| 1. |
- Kabir, Sami, PhD Student, et al.
(författare)
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An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai
- 2022
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 206
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Tidskriftsartikel (refereegranskat)abstract
- Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant enviromental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud in our model. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model. The results conclude that the accuracy of our model to monitor PM2.5 of Shanghai is higher than only CNN and other conventional Machine Learning methods. Real-time validation of our model on near real-time satellite images of April-2021 of Shanghai shows average difference between our calculated PM2.5 concentrations and the actual one within ±5.51.
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| 2. |
- Hossain, Mohammad Shahadat, 1968-, et al.
(författare)
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A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty
- 2018
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Ingår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications. - : Springer. - 1432-7643 .- 1433-7479. ; 22:22, s. 7571-7586
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Tidskriftsartikel (refereegranskat)abstract
- Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.
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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. |
- 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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| 6. |
- Alam, Md. Eftekhar, et al.
(författare)
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An IoT-Belief Rule Base Smart System to Assess Autism
- 2018
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Ingår i: Proceedings of the 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018). - : IEEE. - 9781538682791 - 9781538682807 ; , s. 671-675
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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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| 7. |
- Alizadeh, Morteza, 1987-, et al.
(författare)
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A Survey of Secure Internet of Things in Relation to Blockchain
- 2020
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Ingår i: Journal of Internet Services and Information Security (JISIS). - Seoul, Republic of Korea : Innovative Information Science & Technology Research Group (ISYOU). - 2182-2069 .- 2182-2077. ; 10:3, s. 47-75
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Tidskriftsartikel (refereegranskat)abstract
- Distributed ledgers and blockchain technologies can improve system security and trustworthiness by providing immutable replicated histories of data. Blockchain is a linked list of blocks containing digitally signed transactions, a cryptographic hash of the previous block, and a timestamp stored in a decentralized and distributed network. The Internet of Things (IoT) is one of the application domains in which security based on blockchain is discussed. In this article, we review the structure and architectures of distributed IoT systems and explain the motivations, challenges, and needs of blockchain to secure such systems. However, there are substantial threats and attacks to blockchain that must be understood, as well as suitable approaches to mitigate them. We, therefore, survey the most common attacks to blockchain systems and the solutions to mitigate them, with the objective of assessing how malicious these attacks are in the IoT context.
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| 8. |
- 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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| 9. |
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| 10. |
- Booth, Todd, 1959-, et al.
(författare)
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Stronger Authentication for Password Credential Internet Services
- 2017
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Ingår i: Proceedings of the 2017 Third Conference on Mobile and Secure Services (MOBISECSERV). - Piscataway, NJ : IEEE conference proceedings. - 9781509036325 ; , s. 41-45
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Konferensbidrag (refereegranskat)abstract
- Most Web and other on-line service providers (”Inter- net Services”) only support legacy ID (or email) and password (ID/PW) credential authentication. However, there are numerous vulnerabilities concerning ID/PW credentials. Scholars and the industry have proposed several improved security solutions, such as MFA, however most of the Internet Services have refused to adopt these solutions. Mobile phones are much more sensitive to these vulnerabilities (so this paper focuses on mobile phones). Many users take advantage of password managers, to keep track of all their Internet Service profiles. However, the Internet Service profiles found in password managers, are normally kept on the PC or mobile phone’s disk, in an encrypted form. Our first contribution is a design guideline, whereby the Internet Service profiles never need to touch the client’s disk. Most users would benefit, if they had the ability to use MFA, to login to a legacy Internet Service, which only supports ID/PW credential authentication. Our second contribution is a design guideline, whereby users can choose, for each legacy ID/PW Internet Service, which specific MFA they wish to use. We have also presenting conceptual design guidelines, showing that both of our contributions are minor changes to existing password managers, which can be implemented easily with low overhead.
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