| 1. |
- Dalipi, Fisnik, Senior lecturer, et al.
(författare)
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Sentiment Analysis of Students’ Feedback in MOOCs : A Systematic Literature Review
- 2021
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Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media S.A.. - 2624-8212. ; 4
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Forskningsöversikt (refereegranskat)abstract
- In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.
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| 2. |
- Hedayati, Soudabeh, et al.
(författare)
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MapReduce scheduling algorithms in Hadoop : a systematic study
- 2023
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Ingår i: Journal of Cloud Computing. - : Springer. - 2192-113X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.
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| 3. |
- Katerina, Zdravkova, et al.
(författare)
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Integration of Large Language Models into Higher Education : A Perspective from Learners
- 2024
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Ingår i: 2<em>023 International Symposium on Computers in Education (SIIE)</em>, Setúbal, Portugal, 2023. - : IEEE. - 9798350329315 - 9798350329322
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Konferensbidrag (refereegranskat)abstract
- Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.
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| 4. |
- Maleki, Neda, et al.
(författare)
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DeltaBin : An Efficient Binary Data Format for Low Power IoT Devices
- 2023
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Ingår i: <em>2023 International Conference on Computer, Information and Telecommunication Systems (CITS), Genoa, Italy, 2023</em>. - Genoa, Italy : IEEE Press. - 9798350336108 - 9798350336092
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) notion is quickly influencing t he architectures of data-driven systems d ue to the ever-increasing rapid technological progress in all sectors. The IoT involves the collection and exchange of data from a large number of interconnected devices or sensors. The collected data is structured and transmitted in a variety of different data formats such as JSON, CBOR, BSON, or simply a binary format. The data format used by an IoT device can have a significant i mpact on t he efficiency of its data transmission. In general, using a more compact and efficient data format can help to reduce t he amount of data that needs to be transmitted, which can improve the overall speed and performance of the device. For example, using a binary data format rather than a text-based format can often result in smaller data sizes and faster transmission times. Similarly, using a binary format in a more compressed form can further help to reduce the size of the data being transmitted, which can further improve the efficiency of the transmission. In this paper, we propose Delta Binary (i.e., DeltaBin) to reduce the binary data format by transmitting only changed data. We assess DeltaBin using a real IoT deployment scenario.
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| 5. |
- Maleki, Neda, et al.
(författare)
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DynaSens : Dynamic Scheduling for IoT Devices Sustainability
- 2022
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Ingår i: 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022. - : IEEE. - 9781665485982
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) have shown numerous potential applications that can enhance our quality of life. IoT is becoming a core technology to bring smart homes, smart cities, and smart industries into reality. However, with potential benefits comes a challenge of sustainability, and one major concern is to minimize energy consumption. In a citywide area, managing the operation of such large-scale IoT networking is one of the complex tasks. One of the ways is to utilize dynamic sensing scheduling where the IoT device goes to the sleep mode and prevents unnecessary data transmission. In this paper, we propose a dynamic sensing (DynaSens) algorithm for an IoT-based waste management system. This algorithm helps to reduce the waste bin overflowing, thus, provides better sanitation, and it is also helpful in reducing the fuel cost of waste collection vehicles. Our work utilizes measured values such as current consumption, LiDAR measurement time, and LoRa transmission time as the input data for the simulation experiment to evaluate energy consumption. We also assessed DynaSens using a real dataset obtained from a recycling house. We use Pycom LoPy4 micro-controller as a development board. For a number of garbage-thrown scenarios, DynaSens enables longer battery longevity by reducing the repeated execution of the same tasks. © 2022 IEEE.
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| 6. |
- Maleki, Neda, et al.
(författare)
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Future energy insights : Time-series and deep learning models for city load forecasting
- 2024
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 374
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Tidskriftsartikel (refereegranskat)abstract
- Most of the utility meters in Sweden are now integrated with Internet of Things (IoT) technology. This modern approach significantly enhances our understanding of energy consumption patterns and empowers consumers with detailed insights into their power usage. Additionally, it provides energy companies and grid owners with critical data to facilitate future energy production planning. However, having data at our disposal is only half the battle won. The method employed to forecast energy consumption is equally important due to the complex interplay between long-term trends, seasonal fluctuations, and other unpredictable factors. To optimally utilize this data, we analyzed several robust time-series forecasting models: Random Forest, XGBoost, SARIMAX, FB Prophet, and a Convolutional Neural Network (CNN). Each of these models was chosen for its unique strengths in capturing long-term trends and short-term variations, making them appropriate candidates for predicting power consumption. We showcase the models' performance on the energy consumption data from commercial property owners in 2021 and evaluate their performance based on key performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Relative Root Mean Square Error (RRMSE), Coefficient of determination (R2), R 2 ), and Standard Deviation (SD). Our results demonstrate that while FB Prophet, with its ability to effectively factor in external parameters such as price and temperature, fared well in predicting aggregated consumption, it was effectively outperformed by the CNN classifier. The CNN model demonstrated exceptional prediction capabilities and flexibility in adding additional features to the model. For example, the CNN model with the highest accuracy showed the lowest MSE compared to Random Forest, XGBoost, SARIMAX, and FB Prophet with reductions of 75.70%, 69.48%, 49.45%, and 30.62%, respectively. Additionally, the CNN model showed superior R2 2 values, indicating a better fit to the data. Specifically, the R2 2 value for the CNN model was 0.93% on the training set and 0.60% on the testing set, outperforming the other models in terms of explained variance. We also utilized AutoML to analyze a 4-year dataset (2021-2023) to showcase the generalizability of the models. Using AutoML, the R2 2 value increased from 47% to 83% with an expanded dataset, indicating that other models will also achieve better results. From a qualitative perspective, contrary to the prevailing notion that deep learning models demand substantial resources, our experience revealed that training a CNN model did not pose significantly greater challenges than traditional models. This reinforces the untapped potential of deep learning in time-series forecasting, highlighting that complex problems like electricity consumption forecasts may benefit from advanced solutions like CNN.
