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Purpose The Purpose of this research is to initiate the “Fourth Industrial Revolution” by using the Internet of things (IoT), which can be applied to flammable gas condition monitoring and detection of gas leakage and activate fire extinguisher in case of fire accidents. Liquefied petroleum gas (LPG) leakage and explosions cause many injuries and death each year. By developing an automated and remote LPG ppm condition monitoring and fire extinguisher activation system with the help of a cyber-physical system, the rate of accidents and injuries can be reduced to a significant amount. Design/methodology/approach The IoT enabled the sensors to transmit LPG concentration value reading to a mobile app or cloud server and control actuators by connecting all in the same network. In case of a fire accident, the solenoid valve automatically or can be activated by an android application manually, which will be pre-installed in mobile phones. Another advantage of this system is that the gas cylinder or flammable particle source can be closed by closing the solenoid valves attached to their outlets. The first challenge of Industry 4.0 is to develop a cyber-physical system where all physical entities can be monitored and controlled over the internet or another way remotely or from a single point. Findings This fire extinguisher system can be used everywhere and in all types of firefighting because all types of fire extinguishers are commercially available in cylinders where solenoid valves can be used instead of conventional valves. This system will reduce human effort in the fire safety system and reduce the number of losses owing to fire accidents by taking all actions automatically and from a safe distance. The reliability analysis of this system indicated that the working condition for the best outcome is 20–35°C and the baud rate of the controller should be 11.5 kHz. Originality/value The study of pieces of the literature summarizes that this work is unique in terms of the application of Industry 4.0 in the fire safety system and reliability analysis of this system helped to determine the operating condition for the best performance of this system. Some LPG condition monitoring system was developed using IoT before but had many limitations such as working capability during load shading or emergency cases.

Highlights • Each episode of diarrhea incurred $29 direct and $34 indirect costs to caregivers. • The highest costs of $136 per child were incurred at private healthcare facilities. • Around 46% of caregivers experienced catastrophic healthcare expenditure resulting from diarrheal treatment. • In 2018, an estimated 4% of GDP per capita was spent on diarrheal treatment., Background Diarrhea is a leading cause of morbidity and mortality among under-five children in Bangladesh. Hospitalization for diarrhea can pose a significant burden on households and health systems. The aim of this study was to estimate the cost of illness due to diarrhea from the healthcare facility, caregiver, and societal perspectives in Bangladesh. Method A cross-sectional study with an ingredient-based costing approach was conducted in 48 healthcare facilities in Bangladesh. In total, 899 caregivers of under-five children with diarrhea were interviewed face-to-face between August 2017 and May 2018, followed up over phone after 7–14 days of discharge, to capture all expenses and time costs related to the entire episode of diarrhea. Results The average cost per episode for caregivers was US$62, with $29 direct and $34 indirect costs. From the societal perspective, average cost per episode of diarrhea was $71. In 2018, an estimated $79 million of economic costs were incurred for treating diarrhea in Bangladesh. Using 10% of income as threshold, over 46% of interviewed households faced catastrophic expenditure from diarrheal disease. Conclusion The economic costs incurred by caregivers for treating per-episode of diarrhea was around 4% of the annual national gross domestic product per-capita. Investment in vaccination can help to reduce the prevalence of diarrheal diseases and avert this public health burden.

Background This study estimated the economic cost of treating measles in children under-5 in Bangladesh from the caregiver, government, and societal perspectives. Method We conducted an incidence-based study using an ingredient-based approach. We surveyed the administrative staff and the healthcare professionals at the facilities, recording their estimates supported by administrative data from the healthcare perspective. We conducted 100 face-to-face caregiver interviews at discharge and phone interviews 7 to 14 days post-discharge to capture all expenses, including time costs related to measles. All costs are in 2018 USD ($). Results From a societal perspective, a hospitalized and ambulatory case of measles cost $159 and $18, respectively. On average, the government spent $22 per hospitalized case of measles. At the same time, caregivers incurred $131 and $182 in economic costs, including $48 and $83 in out-of-pocket expenses in public and private not-for-profit facilities, respectively. Seventy-eight percent of the poorest caregivers faced catastrophic health expenditures compared to 21% of the richest. In 2018, 2263 cases of measles were confirmed, totaling $348,073 in economic costs to Bangladeshi society, with $121,842 in out-of-pocket payments for households. Conclusion The resurgence of measles outbreaks is a substantial cost for society, requiring significant short-term public expenditures, putting households into a precarious financial situation. Improving vaccination coverage in areas where it is deficient (Sylhet division in our study) would likely alleviate most of this burden.

An organization’s have various resources such as; human and non- human resources. Employees are human resources. They are also life blood of an organization as like as capital. They plays an important role by performing different activities within and outside the organization. So every organizations should give more attention to the improvement of employees for achieving more effective outcome from them. Also make them efficient to survive in the competitive business world. Many researchers have conducted research activities in the field of Human Research Management, the same cannot be said on employee training and development especially as it concerns developing countries like Bangladesh. The purpose of this study was to evaluate the effects of skill development training on employee performance, using the garments industry in Bangladesh. For understanding the aim of this study, three goals were developed and these focused particularly on investigating the meaning and importance of training, examining the methods are used to develop employee’s skills and finally evaluating the influence of skill development training on employee performance. This conceptual paper also aimed at studying the influence of skill development training on employee performance and to provide suggestion as to how firm can improve its employee performance through effective training programs. The study was based on some garments industry operating in Dhaka, Bangladesh. The qualitative and quantitative both approaches of the data collection were adopted using a questionnaire comprising of some structured questions distributed to 170 respondents between two garments industries; Masco Industries Limited (Composite knit garments) and Walmart Garments, Narayangonj. Based on this sample the results obtained indicate that training have a clear effect on the performance of employees. The results of this study can be useful to Human resource managers, Human resource policy decision makers, as well as government and academic institutions. This research found that strong relationship exists between employee training and development with employees’ performance. The results reveal that the more the employee gets training, the more efficient their level of performance would be.

