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2022 ◽  
Vol 12 ◽  
Author(s):  
Yang Li ◽  
Xuewei Chao

Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments, considering the mapping feature dimensions and base data sizes, were conducted to testify the validity and robustness of this indicator. Both the numerical and the visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the quality assessment of the data information and provides some inspiration for the subsequent research on data mining, as well as for the dataset optimization for practical applications.


SIASAT ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 55-70
Author(s):  
Nur M. Ridha Tarigan ◽  
Rahmat Akbar Syahputra ◽  
Tri Kartika Yudha

In fact, the work performance of employees does not match the expectations of an agency, and they still encounter low employee performance. As a result of low employee performance, the impact on agency productivity decreases and cannot meet the targets set by the agency, so that the agency suffers losses and experiences obstacles in its development. Meanwhile, for employees who have low work performance, it will hinder the career development and income of the employee.Descriptive analysis is a research method that provides an overview regarding situations and events so that this method intends to Base data accumulation applies.From the results of the analysis with the number of respondents as many as 63 people, the following conclusions are drawn: 1) Job satisfaction has a positive and significant effect on employee performance in Simalungun Regency Agriculture Office. 2) Leaders are expected to improve the promotion appraisal system based on performance and work results. 3) Maintaining the current good condition of employees, who are able to work hard to get awards, a well-developed family atmosphere and an interesting and challenging work atmosphere has been created.


Author(s):  
Lucas Woltmann ◽  
Claudio Hartmann ◽  
Dirk Habich ◽  
Wolfgang Lehner

AbstractCardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 90 with our aggregate-based training phase and thus outperform indexes.


2022 ◽  
pp. 165-182
Author(s):  
Jun-Ho Huh

In this design unit, a design to test the performances of varying models was developed for the simulations in the PLC-base data link layer. The design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been designed using OPNET and Power Line Communication is proposed in this book. It is being designed to allow test bed experiments in four layers among OSI 7 layers. This chapter is organized as follows: The Physical Layer and Datalink Layer for Smart Grid Test Bed in Section 1; the Transport Layer for Smart Grid Test Bed in Section 2; and finally, Application Layer for Smart Grid Test Bed in Section.


2022 ◽  
pp. 635-671
Author(s):  
Jun-Ho Huh

In this chapter, a design that allows testing of the performances of various models was developed with OPNET for the simulations in the PLC-base data link layer. As the model proposed earlier, the design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been implemented using OPNET and Power Line Communication is proposed in this book. It is being designed to allow Test Bed experiments in four layers among seven OSI layers. This chapter is organized as follows: the physical layer and datalink layer for Smart Grid Test Bed in Section 1; the transport layer for Smart Grid Test Bed in Section 2; and finally, application layer for Smart Grid Test Bed in Section 3.


2021 ◽  
Vol 8 (4) ◽  
pp. 408-415
Author(s):  
Adebunmi Yetunde Aina ◽  
Ayodele Abosede Ogegbo

The outbreak of the COVID-19 pandemic has compelled education systems across the globe to fully embrace online learning as an alternative to face-to-face classes. This has resulted in a paradigm shift, in which online teaching and assessment practices continue to gain prominence at the tertiary level. Hence, this study explores lecturers' teaching and assessment strategies for teaching university students through online platforms during the COVID-19 pandemic. This study has used a framework of SWOT (strength, weakness, opportunities, and threats) analysis as its theoretical base. Data was collected through semi-structured interviews with five lecturers from three universities in Gauteng Province, South Africa. The collected data was analysed using content analysis. This study found that lecturers were able to manage the teaching and assessment processes during the COVID-19 school restrictions, using a combination of platforms such as Blackboard-collaborate, WhatsApp, Kahoot, and Google Classroom. The findings also revealed that a wide variety of teaching and assessment methodologies, including small group work, collaborative learning, case methods, discussion posts, multiple choice quizzes, chats, game activities, open-ended questions, and essays were utilized on these platforms. Although the methodologies used for teaching and assessing on these platforms require additional preparation time, they also help increase interaction between students and enable immediate grading of scripts and student feedback. Further findings revealed that online assessment is highly susceptible to test/examination malpractices. This study provided recommendations helpful to policymakers, lecturers, and students regarding online teaching and assessment strategies.


