Karl Andersson | Luleå University of Technology (original) (raw)

Papers by Karl Andersson

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2017

Some public and private services are called part of the Critical Infrastructure (CI), which are c... more Some public and private services are called part of the Critical Infrastructure (CI), which are considered as the most important services to protect the functioning of a society and the economy. Many CIs provide services via the Internet and thus cyber-attacks can be performed remotely. It is now very simple and free to find and download software, which automates performing cyber-attacks. A recent example is that two teenagers, with close to no security knowledge, created an on-line business. They would run cyber-attacks (online booter service called vDOS, as reported by Brian Krebs) for a small fee. They reportedly earned over 600,000 USD in a short period of time by conducting a large number of automated DDoS cyber-attacks. Then Krebs was retaliated against, and the highest DDoS attack bandwidth ever recorded, 620 Gbps, was launched against Krebs. In this paper we show how cognitive learning can be used to significantly mitigate any effects of DDoS network attacks, against the critical infrastructure.

Future users of mobile telephones and other handheld devices will benefit from a variety of wirel... more Future users of mobile telephones and other handheld devices will benefit from a variety of wireless access networks including cellular networks, wireless LANs, and wireless MANs. Investments in new wireless infrastructures, new and changed use of radio spectrum, and built-in support for multiple radio access technologies in devices are driving forces behind this trend. The vision of Always Best Connected will finally be reached and users will connect seamlessly to various services delivered over the Internet regardless of media. This paper proposes a combined network and application layer access network decision model for multimedia applications in heterogeneous networking environments. It builds on previous work of a network layer based metric used in combination with multihomed Mobile IP and introduces a mechanism for applications to interact with the mobility management system in the mobile node. This way, applications executing in the mobile node can decide either to take access network decisions on their own or to let the network layer handle mobility management tasks automatically based on default decision criterion decided by the end-user. An extended architecture based on previous work and the upcoming IEEE 802.21 standard for media-independent handover services is presented. The control plane, named "Mobile Mediator Control Function", offers a set of events and commands through an additional service access point. Results from a scenario with a Voice over IP application running in the proposed environment simulated in OPNET Modeler are presented.

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2017

Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the m... more Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the most catastrophic natural disasters. Early warnings help people to take necessary steps to save human lives and properties. Sensors can be used to provide more accurate early warnings due to possibilities of capturing more detail data of surrounding nature. Recent advantages in protocol standardization and cost effectiveness of sensors it is possible to easily deploy and manage sensors in large scale. In this paper, a heterogeneous wireless sensor network is proposed and evaluated to predict natural disaster like flood. In this network CoAP is used as a unified application layer protocol for exchanging sensor data. Therefore, CoAP over SMS protocol is used for exchanging sensor data. Furthermore, the effectiveness of the heterogeneous wireless sensor network for predicting natural disaster is presented in this paper.

Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services, 2017

Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes... more Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes many sufferings. Flood is one of the most catastrophic among the natural calamities. Assessing flood risk helps us to take necessary steps and save human lives. Several heterogeneous factors are used to assess flood risk on the livelihood of an area. Moreover, several types of uncertainties can be associated with each factor. In this paper, we propose a web based flood risk assessment expert system by combining belief rule base with the capability of reading data and generating web-based output. This paper also introduces a generic RESTful API which can be used without writing the belief rule based expert system from scratch. This expert system will facilitate the monitoring of the various flood risk factors, contributing in increasing the flood risk on livelihood of an area. Eventually, the decision makers should be able to take measures to control those factors and to reduce the risk of flooding in an area. Data for the expert system has been collected from a case study area by conducting interviews.

2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), 2018

Because of the increased popularity and fast expansion of the Internet as well as Internet of thi... more 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.

Cognitive Computation, 2021

The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the prese... more The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been teste...

2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), 2018

Among the various natural calamities, flood is considered one of the most catastrophic natural ha... more Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socioeconomic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. Integrated BRBES produces reliable results comparing from the other datadriven approaches. Data for the expert system has been collected targeting different case study

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2017

Wireless Sensor Networks (WSN) have been highly developed which can be used in agriculture to ena... more 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.

Algorithms, 2021

Visual sentiment analysis has become more popular than textual ones in various domains for decisi... more Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual.

Algorithms, 2021

A novel coronavirus (COVID-19), which has become a great concern for the world, was identified fi... more A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the R0-value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated...

Algorithms, 2020

In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from r... more In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX pri...

Sensors, 2020

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT)... more Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian pro...

