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  • 1.
    Abedin, Md. Zainal
    et al.
    University of Science and Technology Chittagong.
    Chowdhury, Abu Sayeed
    University of Science and Technology Chittagong.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Karim, Razuan
    University of Science and Technology Chittagong.
    An Interoperable IP based WSN for Smart Irrigation Systems2017Conference paper (Refereed)
    Abstract [en]

    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.

  • 2.
    Abedin, Md. Zainal
    et al.
    University of Science and Technology, Chittagong.
    Paul, Sukanta
    University of Science and Technology, Chittagong.
    Akhter, Sharmin
    University of Science and Technology, Chittagong.
    Siddiquee, Kazy Noor E Alam
    University of Science and Technology, Chittagong.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Selection of Energy Efficient Routing Protocol for Irrigation Enabled by Wireless Sensor Networks2017In: Proceedings of 2017 IEEE 42nd Conference on Local Computer Networks Workshops, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 75-81Conference paper (Refereed)
    Abstract [en]

    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.

  • 3.
    Abedin, Md. Zainal
    et al.
    University of Science and Technology, Chittagong.
    Siddiquee, Kazy Noor E Alam
    University of Science and Technology Chittagong.
    Bhuyan, M. S.
    University of Science & Technology Chittagong.
    Karim, Razuan
    University of Science and Technology Chittagong.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Performance Analysis of Anomaly Based Network Intrusion Detection Systems2018In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, p. 1-7Conference paper (Refereed)
    Abstract [en]

    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.

  • 4.
    Akter, Shamima
    et al.
    International Islamic University, Chittagong, Bangladesh.
    Nahar, Nazmun
    University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP2019In: Proceedings of 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE, 2019, article id 18566123Conference paper (Refereed)
    Abstract [en]

    Optimization problem like Travelling Salesman Problem (TSP) can be solved by applying Genetic Algorithm (GA) to obtain perfect approximation in time. In addition, TSP is considered as a NP-hard problem as well as an optimal minimization problem. Selection, crossover and mutation are the three main operators of GA. The algorithm is usually employed to find the optimal minimum total distance to visit all the nodes in a TSP. Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance. The proposed crossover operator consists of two crossover point selection and new offspring creation by performing cost comparison. The computational results as well as the comparison with available well-developed crossover operators are also presented. It has been found that the new crossover operator produces better results than that of other cross-over operators.

  • 5.
    Alam, Md. Eftekhar
    et al.
    International Islamic University Chittagong, Bangladesh.
    Kaiser, M. Shamim
    Jahangirnagar University, Dhaka, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An IoT-Belief Rule Base Smart System to Assess Autism2018In: Proceedings of the 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018), IEEE, 2018, p. 671-675Conference paper (Refereed)
    Abstract [en]

    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.

  • 6.
    Chowdhury, Rumman Rashid
    et al.
    University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Hossain, Sazzad
    University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Analyzing Sentiment of Movie Reviews in Bangla by Applying Machine Learning Techniques2019In: Proceedings of the International Conference on Bangla Speech and Language Processing, 2019Conference paper (Refereed)
    Abstract [en]

    This paper proposes a process of sentiment analysis of movie reviews written in Bangla language. This process can automate the analysis of audience’s reaction towards a specific movie or TV show. With more and more people expressing their opinions openly in the social networking sites, analyzing the sentiment of comments made about a specific movie can indicate how well the movie is being accepted by the general public. The dataset used in this experiment was collected and labeled manually from publicly available comments and posts from social media websites. Using Support Vector Machine algorithm, this model achieves 88.90% accuracy on the test set and by using Long Short Term Memory network [1] the model manages to achieve 82.42% accuracy. Furthermore, a comparison with some other machine learning approaches is presented in this paper.

  • 7.
    Chowdhury, Rumman Rashid
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper (Refereed)
    Abstract [en]

    This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.

  • 8.
    Chowdury, Mohammad Salah Uddin
    et al.
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Bin Emranb, Talha
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Ghosha, Subhasish
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Pathak, Abhijit
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Alama, Mohd. Manjur
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Absar, Nurul
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    IoT Based Real-time River Water Quality Monitoring System2019In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 155, p. 161-168Article in journal (Refereed)
    Abstract [en]

    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.

  • 9.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Ahmed, Faisal
    University of Chittagong, Bangladesh.
    Tuj-Johora, Fatema
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty2017In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 41, no 3, article id 43Article in journal (Refereed)
    Abstract [en]

    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 based expert system. 

