Browsing by Author "Rahman, Md. Mahfujur"

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A New Approach to Hiding Data in the Images Using Steganography Techniques Based on AES and RC5 Algorithm Cryptosystem
(IEEE, 2020-09) Hossen, Md. Sagar; Islam, Md. Ashiqul; Khatun, Tania; Hossain, Shahed; Rahman, Md. Mahfujur
In the new era of modern science and technology is developing day by day, data confidentiality is risky, all over the world and it increases rapidly. In this paper, a new approach to hiding the data using steganography techniques is proposed based on AES and RC5 algorithm cryptosystem. Steganography is the beauty of hiding secret data behind the digital images, videos, audios and text to cover the secret communication. A cryptosystem is the process which given our method more perfection. The visual quality of the cover image nice, no one can think about it how confidential data are transmitted using this method. This proposed method and algorithm capacity is highly flexible than other published algorithm. The AES and RC5 algorithm had no complexity and it looks like very well to hide the confidential data.
An In-Depth Analysis of Convolutional Neural Network Architectures with Transfer Learning for Skin Disease Diagnosis
(Elsevier, 2023-01-24) Sadik, Rifat; Majumder, Anup; Biswas, Al Amin; Ahammad, Bulbul; Rahman, Md. Mahfujur
Low contrasts and visual similarity between different skin conditions make skin disease recognition a challenging task. Current techniques to detect and diagnose skin disease accurately require high-level professional expertise. Artificial intelligence paves the way for developing computer vision-based applications in medical imaging, like recognizing dermatological conditions. This research proposed an efficient solution for skin disease recognition by implementing Convolutional Neural Network (CNN) architectures. Computer vision-based applications using CNN architectures, MobileNet and Xception, are used to construct an expert system that can accurately and efficiently recognize different classes of skin diseases accurately and efficiently. The proposed CNN architectures used a transfer learning method in which models are pre-trained on the Imagenet dataset to discover more features. We also evaluated the performance of our proposed approach with some of the most popular CNN architectures: ResNet50, InceptionV3, Inception-ResNet, and DenseNet, thus establishing a comparison to set up a benchmark that will ratify the essence of transfer learning and augmentation. This study uses data from two separate data sources to collect five different types of skin disorders. Different performance evaluation indicators, including accuracy, precision, recall, and F1-score, are calculated to verify the success of our technique. The experimental results revealed the effectiveness of our proposed approach, where MobileNet achieved a classification accuracy of 96.00%, and the Xception model reached 97.00% classification accuracy with transfer learning and augmentation. Moreover, we proposed and implemented a web-based architecture for the real-time recognition of diseases.
An internship report on evaluation of marketing activities of Neelachol Housing Ltd
(Daffodil International University, 2014-11) Rahman, Md. Mahfujur
The housing sector plays vital roles both in the context of the economy of Bangladesh and serving the fundamental human right of shelter which actually call for the awareness and analysis regarding various pertinent issues involving the sector.The objective of this paper is to highlight the marketing activities of Neelachol Housing Ltd. It is a real estate developer in nature. Now a day’s real estate market is very demanding & most of the consumer of this market is facing huge problems like developer selection, procedure, rules etc. This report is not cover entire market but contains all major information about Neelachol Housing Ltd. So, the user could easily evaluate the organization by go through this report. Major portion of this report contains information about products, price, and payment& purchasing procedure, rules of the company, business activities, organizational Structure etc. At present Neelachol Housing LTD ltd is selling plots in two projects (Neelachol Homes& Neelachol river rain).Price of the plots varies with the location and area. The sizes of the plots are 3 &5 khata. The clients have to contact with the sales office of NeelacholHousing Ltd at Banani to purchase the plot. The booking money is Tk.20000/= per khata &down payment is 30%of total price of a plot and rest of the amount could be paid by onetime payment or by 60 or 72equatedmonthly installment. The client should have to pay the installment before falling three consecutive installments due. This is a general rule of the company. If the client fails to do so, his/her allotment should be cancelled. Now, after making the full payment, the client will be paid the registration fees and VAT for the plots. It will take 6(six) months to one year to hand over the plot. There will be a final measurement on the land area after finishing the development activities. Neelachol Housing LTD is new organizations in real estate market but also facing a lot of problems. The major problem are decreasing service quality & ensure a valuable work team, management should avoid nepotism in recruitment process. The firm could recruit some creative person to produce valuable & meaningful advertisement. The allotment cancellation power should be decentralized. The firm has to develop some new project to retain the market share. Despite the significant growth of the housing sector in the recent past, good research dealing with the development of this sector is lacking. This is compounded by the fact that there is a lack of adequate statistical information.
Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement
(Proceedings of International Conference on Trends in Computational and Cognitive Engineering , Springer, 2020-12-17) Rahman, Md. Mahfujur; Al Mamun, Shamim; Kaiser, M. Shamim; Islam, Md. Shahidul; Rahman, Md. Arifur
Face detection and recognition is a prevalent concept in security and access control area which is commonly used in surveillance cameras at public places, attendance etc. But often this type of system can be circumvented by holding a photo or running a video of authorized person to the camera. Therefore, liveliness concept comes up with a solution to detect the person is real or spoofed. In this paper, We proposed a cascade classifier based model for detecting liveliness using deep-learning and Heart-beat measurement. Moreover, we have evaluated our model accuracy with our own dataset of real and fake videos and photos. By using our proposed model of face liveliness detection model, FPR and FNR have declined 16% and 5.22% respectively. In addition, we have also compared proposed system with other state-of-art methods. And here proposed study has achieved an accuracy of 99.46%.
Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement
(Scopus, 2021) Rahman, Md. Mahfujur; Mamun, Shamim Al; Kaiser, M. Shamim; Islam, Md. Shahidul; Rahman, Md. Arifur
Face detection and recognition is a prevalent concept in security and access control area which is commonly used in surveillance cameras at public places, attendance etc. But often this type of system can be circumvented by holding a photo or running a video of authorized person to the camera. Therefore, liveliness concept comes up with a solution to detect the person is real or spoofed. In this paper, we proposed a cascade classifier based model for detecting liveliness using deep-learning and Heart-beat measurement. Moreover, we have evaluated our model accuracy with our own dataset of real and fake videos and photos. By using our proposed model of face liveliness detection model, FPR and FNR have declined 16% and 5.22% respectively. In addition, we have also compared proposed system with other state-of-art methods. And here proposed study has achieved an accuracy of 99.46%.
Chaos in Quantum Mechanics
(BRAC University, 2023-01) Rahman, Md. Mahfujur; Pandey, Sovers Tonmoy; Ali, Dr. Tibra
This thesis provides a detailed view of Out-of-time-order correlators in quantum mechanics. And how we can use OTOC to calculate chaos in quantum mechanics. We present precise OTOC calculations for a circle billiard, a particle in a one dimensional box, a harmonic oscillator, and a stadium shape billiard. We will also take a brief look into chaos and quantum chaos.
Combating COVID-19 Using Machine Learning and Deep Learning: Applications, Challenges, and Future Perspectives
(IEEE, 2023-03-15) Paul, Showmick Guha; Saha, Arpa; Biswas, Al Amin; Zulfiker, Md. Sabab; Arefin, Mohammad Shamsul; Rahman, Md. Mahfujur; Reza, Ahmed Wasif
COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.
HealthBlock: A Secured Healthcare System Using Blockchain
(Springer Nature, 2023-06-03) Rahman, Md. Mahfujur; Sifat, Md. Nur Amin; Rahman, Mostafizur; Rahman, Mushfiqur; Mamun, Shamim Al; Kaise, M. Shamim
Blockchain technology enables a distributed and decentralized environment with no more central authority. To increase the accuracy of electronic healthcare records (EHRs) and establish a secured patient-centric approach, Blockchain can be a smart solution in this case. Blockchain is a distributed ledger technology that allows for the secured transfer of medical records while a transaction is created in the network. As Blockchain is decentralized and transparent if one user tampers with medical records of transactions, all other nodes/participants would cross-reference each other and easily pinpoint the node/participants with the wrong information. In this paper, we proposed a smart contact-based Blockchain technology and implemented it in the Hyperledger framework to make the healthcare system more confidential and secure. Moreover, Smart contracts give us more security while transacting data and reduce the entire transaction cost, and also make faster transaction speed.
Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study
(Springer Nature, 2023-01-25) Hossain, Sheikh Mufrad; Rahman, Md. Mahfujur; Barros, Alistair; Whaiduzzaman, Md.
The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student’s academic performance. In this research, we aim to identify the factors that impact the student’s academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers.
