A collaborative project was done to collect real time data from river water and sent to server for data analysis. Different kinds of sensors like Ph, Turbidity, Temperature, etc has been used for data collection. Using WIFI module, those is transferred to server.

For smart irrigation system, continuous monitoring of soil with data analysis has been done. Once real time data came to a threshold value, irrigation system is auto enabled and user is notified about all the actions.

This project was intend to detect obstacle in front and fire inside, data was shared live to the control room using IoT. It was first initiated as a prototype by using Arduino controller and model train. Project has been presented in an exhibition at BGC Trust University Bangladesh and 2nd price is achieved.

Smart home concept has been applied, connected multiple home devices to internet for remote control. Home electrical and electronics was able to controlled by remote system using IoT in both real time and pre-defined commands.

Object was detected using ultrasonic sensor. Using a good ranged sensor intrusion detection is possible for in a very low cost. Once detected an alarm has been send to user.

We extended the idea of remote to server control from remote location. The car was able to connect to WIFI and based on pre-set or live command can move.

Our robot was able to follow complex paths such as cross over and circular

Preliminary round – Dhaka Regional

Preliminary round – Dhaka Regional

Organizer: ICT ministry of Bangladesh Host: Military Institute of Science and Technology (MIST)

Organizer: ICT ministry of Bangladesh

Organizer: ICT ministry of Bangladesh

Organizer: Chittagong University of Engineering & Technology
Year: 2017-2019

Organizer: University of Science & Technology Chittagong
Year: 2018

Organizer: Outsbook
Year: 2017

Organizer: BGC Trust University Bangladesh

Organizer: BGC Trust University Bangladesh

Organizer: BGC Trust University Bangladesh

Organizer: Outsbook
Year: 2015

Semantic segmentation plays a vital role in various computer vision applications. One of the convolutional networks which have been scholarly accepted as the prominent solution for semantic segmentation is U-Net, known for its effectiveness in capturing spatial context with fewer training images. In real-world applications of analysing liquid spray images, motion blur and defocus blur distortions likely appear due to camera positioning. The robustness of U-Net segmenting the object in the presence of image distortions still needs to be explored in the context of liquid spray images. This study aims to analyse the visual impact of U-Net for semantic segmentation in liquid spray images. Gaussian blurring is applied with different levels of distortion to the test image to simulate the blurring effect caused by the camera defocusing and unstable motion-positioning. Surprisingly, the experimental results showed that U-Net could recognise many initially unrecognisable objects in the test image after applying Gaussian blurring. Following this finding, a hypothesis is proposed that inducing an appropriate blurring artefact into the test image will significantly maximise the contour boundaries visibility, allowing the U-N et to segmentise the recognisable contour efficiently. A closer inspection of the pixel histogram revealed a narrow range of high-frequency values. The reduction of pixel variation makes the object appear connected without sharp separation, improving the continuity of the contour boundary. This quantitative observation is harmonised with the above hypothesis. Code is available at https://github.com/lynerlwl/unet-gaussian-blurring

Accurate analysis of the spray formation process is of utmost importance because it governs the fuel injector performance in air-breathing engines. Historically, optical diagnostic measurements in this region have been limited, hindering progress in understanding the underpinning physics. Due to the rapid growth in computer power, first principle simulations of the primary breakup process are now available, providing access to the full spatiotemporal fields. This data is essential to investigate the physical instability modes. However, it typically relies on subjective criteria to identify the emerging structures in the spray formation process. Since computer vision tools are known to help automate such classification tasks, this work focuses on developing a framework that can be applied to help identify critical regions during the breakup process. The emerging spray structures identified and tracked include the well-known lobes, ligaments, and droplets that arise during the fuel injection process. Analysis shows that our approach yields promising results providing helpful statistics of the fundamental breakup dynamics for engineering analysis.

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to …

Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, “Finger Spelling, A” dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image …

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family’s Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows “Root Mean Square Error” (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.

