Manifesting a mobile application on safety which ascertains women salus in Bangladesh

This paper reflects on the indemnity of women in our society. The proposed model ensures the embodiment of a mobile application. The algorithm, we developed for this model focuses the safety issues which is applicable to both inside as well as outside of the house for the women in Bangladesh. The solution of this problems can be done through some interrelated features such as i) SOS button pressing which ensures automatic calling, instant location tracking system through GPS of the phone and sending tracked location to all trusted numbers, automatically secrete video recording system ii) voice command detection which assures exact same features as SOS button pressing iii) phone shaking features serve user instant immunity by calling a trusted number. This research also assures experimented data analysis at Dhaka city based on respond time, the time it takes to arrive the SMS and Phone call and current location of the victim. Also do a short comparison among the most popular safety related mobile applications

Improved Solar Photovoltaic Array Model with FLC Based Maximum Power Point Tracking

This paper presents an improved model of solar photovoltaic (PV) array along with the implementation of fuzzy logic as maximum power point tracking (MPPT). The proposed PV array behavioral model is more accurate and with reduced complexity though considered discrete components. The PV array model was well verified by considering the effect of change of environmental conditions, mainly intensity of solar irradiation (insolation) and temperature. The model was tested by feed a single phase inverter. MPPT control the operating voltage of  PV arrays in order to maximize their power output as a result maximize the array efficiency and minimize  the overall system cost. Using a Fuzzy logic based algorithm, the duty cycle of the converter inserted between source and load is adjusted continuously to track the MPP and compared with the conventional perturb and observed (P&O) method for changing environmental conditions. It was found that the Fuzzy logic based method can track the MPP more precisely and rapidly than the conventional one. In P&O method, if step size of input variable is very small, the accuracy in tracking MPP is sufficient but tracking speed becomes too slow. On the other hand if the step size is increased to imitate the rapidly changing weather conditions, accuracy deteriorates and unexpected results occur due to oscillation around a mean point although tracking speed increased. But in the case of proposed FLC whatever the step size of input variable it best suited to track MPP continuously and accurately. The obtained simulation results validate the competent of the solar PV array model as well as the fuzzy controller.DOI:http://dx.doi.org/10.11591/ijece.v2i6.132

Feature selection and prediction of heart disease using machine learning approaches

Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%

Automatic brain tumor detection using feature selection and machine learning from MRI Images

A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis

Comparison Between Reduced Susceptibility to Disinfectants and Multidrug Resistance Among Hospital Isolates of Pseudomonas aeruginosa and Staphylococcus aureus in Bangladesh

Disinfectants have been used largely in hospitals, health care centers and different pharmaceuticals for the removal of microorganisms. It is evident that microorganisms are showing reduced sensitivity against many disinfectants or their minimum inhibitory concentration (MIC) is increasing day by day due to improper use. The aim of this study was to compare the reduced susceptibility to disinfectants and antibiotics of 25 hospital isolates of Pseudomonas aeruginosa and 40 hospital isolates of Staphylococcus aureus isolated from 5 different hospitals at Noakhali region of Bangladesh. Susceptibility of the selected isolates to two disinfectants (savlon and herpic) and ten separate antimicrobial agents for both P. aeruginosa and S. aureus were investigated and compared. Multidrug resistant pattern of all the hospital isolates were determined by agar diffusion method and MIC of the disinfectants were determined by the serial dilution method. All the hospital isolates of P. aeruginosa and S. aureus were multidrug resistant. No severe evident resistance to disinfectants was seen among the 25 isolates of P. aeruginosa and 40 isolates of S. aureus. Interestingly, satisfactory MIC of savlon for 25 isolates of P. aeruginosa and 40 isolates of S. aureus reached at 0.5% to 0.7% (v/v) solution whereas satisfactory MIC of herpic reached at 2% to 2.5% (v/v) solution for all hospital isolates but four isolates of S. aureus showed MIC against herpic at 1.75% (v/v) solution. No sign of co-resistant of disinfectant and antibiotics were found. So, it can be concluded that disinfectants (savlon and herpic) can’t be responsible for P. aeruginosa and S. aureus to become multidrug resistant, when the semi inhibitory dilution of these disinfectants are used

