| 1. |
- Andersson, Ken, et al.
(författare)
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Secure Computation on 4G/5G Enabled Internet-of-Things
- 2019
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Ingår i: Wireless Communications & Mobile Computing. - : Hindawi Limited. - 1530-8669 .- 1530-8677. ; 2019, s. 1-1
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- The rapid development of Internet-of-ings (IoT) techniques in G/ G deployments is witnessing the generation of massive amounts of data which are collected, stored, processed, and presented in an easily interpretable form. Analysis of IoT data helps provide smart services such as smart homes, smart energy, smart health, and smart environments through G and G technologies. At the same time, the threat of the cyberattacks and issues with mobile internet security is becoming increasingly severe, which introduces new challenges for the security of IoT systems and applications and the privacy of individuals thereby. Protecting IoT data privacy while enabling data availability is an urgent but difficult task.
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| 2. |
- Mårild, Karl, 1982, et al.
(författare)
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Histologic activity in inflammatory bowel disease and risk of serious infections : A nationwide study
- 2024
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Ingår i: Clinical Gastroenterology and Hepatology. - : Elsevier. - 1542-3565 .- 1542-7714. ; 22:4, s. 831-846
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND AIMS: Individuals with inflammatory bowel disease (IBD) are at increased risk of serious infections, but whether this risk varies by histological disease activity is unclear.METHODS: A national population-based study of 55,626 individuals diagnosed with IBD in 1990-2016 with longitudinal data on ileo-colorectal biopsies followed through 2016. Serious infections were defined as having an inpatient infectious disease diagnosis in the Swedish National Patient Register. We used Cox regression to estimate hazard ratios (HRs) for serious infections in the 12 months following documentation of histologic inflammation (vs. histological remission), adjusting for social and demographic factors, chronic comorbidities, prior IBD-related surgery and hospitalization. We also adjusted for IBD-related medications in sensitivity analyses.RESULTS: With histological inflammation vs. remission, there was 4.62 (95%CI=4.46-4.78) and 2.53 (95%CI=2.36-2.70) serious infections per 100 person-years of follow-up, respectively (adjusted [a]HR=1.59; 95%CI=1.48-1.72). Histological inflammation (vs. remission) were associated with an increased risk of serious infections in ulcerative colitis (UC, aHR=1.68; 95%CI=1.51-1.87) and Crohn's disease (CD, aHR=1.59; 95%CI=1.40-1.80). The aHRs of sepsis and opportunistic infections were 1.66 (95%CI=1.28-2.15) and 1.71 (95%CI=1.22-2.41), respectively. Overall, results were consistent across age groups, sex and education level and remained largely unchanged after adjustment for IBD-related medications (aHR=1.47; 95%CI=1.34-1.61).CONCLUSION: Histological inflammation of IBD was an independent risk factor of serious infections, including sepsis, suggesting that achieving histological remission may reduce infections in IBD.
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| 3. |
- Abedin, Md. Zainal, et al.
(författare)
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An Interoperable IP based WSN for Smart Irrigation Systems
- 2017
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Konferensbidrag (refereegranskat)abstract
- Wireless Sensor Networks (WSN) have been highly developed which can be used in agriculture to enable optimal irrigation scheduling. Since there is an absence of widely used available methods to support effective agriculture practice in different weather conditions, WSN technology can be used to optimise irrigation in the crop fields. This paper presents architecture of an irrigation system by incorporating interoperable IP based WSN, which uses the protocol stacks and standard of the Internet of Things paradigm. The performance of fundamental issues of this network is emulated in Tmote Sky for 6LoWPAN over IEEE 802.15.4 radio link using the Contiki OS and the Cooja simulator. The simulated results of the performance of the WSN architecture presents the Round Trip Time (RTT) as well as the packet loss of different packet size. In addition, the average power consumption and the radio duty cycle of the sensors are studied. This will facilitate the deployment of a scalable and interoperable multi hop WSN, positioning of border router and to manage power consumption of the sensors.
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| 4. |
- Abedin, Md. Zainal, et al.
(författare)
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Performance Analysis of Anomaly Based Network Intrusion Detection Systems
- 2018
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Ingår i: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops). - Piscataway, NJ : IEEE Computer Society. ; , s. 1-7
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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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| 5. |
- Abedin, Md. Zainal, et al.
