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1دورية أكاديمية
المؤلفون: Dilek AYDOGAN-KILIC, Deniz Kenan KILIC, Izabela Ewa NIELSEN
المصدر: Applied Computer Science, Vol 20, Iss 2 (2024)
مصطلحات موضوعية: Artificial intelligence, Natural language processing (NLP), Classification problem, International classification of diseases (ICD), Bidirectional Encoder Representations from Transformers (BERT), MIMIC-III, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
الوصف: The International Classification of Diseases (ICD) is utilized by member countries of the World Health Organization (WHO). It is a critical system to ensure worldwide standardization of diagnosis codes, which enables data comparison and analysis across various nations. The ICD system is essential in supporting payment systems, healthcare research, service planning, and quality and safety management. However, the sophisticated and intricate structure of the ICD system can sometimes cause issues such as longer examination times, increased training expenses, a greater need for human resources, problems with payment systems due to inaccurate coding, and unreliable data in health research. Additionally, machine learning models that use automated ICD systems face difficulties with lengthy medical notes. To tackle this challenge, the present study aims to utilize Medical Information Mart for Intensive Care (MIMIC-III) medical notes that have been summarized using the term frequency-inverse document frequency (TF-IDF) method. These notes are further analyzed using deep learning, specifically bidirectional encoder representations from transformers (BERT), to classify disease diagnoses based on ICD codes. Even though the proposed methodology using summarized data provides lower accuracy performance than state-of-the-art methods, the performance results obtained are promising in terms of continuing the study of extracting summary input and more important features, as it provides real-time ICD code classification and more explainable inputs.
وصف الملف: electronic resource
العلاقة: https://ph.pollub.pl/index.php/acs/article/view/5894Test; https://doaj.org/toc/2353-6977Test
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2دورية أكاديمية
المصدر: Supply Chain Analytics, Vol 7, Iss , Pp 100072- (2024)
مصطلحات موضوعية: Vehicle routing problem, Supply chain management, Green logistics, Decision anaysis, Mathematical modeling, Systematic review, Marketing. Distribution of products, HF5410-5417.5, Management. Industrial management, HD28-70
الوصف: The vehicle routing problem (VRP) is a combinatorial optimization problem that determines optimal routes to enhance distribution efficiency. One of the most popular strategies in freight distribution is multi-echelon distribution. Multi-echelon distribution networks often apply to supply chain management, land transportation, the maritime industry, aviation, etc., and rely on VRP. This comprehensive review systematically analyses 382 papers retrieved through the Scopus database. We use a bibliometric and network analysis tool to complete a systematic literature mapping identifying key interrelationships and research clusters. The analysis depicts five main research clusters: green logistics and decision analysis, scheduling and inventory optimization, VRP for city logistics, mathematical modeling and optimization, and outbound logistics and customer service, identified based on author keywords of the systematically derived paper pool. Each cluster is provided with foundational knowledge, concepts, theories, and employed techniques. Finally, future studies are suggested to explore more comprehensive investigation in highly discussed domains like city logistics problems in e-commerce, vehicle routing problems for sustainable logistics, and technological advancement-based applications.
وصف الملف: electronic resource
العلاقة: http://www.sciencedirect.com/science/article/pii/S2949863524000153Test; https://doaj.org/toc/2949-8635Test
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3دورية أكاديمية
المؤلفون: Deniz Kenan Kılıç, Peter Nielsen, Amila Thibbotuwawa
المصدر: Energies, Vol 17, Iss 12, p 2909 (2024)
مصطلحات موضوعية: long short-term memory (LSTM), electricity price forecasting (EPF), intraday electricity market, time series, energy trading, power market, Technology
الوصف: For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.
وصف الملف: electronic resource
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4دورية أكاديمية
المؤلفون: Fernando, W. Madushan, Thibbotuwawa, Amila, Perera, H. Niles, Nielsen, Peter, Kilic, Deniz Kenan
المصدر: Fernando , W M , Thibbotuwawa , A , Perera , H N , Nielsen , P & Kilic , D K 2024 , ' An integrated vehicle routing model to optimize agricultural products distribution in retail chains ' , Cleaner Logistics and Supply Chain , vol. 10 , 100137 . https://doi.org/10.1016/j.clscn.2023.100137Test
مصطلحات موضوعية: Agricultural products, City logistics, Integrated VRP, Perishable supply chains, Retail chain
الوصف: The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.
الإتاحة: https://doi.org/10.1016/j.clscn.2023.100137Test
https://vbn.aau.dk/da/publications/a8c7c32e-f7d9-4a67-8b10-c7bdf1081e9eTest
http://www.scopus.com/inward/record.url?scp=85182253675&partnerID=8YFLogxKTest -
5دورية أكاديمية
مصطلحات موضوعية: Artificial Intelligence cs.AI, Information Retrieval cs.IR, Machine Learning cs.LG, FOS Computer and information sciences
الوصف: Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today's vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don't know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to ... : It was published online on 15 May 2024 in Human-Centric Intelligent Systems, Springer ...
