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Abstract: As we move toward Agriculture 4.0, there is increasing attention and pressure on the productivity of food production and processing. Optimizing efficiency in critical food processes such as corn drying is essential for long-term storage and economic viability. By using innovative technologies such as machine learning, neural networks, and LSTM modeling, a predictive model was implemented for past data that include various drying parameters and weather conditions. As the data collection of 3826 samples was not originally intended as a dataset for predictive models, various imputation techniques were used to ensure integrity. The model was implemented on the imputed data using a multilayer neural network consisting of an LSTM layer and three dense layers. Its performance was evaluated using four objective metrics and achieved an RMSE of 0.645, an MSE of 0.416, an MAE of 0.352, and a MAPE of 2.555, demonstrating high predictive accuracy. Based on the results and visualization, it was concluded that the proposed model could be a useful tool for predicting the moisture content at the outlets of continuous drying systems. The research results contribute to the further development of sustainable continuous drying techniques and demonstrate the potential of a data-driven approach to improve process efficiency. This method focuses on reducing energy consumption, improving product quality, and increasing the economic profitability of food processing
Keywords: drying, moisture prediction, big data, artificial intelligence, LSTM
Published in DKUM: 21.03.2025; Views: 0; Downloads: 13
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Abstract: Background: The development of the sports industry was impacted by the era of Big Data due to the rapid growth of information technology. Unfortunately, that has become an increasingly challenging Issue. Objectives: The purpose of the research was to analyze the scientific production of Big Data in sports and sports-related activities in two databases, Web of Science and Scopus. Methods/Approach: Bibliometric analysis and topic mining were done on 51 articles selected after four exclusion criteria (written in English, journal articles, the final stage of publication, and a detailed review of all full texts). The software tool used was Statistica Data Miner. Results: We found that the first articles appeared in Scopus in 2013 and WoS in 2014. USA and China are countries which produced the most articles. The most common research areas in WoS and Scopus are Public environmental and occupational health, Medicine, Environmental science ecology, and Engineering. Conclusions: We conducted that further research and literature review will be required as this is a broad and new topic.
Keywords: big data, sport, bibliometric study, topic study, health care management, services, decision making
Published in DKUM: 05.07.2024; Views: 154; Downloads: 12
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Abstract: Reporting is present in every company. Whether it is small or big, it cannot be avoided. It plays a crucial role in the process and progress of business. The quality of reporting affects the development of the work environment and the company. Since business report is a document that contains business information, which supports the decisions about the future-oriented business decisions, it is very important for it to be designed in such a way that it contains the key information for the recipient and provides support for business decisions. The reporting process can take place horizontally upwards or downwards. Content and structure vary depending on the recipient of the report. We live in an age when our every step is accompanied by digitization, computerization, artificial intelligence, mass data, the Internet of Things, machine learning, and robotics. These changes have also affected the reporting process as well as its processes. The processes of data acquisition, processing and sharing have changed. Furthermore, the data quantity has increased, whereas the speed of the time in which to prepare the reports has decreased. We can have data without information, but we cannot have information without data. There is never enough time, especially nowadays when we are used to having everything at our fingertips. These are two conflicting factors – having more data and less time to prepare quality reports. The systems are developed to optimize the process, increase efficiency and quality and, what is nowadays most important, they have been created to obtain mass data in the shortest possible time. Therefore, it is important to adapt and implement software that can help achieve our daily tasks. We must know how to process huge amounts of real-time data and deliver the information they contain. It is crucial for companies to keep up with the environment and implement changes and innovations into their business process. A company is like a living organism for it must constantly evolve and grow. As soon as it stops growing and evolving, it can fail because it starts lagging and is therefore no longer competitive to others. To deliver faster feedback, companies need data of better quality. There are tools that can improve the business process, better facilitating the capacity of the human agents. The goal is to harness the employees’ full potential and knowledge for important tasks, such as analyzing, reviewing, and understanding data and acting upon them, invoking information technology to automate repetitive processes and facilitate better communication. The focus in this master’s thesis is on the reporting process in Group X. Group X is one of the world leaders in the automotive industry, a multinational corporation based in Canada with subsidiaries around the world. The complexity of the business reporting that is implemented for the Headquarters in Canada has to address the complexity of the multinational corporation to support the decision process. The aim of the thesis is to propose a reporting process for preparing and producing reports with a huge amount of data in a very time-efficient manner. We start by examining the existing processes and upon that, identifying the processes required for the reports to reach the final recipients. Our goal is to identify the toolset, which would increase efficiency, accuracy, credibility, and reduce errors in the fastest possible time. We investigate a short-term and a long-term solution. By a short-term solution, we mean a system, program, or a tool that can help us increase our potential by using digital resources, which are already existing in the organization. By a long-term solution, we mean a solution, which requires employment of specialized future tools in the field of reporting and in repetitive processes, which we can identify with current knowledge and expectations for development. This includes machine learning, robotic process automatization, artificial intelligence.
Keywords: Consolidated reporting, reporting process, robotic process automatization, business intelligence, artificial intelligence, machine learning, SharePoint, Big Data, digital transformation, electronic data interchange.
Published in DKUM: 01.09.2021; Views: 894; Downloads: 10
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Abstract: In the provided research, some of the Big Data most prospective usage domains connect with distinguished player groups found in the business ecosystem. Literature analysis is used to identify the state of the art of Big Data related research in the major domains of its use—namely, individual marketing, health treatment, work opportunities, financial services, and security enforcement. System theory was used to identify business ecosystem major player types disrupted by Big Data: individuals, small and mid-sized enterprises, large organizations, information providers, and regulators. Relationships between the domains and players were explained through new Big Data opportunities and threats and by players’ responsive strategies. System dynamics was used to visualize relationships in the provided model.
Keywords: business ecosystems, Big Data, information providers, system dynamics
Published in DKUM: 03.04.2017; Views: 1279; Downloads: 419
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Abstract: Številna podjetja se že poslužujejo upravljanja odnosov s strankami in CRM-rešitev. CRM lahko definiramo kot upravljavsko filozofijo, kjer se cilji podjetja najlažje dosežejo skozi identificiranje in zadovoljevanje potrošniških želj (tako izjavljenih kot tudi neizjavljenih) in potreb. CRM pomaga pri profiliranju potencialnih strank, razumevanju njihovih potreb in grajenju odnosov, tako da se jim ponudi najprimernejši izdelek. Na drugi strani pa imamo Big Data, ki sicer po podjetjih še ni tako razširjena zadeva kot CRM, vendar vedno bolj pridobiva na popularnosti. Big Data so nizi podatkov, katerih obseg presega sposobnosti zajema, hranjenja, upravljanja in analiziranja s pomočjo klasičnih programskih rešitev. Uvedba CRM-rešitve in integracija Big Data sami po sebi pa ne bosta prinesli dobička. Za izboljšanja delovanja podjetja, poslovnega izida in prihodkov potrebujemo ljudi, ki bodo dana orodja in podatke znali analizirati in izkoristiti v svoj prid. Ne smemo pozabiti ene precej pomembne stvari. Lahko uvedemo najboljšo in najdražjo rešitev in nam ta ne bo prav nič koristila, če je sami ne bomo znali maksimalno izkoristiti. CRM-rešitve in Big Data sta samo pripomočka, ki nam precej olajšata razumevanje strank, vendar sama po sebi ne bosta storila ničesar.
Keywords: Big Data, velike količine podatkov, CRM, upravljanje odnosov s strankami, Salesforce, Datameer.
Published in DKUM: 24.10.2016; Views: 1413; Downloads: 212
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