This paper represents a generic feature extraction approach to handle multiple attribute decision analysis problems. For that purpose, available decision support frameworks are carefully studied and the basic types of attributes involved in the decision problems are identified. Based on this analysis, a generic decision support scheme is proposed that can deal with all sorts of attributes in order to deduce the optimal solution for any decision problem. The proposed framework is capable of handling multiple attributes throughout the process of providing a flawless solution for the decision problem under both risk and uncertainty. This paper provides detailed information about the sources of uncertainty in the decision-making process and proposes a sophisticated approach for capturing all sorts of uncertainties. In the proposed approach, a cross assessment of every attribute against the corresponding attribute of the other alternatives is conducted to extract the significant features of an attribute. The relative importance of every attribute is considered as a supporting knowledge representation parameter in order to optimize the attribute-assessment process. The final decision is made based on the numerical scores seized by the alternatives. The paper also represents a numerical study to demonstrate the potential applications of the proposed methodology.

PurposeUnderdeveloped along with developing countries face enough crisis of human resources in high-tech industries. As a result, the amount of waste is higher along with less productivity. Highly qualified and smart employees are required as the term “Industry 4.0” introduces a lot of sophisticated and complex technologies that dominates productivity. But in those countries, to cope up with this terminology; neither the educational curriculum and method of teaching has been modified nor changed yet nor will be modified soon. So, the engineering students should be introduced with the aims and technologies of Industry 4.0 and its key technologies so that they can prepare themselves as highly skilled and qualified human resources besides academic courses.Design/methodology/approachThis article mainly depicts the causes of poor technical skills of the engineering students of Bangladesh and a new learning process has been proposed. The model proposed is basically inspired by Kaizen philosophy consisting of initiation, sharing and implementation cycles. The key aspects of human resource quality and the possibility of gaining innovative human resources for smart industries are given equal priority here. The prime factor to evaluate any learning outcome is quality and this research is a structured guideline to achieve sustainable quality education needed for “Industry 4.0” to engineering students.FindingsThis paper provides statistical analysis on “Fitness of undergraduate engineering students for Industry 4.0”. After applying the proposed methodology on 130 students from different public engineering institutes of Bangladesh, the significant improvement of effectiveness in learning various topics of “Industry 4.0” besides academic studies has justified the method to be a successful one measuring by the criteria for fitness of human resource. This research aims to clarify the sustainable and work-based learning goals and methods for Science, Technology, Engineering and Management (STEM) students and professional courses.Research limitations/implicationsThis is a research where the proposed methodology was applied upon academically enrolled engineering students and the result was very satisfactory. A sustainable learning strategy for developing professional skills from academic life can result in better productivity at the beginning of a career. However, this methodology can be applied for job holders or professionals besides their office hours thus it is the future scope of this research methodology.Originality/valueThis research represents the crucial factors related to technical education and skill development for the upcoming challenges and possible remedies needed for Industry 4.0. Therefore, students should become aware of their carrier challenges and the administration concerned for education to minimize the scarcity of skilled human resources needed for the Fourth Industrial revolution.

Fading increases the aesthetic properties of garments produced from denim fabrics. Different fading processes (e.g., laser fading, hand sand brushing, and potassium permanganate (PP) spraying) are applied on denim fabric to modify fabric appearance. A new approach was developed using gamma irradiation prior to the fading process. A design was produced using a CO2 laser for 130 μs. The same design was produced by hand sand brushing and PP spraying. Physical, mechanical, color, and other aesthetic properties were tested, including fabric grams per square meter (GSM), fastness to rubbing and abrasion resistance, tensile and tearing strength, color change (ΔE) values, yellowness index (YI), and color strength (K/S).

The spacecraft designed for long-duration service are susceptible to hypervelocity impacts of micrometeoroids and orbiting space debris. Such impacts on spacecraft structures can cause spacecraft failure and loss of life. In order to adequately protect the spacecraft bulkhead and flight critical systems, many high-strength composite materials have been developed for debris bumper. GLAss fiber REinforced aluminum is one of the high-performance composites. The review of articles, however, yielded no single study, which has been dedicated to interrogate the damage mechanics of GLAss fiber REinforced aluminum upon collision with micrometeoroids. This study, therefore, aimed at the numerical investigation of penetration phenomena of thick GLAss fiber REinforced aluminum 5-6/5-0.4 laminates upon hypervelocity impact of a projectile. The numerical study employed a predictive model that merged the smoothed particle hydrodynamics and the finite element methods. The model could predict the colossal damage modes of GLAss fiber REinforced aluminum. As seen, the normal impact of a 2 mm diameter spherical 2024-T3 aluminum projectile on GLAss fiber REinforced aluminum, at a relative velocity of 7.11 km/s, resulted into membrane stretching and fiber failure of the glass fiber reinforced epoxy composite laminates. By contrast, the aluminum layers experienced an enormous strain-rate and consequently, suffered thinning, fracture and large mass erosion. The perpetual release waves fragmented the projectile and dispersed the projectile mass prior to the further penetration of GLAss fiber REinforced aluminum. To verify the accuracy of the numerical model, experiments had been conducted by using a two-stage light-gas gun. The experiments generated damage modes of GLAss fiber REinforced aluminum in good correspondence to that of the predictive model. Yet, disparity between the estimations of experiments and simulations had been apparent, which is anticipated due to the phase change of material had not been accounted for in the analysis.

