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نتیجه جستجو - تجزیه و تحلیل پیش بینی

تعداد مقالات یافته شده: 21
ردیف عنوان نوع
1 A novel machine learning pipeline to detect malicious anomalies for the Internet of Things
پایپ لاین یادگیری ماشینی جدید برای شناسایی ناهنجاری های مخرب برای اینترنت اشیا-2022
Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The anomalies are considered as samples that do not follow a normal pattern and significantly differ from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data generated from IoT devices. There has been wide-scale research done on anomaly detection problems in general, i.e., finding the samples in data that differ significantly from the expected values. However, there has been limited work done to figure out the inherent cause of the anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed, the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data manipulation by an attacker has not been explored in detail.
In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement.
keywords: IoT | Anomaly detection | Ensemble learning | Predictive analytics
مقاله انگلیسی
2 Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستم‌های تولید هوشمند با استفاده از تجزیه و تحلیل پیش‌بینی‌کننده-2022
Smart manufacturing systems (SMS) are one of the most important applications in the Industry 4.0 era, offering numerous advantages over traditional production systems and rapidly being used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT), Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more. SMS is an innovative and popular manufacturing setup that produces increasingly intelligent production systems; yet, designers must adapt to business tastes and requirements. This study employs an analytical and descriptive research technique to identify and assess functional and non-functional, technological, economic, social, and performance evaluation components that are essential to SMS evaluation. A predictive analytics framework, which is a key component of many decision support systems, is used to assess corporate needs as well as proposed and prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system
مقاله انگلیسی
3 Future Generation Computer Systems 116 (2021) 209–219
سیستم های کامپیوتری نسل آینده 116 (2021) 209-219-2021
An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best combination to use when solving the problem at hand. Often, such knowledge is not explicitly captured by the organisation. To address this problem, this paper presents the design of an innovative Information Technology (IT) platform which enables data sharing between different analytics models and provides the ability to extend or customise models or data sources without necessarily involving the analysts who created them. It can make analytics knowledge readily available and modifiable for future use and problem-solving by analysts and other stakeholders. In the context of our work, we organise analytics knowledge around the concept of a research variable, which analysts often use when defining and proving a hypothesis. By focusing on such a concept, this platform is particularly suited to develop empirical data analytics applications in any domain. This paper presents the architecture of this platform, including the knowledge base and the Application Programming Interface (API) layer. Capabilities of this platform are illustrated through a software prototype and a use case on property price prediction across Sydney, Australia.
keywords: تجزیه و تحلیل پیش بینی | مدیریت دانش | دانش محور | مدل سازی معنایی | هستی شناسی | Predictive analytics | Knowledge management | Knowledge base | Semantic modelling | Ontologies
مقاله انگلیسی
4 A deep reinforcement learning approach for real-time sensor-driven decision making and predictive analytics
یک رویکرد یادگیری تقویتی عمیق برای تصمیم گیری در زمان واقعی مبتنی بر حسگر و تجزیه و تحلیل پیش بینی-2020
The increased complexity of sensor-intensive systems with expensive subsystems and costly repairs and failures calls for efficient real-time control and decision making policies. Deep reinforcement learning has demonstrated great potential in addressing highly complex and challenging control and decision making problems. Despite its potential to derive real-time policies using real-time data for dynamic systems, it has been rarely used for sensordriven maintenance related problems. In this paper, we propose two novel decision making methods in which reinforcement learning and particle filtering are utilized for (i) deriving real-time maintenance policies and (ii) estimating remaining useful life for sensor-monitored degrading systems. The proposed framework introduces a new direction with many potential opportunities for system monitoring. To demonstrate the effectiveness of the proposed methods, numerical experiments are provided from a set of simulated data and a turbofan engine dataset provided by NASA.
Keywords: Particle filters | Deep reinforcement learning | Real-time control | Decision-making | Remaining useful life estimation
مقاله انگلیسی
5 Analytical study on use of AI techniques in tourism sector for smarter customer experience management
مطالعه تحلیلی در مورد استفاده از تکنیک های هوش مصنوعی در بخش گردشگری برای مدیریت دقیقتر تجربه مشتری-2020
Artificial Intelligence is the new prime factor for paradigm shift of the new age technologies. It has created a new realm in every field- from education to entertainment or from biotechnology to manufacturing industry. Though tourism is a late runner in this race, but this sector has also witnessed a huge change with the magical touch of AI. This sector being one of the highly emerging sectors, contributing very high GDP , has adapted several machine learning techniques or data analytics, which has made tourism model smarter and dynamic. In India , tourism has an ample scope to grow and Indian tourism sectors are also adapting several popular AI techniques like deep learning, Artificial neural network, predictive analytics, robotics or new technologies like virtual reality or augmented reality. This technological adaptation has made their services much better, heled in dynamic pricing, or for smart customer experience management. This paper has conducted a study on Indian tourism sectors providing online services and discusses about the current AI technologies used by them while exploring the pros and cons faced by them . The paper is alienated in three different segments- section 1 contains introduction part, section 2 discusses about related works in similar area, third section deliberates about different AI techniques adapted by Indian tourism sectors along with their pro and cons.