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| 7. |
- Maleki, Neda, et al.
(författare)
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Unraveling Energy Consumption Patterns : Insights Through Data Analysis and Predictive Modeling
- 2023
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Ingår i: 15th International Conference on Applied Energy.
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Konferensbidrag (refereegranskat)abstract
- Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.
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| 8. |
- Manzoni, Pietro, et al.
(författare)
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Crowdsourcing Through TinyML as aWay to Engage End-Users in IoT Solutions
- 2023. - 1
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Ingår i: Mobile Crowdsourcing. - Switzerland : Springer. - 9783031323973 - 9783031323966 ; , s. 359-387
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization. Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted.
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| 9. |
- Mohammadian, Mehrdad, et al.
(författare)
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Persis : A Persian Font Recognition Pipeline Using Convolutional Neural Networks
- 2022
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Ingår i: <em>2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)</em>, Mashhad, Iran, Islamic Republic of. - : IEEE. ; , s. 196-204
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Konferensbidrag (refereegranskat)abstract
- What happens if we see a suitable font for our design work but we do not know its name? Visual Font Recognition (VFR) systems are used to identify the font typeface in an image. These systems can assist graphic designers in identifying fonts used in images. A VFR system also aids in improving the speed and accuracy of Optical Character Recognition (OCR) systems. In this paper, we proposed the first publicly available datasets in the field of Persian font recognition and employed Convolutional Neural Networks (CNN) to address the Persian font recognition problem. The results show that the proposed pipeline obtained 78.0% top-1 accuracy on our new datasets, 89.1% in the IDPL-PFOD dataset, and 94.5% in the KAFD dataset. Furthermore, the average time spent in the entire pipeline for one sample of our proposed datasets is 0.54 and 0.017 seconds for CPU and GPU, respectively. We conclude that CNN methods can be used to recognize Persian fonts without the need for additional pre-processing steps such as feature extraction, binarization, normalization, etc.
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| 10. |
- Musaddiq, Arslan, et al.
(författare)
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Industry-Academia Cooperation : Applied IoT Research for SMEs in South-East Sweden
- 2023
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Ingår i: Internet of Things. GIoTS 2022. - Cham : Springer. - 9783031209352 - 9783031209369 ; , s. 397-410
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Konferensbidrag (refereegranskat)abstract
- This paper presents the activities of the Applied IoT Lab at the Department of Computer Science and Media Technology, Linnaeus University (LNU), Kalmar, Sweden. The lab is actively engaged in IoT-based educational programs, including a series of workshops and pilot cases. The lab is funded by the European Union and two Swedish counties – Kalmar and Kronoberg. The workshops and pilot cases are part of the research project named IoT Lab for Small and Medium-sized Enterprises (SMEs). One of the lab’s main objectives is to strengthen and support local companies with IoT. The project IoT Lab for SMEs also aims to spread knowledge and inspire the local community about the possibilities of using IoT technologies by organizing open lab days, in-depth lectures, and seminars. This paper introduces Applied IoT Lab at LNU, its educational programs, and industry-academic cooperation, including workshops and a number of ongoing pilot cases.
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| 11. |
- Musaddiq, Arslan, et al.
(författare)
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Integrating Object Detection and Wide Area Network Infrastructure for Sustainable Ferry Operation
- 2023
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Ingår i: <em>2023 IEEE International Conference on Imaging Systems and Techniques (IST)</em>, Copenhagen, Denmark. - : IEEE. - 9798350330830 - 9798350330847
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Konferensbidrag (refereegranskat)abstract
- Low-Power Wide-Area Network (LPWAN) technologies offer new opportunities for data collection, transmission, and decision-making optimization. Similarly, a wide range of use cases of computer vision and object detection algorithms can be found across different industries. This paper presents a case study focusing on the utilization of LPWAN infrastructure, specifically the Helium network, coupled with computer vision and object detection algorithms, to optimize passenger ferry operation. The passenger ferry called M/S Dessi operates between Kalmar and Färjestaden in Sweden during the summer season. By implementing an Edge-computing solution, real-time data collection and communication are achieved, enabling accurate measurement of passenger flow. This approach is superior to traditional methods of collecting passenger data, such as manual counting or CCTV surveillance. Real-time passenger data is invaluable for traffic planning, crowd prediction, revenue enhancement, and speed and fuel optimization. The utilization of the Helium network ensures reliable and long-distance data transmission, extending the system’s applicability to multiple ferries and distant locations. The proposed approach can be utilized to integrate passenger ferries that operate in close proximity to urban areas into society’s digital transformation efforts. This study highlights the potential of LPWAN, computer vision, and object detection in enhancing passenger ferry operations, contributing to enhanced efficiency and sustainability.