Implementation of Real-Time Automated Attendance System Using Deep Learning
(Daffodil International University, 2022-02-05) Hasan, Hafiz Mahdi; Rahman, Md. Mahfujur; Khan, Md. Al-Amin; Meghla, Tamara Islam; Mamun, Shamim Al; Kaiser, Shamim
In comparison to general manual operations, contemporary technology always saves time and is often more hassle-free when it comes to verifying human authenticity using their biometrical components. However, despite the fact that face recognition technology has been used in a variety of sectors such as human identification systems, this work is the first to describe how the Face Recognition Technique can be integrated with a deep learning approach. Advanced deep learning techniques can make the attendance system completely automated, highly secure, easier to use, and faster to implement than older systems. Nowadays, the Attendance System is becoming increasingly automated, resulting in time-saving, effective, and beneficial solutions that reduce the burden on administration and organizations. In this paper, we suggest an automatic attendance mechanism that is based on Deep Convolutional Neural Networks (DCNN). SeetaFace, a deep convolutional neural network-based face detection system, is employed in this research effort to detect faces in real-time video capture. This implementation is a VIPLFaceNet implementation, to be more specific. AlexNet, which is also a DCNN, is used for image categorization. The experimental results bring four short similarity situations of the classroom such as absence, delayed appearances, early leave, and unauthorized entry during class or session along with the name, student id, and section and passes this information to the attendance sheet which will evaluate the students/persons in the classroom. This methodology saves time when compared to the traditional method of attendance marking, as well as allows organizations to conduct stress-free observations of students and staff.
IoT Based Smart Human Traffic Monitoring System Using Raspberry Pi
(Springer Nature, 2022-10-21) Hussain, M. M. Musharaf; Rahman, Md. Mahfujur; Uddi, Md. Shoreef; Arefin, Mohammad Shamsul
Ensuring security is the major concern in this modern era in order to lead a healthy relaxed life. In many commercial and non-commercial sectors, people counting and surveillance are important for ensuring security. The number of people entering and leaving important places like business establishments, shops or shopping malls, office buildings, server rooms, or data centers has become essential for security officers or operators to have useful information at the right time. Nowadays, CCTV-based security systems are widely used, which do not send real-time alert notifications to the authorities after any untoward incident occurs unless a security team monitors the system 24/7. The emergence of Industry 4.0 in the current economic trend promotes the usage of Artificial Intelligence (AI) in service development. Computer Vision has played a major role in the image processing and traffic surveillance sector. This study aims to design and implement a cost-effective IoT-based smart Human Traffic Monitoring (HTM) system capable of detecting authorized and unauthorized people as well as storing relevant information. One of this research’s major concerns is creating the functionality to notify the authority in real-time and the ability to interact with the system remotely. To accomplish our desired goal, we use a low-cost Raspberry Pi machine for processing and transmitting data, a PIR sensor for detecting motions, and a Pi camera for capturing images.
One Day International (ODI) Cricket Match Prediction in Logistic Analysis
(International Journal of Human Movement and Sports Sciences, IEEE, 2020) Chowdhury, Shanjida; Islam, K. M. Anwarul; Rahman, Md. Mahfujur; Raisa, Tahsin Sharmila; Zayed, Nurul Mohammad
Cricket is now a game of enchantment, refreshing and physical strength of 22 players. As the popularity of one-day international (ODI) games increases, it is essential to understand the game results' potential predictors. The advantage at home ground, coin-toss result, decision on first batting or fielding first, and day-to-day games are such popular cricket literature variables. The Indian subcontinent is like a game of thrill, war, friendship, and finally, and the best fighting teams are India and Pakistan. This study emphases a comprehensive analysis of the importance of these important predictors by various statistical calculations. For all ODI matches played by India & Pakistan from 1978 to 2019, information was manually collected from the website, www.espncricinfo.com. For purposes of model-building, logistic regression is applied retrospectively to data already obtained from previously played matches. Univariate, bivariate, binary and skewed logistic regression on the multivariate context are considered. In bivariate analysis, only the day/day-night format match is statistically significant at a 5% level of significance in favor of winning the Indian team. In binary logistic regression, the odds of winning team India was 70.6% times more for home ground and 2.28 times more for data compared to day-night times match. For skewed logistic, the odds of conquering for both teams increase, but the model performs comparatively worse than binary logistic regression.
