Digital Livestock Farming
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
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
Implementation of a Vision-Based Worker Assistance System in Assembly: a Case Study
پیاده سازی سیستم کمک کارگری مبتنی بر دید در مونتاژ: مطالعه موردی-2021
The current introduction of Industry 4.0 is very challenging for industrial companies. On the one hand, there is an urge to implement concepts such as digital worker assistance systems or cyber-physical production systems, but besides theoretical work, there is very little research that shows examples of its practical implementation. Furthermore, there is currently a lack of a clear model of how sensor-based worker assistance systems for data acquisition and analytics can be designed and systematically implemented. In the present research, a model for a vision-based worker assistance system for assembly was developed based on an industrial case study regarding a manual assembly line. The proposed model consists of five integrated modules: data acquisition, data preprocessing, data storage, data analysis, and simulation. The data acquisition module was constructed in the assembly workstation of the production line by implementing a depth camera, which together with an algorithm developed in Python for preprocessing, tracks the activities of the operator and inserts the processing times into a SQL table of the data storage module. This module contains all the relevant information of the production system, from the shop floor to the Manufacturing Execution System, enabling vertical integration. The data analysis module, aimed at the streaming and predictive analytics, was deployed in the RStudio platform. Likewise, the simulation module was conceptualized to retrieve real-time data from the shop floor and to select the best strategy. To evaluate the model testing of the proposed system in real production was performed. The results of this use case provide useful information for academia as well as practitioners how to implement vision-based worker assistance systems.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the 8th CIRP Global Web Conference – Flexible Mass Customisation
Keywords: industry 4.0 | data analytics | cyber-physical production system | computer vision | smart manufacturing | assembly ponsibility
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
Prescriptive analytics: Literature review and research challenges
تجزیه و تحلیل تجربی: مرور ادبیات و چالش های تحقیقاتی-2020
Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to prescriptive analytics and prominent methods for its implementation, provides clarity on the research field of prescriptive analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.
Keywords: Analytics | Prescriptive analytics | Business analytics | Big data | Literature review
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
Predicting and explaining corruption across countries: A machine learning approach
پیش بینی و توضیح فساد در سراسر کشور: رویکرد یادگیری ماشینی-2020
In the era of Big Data, Analytics, and Data Science, corruption is still ubiquitous and is perceived as one of the major challenges of modern societies. A large body of academic studies has attempted to identify and explain the potential causes and consequences of corruption, at varying levels of granularity, mostly through theoretical lenses by using correlations and regression-based statistical analyses. The present study approaches the phenomenon from the predictive analytics perspective by employing contemporary machine learning techniques to discover the most important corruption perception predictors based on enriched/enhanced nonlinear models with a high level of predictive accuracy. Specifically, within the multiclass classification modeling setting that is employed herein, the Random Forest (an ensemble-type machine learning algorithm) is found to be the most accurate prediction/classification model, followed by Support Vector Machines and Artificial Neural Networks. From the practical standpoint, the enhanced predictive power of machine learning algorithms coupled with a multi-source database revealed the most relevant corruption-related information, contributing to the related body of knowledge, generating actionable insights for administrator, scholars, citizens, and politicians. The variable importance results indicated that government integrity, property rights, judicial effectiveness, and education index are the most influential factors in defining the corruption level of significance
Keywords: Corruption perception | Machine learning | Predictive modeling | Random forest | Society policies and regulations |Government integrity | Social development
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
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
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