The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

Introduction: There are huge numbers of drilling platforms in the world and once the worker on those platforms meet with an accident, the situation could be very serious. The consequence of this could be environmental, economic and in some cases fatal. Middle East, being one of the oil rich regions hence some of the largest operator works here. Companies here own various types of jack up rigs ranging from old generation rigs to the latest cyber-rig. This paper addresses what oil rig workers have to say about their interaction with machines, and how Human Machine Interaction (HMI) in Offshore Drilling can be improved with design. Method: A case study approach was undertaken The analysis in this paper draws on the interviews conducted with two different employees involved in operating the drilling operations conducted in the driller’s cabin of newly designed offshore rigs. A semi-structured approach was adopted, using themes identified through analysis of the preceding. The interviews were transcribed by the research team. Each interview was analyzed thematically with existing system and reported discrepancy Results: The study on Human Machine Interaction (HMI) and Human Factor regarding this has been conducted in the latest generation cyber rigs. There are many aspects of HMI and ergonomics but in this study a special concentration has been given to deal with the ergonomic standpoint and evaluates the drillers console controls. Conclusions: When comparison is done with the existing machinery, few modifications can be thought of for better human machine interaction. A better human machine interaction system will ensure a more productive environment for the oil-rig workers.

Spur gears are the most well-known kind of gears used in hybrid vehicle’s power transmission. They have straight teeth, and are mounted on parallel shafts. In some cases, many spur gears are utilized without a moment's delay to make huge rigging decreases. In this paper how stress creates on a spur equip under various conditions and conditions and reenactments of a rigging system (two spur gears) is assessed by Ansys workbench. For this static structural and dynamic analysis modeling is utilized. A couple of spurs equip tooth in real life is by and large subjected to two sorts of cyclic stress: contact stress and twisting stress including bowing fatigue. The two stresses may not accomplish their greatest esteems at a similar purpose of contact fatigue. These sorts of failure can be limited by analysis of the issue amid the outline organize and making appropriate tooth surface profile with legitimate assembling strategies.

Burn injury is a common clinical concern that leads to morbidity and mortality, especially in the developing countries. Nowadays, it is a world-wide challenge for the patients due to the long-term hospital stay and undue side effects for microbial contamination of marketed burn dressing materials. So, scientists are trying hard to find out materials with fewer side effects, more cost-effective, and easily available to reduce the sufferings of burn patients. For this, researchers from all over the world are giving great focus on the biomaterials such as human amniotic membrane (AM) and various plant extracts. AM and Centella asiatica (CA), a medicinal plant, are individually used for burn and wound healing. AM heals quicker than any other wound healing components in the case of 1st- and 2nd-degree burn. Furthermore, CA contains various important alkaloids, phenolics, asiatic acid, madecassic acid, asiaticoside, and madecassoside. Among them, asiatic acid and madecassic acid exert anti-inflammatory, apoptotic effect, while asiaticoside and madecassoside stimulate collagen (I, II, III) synthesis, new blood vessel formation that ultimately helps to burn wound healing. Moreover, vimentin and α-SMA produced by CA also help in faster healing. Since burn patients suffer a lot of difficulties and complexities due to the slow rate of epithelization, a time-consuming process and several side effects of the available burn-healing agents, this review focuses on the synergistic and complementary effects of AM and CA for faster burn and wound healing. Burn injury is increasing day by day worldwide and this phenomenon is suspected to be persistent. Although treatment exists, it is expensive and takes a longer period to heal and sometimes shows adverse effects. Since AM and CA are available and widely used as burn and wound dressing material, a gel mixed with both of them may be used to boost up burn healing.

Background District hospitals (DHs) provide secondary level of healthcare to a wide range of population in Bangladesh. Efficient utilization of resources in these secondary hospitals is essential for delivering health services at a lower cost. Therefore, we aimed to estimate the technical efficiency of the DHs in Bangladesh. Methods We used input-oriented data envelopment analysis method to estimate the variable returns to scale (VRS) and constant returns to scale (CRS) technical efficiency of the DHs using data from Local Health Bulletin, 2015. In this model, we considered workforce as well as number of inpatient beds as input variables and number of inpatient, outpatient, and maternal services provided by the DHs as output variables. A Tobit regression model was applied for assessing the association of institutional and environmental characteristics with the technical efficiency scores. Results The average scale, VRS, and CRS technical efficiency of the DHs were estimated to 85%, 92%, and 79% respectively. Population size, poverty headcount, bed occupancy ratio, administrative divisions were significantly associated with the technical efficiency of the DHs. The mean VRS and CRS technical efficiency demonstrated that the DHs, on an average, could reduce their input mix by 8% and 21% respectively while maintaining the same level of output. Conclusion Since the average technical efficiency of the DHs was 79%, there is little scope for overall improvements in these facilities by adjusting inputs. Therefore, we recommend to invest further in the DHs for improvement of services. The Ministry of Health and Family Welfare (MoHFW) should improve the efficiency in resource allocation by setting an input-mix formula for DHs considering health and socio-economic indicators (e.g., population density, poverty, bed occupancy ratio). The formula can be designed by learning from the input mix in the more efficient DHs. The MoHFW should conduct this kind of benchmarking study regularly to assess the efficiency level of health facilities which may contribute to reduce the wastage of resources and consequently to provide more affordable and accessible public hospital care.

Foot and Mouth Disease is the most highly contagious disease around the world. Early detection of FMD is crucial for controlling this disease. Convolutional Neural Networks is the most convenient architecture in the state-of-the-art of Image Processing and Computer Vision sector. As per our knowledge, there was no method for FMD detection in the livestock sector which has been proposed by using CNN architecture. This proposed model aims that early detection of the highly infectious FMD using different types of CNN architectures with a pre-trained model like GoogleNet, AlexNet, and Residual Networks (ResNet). All the essential steps for executing the FMD with the Healthy cattle detection process are thoroughly explained in the paper, from the data assemble to the procedure and result. The proposed system is prominent to be successful, achieving results with 95% accuracy, which perhaps inhibit the human mistakes in the diagnosis process and would be conducive to recognize epidemic disease especially the FMD for veterinarians and cattle farmers.