(författare)
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Selection of Energy Efficient Routing Protocol for Irrigation Enabled by Wireless Sensor Networks
- 2017
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Ingår i: Proceedings of 2017 IEEE 42nd Conference on Local Computer Networks Workshops. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509065844 - 9781509065837 ; , s. 75-81
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Konferensbidrag (refereegranskat)abstract
- Wireless Sensor Networks (WSNs) are playing remarkable contribution in real time decision making by actuating the surroundings of environment. As a consequence, the contemporary agriculture is now using WSNs technology for better crop production, such as irrigation scheduling based on moisture level data sensed by the sensors. Since WSNs are deployed in constraints environments, the life time of sensors is very crucial for normal operation of the networks. In this regard routing protocol is a prime factor for the prolonged life time of sensors. This research focuses the performances analysis of some clustering based routing protocols to select the best routing protocol. Four algorithms are considered, namely Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP) and Energy Aware Multi Hop Multi Path (EAMMH). The simulation is carried out in Matlab framework by using the mathematical models of those algortihms in heterogeneous environment. The performance metrics which are considered are stability period, network lifetime, number of dead nodes per round, number of cluster heads (CH) per round, throughput and average residual energy of node. The experimental results illustrate that TEEN provides greater stable region and lifetime than the others while SEP ensures more througput.
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| 6. |
- Adman, Per, et al.
(författare)
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171 forskare: ”Vi vuxna bör också klimatprotestera”
- 2019
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Ingår i: Dagens nyheter (DN debatt). - Stockholm. - 1101-2447.
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- DN DEBATT 26/9. Vuxna bör följa uppmaningen från ungdomarna i Fridays for future-rörelsen och protestera eftersom det politiska ledarskapet är otillräckligt. Omfattande och långvariga påtryckningar från hela samhället behövs för att få de politiskt ansvariga att utöva det ledarskap som klimatkrisen kräver, skriver 171 forskare i samhällsvetenskap och humaniora.
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| 7. |
- Afroze, Tasnim, et al.
(författare)
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Glaucoma Detection Using Inception Convolutional Neural Network V3
- 2021
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Ingår i: Applied Intelligence and Informatics. - Cham : Springer. ; , s. 17-28
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Konferensbidrag (refereegranskat)abstract
- Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.
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| 8. |
- Ahmed, Faisal, et al.
(författare)
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An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty
- 2021
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Ingår i: Applied Sciences. - Basel, Switzerland : MDPI. - 2076-3417. ; 11:13
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.
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| 9. |
- Ahmed, Faisal, et al.
(författare)
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Comparative Performance of Tree Based Machine Learning Classifiers in Product Backorder Prediction
- 2023. - 1
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Ingår i: Intelligent Computing & Optimization. - Cham : Springer. ; , s. 572-584
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Bokkapitel (refereegranskat)abstract
- Early prediction of whether a product will go to backorder or not is necessary for optimal management of inventory that can reduce the losses in sales, establish a good relationship between the supplier and customer and maximize the revenues. In this study, we have investigated the performance and effectiveness of tree based machine learning algorithms to predict the backorder of a product. The research methodology consists of preprocessing of data, feature selection using statistical hypothesis test, imbalanced learning using the random undersampling method and performance evaluating and comparing of four tree based machine learning algorithms including decision tree, random forest, adaptive boosting and gradient boosting in terms of accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve and area under the precision and recall curve. Three main findings of this study are (1) random forest model without feature selection and with random undersampling method achieved the highest performance in terms of all performance measure metrics, (2) feature selection cannot contribute to the performance enhancement of the tree based classifiers, and (3) random undersampling method significantly improves performance of tree based classifiers in product backorder prediction.
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| 10. |
- Ahmed, Mumtahina, et al.
(författare)
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Explainable Text Classification Model for COVID-19 Fake News Detection
- 2022
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Ingår i: Journal of Internet Services and Information Security (JISIS). - : Innovative Information Science & Technology Research Group. - 2182-2069 .- 2182-2077. ; 12:2, s. 51-69
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence has achieved notable advances across many applications, and the field is recently concerned with developing novel methods to explain machine learning models. Deep neural networks deliver the best performance accuracy in different domains, such as text categorization, image classification, and speech recognition. Since the neural network models are black-box types, they lack transparency and explainability in predicting results. During the COVID-19 pandemic, Fake News Detection is a challenging research problem as it endangers the lives of many online users by providing misinformation. Therefore, the transparency and explainability of COVID-19 fake news classification are necessary for building the trustworthiness of model prediction. We proposed an integrated LIME-BiLSTM model where BiLSTM assures classification accuracy, and LIME ensures transparency and explainability. In this integrated model, since LIME behaves similarly to the original model and explains the prediction, the proposed model becomes comprehensible. The performance of this model in terms of explainability is measured by using Kendall’s tau correlation coefficient. We also employ several machine learning models and provide a comparison of their performances. Therefore, we analyzed and compared the computation overhead of our proposed model with the other methods because the model takes the integrated strategy.
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