  • 10.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Al Hasan, Abdullah
    University of Chittagong, Bangladesh.
    Guha, Sunanda
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Expert System to Predict Earthquake under Uncertainty2018In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 9, no 2, p. 26-41Article in journal (Refereed)
    Abstract [en]

    The impact of earthquake is devastating, which has the capability to stop the socio-economic activities of a region within a short span of time. Therefore, an earlier prediction of earthquake could play an important role to save human lives as well as socio-economic activities. The signs of animal behavior along with environmental and chemical changes in nature could be considered as a way to predict the earthquake. These factors cannot be determined accurately because of the presence of different categories of uncertainties. Therefore, this article presents a belief rule based expert system (BRBES) which has the capability to predict earthquake under uncertainty. Historical data of various earthquakes of the world with specific reference to animal behavior as well as environmental and chemical changes have been considered in validating the BRBES. The reliability of our proposed BRBES’s output is measured in comparison with Fuzzy Logic Based Expert System (FLBES) and Artificial Neural Networks (ANN) based system, whereas our BRBES’s results are found more reliable than that of FLBES and ANN. Therefore, this BRBES can be considered to predict the occurrence of an earthquake in a region by taking account of the data, related to the animal, environmental and chemical changes.

  • 11.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Habib, Israt Binteh
    University of Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule based Expert System to Diagnose Dengue Fever under Uncertainty2017In: Proceedings of Computing Conference 2017 / [ed] Liming Chen, Nikola Serbedzija, Kami Makki, Nazih Khaddaj Mallat, Kohei Arai, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 179-186Conference paper (Refereed)
    Abstract [en]

    Dengue Fever is a debilitating mosquito-borne disease, causing sudden fever, leading to fatality in many cases. A Dengue patient is diagnosed by the physicians by looking at the various signs, symptoms and risk factors of this disease. However, these signs, symptoms and the risk factors cannot be measured with 100% certainty since various types of uncertainties such as imprecision, vagueness, ambiguity, and ignorance are associated with them. Hence, it is difficult for the physicians to diagnose the dengue patient accurately since they don’t consider the uncertainties as mentioned. Therefore, this paper presents the design, development and applications of an expert system by incorporating belief rule base as the knowledge representation schema as well as the evidential reasoning as the inference mechanism with the capability of handling various types of uncertainties to diagnose dengue fever. The results generated from the expert system are more reliable than from fuzzy rule based system or from human expert.

  • 12.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Rahaman, Saifur
    Department of Computer Science and Engineering, International Islamic University Chittagong.
    Kor, Ah-Lian
    School of Computing, Creative Technologies and Engineering, Leeds Beckett University.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Pattison, Colin
    School of Computing, Creative Technologies and Engineering, Leeds Beckett University.
    A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty2017In: IEEE Transactions on Sustainable Computing, ISSN 2377-3782, Vol. 2, no 2, p. 140-153Article in journal (Refereed)
    Abstract [en]

    A rapidly emerging trend in the IT landscape is the uptake of large-scale datacenters moving storage and data processing to providers located far away from the end-users or locally deployed servers. For these large-scale datacenters, power efficiency is a key metric, with the PUE (Power Usage Effectiveness) and DCiE (Data Centre infrastructure Efficiency) being important examples. This article proposes a belief rule based expert system to predict datacenter PUE under uncertainty. The system has been evaluated using real-world data from a data center in the UK. The results would help planning construction of new datacenters and the redesign of existing datacenters making them more power efficient leading to a more sustainable computing environment. In addition, an optimal learning model for the BRBES demonstrated which has been compared with ANN and Genetic Algorithm; and the results are promising.

  • 13.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Rahaman, Saifur
    International Islamic University Chittagong.
    Mustafa, Rashed
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty2018In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, no 22, p. 7571-7586Article in journal (Refereed)
    Abstract [en]

    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.

  • 14.
    Hossain, Mohammad Shahadat
    et al.
    University of Chittagong, Bangladesh.
    Sultana, Zinnia
    International Islamic University Chittagong, Bangladesh.
    Nahar, Lutfun
    International Islamic University Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Intelligent System to Diagnose Chikungunya under Uncertainty2019In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 10, no 2, p. 37-54Article in journal (Refereed)
    Abstract [en]