While there is increasing evidence on the trends and impacts of climate change, both globally and in Bangladesh, there is a limited quantitative analysis on the impacts of natural disasters on population health and the association with socio-economic characteristics. Using data from the ‘Bangladesh Disaster-Related Statistics-2015’, we assessed disaster-related health outcomes namely, injury, disability, and death. We applied three logistic regression models to examine the relationships between individual socio-economic characteristics, reported disaster-related injury, disability, and death. Our study found that 145.9 disaster-related injuries, 14.4 disabilities, and 21.0 deaths per 100,000 population were reported in Bangladesh over 2009–2014. Floods were responsible for the highest percentage of injury, disability, and death followed by thunderstorms. The elderly and children were more likely to suffer from disaster-related injuries compared to adults. Disaster preparedness was a protective factor for both disasters related injury and disability. We observed geographic variation in the number of injuries, disability, and death across the districts in Bangladesh. Policy-makers can use the study findings to strengthen risk-monitoring, assessment, and preparedness strategies and actions for extreme weather events related to the rapidly changing climate.

Brain-computer interface (BCI) can be a hope for the people who are not capable of interacting with the external environment. Classification accuracy is a vital issue for implementing BCI system. This study aims to compare the performance of four different machine learning models for fNIRS-based BCI and find out a suitable one. We also present a method of ensemble the models for slight enhancement of accuracy from the best performing model. We use fNIRS data acquired for two different mental tasks- right-hand and lefthand motor imagery movements. The signals were taken from the prefrontal cortex of six healthy subjects using the multichannel fNIRS system. Mean, median, variance, and slope were used as features for classification. Based on the features, four different models -Quadratic discriminant analysis (QDA), Naive Bayes approach, support vector machine (SVM), and Random forest, were used to figure out the best-fit algorithm. Eventually, the average test accuracies are found as 85%, 83%, 87.5%, and 92.5%, respectively. We present a 0.4% enhancement of accuracy through ensemble the models.

The goal of this paper is to create a Voice Activated Braille as a portable device that will help individuals who are blind or visually impaired without any partial help to recognize certain written characters. This is a system controlled by Arduino that will be able to direct blind people. The system acts mostly as a guide for the blind, a particular virtual environment for the visually disadvantaged, making them similarly optimistic to the rest of the world's regular citizens. Blind people will benefit from these innovative braille methods to tackle the challenge of information technology around the world. This system is compact and will keep blind people from partial assistance and can also help them read something without any help. Thus, the daily inconvenience of blind individuals can be significantly decreased and they can enjoy a certain level of freedom and activity.

In this paper, a fuzzy rule-based K Nearest Neighbor (KNN) approach is proposed to forecast rainfall. All the existing rainfall forecasting systems are first examined, and all the climatic factors that cause rainfall are then briefly analyzed. Based on that analysis, a new hybrid method is proposed to forecast rainfall for a certain day. In the proposed system, a robust knowledge base is constructed using some belief rules. The belief rules are formed using the fuzzy membership functions on some historical data provided by Bangladesh Meteorological Department (BMD). A detailed description of the collected data as well as its observation stations is provided in this paper. Finally, KNN is applied to forecast rainfall based on some given inputs. A numeric study is provided to illustrate the forecasting accuracy of the proposed methodology.

The potato disease epidemic can cause a huge threat to food safety. In this chapter, early detection of potato disease is described through deep learning strategies. A dataset is generated using 1574 images of various diseases. This dataset is expanded to 7870 images through the data augmentation technique by utilizing scaling and rotation. Experimentation is performed by dividing the data into training and testing categories at a ratio of 8:2. Three different deep convolutional neural network architectures, such as AlexNet, ResNet, and GoogLeNet are used. The results are compared based on accuracy, precision, recall, and F1 score, and it is found that the ResNet gives the best performance for this particular application.

This paper represents an expert system to presume systemic lupus erythematosus (SLE) at its early development stage. SLE is an autoimmune disease that assaults almost every organ immediately and puts the affected individual to death in the briefest time frame. Statistical data shows a large number of people lose their lives to SLE due to its deferred suspicion as well as detection. Researchers in their discourse accentuate the speculating SLE at its underlying stage, which can be followed by some particular signs and manifestations. The proposed system considers all the potential signs and symptoms of SLE to develop its knowledge base. The system has been developed by using the belief rule-based inference methodology using the evidential reasoning (RIMER) approach. The domain knowledge and additional knowledge representation parameters are collected from some domain experts. The system has been tested over thousands of patients and found accurate on an average of 99.67%.

From the farmers’ perspective, it is very important to detect the beneficial and non-beneficial paddy pests for reducing the use of pesticides with increased productivity. In this chapter, a region-based deep convolutional neural network (Faster R-CNN) where a deep CNN is connected with a region proposal network (RPN) is used to perform the detection and identification paddy pests from the images. The main focus of this chapter is to find out not only harmful pests but also beneficial pests of paddy field so that farmers can make decisions when to use pesticides and when not to. A dataset with a large number of beneficial and harmful pests’ images from the paddy field was created for experimentation. Three models of Faster R-CNN based on ResNet-101, VGG-16, and MobileNet have been applied, and it is found that ResNet-101 gives the highest accuracy of 96.93% for beneficial and 95.07% for non-beneficial pest detection and identification compared to other networks.

While looking for jobs, job seekers like to register themselves in different job portal that has a huge number of job circulars. As from all the job circulars, it is easier to look for the job of their liking. At the same time, these job portals gather some information on the jobseekers that they use to apply to different jobs. Whenever a new job circular is posted in the portal, it is advertised to the job seekers through email notification. To make the email notification more personalized and relevant to the job seekers, a decision tree algorithm is implemented. This decision tree makes the decision of which job circular should be advertised to which job seekers based on the job seeker's field of interest, expected salary and experience. This helps jobseekers relate to the notification and be more engaged in the job portal.

This paper is mainly focused on the low cost technique to measure both AC and DC voltage along with current by using very low cost components. The system can also be easily monitored via a smartphone. The work is intended for the engineering students as most of the Voltmeter/Ammeter is very expensive. Not only that, the acquired data can be stored as per the requirements. New features can be added very easily with this ammeter/voltmeter as well. Whole task was implemented with the help of popular micro-controller ATmega328p to reduce the cost.

Weather forecasting has several effects in our everyday life from farming to event planning. In the northwestern part of Bangladesh, various natural calamities cause the tragic death of many people and economic loss which impacts the total economic growth in Bangladesh. However, the nonlinear relationship between the input parameters and output data in the weather forecasting system makes it more complex. This study investigates the machine learning-based weather forecasting model for the north-western part of Bangladesh to enhance the accuracy of forecasting results in short periods. Artificial neural networks and extreme learning machine algorithms were used for a strong weather prediction purpose. In this experiment, thirty years of historical weather data of temperature, rain, wind, and humidity from seven weather stations in the northwestern part were collected from the Bangladesh Meteorological Department (BMD). The Extreme Machine Learning (ELM) model performs better than Artificial Neural Network and the accuracy rate is 95%.

BackgroundNational healthcare financing strategy recommends tax-based equity funds and insurance schemes for the poor and extreme poor living in urban slums and pavements as the majority of these population utilise informal providers resulting in adverse health effects and financial hardship. We assessed the effect of a health voucher scheme (HVS) and micro-health insurance (MHI) scheme on healthcare utilisation and out-of-pocket (OOP) payments and the cost of implementing such schemes.MethodsHVS and MHI schemes were implemented by Concern Worldwide through selected NGO health centres, referral hospitals, and private healthcare facilities in three City Corporations of Bangladesh from December 2016 to March 2020. A household survey with 1,294 enrolees, key-informant interviews, focus group discussions, consultative meetings, and document reviews were conducted for extracting data on healthcare utilisation, OOP payments, views of enrolees, and suggestions of implementers, and costs of services at the point of care.ResultsHealthcare utilisation including maternal, neonatal and child health (MNCH) services, particularly from medically trained providers, was higher and OOP payments were lower among the scheme enrolees compared to corresponding population groups in general. The beneficiaries were happy with their access to healthcare, especially for MNCH services, and their perceived quality of care was fair enough. They, however, suggested expanding the benefits package, supported by an additional workforce. The cost per beneficiary household for providing services per year was €32 in HVS and €15 in MHI scheme.ConclusionHVS and MHI schemes enabled higher healthcare utilisation at lower OOP payments among the enrolees, who were happy with their access to healthcare, particularly for MNCH services. However, they suggested a larger benefits package in future. The provider’s costs of the schemes were reasonable; however, there are potentials of cost containment by purchasing the health services for their beneficiaries in a competitive basis from the market. Scaling up such schemes addressing the drawback would contribute to achieving universal health coverage.

The Optical Burst Switching (OBS) network is mostly victimized to the Denial of Service (DOS) attack, referred as Burst Header Packet (BHP) flooding attack can prevent reasonable traffics from keeping the necessary resources at transitional core nodes. The attack scenario is to flood the malicious BHP without acknowledging Data Bursts (DB) which can affect low bandwidth utilization, degrade network performance, high data loss rate and ultimately DOS. Therefore, machine predicted analysis has become very promising in recent decades that can effectually identify the attack in the optical switching network. However, due to a very small number of samples of the datasets, traditional machine learning approaches such as Naive Bayes, K-Nearest Neighbour’s (KNN) and Support Vector Machine (SVM) cannot analyse the data efficiently. In this regard, we intend a Deep Convolution Neural Network (DCNN) model to automatically detect the edge nodes at an early stage. Finally, presented that proposed deep model is working enhanced rather than any other traditional model (e.g. Naive Bayes, SVM and KNN).

Rising usage of web applications in every aspect of life has created intense need of enhancement of user experience and formation of intelligent web applications. This research work was conducted with a view to do so. This paper focuses on the reorganization of web application content based on user’s specific interests while browsing any web application by providing new algorithms and a new approach.

Machine learning methods such as deep neural networks have remarkably improved plant species classification in recent years. It is very challenging task to classify plant species based on their categories. In this work, deep learning approach is explained to identify and classify succulent plant species using VGG19, three layers CNN and five layers CNN network on our dataset. The proposed architecture achieved a significant result from VGG19 and three layers CNN model. In succulent plant image dataset, there are 10 different classes of succulent and non-succulent plants. The dataset consists of 3632 succulent plant images and 200 non-succulent plant images. The model achieved 99.77% accuracy which performs better than VGG19 and three layers CNN model.

This paper represents a novel strategy for developing a disease diagnosis gadget from a patient’s statement. For that, the system solely accepts patients’ statements in a natural language like English and analyzes the patients’ statements to prognosis the symptoms the affected person is presently suffering from. The framework forms the patients’ discourse and afterward utilizes Term Frequency (TF) to find the indications of a malady. Cosine Similarity is utilized to settle on a final decision with respect to regarding disease diagnosis task. Cosine Similarity quantifies the similitude between two non-zero vectors in a vector space model where one of the vectors is constructed with the symptoms the patient is encountering and the rest is developed during knowledge base setup. The framework is tested over 1013 patients with various ailments and its accuracy up to 98.3%.