Performance Analysis of Machine Learning Approaches In Software Complexity Prediction
(Scopus, 2021) Reza, Sayed Moshin; Rahman, Md. Mahfujur; Parvez, Hasnat; Badreddin, Omar; Mamun, Shamim Al
Software design is one of the core concepts in software engineering. This covers insights and intuitions of software evolution, reliability, and maintainability. Effective software design facilitates software reliability and better quality management during development which reduces software development cost. Therefore, it is required to detect and maintain these issues earlier. Class complexity is one of the ways of detecting software quality. The objective of this paper is to predict class complexity from source code metrics using machine learning (ML) approaches and compare the performance of the approaches. In order to do that, we collect ten popular and quality maintained open source repositories and extract 18 source code metrics that relate to complexity for class-level analysis. First, we apply statistical correlation to find out the source code metrics that impact most on class complexity. Second, we apply five alternative ML techniques to build complexity predictors and compare the performances. The results report that the following source code metrics: Depth inheritance tree (DIT), response for class (RFC), weighted method count (WMC), lines of code (LOC), and coupling between objects (CBO) have the most impact on class complexity. Also, we evaluate the performance of the techniques, and results show that random forest (RF) significantly improves accuracy without providing additional false negative or false positive that work as false alarms in complexity prediction.
Performance Analysis of Machine Learning Approaches in Stroke Prediction
(Scopus, 2020-12-28) Emon, Minhaz Uddin; Keya, Maria Sultana; Meghla, Tamara Islam; Rahman, Md. Mahfujur; Al Mamun; M Shamim; Kaiser, M Shamim
Most of strokes will occur due to an unexpected obstruction of courses by prompting both the brain and heart. Early awareness for different warning signs of stroke can minimize the stroke. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average glucose level, smoking status, previous stroke and age. Using these high features attributes, ten different classifiers have been trained, they are Logistics Regression, Stochastic Gradient Descent, Decision Tree Classifier, AdaBoost Classifier, Gaussian Classifier, Quadratic Discriminant Analysis, Multi layer Perceptron Classifier, KNeighbors Classifier, Gradient Boosting Classifier, and XGBoost Classifier for predicting the stroke. Afterwards, results of the base classifiers are aggregated by using the weighted voting approach to reach highest accuracy. Moreover, the proposed study has achieved an accuracy of 97%, where the weighted voting classifier performs better than the base classifiers. This model gives the best accuracy for the stroke prediction. The area under curve value of weighted voting classifier is also high. False positive rate and false negative rate of weighted classifier is lowest compared with others. As a result, weighted voting is almost the perfect classifier for predicting the stroke that can be used by physicians and patients to prescribe and early detect a potential stroke.
Predicting Students
(Scopus, 2020) Zulfiker, Md. Sabab; Kabir, Nasrin; Biswas, Al Amin; Chakraborty, Partha; Rahman, Md. Mahfujur
Every year thousands of students get admitted into different universities in Bangladesh. Among them, a large number of students complete their graduation with low scoring results which affect their careers. By predicting their grades before the final examination, they can take essential measures to ameliorate their grades. This article has proposed different machine learning approaches for predicting the grade of a student in a course, in the context of the private universities of Bangladesh. Using different features that affect the result of a student, seven different classifiers have been trained, namely: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Decision Tree, AdaBoost, Multilayer Perceptron (MLP), and Extra Tree Classifier for classifying the students’ final grades into four quality classes: Excellent, Good, Poor, and Fail. Afterwards, the outputs of the base classifiers have been aggregated using the weighted voting approach to attain better results. And here this study has achieved an accuracy of 81.73%, where the weighted voting classifier outperforms the base classifiers.
Prediction and Optimization of Surface Roughness by Desirability Analysis
(Department of Mechanical and Production Engineering (MPE),Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh, 2012-11-15) Ullah, S M Tawfiq; Khan, Ragib Ishraq; Rahman, Md. Mahfujur
This project deals with the prediction and optimization of surface roughness by desirability approach. There are some machining parameters that have significant effects on the surface of a metal and cause surface roughness. Now a days, it is a big concern to reduce the surface roughness for various machining operation by changing the value of machining parameters like feed rate, cutting speed etc. Here, in this project there were observations of rough surface at different feed and cutting speed. However, a CNC drilling machine can have different operation along with drilling. Obviously, there is roughness in the machined surface of a drilled hole. In this project there an effort has been made to develop a mathematical model of a CNC drilling machine for reducing surface roughness as much as possible. For getting the optimum values of machining parameters, “Desirability approach” and “ANOVA” were applied. