzzy results. For security reasons, we use two separate servers that cannot communicate with each other. Our experiment result shows that our system is effective and scalable to handle large number of encrypted files.
文摘Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.
文摘This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introduced by a quarter-car model. A novel mutation mechanism is employed in MPSO to enhance global search ability and increase convergence speed of basic PSO (BPSO) algorithm. MPSO optimization is used to find the optimum values of parameters by minimizing the sum of squares error. The performance of the MPSO is compared with other optimization methods including BPSO and Genetic Algorithm (GA) in offline parameter identification. The simulating results show that this algorithm not only has advantage of convergence property over BPSO and GA, but also can avoid the premature convergence problem effectively. The MPSO algorithm is also improved to detect and determine the variation of parameters. This novel algorithm is successfully applied for online parameter identification of suspension system.
文摘The paper addresses a literature review of the technologies used in the transmission of measuring and logging data during well drilling. It presents a discussion about efficiency in data density transmission and reliability, especially when it comes to software and automated tools. Initially, this paper analyzes the principle of the telemetry systems, considering the mud pulse telemetry, acoustic telemetry, electromagnetic telemetry and wired drill pipe telemetry. They were detailed highlighting information about functionality, data transmission and its linkage to supporting software. Focus is also given to details of the main advantages and disadvantages of each technology considering the influences of lithology, drilling fluid and formation fluids in the reliability and capacity of data transmission.
文摘As online trade and interactions on the internet are on the rise, a key issue is how to use simple and effective evaluation methods to accomplish trust decision-making for customers. It is well known that subjective trust holds uncertainty like randomness and fuzziness. However, existing approaches which are commonly based on probability or fuzzy set theory can not attach enough importance to uncertainty. To remedy this problem, a new quantifiable subjective trust evaluation approach is proposed based on the cloud model. Subjective trust is modeled with cloud model in the evaluation approach, and expected value and hyper-entropy of the subjective cloud is used to evaluate the reputation of trust objects. Our experimental data shows that the method can effectively support subjective trust decisions and provide a helpful exploitation for subjective trust evaluation.
文摘Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability.