The maxillary sinus is the most prominent in humans. Maxillary Sinus Volume (MSV) has grown in popularity as a tool to predict the success of various dental procedures and oral surgeries and determine a person's gender in cases such as forensic investigations when only partial skulls are available. Because it is an irregularly shaped cavity that may be difficult to measure manually, robust imaging techniques such as cone-beam computed tomography (CBCT) used in conjunction with machine learning (ML) algorithms may offer quick and vigorous ways to make accurate predictions using sinus data. In this retrospective study, we used data from 150 patients with normal maxillary sinuses to train and evaluate a Python ML algorithm for its ability to predict MSV from basic patient demographics (age, gender) and sinus length measurements in three directions (anteroposterior, mediolateral, and superoinferior). The …

In digital image processing, segmentation is a process by which we can partition an image based on some variables to extract necessary elements. Unlike typical objects, it is complicated to segment dynamic objects from a synthetic fluid dataset where properties like position and shape change over time. Experiments on image segmentation over this dataset are conducted using U-Net (semantic segmentation) and Mask R-CNN (instance segmentation) to compare their results. The training dataset is generated from seven labelled images through data augmentation. Training on 1000 and validating on 200 images, Mask R-CNN achieved more epochs quickly. Around 1000 epochs for Mask R-CNN and 500 epochs for U-Net, both models reached a similar result in terms of F1 score and can segment the object in the new images.

Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more. Two basic grounds of voice or speech analysis are to detect human gender and the geographical region based on accent. This study presents a three-layer feature extraction method from the raw human voice to detect the gender as male or female, as well as the region from where that voice belongs. Fundamental frequency, spectral entropy, spectral flatness, and mode frequency have been calculated in the first layer of feature extraction. On the other hand, Mel Frequency Cepstral Coefficient has been used to extract the features in the second layer and linear predictive coding in the third layer. Regular voice contains some noises which have bee

Identifying people’s ages and events by the use of gait information is a popular issue in our daily applications. The most popular application is health, security, entertainment and charging. A variety of algorithms for data mining and deep learning have been proposed. Many different technologies may be used to keep track of people’s ages and behaviors. Existing approaches and technologies are limited by their performance, as well as their privacy and deployment costs. For example CCTV or Kinect sensor technology constitutes a privacy offense and most people do not want to make pictures or videos when they are working every day. The inertial sensor-based gait data collection is a recent addition to the gait analysis field. We have identified the age of people in this paper from an inertial sensor-data. We obtained the gait data from the University of Osaka. Convolution Neural Network (CNN) and LSTM …

The identification of people’s gender and events in our everyday applications by means of gait knowledge is becoming important. Security, safety, entertainment, and billing are examples of such applications. Many technologies could also be used to monitor people’s gender and activities. Existing solutions and applications are subject to the privacy and the implementation costs and the accuracy they have achieved. For instance, CCTV or Kinect sensor technology for people is a violation of privacy, since most people don’t want to make their photos or videos during their daily work. A new addition to the gait analysis field is the inertial sensor-based gait dataset. Therefore, in this paper, we have classified people’s gender from an inertial sensor-based gait dataset, collected from Osaka University. Four machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Bagging, and …

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.

SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of …

Nowadays the interaction between humans and machines is quite possible and friendly because of the speech recognition system. The gender identification system has been used in many fields like security systems, robotics, artificial intelligence, call center, etc. This paper narrates a novel method to extract the features from audio speech to recognize gender as male or female. At first, we have done data pre-processing to get the noise-free smooth data. Then used this pre-processed data in a multi-layer architecture model to extract the features. In the first layer, we have calculated fundamental frequency using autocorrelation function, spectral entropy, spectral flatness and mode frequency. In the second layer, we have used linear interpolation function to map the pre-processed data into a suitable range and used the Mel Frequency Cepstral Coefficient (MFCC) to extract the features from these mapped data. Three …

Visually impaired people face a lot of challenges in their day by day life. Due to blindness most of the time they depend on others for their daily movements. Many assistive technologies have been developed for blind people; most of them are expensive and designed in a complicated way. So in this paper, we represent a complete wearable navigation system for blind people based on the low expanse and truly subtle sensors, for example, Pi camera and Ultrasonic sensor. Live video analysis has been done to detect human faces and ultrasonic sensors are used to detect objects as obstacles. Raspberry Pi has been used as the main controller board. The indoor path has been pre-trained and saved in a database for blind assistance by voice command using Google Text To Speech (gTTS) API so that blind people can navigate independently. In an emergency, the blind person can seek help from the specific person …

Line follower robot is one kind of autonomous robot which follows a line until that line exists. Generally, the line is drawn on the floor. It can be either black or white. The line can also be normal visible color or invisible magnetic field or electric field. The robot follows the line by using Infra-Red Ray (IR) sensors. There are five IR sensors which makes it an IR sensor array. These sensors read the line and send that reading to Arduino and then control the robot movement. In this paper, the authors will explain about the robot design, implementation, coding, testing, problems they faced and their solutions.