Visually impaired person cannot perform their activities like ordinary person. It becomes more difficult when a person is visually impaired along with deafness. Sometimes, it is quite impossible to communicate with such people whether in speaking or writing format. Therefore, Expert Braille Communicating System (EBCS) is a promising solution which provides braille writing format from voice. Unfortunately, people of underdeveloped countries are not getting the facilities of advanced electronic braille system for overpricing. In this work, an attempt has taken on designing a modified braille device which is economical and more compelling than previous. The device takes voice as input from an android apps and can convert the voice signal into text format of braille and prints the braille letter in paper. EBCS is trained with 1 epoch and the accuracy is achieved of 97.6%. By implementing the considered Braille system, visually impaired person can entirely engage themselves in the society.

Security has emerged to be a concerning issue in cloud computing as numerous sensitive data are processed and transferred over the cloud servers. In this paper, a variable length key based security mechanism has been designed, developed, and implemented by using the Advanced Encryption Standard (AES) protocol for the secured delivery of cloud data which also works as SaaS (Software as a Services). The length of the used key is given as input by the user followed by the selection of a secret key which is fed into the AES cryptographic system with the desired cloud data message thus producing the Cipher text that is to be transmitted to the destination. In the receiver end, the reverse process is performed with the same key on the received Cipher text and the plaintext is retrieved. Using Python to implement and validate the security process, several messages from cloud users are used and the result for each input is analyzed. The result of our study is then compared with the two existing approaches which clearly shows the advancement of the proposed approach. This process can be applied in any secured electronic message transactions for cloud data.

The drowsiness detection system is a non-evasive system which uses vision-based concepts. In this work, we present a method based on image processing technique for detecting driver drowsiness by considering eye blink rate and pressure sensor value from hand gloves. We applied the Haar Cascade Classifier and Viola–Jones algorithm to detect face and eyes. A camera is focused on the face of the subject from an adequate distance. The system first detects the driver’s face. Then it confirms the region of interest: eye. It then starts counting the blinking of the eye and hand grip pressure from the gloves. If the blinking rate is higher than the normal rate for a given period of time and the hand pressure value is lower than the threshold, the system confirms that the driver’s condition is drowsy. It then gives a warning signal to the driver. If the system cannot measure any blink for a fixed period of time and hand pressure value is extremely lower than the threshold, the proposed system confirms that the driver is sleepy. It then either slows down to the point of stopping the vehicle or gives a strong alarm to wake up the driver. Experimental evaluation is done with 5 mock-drivers of different age groups in a total of more than 4 h of recording time. Result shows that the system is performing as expected to detect driver drowsiness. Eye blinking rate together with hand grip pressure gives a better performing system making it an excellent candidate for future exploration in the field of automotive.

Automatic font recognition or similar font suggestions from an image or picture are the core design works for many designers. This paper proposes a framework based on Convolutional Neural Network (CNNs) to the widely neglected problem of Bangla font recognition by the vision community. First of all, we build up the available large-scale dataset consisting of both labeled synthetic data by Adobe and partly labeled real-world data. Next, CNN is trained to classify images into predefined font classes. Global average pooling layer is proposed instead of fully connected layers over feature maps in the classification layer to correspondence between feature maps and output. Thus, the feature maps can be easily interpreted as font categories confidence maps. We show that our method achieves state-of-the-art performance on a challenging dataset of 10 selected Bangla computer fonts with 96% line-level accuracy. Large-scale experiments show that our approach is exceptionally viable on our synthetic test images and achieves promising results on real-world test images.

The aim of this study is to build a classifier for predicting a disease existence by learning a least conventional set of features extracted from the clinical dataset. Rough set, indiscernibility relation method along with a feedforward neural network is applied and divided the whole process into two parts. At first part, the rough set method is considered as a reduction of features and selected as subset of attributes. In the next part, classification via feedforward artificial neural network is applied to the selected reduction on the dataset. Obtaining datasets of skin cancer disease from the Engineering in Medicine and Biology Society (EMBC) has been used to test the classifier. Our proposed method obtained 95% accuracy for melanoma skin cancer detection. In this regard, this (ANN) model is proposed intended for detecting automatically the cancer patients at a primary stage. Finally, our proposed model is working improved as opposed to some other conventional model (for example RF and SVM).

Brain stroke become a serious cardiovascular and cerebral disease causes of human death. Precisely predicting stroke effect from a set of predictive attributes may classify high-risk patients and guide cure approaches, leading to reduce relative incidence. In respect to, we have collected the information regarding brain stroke patient’s data from five renowned hospitals in Bangladesh with connectivity in patients with acute thalamic ischemic stroke (melanoma), Atypical Nevus (cancer risk) and Common Nevus (No cancer risk). In this work, we propose an ensemble based Modified Bootstrap Aggregating (Bagging) technique for pattern classification. Existing bagging algorithm, can usually progress the performance of a single classifier. However, they typically need larger space as well as quite time-consuming predictions. However, our proposed accuracy based pruning bagging method can improve the classification performance and reduce ensemble size. In general, our proposed modified bagging technique is more appropriate than traditional bagging technique for the prediction of brain stroke disease patients with greater accuracy of 96%.

As Convolutional Neural Network (CNN) is achieving the state-of-the-art in the field of image classification, this research work focuses on the finding prominent accuracy of the jute leaf image diseases using deep learning approach. Acquiring the better performance in disease identification is the main purpose of this paper. Among different types of jute leaf diseases, Chlorosis and Yellow Mosaic have been selected to recognize the diseased leaves from the healthy leaves. As per our knowledge, no other method for leaf disease detection of Jute plant has been proposed for the first time. Using a dataset of 600 images, proposed model is aimed to classify two common jute leaf diseases. CNN achieves an overall accuracy of 96% without applying any image preprocessing and feature extraction method. The results suggest that proposed deep learning model provides an improved solution in disease control for jute leaf diseases with high accuracy.