2021 ◽  
Vol 3 ◽  
pp. 1-2
Author(s):  
Maroale Chauke ◽  
Nicolene Fourie ◽  
Vutomi Ndlovu ◽  
Yvonne Moema
Keyword(s):  
Data Set ◽  


2021 ◽  
pp. 5-12
Author(s):  
V. Lysenko ◽  
◽  
I. Chernova ◽  

The article is devoted to the issue of formalization of knowledge in the management of entomophages production. The analysis of scientific works concerning the chosen direction of researches is carried out in the work, their urgency is substantiated. Factors influencing the efficiency of entomophages production management as a complex biotechnical system of ergatic type are identified. The aim of the research was to formalize knowledge in the management of entomophages production to increase the efficiency of its operation. The object of research is the processes of entomophages production management. Research methods - system modeling, precedent-based management, data mining. A model for presenting knowledge for entomophages production management based on precedents in the form of an associative map using the FreeMind software environment has been developed. A method for management the production of entomophages based on the application of a precedent approach and data mining has been developed. A precedent knowledge base has been developed using the Microsoft Access software environment for systematization of information on human operator actions in entomophages production management processes. The functioning of the knowledge base is based on production rules. The expediency of formalization of knowledge in the management of entomophages production is scientifically substantiated, which creates the conditions for increasing the efficiency of production and the quality of entomological products. This is achieved by reducing the time for the formation of strategies for its management through the use of previous experience and structuring information on the actions of the operator-technologist in management processes in the form of a precedent knowledge base. Key words: management, entomophages production, precedent, knowledge base, data mining


2021 ◽  
Author(s):  
Ridda Ali ◽  
Sophie Abrahams ◽  
Anna Berryman ◽  
Collin Bleak ◽  
Nor Aishah Hamzah ◽  
...  

We were asked by Innovation Embassy to work with a large dataset centred around gambling investment, with the task of making a predictive function for computing Customer Lifetime Value (CLV), and also to see if there are ways of detecting fraudulent financial practices and addictive gambling patterns. We had moderate success with the data as it stands, but we were partly held back for two main reasons: the ability to discern a solid definition of CLV due to highly inconsistent data and data that contained many large and incomputable gaps. Different machine learning algorithms were used to find CLV functions based on key variables. We also describe a short and explicit list of ways where the base data can be improved to support effective calculation of CLV. Our key findings suggest that the average customer's CLV is 1035 and ~80% of revenue is brought in from ~10% of the clients.


2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Ahmed Nur ◽  
Aditya Agrawal

Abstract Aims To evaluate early perioperative outcomes following emergency and elective laparoscopic cholecystectomies in a district general hospital against the national average. Methods A retrospective audit was carried out on consecutive Laparoscopic Cholecystectomies performed between January 2020 and June 2018. All indications were included. Demographics and base data included; age, gender, ASA grade, type of surgery (Emergency/Elective), number of symptomatic days preoperatively, preoperative bloods, preoperative ERCP, operative findings, postoperative complications and length of stay. Data was gathered from physical and electronic patient records. Results 166 laparoscopic cholecystectomies were included in the audit. Of the 166 included patients, 48 were male and 118 were female. Mean age at time of operation was 53.4 years. 106 of the laparoscopic cholecystectomies were carried out as Elective cases and 60 were performed as Emergencies. 100% of cases were performed laparoscopically, with 3 cases requiring conversion to open intraoperatively. Postoperatively, 5 patients had post-op pneumonia. There were 3 documented cases of bile leak with 1 patient requiring ERCP as a result. There were 2 cases of wound infection requiring re-admission. Other documented complications included; umbilical port sit abscess requiring incision and drainage, collection in the gallbladder fossa, small bowel injury and a post-op drop in Haemoglobin requiring transfusion. Conclusions Outcomes in this cohort of patients undergoing laparoscopic cholecystectomies are comparable to national data. The focus of further evaluation from this cohort should be to compare outcomes between Emergency and Elective Laparoscopic Cholecystectomies, with Emergency cases further stratified according to the number of symptomatic days preoperatively.


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