Mohammad Shahadat Hossain

A belief rule based clinical decision support system to assess suspicion of heart failure from signs, symptoms and risk factors

A Clinical Decision Support System (CDSS) to assess suspicion of a disease would avoid unnecessary cost of medical diagnosis. Heart Failure (HF) is a complex clinical syndrome of cardiac disorder. In the present paper, a Belief rule based (BRB) CDSS has been proposed to assess suspicion of HF by using signs, symptoms and risk factors. The recently developed generic rulebased inference methodology using the evidential reasoning approach (RIMER) was considered as the methodology for developing this CDSS. Netbean7.2s GUI and MySQL server have been employed to develop the system. This belief rule based CDSS can deal with various types of uncertainties found in clinical sings, symptoms, risk factors and domain knowledge. The knowledge based for this system has been developed by taking account of real patient data, obtained in consultation with specialists. The CDSS has been tested by using the data taken from the patients with breathlessness. It has been observed that the results generated by the system is reliable and thus facilitates to take decision to avoid unnecessary costly medical diagnosis.

Performance Analysis of an IP based Protocol Stack for WSNs

Wireless sensor networks (WSNs) are the key enablers of the Internet of Things (IoT) paradigm. Traditionally, sensor network research has been to be unlike the internet, motivated by power and device constraints. The IETF 6LoWPAN draft standard changes this, defining how IPv6 packets can be efficiently transmitted over IEEE 802.15.4 radio links. Due to this 6LoWPAN technology, low power, low cost microcontrollers can be connected to the internet forming what is known as the Wireless embedded Internet. Another IETF recommendation, CoAP allows these devices to communicate interactively over the Internet. The integration of such tiny, ubiquitous electronic devices to the Internet enables interesting real-time applications. We evaluate the performance of a stack consisting of CoAP and 6LoWPAN over the IEEE 802.15.4 radio link using the Contiki OS and Cooja simulator, along with the CoAP framework Californium (Cf).

A well-being model of small-scale microenterprise development to alleviate poverty : A case study of Bangladesh village

Purpose – The papers purpose is to present and empirically validate a learning model of participatory grassroots development among the poor and needy in Bangladesh.Design/methodology/approach – The approach used is conceptual modeling and its empirical validation for a case study of poor womens sewing project in an interior village of Chittagong, Bangladesh.Findings – A perpetual charity‐fund with endogenous values and productive transformation of the needy at the grassroots can prove to be an effective approach to socioeconomic development.Research limitations/implications – The empirical validation can be enhanced with more data being generated with experience in the womens sewing project in the near future.Practical implications – This is a policy‐oriented paper with practical ways and means‐test for implementation in development planning.Originality/value – A formal modeling of grassroots development premised on human resource development and perpetual charity‐fund for financing and their empirical...

Smart risk assessment systems using belief-rule-based DSS and WSN technologies

Smart risk assessment systems are becoming more and more important in the society. If the chances of reducing and managing certain [[risks are increased, the impacts can be controlled and reduced significantly. This article surveys different belief-rule-based decision support systems and various wireless sensor network technologies that can be used in collaboration to build interesting risk assessment applications. We propose a model for building such an environment and describe a potential application of our proposed model for assessing flood risks in a case study.

A novel anomaly detection algorithm for sensor data under uncertainty

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, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications.

A web based belief rule based expert system to predict flood

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.

Heterogeneous wireless sensor networks for flood prediction decision support systems

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.

A Belief Rule-Based Expert System to Diagnose Influenza

Influenza is a viral disease that usually affects the nose, throat, bronchi, and seldom lungs. This disease spreads as seasonal epidemics around the world, with an annual attack rate of estimated at 5%-10% in adults and 20%-30% in children. Thus, influenza is regarded as one of the critical health hazards of the world. Early diagnosis (consisting of determination of signs and symptoms) of this disease can lessen its severity significantly. Examples of signs and symptoms of this disease consist of cough, fever, headache, bireme, nasal congestion, nasal polyps and sinusitis. These signs and symptoms cannot be measured with near-100% certainty due to varying degrees of uncertainties such as vagueness, imprecision, randomness, ignorance, and incompleteness. Consequently, traditional diagnosis, carried out by a physician, is unable to deliver desired accuracy. Hence, this paper presents the design, development and application of an expert system to diagnose influenza under uncertainty. The recently developed generic belief rule-based inference methodology by using the evidential reasoning (RIMER) approach is employed to develop this expert system, termed as Belief Rule Based Expert System (BRBES). The RIMER approach can handle different types of uncertainties, both in knowledge representation, and in inference procedures. The knowledge-base of this system was constructed by using records of the real patient data along with in consultation with the Influenza specialists of Bangladesh. Practical case studies were used to validate the BRBES. The system generated results are effective and reliable than from manual system in terms of accuracy.

A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty

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.

A belief rule based expert system to assess clinical bronchopneumonia suspicion

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.