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Sökning: WFRF:(Andersson M) > Luleå tekniska universitet

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  • Ingri, Johan, et al. (författare)
  • Hydrogeochemistry of sulfur isotopes in the Kalix River catchment, northern Sweden
  • 1997
  • Ingår i: Applied Geochemistry. - 0883-2927 .- 1872-9134. ; 12:4, s. 483-496
  • Tidskriftsartikel (refereegranskat)abstract
    • The 34S-to-32S ratio in dissolved SO4 has been studied in the Kalix River, Northern Sweden, and its catchment. Weekly sampling over 17 months revealed temporal variations from +5.3‰ up to +7.4‰ in the δ34S values in the river. Snow and rain samples showed lower δ34S values (average +5.6‰ and +5.0‰, respectively). The atmosphere is the major source for S in surface waters in the catchment, and the heavier δ34S values in the river are a result of SO4 reduction within the catchment. Most of the temporal variations in the δ34S value in the river are caused by a mixing of water from the mountain areas (relatively light δ34S) and the woodland. The δ34S value is relatively heavy in the woodland tributaries because of bacterial SO4 reduction in peatland areas influenced by groundwater. The highest δ34S values were measured during the spring flood, in June and in November. These heavy δ34S values are related to different types of water with diverse origins. The heavy δ34S values coinciding with the early spring flood originate from peatland areas in the woodland. Relatively heavy δ34S values (up to +14.4‰) were registered in mire water. Smaller variations of the δ34S value during summer and early autumn most likely were caused by the input of ground-mire water during heavy rains. A correlation between increased TOC concentrations and increased δ34S values was observed. The heavy δ34S values in June and November probably originate from SO4 reduction in bottom water and sediments in lakes within the catchment. Bottom water, enriched in 34S---SO4, was transported in the river during the spring and autumn overturn.
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  • Reiz, S, et al. (författare)
  • Epidural morphine for postoperative pain relief
  • 1981
  • Ingår i: Acta Anaesthesiologica Scandinavica. - : Wiley. - 0001-5172 .- 1399-6576. ; 25:2, s. 111-114
  • Tidskriftsartikel (refereegranskat)abstract
    • Thirty-three patients were randomly assigned to two groups to study the analgesic potency, duration of action and side effects of epidural and intramuscular morphine after hip surgery. Two milligrams of preservative-free morphine chloride in 10 ml of normal saline in the epidural space was compared to 10 mg of intramuscularly administered morphine. There was a more rapid onset of action after intramuscular morphine. However, the quality of pain relief was substantially higher and the duration of action markedly longer after epidural morphine. The total dose required in the epidural group was 3.6 mg and in the intramuscular group 41 mg during the 15-h observation period. The side effects of epidural morphine were few and mild, the most embarrassing being urinary retention (20%). Nausea and/or vomiting was less common after epidural morphine (20% versus 55%). Pruritus or respiratory depression which have been reported previously were not encountered. However, it is recommended that preservative-free solution are used to avoid itching and that the patients are monitored, as respiratory depression may occur long after administration of epidural opiate
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  • Shafkat Raihan, S.M., et al. (författare)
  • A BRBES to Support Diagnosis of COVID-19 Using Clinical and CT Scan Data
  • 2022
  • Ingår i: Proceedings of the International Conference on Big Data, IoT, and Machine Learning. - Singapore : Springer. ; , s. 483-496
  • Konferensbidrag (refereegranskat)abstract
    • In the prevailing COVID-19 pandemic, accurate diagnosis plays a vital role in preventing the mass transmission of the SARS-CoV-2 virus. Especially patients with pneumonia need correct diagnosis for proper treatment of their respiratory distress. However, the current standard diagnosis method, RT-PCR testing has a significant false negative and false positive rate. As alternatives, diagnosis methods based on artificial intelligence can be applied for faster and more accurate diagnosis. Currently, various machine learning and deep learning techniques are being researched on to develop better COVID-19 diagnosis system. However, these approaches do not consider the uncertainty in data. Deep learning approaches use backpropagation. It is an unexplainable black box approach and is prone to problems like catastrophic forgetting. This article applies a belief rule-based expert system (BRBES) for diagnosis of COVID-19 on hematological data and CT scan data of lung tissue infection of adult pneumonia patients. The system is optimized with nature-inspired optimization algorithm—BRBES-based adaptive differential evolution (BRBaDE). This model has been evaluated on a real-world dataset of COVID-19 patients published in a previous work. Also, performance of the BRBaDE has been compared with BRBES optimized with genetic algorithm and MATLAB’s fmincon function where BRBaDE outperformed genetic algorithm and fmincon and showed best accuracy of 73.91%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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  • Abedin, Md. Zainal, et al. (författare)
  • Performance Analysis of Anomaly Based Network Intrusion Detection Systems
  • 2018
  • Ingår i: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops). - Piscataway, NJ : IEEE Computer Society. ; , s. 1-7
  • Konferensbidrag (refereegranskat)abstract
    • Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool.
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  • Al Banna, Md. Hasan, et al. (författare)
  • Attention-based Bi-directional Long-Short Term Memory Network for Earthquake Prediction
  • 2021
  • Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 56589-56603
  • Tidskriftsartikel (refereegranskat)abstract
    • An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model’s earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.
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  • Alam, Md. Eftekhar, et al. (författare)
  • An IoT-Belief Rule Base Smart System to Assess Autism
  • 2018
  • Ingår i: Proceedings of the 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018). - : IEEE. - 9781538682791 - 9781538682807 ; , s. 671-675
  • Konferensbidrag (refereegranskat)abstract
    • An Internet-of-Things (IoT)-Belief Rule Base (BRB) based hybrid system is introduced to assess Autism spectrum disorder (ASD). This smart system can automatically collect sign and symptom data of various autistic children in realtime and classify the autistic children. The BRB subsystem incorporates knowledge representation parameters such as rule weight, attribute weight and degree of belief. The IoT-BRB system classifies the children having autism based on the sign and symptom collected by the pervasive sensing nodes. The classification results obtained from the proposed IoT-BRB smart system is compared with fuzzy and expert based system. The proposed system outperformed the state-of-the-art fuzzy system and expert system.
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