الإتاحة: https://doi.org/10.48550/arxiv.2307.0457310.1007/s44230-024-00070-6Test
https://arxiv.org/abs/2307.04573Test -
6دورية أكاديمية
المصدر: Algorithms; Volume 16; Issue 3; Pages: 172
مصطلحات موضوعية: robotic arm-based batching systems, scheduling robotic arms, robotic task-sequencing problem, real-time decision making, give-away minimization
الوصف: In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor belt is picked up by which robotic arm at what position, and on which tray the picked up item will be placed. This problem involves critical constraints, such as sequence-dependent processing time and dynamic item and tray positions. Moreover, due to the speed of the infeed conveyor and latency in the information about entering items into the system, this scheduling problem must be solved in near real time. To address these challenges, we propose a scheduling solution that first decomposes the original scheduling problem into sub-problems, where a sub-problem formulated as a goal program schedules robotic arms only for a single tray. The performance of the proposed solution approach is then tested under a simulation environment, and from the experiments, the proposed approach produces acceptable performance.
وصف الملف: application/pdf
العلاقة: Combinatorial Optimization, Graph, and Network Algorithms; https://dx.doi.org/10.3390/a16030172Test
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7دورية أكاديمية
المؤلفون: Kılıç, Deniz Kenan, Uğur, Ömür
المصدر: Kılıç , D K & Uğur , Ö 2023 , ' Hybrid wavelet-neural network models for time series ' , Applied Soft Computing , vol. 144 , 110469 . https://doi.org/10.1016/j.asoc.2023.110469Test
مصطلحات موضوعية: Long short-term memory (LSTM), Multiresolution analysis (MRA), Nonlinear models, Recurrent neural network (RNN), TIme Series Analysis, Wavelet neural network (WNN), Wavenet
الوصف: The use of wavelet analysis contributes to better modeling for financial time series in the sense of both frequency and time. In this study, S&P500 and NASDAQ data are separated into several components utilizing multiresolution analysis (MRA). Subsequently, using an appropriate neural network structure, each component is modeled. In addition, wavelets are used as an activation function in long shortterm memory (LSTM) networks to form a hybrid model. The hybrid model is merged with MRA as a proposed method in this paper. Four distinct strategies are employed: LSTM, LSTM+MRA, hybrid LSTM-Wavenet, and hybrid LSTM-Wavenet+MRA. Results show that the use of MRA and wavelets as an activation function together reduces the error the most.
وصف الملف: application/pdf
الإتاحة: https://doi.org/10.1016/j.asoc.2023.110469Test
https://vbn.aau.dk/da/publications/1955a46d-60b5-49de-b713-2914411f00bcTest
https://vbn.aau.dk/ws/files/550215292/1-s2.0-S1568494623004878-main.pdfTest
http://www.scopus.com/inward/record.url?scp=85163764964&partnerID=8YFLogxKTest -
8دورية أكاديمية
المؤلفون: Nielsen, Kasper Gaj, Sung, Inkyung, Yafrani, Mohamed El, Kılıç, Deniz Kenan, Nielsen, Peter
المصدر: Nielsen , K G , Sung , I , Yafrani , M E , Kılıç , D K & Nielsen , P 2023 , ' A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts ' , Algorithms , vol. 16 , no. 3 , 172 . https://doi.org/10.3390/a16030172Test
مصطلحات موضوعية: give-away minimization, real-time decision making, robotic arm-based batching systems, robotic task-sequencing problem, scheduling robotic arms
الوصف: In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor belt is picked up by which robotic arm at what position, and on which tray the picked up item will be placed. This problem involves critical constraints, such as sequence-dependent processing time and dynamic item and tray positions. Moreover, due to the speed of the infeed conveyor and latency in the information about entering items into the system, this scheduling problem must be solved in near real time. To address these challenges, we propose a scheduling solution that first decomposes the original scheduling problem into sub-problems, where a sub-problem formulated as a goal program schedules robotic arms only for a single tray. The performance of the proposed solution approach is then tested under a simulation environment, and from the experiments, the proposed approach produces acceptable performance.
وصف الملف: application/pdf
الإتاحة: https://doi.org/10.3390/a16030172Test
https://vbn.aau.dk/da/publications/a04aa45a-7ee5-48c8-a24f-6c092168408dTest
https://vbn.aau.dk/ws/files/536324075/algorithms_16_00172_v2.pdfTest
http://www.scopus.com/inward/record.url?scp=85151097551&partnerID=8YFLogxKTest -
9دورية أكاديمية
مصطلحات موضوعية: Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Machine Learning
الوصف: In today's vast literature landscape, a manual review is very time-consuming. To address this challenge, this paper proposes a semi-automated tool for solution method review and selection. It caters to researchers, practitioners, and decision-makers while serving as a benchmark for future work. The tool comprises three modules: (1) paper selection and scoring, using a keyword selection scheme to query Scopus API and compute relevancy; (2) solution method extraction in papers utilizing OpenAI API; (3) sensitivity analysis and post-analyzes. It reveals trends, relevant papers, and methods. AI in the oncology case study and several use cases are presented with promising results, comparing the tool to manual ground truth. ; Comment: The paper is under review in Expert Systems with Applications, Elsevier
العلاقة: http://arxiv.org/abs/2307.04573Test
الإتاحة: http://arxiv.org/abs/2307.04573Test
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10دورية أكاديمية
المؤلفون: Kılıç, Deniz Kenan1 (AUTHOR) denizkk@mp.aau.dk, Nielsen, Peter1 (AUTHOR), Thibbotuwawa, Amila2,3 (AUTHOR)
المصدر: Energies (19961073). Jun2024, Vol. 17 Issue 12, p2909. 15p.
مصطلحات موضوعية: *ELECTRICITY pricing, *ENERGY development, *ELECTRICITY markets, *RENEWABLE energy sources, *MARKETING strategy, *DEEP learning
مصطلحات جغرافية: DENMARK
مستخلص: For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data. [ABSTRACT FROM AUTHOR]
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