Keywords : ChatBot | Artificial neural network | Machine Learning | Robotics | Predictive Analytics | Recommendation System
مقاله انگلیسی
6 The impact of entrepreneurship orientation on project performance: A machine learning approach
تأثیر گرایش کارآفرینی بر عملکرد پروژه: یک رویکرد یادگیری ماشینی-2020
Recent studies in project management have shown the important role of entrepreneurship orientation of the individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical success factor in project performance has been considered as an important issue, it is also important to develop a measurement system for predicting performance based on the degree of an individual’s entrepreneurial orientation. In this study, we use predictive analytics by proposing a machine learning approach to predict individuals’ project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks, and random forest. Our results showed that the best method for predicting project performance is lasso. After identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Information Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial orientation (proactiveness) as the most important factors. This study contributes to the relationship between entrepreneurship skills and project performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.
Keywords: Project performance | Entrepreneurship orientation | Machine learning | Supervised learning | Predictive analytics
مقاله انگلیسی
7 A new model to compare intelligent asset management platforms (IAMP)
مدل جدیدی برای مقایسه سیستم عامل های مدیریت دارایی هوشمند (IAMP)-2020
Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0)
Keywords: Asset Management | Industrial IoT | Digitalization | Predictive Analytics | Intelligent assets management systems
مقاله انگلیسی
8 Factor-based big data and predictive analytics capability assessment tool for the construction industry
داده های بزرگ مبتنی بر فاکتور و ابزار ارزیابی قابلیت تحلیلی پیش بینی کننده برای صنعت ساخت و ساز-2020
Big data and predictive analytics have huge potential to create value to the construction industry. However, there is a lack of benchmarking system to evaluate organizations competency to adopt big data and predictive analytics. Hence, this study aims to develop a big data and predictive analytics capability assessment tool that can measure construction organizations capability in big data and predictive analytics implementation and that also highlights strengths and weaknesses of the organization to provide a benchmark in the process of big data and predictive analytics implementation. 21 determinants were identified and assessed in sense of their impacts on an organizations capability to implement big data and predictive analytics. These determinants were categorized into five determinant groups and assigned weights, to form the basis for the big data and predictive analytics capability assessment tool. The developed tool was then validated with four construction organizations to reflect their big data and predictive analytic capability levels, strengths and weaknesses. The findings of this study contribute to knowledge and practice by identifying the determinants impacting construction organizations capability to adopt big data and predictive analytics and in the development of a computerized assessment tool which also serves as a benchmarking tool for construction organizations in the implementation of big data and predictive analytics.
Keywords: Big data | Predictive analytics | Capability assessment tool | Construction industry | Organization capability
مقاله انگلیسی
9 The impact of entrepreneurship orientation on project performance: A machine learning approach
تأثیر گرایش کارآفرینی بر عملکرد پروژه: یک رویکرد یادگیری ماشین-2020
Recent studies in project management have shown the important role of entrepreneurship orientation of the individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical success factor in project performance has been considered as an important issue, it is also important to develop a measurement system for predicting performance based on the degree of an individual’s entrepreneurial orientation. In this study, we use predictive analytics by proposing a machine learning approach to predict individuals’ project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks, and random forest. Our results showed that the best method for predicting project performance is lasso. After identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Infor mation Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial orientation (proactiveness) as the most important factors. This study contributes to the relationship between entrepreneurship skills and project performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.
Keywords: Project performance | Entrepreneurship orientation | Machine learning | Supervised learning | Predictive analytics
مقاله انگلیسی
10 Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil
داده کاوی آموزشی: تجزیه و تحلیل پیش بینی کننده عملکرد علمی دانش آموزان مدارس دولتی در پایتخت برزیل-2019
In this article, we present a predictive analysis of the academic performance of students in public schools of the Federal District of Brazil during the school terms of 2015 and 2016. Initially, we performed a descriptive statistical analysis to gain insight from data. Subsequently, two datasets were obtained. The first dataset contains variables obtained prior to the start of the school year, and the second included academic variables collected two months after the semester began. Classification models based on the Gradient Boosting Machine (GBM) were created to predict academic outcomes of student performance at the end of the school year for each dataset. Results showed that, though the attributes ‘grades and ‘absences were the most relevant for predicting the end of the year academic outcomes of student performance, the analysis of demographic attributes reveals that ‘neighborhood’, ‘school’ and ‘age’ are also potential indicators of a students academic success or failure.
Keywords: Educational data mining | Academic performance | Predictive analysis | Decision tree | Gradient boosting machine | H2O
مقاله انگلیسی
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