Background In recent years, developing countries like Bangladesh are facing a higher burden of non-communicable diseases such as hypertension as a result of demographic transition. Prevalence of hypertension is often studied in this setting. However, evidence on undiagnosed hypertension is not widely available in the existing literature. Therefore, the current study focuses on inequalities in the prevalence of undiagnosed hypertension in Bangladesh. Methods A total of 8835 participants aged 35+ years were included in this study using nationally representative Bangladesh Demographic and Health Survey 2011 (BDHS). In the survey, systolic blood pressure (SBP) and diastolic blood pressure (DBP) of these participants were measured three times with approximately 10 minutes of an interval between each measurement. Any respondent with either SBP ≥ 140 mmHg or DBP ≥ 90 mmHg was considered as patient with hypertension as per the guidelines from American Heart Association. Among the participants, undiagnosed hypertension was defined as having SBP > =140 mmHg or DBP > =90 mmHg and never taking prescribed medicine or being told by health professionals to lower/control blood pressure. Multiple logistic regression analysis was applied for identifying factors associated with undiagnosed hypertension. Further, socioeconomic inequalities in the prevalence of undiagnosed hypertension were estimated using Concentration Index (C). Results We found 978 (59.9% of the total) were undiagnosed among 1685 hypertensive patients studied. Regression analysis showed individuals with being underweight, having poor socioeconomic conditions, and lower educational qualifications were more likely to have undiagnosed hypertension. A similar association between undiagnosed hypertension and socioeconomic quintiles was observed using concentration index (C = − 0.07). On the other hand, individuals from higher age group (50–64 or above), female sex, and Sylhet region were at lower risk of undiagnosed hypertension. Conclusions This study showed that a large proportion of the cases with hypertension are remained undiagnosed in Bangladesh, especially among the poor and low educated population. Screening and awareness building initiatives on hypertension should be taken for this group of population to reduce the burden of undiagnosed hypertension.

Cloud computing has become the latest technological computing area providing a large number of advantage to the different organization with its different business model at low cost. Nevertheless, there is always a security concern when uploading sensitive data in the cloud server. Client-side encryption is a common and better solution for ensuring end users that a third party user cannot access the uploading data. Cloud service providers maintain different techniques to protect data but like google drive, most of the company do not use client- side encryption. In this paper, we propose a way to protect the data from hacking or losing when storing or uploading the data in the cloud server using the combination of Advanced Encryption Standard and Secure Hash Algorithm with Initial Vector.

Perovskite halides are the most promising current candidates for the construction of solar cells and other photovoltaic devices. This is the first theoretical approach to explore the effects of Mn-doping on the optoelectronic performance of the lead-free halide CsGeBr3 and the lead-bearing halide CsPbBr3. We have performed the first-principles calculations to investigate the structural, mechanical, electronic, and optical properties of pure and Mn-doped CsGeBr3 and CsPbBr3 perovskite halides in detail. The lattice constants of Mn-doped halides were slightly reduced compared to their pure phases, which is common in materials after doping. The structural stability of both undoped and doped halides was confirmed by their formation enthalpy. Analysis of the mechanical properties revealed the mechanical stability of both undoped and Mn-doped CsGeBr3 and CsPbBr3. The lower values of the bulk modulus suggested potential optoelectronic applications for the halides being studied. Remarkably, the partial substitution of Ge with Mn narrows the bandgap of both Pb-free and Pb halides, enhancing the electron transfer from the valence band to the conduction band, which increased the absorption and conductivity, essential for superior optoelectronic device applications. The combined analysis of mechanical, electronic, and optical properties indicated that the Mn-doped halides, CsGeBr3 and CsPbBr3, are more suitable for the solar cells and optoelectronic applications than undoped CsGeBr3 and CsPbBr3.

Deicing is a mandatory procedure to ensure the required aerodynamic lift of aircraft. Countless experimental and numerical investigations have been conducted to determine the deicing efficiency of various ice protection systems. The literature review, however, yielded no single study that has been dedicated to investigate the deicing performance of a GLAss fiber REinforced aluminum (GLARE)-based electro-thermal ice protection system. Of notes, the upper fuselage of the front- and the rear section of Airbus A380 comprises circa 469 m2 of GLARE. This study, therefore, proposed a phase change model based on thermo-dynamics to determine the power consumption and the de-icing performance of a GLARE laminate embedded with recurring S-shaped copper heater elements. The model addressed the influence of copper mesh length and orthotropic properties of GLARE on deicing. As a test case, the power budget for deicing a GLARE 5A-3/2 laminate was predicted numerically. The numerical analysis demonstrated that a surface power density of 14.62 kW/m2 sufficed to deice a GLARE 5A-3/2 laminate covered with a 0.8 mm thick ice layer at an ambient temperature of 267.15 K. To corroborate the numerical predictions, deicing experiments had been conducted in a miniature icing wind tunnel by incorporating icing conditions comparable to that of the numerical analysis. It was found that the predicted surface power density was in excellent agreement with that determined in the deicing experiment. In the experiment, an infrared camera measured the temperature gradient of the GLARE specimen, which was also in close proximity to that of the GLARE model in the numerical analysis.