Procedia Computer Science, 2019

Now-a-days, the natural irrigation system is under pressure due to the growing water shortages, w... more Now-a-days, the natural irrigation system is under pressure due to the growing water shortages, which are mainly caused by population growth and climate change. Therefore, the control of water resources to increase the allocation of retained water is very important. It has been observed in the last two decades, especially in the Indian sub-continent, the change of climate affects the agricultural crops production significantly. However, the prediction of good harvests before harvesting, enables the farmers as well as the government officials to take appropriate measures of marketing and storage of crops. Some strategies for predicting and modelling crop yields have been developed, although they do not take into account the characteristics of climate, and they are empirical in nature. In the proposed system, a Cuckoo Search Algorithm has been developed, allowing the allocation of water for farming under any conditions. The various parameters such as temperature, turbidity, pH., moisture have been collected by using Internet of Things (IoT) platform, equipped with related sensors and wireless communication systems. In this IoT platform the sensor data have been displayed in the cloud environment by using ThingSpeak. The data received in the ThingSpeak used in the proposed Cuckoo Search Algorithm, allowing the selection of appropriate crops for particular soil.

Procedia Computer Science, 2019

Current water quality monitoring system is a manual system with a monotonous process and is very ... more Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.

Soft Computing, 2017

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting ... more 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 Communicated by V. Loia.

Journal of medical systems, 2017

The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs... more The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule b...

2016 Future Technologies Conference (FTC), 2016

Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or in... more Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or interstitial are affected. Usually the diagnosis of Bronchopneumonia is carried out using signs and symptoms of this disease, which cannot be measured since they consist of various types of uncertainty. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, development and application of an expert system for assessing the suspicion of Bronchopneumonia under uncertainty. The Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than from the results generated by a manual system.

Soft Computing, 2016

It is an era of Internet of Things, where various types of sensors, especially wireless, are wide... more It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, Communicated by V. Loia.

Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, 2015

Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natura... more Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natural calamities, flood is one of the most catastrophic. Predicting flood helps us to take necessary precautions and save human lives. Several types of data (meteorological condition, topography, river characteristics, and human activities) are used to predict flood water level in an area. In our previous works, we proposed a belief rule based flood prediction system in a desktop environment. In this paper, we propose a web-service based flood prediction expert system by incorporating belief rule base with the capability of reading sensor data such as rainfall, river flow on real time basis. This will facilitate the monitoring of the various flood-intensifying factors, contributing in increasing the flood water level in an area. Eventually, the decision makers would able to take measures to control those factors and to reduce the intensity of flooding in an area.

2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2015

Recent advancements in the fields of sensor equipment and wireless sensor networks have opened th... more Recent advancements in the fields of sensor equipment and wireless sensor networks have opened the window of opportunity for many innovative applications. In this paper, we propose a new architecture for building decision support systems using heterogeneous wireless sensor networks. The architecture is built around standard hardware and existing wireless sensor networks technology. We show the effectiveness of the proposed architecture by applying it to a flood prediction scenario.

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2017

Some public and private services are called part of the Critical Infrastructure (CI), which are c... more Some public and private services are called part of the Critical Infrastructure (CI), which are considered as the most important services to protect the functioning of a society and the economy. Many CIs provide services via the Internet and thus cyber-attacks can be performed remotely. It is now very simple and free to find and download software, which automates performing cyber-attacks. A recent example is that two teenagers, with close to no security knowledge, created an on-line business. They would run cyber-attacks (online booter service called vDOS, as reported by Brian Krebs) for a small fee. They reportedly earned over 600,000 USD in a short period of time by conducting a large number of automated DDoS cyber-attacks. Then Krebs was retaliated against, and the highest DDoS attack bandwidth ever recorded, 620 Gbps, was launched against Krebs. In this paper we show how cognitive learning can be used to significantly mitigate any effects of DDoS network attacks, against the critical infrastructure.

Future users of mobile telephones and other handheld devices will benefit from a variety of wirel... more Future users of mobile telephones and other handheld devices will benefit from a variety of wireless access networks including cellular networks, wireless LANs, and wireless MANs. Investments in new wireless infrastructures, new and changed use of radio spectrum, and built-in support for multiple radio access technologies in devices are driving forces behind this trend. The vision of Always Best Connected will finally be reached and users will connect seamlessly to various services delivered over the Internet regardless of media. This paper proposes a combined network and application layer access network decision model for multimedia applications in heterogeneous networking environments. It builds on previous work of a network layer based metric used in combination with multihomed Mobile IP and introduces a mechanism for applications to interact with the mobility management system in the mobile node. This way, applications executing in the mobile node can decide either to take access network decisions on their own or to let the network layer handle mobility management tasks automatically based on default decision criterion decided by the end-user. An extended architecture based on previous work and the upcoming IEEE 802.21 standard for media-independent handover services is presented. The control plane, named "Mobile Mediator Control Function", offers a set of events and commands through an additional service access point. Results from a scenario with a Voice over IP application running in the proposed environment simulated in OPNET Modeler are presented.

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2017

Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the m... more Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the most catastrophic natural disasters. Early warnings help people to take necessary steps to save human lives and properties. Sensors can be used to provide more accurate early warnings due to possibilities of capturing more detail data of surrounding nature. Recent advantages in protocol standardization and cost effectiveness of sensors it is possible to easily deploy and manage sensors in large scale. In this paper, a heterogeneous wireless sensor network is proposed and evaluated to predict natural disaster like flood. In this network CoAP is used as a unified application layer protocol for exchanging sensor data. Therefore, CoAP over SMS protocol is used for exchanging sensor data. Furthermore, the effectiveness of the heterogeneous wireless sensor network for predicting natural disaster is presented in this paper.

Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services, 2017

Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes... more Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes many sufferings. Flood is one of the most catastrophic among the natural calamities. Assessing flood risk helps us to take necessary steps and save human lives. Several heterogeneous factors are used to assess flood risk on the livelihood of an area. Moreover, several types of uncertainties can be associated with each factor. In this paper, we propose a web based flood risk assessment expert system by combining belief rule base with the capability of reading data and generating web-based output. This paper also introduces a generic RESTful API which can be used without writing the belief rule based expert system from scratch. This expert system will facilitate the monitoring of the various flood risk factors, contributing in increasing the flood risk on livelihood of an area. Eventually, the decision makers should be able to take measures to control those factors and to reduce the risk of flooding in an area. Data for the expert system has been collected from a case study area by conducting interviews.

2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), 2018

Because of the increased popularity and fast expansion of the Internet as well as Internet of thi... more 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.

Cognitive Computation, 2021

The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the prese... more The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been teste...

2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), 2018

Among the various natural calamities, flood is considered one of the most catastrophic natural ha... more Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socioeconomic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. Integrated BRBES produces reliable results comparing from the other datadriven approaches. Data for the expert system has been collected targeting different case study

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2017

Wireless Sensor Networks (WSN) have been highly developed which can be used in agriculture to ena... more 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.

Algorithms, 2021

Visual sentiment analysis has become more popular than textual ones in various domains for decisi... more Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual.

Algorithms, 2021

A novel coronavirus (COVID-19), which has become a great concern for the world, was identified fi... more A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the R0-value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated...

Algorithms, 2020

In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from r... more In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX pri...

Sensors, 2020

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT)... more Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian pro...

Procedia Computer Science, 2019

Now-a-days, the natural irrigation system is under pressure due to the growing water shortages, w... more Now-a-days, the natural irrigation system is under pressure due to the growing water shortages, which are mainly caused by population growth and climate change. Therefore, the control of water resources to increase the allocation of retained water is very important. It has been observed in the last two decades, especially in the Indian sub-continent, the change of climate affects the agricultural crops production significantly. However, the prediction of good harvests before harvesting, enables the farmers as well as the government officials to take appropriate measures of marketing and storage of crops. Some strategies for predicting and modelling crop yields have been developed, although they do not take into account the characteristics of climate, and they are empirical in nature. In the proposed system, a Cuckoo Search Algorithm has been developed, allowing the allocation of water for farming under any conditions. The various parameters such as temperature, turbidity, pH., moisture have been collected by using Internet of Things (IoT) platform, equipped with related sensors and wireless communication systems. In this IoT platform the sensor data have been displayed in the cloud environment by using ThingSpeak. The data received in the ThingSpeak used in the proposed Cuckoo Search Algorithm, allowing the selection of appropriate crops for particular soil.

Procedia Computer Science, 2019

Current water quality monitoring system is a manual system with a monotonous process and is very ... more Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.

Soft Computing, 2017

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting ... more 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 Communicated by V. Loia.

Journal of medical systems, 2017

The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs... more The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule b...

2016 Future Technologies Conference (FTC), 2016

Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or in... more Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or interstitial are affected. Usually the diagnosis of Bronchopneumonia is carried out using signs and symptoms of this disease, which cannot be measured since they consist of various types of uncertainty. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, development and application of an expert system for assessing the suspicion of Bronchopneumonia under uncertainty. The Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than from the results generated by a manual system.

Soft Computing, 2016

It is an era of Internet of Things, where various types of sensors, especially wireless, are wide... more It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, Communicated by V. Loia.

Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, 2015

Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natura... more Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natural calamities, flood is one of the most catastrophic. Predicting flood helps us to take necessary precautions and save human lives. Several types of data (meteorological condition, topography, river characteristics, and human activities) are used to predict flood water level in an area. In our previous works, we proposed a belief rule based flood prediction system in a desktop environment. In this paper, we propose a web-service based flood prediction expert system by incorporating belief rule base with the capability of reading sensor data such as rainfall, river flow on real time basis. This will facilitate the monitoring of the various flood-intensifying factors, contributing in increasing the flood water level in an area. Eventually, the decision makers would able to take measures to control those factors and to reduce the intensity of flooding in an area.

2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2015

Recent advancements in the fields of sensor equipment and wireless sensor networks have opened th... more Recent advancements in the fields of sensor equipment and wireless sensor networks have opened the window of opportunity for many innovative applications. In this paper, we propose a new architecture for building decision support systems using heterogeneous wireless sensor networks. The architecture is built around standard hardware and existing wireless sensor networks technology. We show the effectiveness of the proposed architecture by applying it to a flood prediction scenario.