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| 12. |
- Musaddiq, Arslan, et al.
(författare)
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Internet of Things for Digital Transformation and Sustainable Growth of SME's
- 2024
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Ingår i: 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 29-31 July 2024. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350349597 - 9798350349603 ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- Small and medium-sized enterprises (SMEs)can significantly enhance their efficiency and productivity with the integration of emerging technologies like the Internet of Things (IoT). However, limited resources and a lack of expertise in information and communication technologies often present challenges for SMEs in their journey towards digitalization. This paper outlines the IoT roadmap, detailing the necessary knowledge and support required to support SMEs in using IoT technologies. Drawing upon several pilot case studies as illustrative examples, the paper underscores the value that IoT and related platforms can offer SMEs within the framework of smart and sustainable development. Additionally, it highlights the challenges typically encountered in the adoption and integration of these technologies
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| 13. |
- Musaddiq, Arslan, et al.
(författare)
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Internet of Things for Wetland Conservation using Helium Network : Experience and Analysis
- 2022
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Ingår i: 12th International Conference on the Internet of Things, IoT 2022, Delft 7 - 10 November 2022. - New York, NY, USA : ACM Digital Library. - 9781450396653 ; , s. 143-146
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.
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| 14. |
- Musaddiq, Arslan, et al.
(författare)
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Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments : Theoretical Perspective and Challenges
- 2023
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:19
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study.
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| 15. |
- Palma, Francis, et al.
(författare)
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Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs
- 2022
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Ingår i: Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022. - : IEEE. - 9781665481465 ; , s. 20-30
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Konferensbidrag (refereegranskat)abstract
- Application Programming Interfaces (APIs) define how Web services, middle-wares, frameworks, and libraries communicate with their clients. An API that conforms to REpresentational State Transfer (REST) design principles is known as REST API. At present, it is an industry-standard for interaction among Web services. There exist mainly three categories of APIs: public, partner, and private. Public APIs are designed for external consumers, whereas partner APIs are designed aiming at organizational partners. In contrast, private APIs are designed solely for internal use. The API quality matters regardless of their category and intended consumers. To assess the (linguistic) design of APIs, researchers defined linguistic patterns (i.e., best API design practices) and linguistic antipatterns (i.e., poor API design practices.) APIs that follow linguistic patterns are easy to understand, use, and maintain. In this study, we analyze and compare the design quality of public, partner, and private APIs. More specifically, we made a large survey by analyzing and performing the detection of nine linguistic patterns and their corresponding antipatterns on more than 2,500 end-points from 37 APIs. Our results suggest that (1) public, partner, and private APIs lack quality linguistic design, (2) among the three API categories, private APIs lack linguistic design the most, and (3) end-points are amorphous, contextless, and non-descriptive in partner APIs. End-points have contextless design and poor documentation regardless of the API categories.
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| 16. |
- Pena, Blanca, et al.
(författare)
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A Review on Applications of Machine Learning in Shipping Sustainability
- 2020
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Ingår i: SNAME Maritime Convention 2020 – A Virtual Event 29 September- 2 October. - : Society of Naval Architects and Marine Engineers (SNAME).
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Konferensbidrag (refereegranskat)abstract
- The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges.
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| 17. |
- Xie, Xianwei, et al.
(författare)
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Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods
- 2023
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Ingår i: Journal of Marine Science and Engineering. - : MDPI. - 2077-1312. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R22 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R22 also can reach 0.9954, which can provide decision support for the operation of shipping companies.
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| 18. |
- Zapico, Jorge Luis, et al.
(författare)
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Insect biodiversity in agriculture using IoT : opportunities and needs for further research
- 2022
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Ingår i: IEEE Global Communications Conference, 7-11 December 2021, Madrid, Spain. - : IEEE. - 9781665423908 - 9781665423915 ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- Recent research points out an alarming decline in insect biodiversity and biomass. Changing agriculture practices, together with climate change, are a main driver behind this decline. Biodiversity interventions in agriculture can therefore play an important role for insect conservation. Validating the impact of such interventions is limited by expertise and labor intensive methods, and there is a growing number of projects exploring how IoT could help. For instance using remote sensors to capture insect images and sound fingerprints non-intrusively, and machine learning models to automatically classify the obser- vation in different taxa. This article will: (a) explore recent advances in Internet of Things, Edge ML and LPWAN technologies and their application for monitoring insect biodiversity; (b) discuss opportunities, needs and ideas for validating the impact of biodiversity inter- ventions in agriculture using these technologies; and (c) outline future research opportunities.
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