    Chikungunya is a virus-related disease, bring about by the virus called CHIKV that spreads throughmosquito biting. This virus first found in Tanzania, while blood from patients was isolated. Thecommon signs and symptoms, associated with Chikungunya are considered as fever, joint swelling,joint pain, muscle pain and headache. The examination of these signs and symptoms by the physician constitutes the typical preliminary diagnosis of this disease. However, the physician is unable tomeasure them with accuracy. Therefore, the preliminary diagnosis in most of the cases could sufferfrom inaccuracy, which leads to wrong treatment. Hence, this paper introduces the design and implementation of a belief rule based expert system (BRBES) which is capable to represent uncertainknowledge as well as inference under uncertainty. Here, the knowledge is illustrated by employing belief rule base while deduction is carried out by evidential reasoning. The real patient data of250 have been considered to demonstrate the accuracy and the robustness of the expert system. Acomparison has been performed with the results of BRBES and Fuzzy Logic Based Expert System(FLBES) as well as with the expert judgment. Furthermore, the result of BRBES has been contrastedwith various data-driven machine learning approaches, including ANN (Artificial Neural networks)and SVM (Support Vector Machine). The reliability of BRBESs was found better than those of datadriven machine learning approaches. Therefore, the BRBES presented in this paper could enable thephysician to conduct the analysis of Chikungunya more accurately.

  • 15.
    Islam, Md. Zahirul
    et al.
    Department of Computer Science and Engineering University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by traditional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method using CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).

  • 16.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ruci, Xhesika
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kor, Ah-Lian
    School of Computing, Creative Technologies and Engineering Leeds Beckett University, Leeds, UK.
    Capacity Management of Hyperscale Data Centers Using Predictive Modelling2019In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 18, article id 3438Article in journal (Refereed)
    Abstract [en]

    Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

  • 17.
    Jamil, Mohammad Newaj
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Technological innovation capability (TIC) is a complicated and subtle concept which is based on multiple quantitative and qualitative criteria. The cores of a firm’s long-term competitive dominance are defined by technological innovation capability which is the incentive for a firm’s innovation. Various types of uncertainty can be noticed while considering multiple criteria for evaluating TIC. In order to evaluate TIC in a reliable way, a Belief Rule Base (BRB) Expert System can be used to handle both quantitative and qualitative data and their associated uncertainties. In this paper, a RESTful API-based BRB expert system is introduced to evaluate technological innovation capability by taking uncertainties into consideration. This expert system will facilitate firms’ managers to obtain a recapitulation of the TIC evaluation. It will help them to take essential steps to ensure corporate survival and strengthen their weak capabilities continuously to facilitate a competitive advantage. Other users can also use this API to apply BRB for a different domain. However, a comparison between the knowledge-driven approach (BRBES) and several data-driven models has been performed to find out the reliability in evaluating TIC. The result shows that the outcome of BRBES is better than other data-driven approaches.

  • 18.
    Monrat, Ahmed Afif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming2018In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, p. 8-45Conference paper (Refereed)
    Abstract [en]

    Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic 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. The integrated BRBES produces reliable results in comparison to other data-driven approaches. Data for the expert system has been collected by considering different case study areas of Bangladesh to validate the system.

  • 19.
    Monrat, Ahmed Afif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Challenges and Opportunities of Using Big Data for Assessing Flood Risks2018In: Applications of Big Data Analytics: Trends, Issues, and Challenges / [ed] Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya, Cham: Springer, 2018, p. 31-42Chapter in book (Refereed)
    Abstract [en]

    Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has disastrous impact on the socioeconomic lifeline of a country. Nowadays, business organizations are using Big Data to improve their strategies and operations for revealing patterns and market trends to increase revenues. Eventually, the crisis response teams of a country have turned their interest to explore the potentialities of Big Data in managing disaster risks such as flooding. The reason for this is that during flooding, crisis response teams need to take decisions based on the huge amount of incomplete and inaccurate information, which are mainly coming from three major sources, including people, machines, and organizations. Hence, Big Data technologies can be used to monitor and to determine the people exposed to the risks of flooding in real time. This could be achieved by analyzing and processing sensor data streams coming from various sources as well as data collected from other sources such as Twitter, Facebook, and satellite and also from disaster organizations of a country by using Big Data technologies. Therefore, this chapter explores the challenges, the opportunities, and the methods, required to leverage the potentiality of Big Data to assess and predict the risk of flooding.

  • 20.
    Pathak, Abhijit
    et al.
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Uddin, Mohammad Amaz
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Abedin, Md. Jainal
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mustafa, Rashed
    University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    IoT based Smart System to Support Agricultural Parameters: A Case Study2019In: Proceedings of the 6th International Symposium on Emerging Inter-networks, Communication and Mobility (EICM), Elsevier, 2019, p. 648-653Conference paper (Refereed)
    Abstract [en]

    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

  • 21.
    Siddiquee, Kazy Noor E Alam
    et al.
    University of Science and Technology Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Khan, Faria Farjana
    University of Science and Technology Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    A Scalable and Secure MANET for an i-Voting System2017In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 8, no 3, p. 1-17, article id 1Article in journal (Refereed)
    Abstract [en]