BDHusk: A comprehensive dataset of different husk species images as a component of cattle feed from different regions of Bangladesh

This study presents a recently compiled dataset called “BDHusk,” which encompasses a wide range of husk images representing eight different husk species as a component of cattle feed sourced from different locales in Sirajganj, Bangladesh. The following are eight husk species: Oryza sativa, Zea mays, Triticum aestivum, Cicer arietinum, Lens culinaris, Glycine max, Lathyrus sativus, and Pisum sativum var. arvense L. Poiret. This dataset consists of a total of 2,400 original images and an additional 9,280 augmented images, all showcasing various husk species. Every single one of the original images was taken with the right backdrop and in enough amount of natural light. Every image was appropriately positioned into its respective subfolder, enabling a wide variety of machine learning and deep learning models to make the most effective use of the images. By utilizing this extensive dataset and employing various machine learning and deep learning techniques, researchers have the potential to achieve significant advancements in the fields of agriculture, food and nutrition science, environmental monitoring, and computer sciences. This dataset allows researchers to improve cattle feeding using data-driven methods. Researchers can improve cattle health and production by improving feed compositions. Furthermore, it not only presents potential for substantial advancements in these fields but also serves as a crucial resource for future research endeavors

WaterHyacinth: A comprehensive image dataset of various Water hyacinth species from different regions of Bangladesh

The “WaterHyacinth” dataset, a recently gathered collection of images featuring four distinct species of Water hyacinth from different regions of Bangladesh, is presented in this article. There are four different classifications: Lemna minor, Eichhornia crassipes, Monochoria korsakowii, and Pistia stratiotes. The collection consists of 1790 original images and in addition 4050 augmented photos of Water hyacinth species. Every original picture was captured with the appropriate background and in sufficient natural light. Every image was correctly placed in its corresponding subfolder, providing optimal use of the pictures by various machine learning and deep learning models. Researchers could make major progress in agriculture, environmental monitoring, aquatic science, and remote sensing domains by utilizing this enormous dataset and various machine learning and deep learning approaches. In addition to opening opportunities for significant developments in these domains, it offers an essential asset for further study

An advanced Bangladeshi local fish classification system based on the combination of deep learning and the internet of things (IoT)

Fish classification leads to the automated machine-based fish separation system. In terms of classification and real-time data monitoring, deep learning and the Internet of Things (IoT) each provides an efficient solution. This paper focuses on the development of an embedded system based on the principles of Deep Learning and IoT. The proposed methodology is classified into interconnected parts. The first part describes the working principles of DL with along the dataset building, model analysis and overall system architecture. A new dataset from eight different Bangladeshi fish species. In the process of DL, First, two sets of datasets have been created namely, setup-1(S1) containing original images and setup-2(S2) containing Unsharp masked photos. Then, seven conventional ImageNet pertained state-of-the-art deep learning models on both benchmarking setups: InceptionV3, Xception, DenseNet121, DenseNet169, DenseNet201, InceptionResNetV2, and ResNet152V2. In the process of IoT, the architectural design of a smart contained has been deployed with the aid of several kinds of sensors and microcontrollers. This research has found satisfactory results with the DL models and IoT-based components. The best benchmark accuracy for setup-1 was 96% for all of the DenseNet121, DenseNet169, and DenseNet201 architecture, and for setup-2, it was 96% for the Xception model. Finally, we have constructed a hybrid (CNN + Convolutional LSTM) model, for which the accuracy was 97%, outperforming all of the abovementioned state-of-the-art methods. Besides, the research has performed some experiments with the IoT-based Solution. Though the proposed solution has exhibited some drawbacks, but it can be practicable in real-time solutions