The aim of this study is to emphasize on the clinical uses of amniotic membrane (AM) in multiple ocular disorders. AM possesses many properties including promotion of epithelialization, anti-fibrotic, anti-apoptotic, anti-angiogenic properties. Epithelial wound healing on the eye surface is promoted due to several epidermal and keratocyte growth factors as well as by its anti-inflammatory and anti-scarring effects due to interleukin-10, interleukin-1 receptor antagonists and inhibition of transforming growth factor- beta (TGF-β) signal transduction by AM in the corneal and conjunctival fibroblasts respectively. It plays a crucial role by functioning as a substitute of basement membrane (BM) and as a temporary graft material. It is not only being used as an eye graft but also its extract can be used as an eye drop for corneal and external eye diseases. So its proper preparation, preservation and clinical application can bring a revolutionary change in the treatment of different ocular disorders.

An Iterative method of ind ividual nameplate detection using color images acquired fro m a high position is proposed for guidance of nighttime vehicles and other similar purposes. Segmentation is a very critical and d ifficult stage to accomplish in co mputer aided detection systems. Fundamentally the method contains iterative automatic thresholding and selecting the best threshold value which is applied to the original or enhanced dark night images. The main focus of the iteration based threshold to distinguish the image of the background and foreground. This method was tested on an actual outdoor vehicle images and results obtained fro m automat ic thresholding of the experimental images are showing the validity of the method.

Appearance based local feature methods are widely used for facial expression recognition because of their simplicity and high accuracy rates of recognition. However, the achieved accuracy rates and running time yet need to be improved. A new appearance based local feature method, called Local Distinctive Gradient Pattern (LDGP) is proposed in this paper. It derives two 4-bit local binary patterns from two different layers for a pixel by comparing the gray color intensity value of the pixel with its neighboring pixels in four distinct directions. Since each face image is divided into equal sized blocks, two histograms for the two 4-bit LDGP patterns of all pixels in each block can be constructed. The histograms of all blocks are then concatenated to build the feature vector for the given image. To evaluate the effectiveness of the proposed descriptor, experiments were conducted on the popular JAFFE dataset using Support Vector Machine (SVM) as the classifier. Extensive experimental results with seven prototype expressions show that proposed LDGP descriptor is superior to other appearance-based feature descriptors in terms of accuracy rates of recognition.

The transmission of binary information by using chaotic switching named as chaos-shift keying (CSK) with the generation of chaos using cubic map is presented. A system has been proposed for calculating the analytical bit-error rate (BER) of a coherent chaos-shift keying (CSK) digital communication system under an additive white Gaussian noise environment with the consideration of ideal synchronization at the receivers. The bit-error rate of the CSK system is compared with the conventional modulation techniques like ASK, FSK, and PSK. Generating the chaos with the simple cubic map, it is shown that the performance of coherent CSK system can be improved with the variations of the initial conditions of chaos generation. The bit-error rate of the CSK system is improved for both single-user and multiple-user environment.

This study is a part of a large scale urban development project called POLYCITY dealing with how to make cities more energy efficient. The project is already in progress in three different countries like Italy, Spain and Germany. We will keep our analysis focused only on German part Scharnhauser Park, which is located in Stuttgart. In this project the working and living places are integrated in order to get a sustainable city quarter with special emphasis on minimum travel distance and low energy consumption. As renewable energies are an additional source to conventional ones, they are more and more important for the production of the energy. The interesting feature is that a great deal of the necessary energy will be provided from roof top PV-systems and wood fired cogeneration plants. Laser Scanning data along with GeoMedia are used for finding the potentials of roof top. The analysis is also supported with Web Application to disseminate the information to the public. This approach will set a good example for supplying green energy in residential quarters and will provide a basis for the sustainability of similar communities.

Tuberculosis is known to involve any segment of gastrointestinal tract. While tuberculosis of the small bowel is common, isolated primary tuberculosis of the large bowel is quite uncommon. We report two cases of isolated colonic tuberculosis, successfully treated with primary resection. doi: 10.3329/taj.v17i1.3491 TAJ 2004; 17(1) : 52-54

Background Rapidly increasing healthcare costs and the growing burden of non-communicable diseases have increased the out-of-pocket (OOP) spending (63.3% of total health expenditure) in Bangladesh. This increasing OOP spending for healthcare has catastrophic economic impact on households. To reduce this burden, the Health Economics Unit (HEU) of the Ministry of Health and Family Welfare has developed the Shasthyo Surokhsha Karmasuchi (SSK) health protection scheme for the below-poverty line (BPL) population. The key actors in the scheme are HEU, contracted scheme operator and hospital. Under this scheme, each enrolled household is provided 50,000 BDT (620 USD) coverage per year for healthcare services against a government financed premium of 1000 BDT (12 USD). This initiative faces some challenges e.g., delays in scheme activities, registering the targeted population, low utilization of services, lack of motivation of the providers, and management related difficulties. It is also important to estimate the financial requirement for nationwide scale-up of this project. We aim to identify these implementation-related challenges and provide feedback to the project personnel. Methods This is a concurrent process documentation using mixed-method approaches. It will be conducted in the rural Kalihati Upazila where the SSK is being implemented. To validate the BPL population selection process, we will estimate the positive predictive value. A community survey will be conducted to assess the knowledge of the card holders about SSK services. From the SSK information management system, numbers of different services utilized by the card holders will be retrieved. Key-informant interviews with personnel from three key actors will be conducted to understand the barriers in the implementation of the project as per plan and gather their suggestions. To estimate the project costs, all inputs to be used will be identified, quantified and valued. The nationwide scale-up cost of the project will be estimated by applying economic modeling. Discussion SSK is the first ever government initiated health protection scheme in Bangladesh. The study findings will enable decision makers to gain a better understanding of the key challenges in implementation of such scheme and provide feedback towards the successful implementation of the program. Electronic supplementary material The online version of this article (10.1186/s12913-018-3337-x) contains supplementary material, which is available to authorized users.