    Internet Voting (i-Voting) is an online electronic voting process where a voter can vote staying online from anywhere or connected to a wireless network of a target place. In this paper, a wireless network built with a MANET has been considered for the voting process. National parliamentary voting process of Bangladesh has been taken as the case study. The MANET of the voting process is built using some stationary wireless nodes and mobile wireless nodes. Voters carry mobile wireless nodes using which they can vote. Stationary wireless nodes are installed and deployed in the MANET built in a polling area selected by the National Agency of Election process. These nodes are directly in connection with the national database of voters. Stationary nodes perform the authentication and validation processes of the voter (a mobile node) before the vote is given and casted. The secured transaction of data is the goal to be occurred and routed after a strong authentication and validation of the user has been confirmed. The whole process is completed in a scalable wireless network with a distributed goal based approach. Total processes are followed by secured routing of data in this MANET. The optimal routing protocol among OLSR, AODV, DSR, TORA and GRP has been chosen. Denial of Service (DoS) attacks have been considered as the major threat on nodes in this MANET. The simulation work is done in the OPNET simulator.

  • 22.
    Siddiquee, Kazy Noor E Alam
    et al.
    University of Science and Technology, Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Moreno Arrebola, Francisco Javier
    HeidelbergCement, Spain.
    Abedin, Md. Zainal
    University of Science and Technology, Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Estimation of Signal Coverage and Localization in Wi-Fi Networkswith AODV and OLSR2018In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 9, no 3, p. 11-24, article id 2Article in journal (Refereed)
    Abstract [en]

    For estimation of signal coverage and localization, path loss is the major component for link budget of any communication system. Instead of traditional Doppler shift or Doppler spread techniques, the path loss has been chosen for IEEE 802.11 (Wi-Fi) signals of 2.5 and 5 GHz to measure the signal coverage and localization in this research. A Wi-Fi system was deployed in a MANET (Mobile Adhoc NETwork), involving both mobile and stationary nodes. The Adhoc network was also assessed in a routing environment under AODV and OLSR protocols. The proposal was evaluated using the OPNET Modeler simulation environment.

  • 23.
    Siddiquee, Kazy Noor E Alam
    et al.
    University of Science and Technology Chittagong.
    Dhiman, Sarma
    Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati.
    Nandi, Avijit
    Department of Computer Science and Engineering, University of Science and Technology Chittagong Foy's Lake, Chittagong, Bangladesh.
    Akhter, Sharmin
    Department of Computer Science and Engineering, University of Science and Technology Chittagong Foy's Lake, Chittagong, Bangladesh.
    Hossain, Sohrab
    Department of Computer Science and Engineering, University of Science and Technology Chittagong, Foy's Lake, Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method2017In: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 258-263, article id 17595122Conference paper (Refereed)
    Abstract [en]

    This paper presents the real time vehicle detection and tracking system, based on data, collected from a single camera. In this system, vehicles are detected by using Haar Feature-based Cascaded Classifier on static images, extracted from the video file. The advantage of this classifier is that, it uses floating numbers in computations and hence, 20% more accuracy can be achieved in comparison to other classifiers and features of classifiers such as LBP (Local Binary Pattern). Tracking of the vehicles is carried out using Lucas-Kanade and Horn Schunk Optical Flow method because it performs better than other methods such as Morphological and Correlation Transformations. The proposed system consists of vehicle detection and tracking; and it is evaluated by using real data, collected from the route networks of Chittagong City of Bangladesh.

  • 24.
    Siddiquee, Kazy Noor E Alam
    et al.
    University of Science and Technology Chittagong.
    Khan, Faria Farjana
    University of Science and Technology Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Optimal Dynamic Routing Protocols for Agro-Sensor Communication in MANETs2017Conference paper (Refereed)
    Abstract [en]

    Recent developments in the area of Wireless sensor networks and Mobile ad hoc networks provide flexible and easy- to-deploycommunication means for a wide range of appli- cations without any need for an infrastructure being pre-con- figured. Our paper studies performance of proactive and reactive routing protocols in a scenario with agro-sensors. Our results, achieved by simulating a network both in OPNET Modeler and NS2, show that the AODV routing protocol performs better for a large-scale network (where node density is higher) while the DSR routing protocol performs better in a small-scale network given the particular scenario we studied.

  • 25.
    Uddin Ahmed, Tawsin
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Facial Expression Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper (Refereed)
    Abstract [en]

    Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.

  • 26.
    Ul Islam, Raihan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters2017In: Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS): The 8th IEEE INFOCOM International Workshop on Mobility Management in the Networks of the Future World (MobiWorld'17),, Piscataway, NJ: IEEE Communications Society, 2017, p. 30-35Conference paper (Refereed)
    Abstract [en]

    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.

1 - 26 of 26
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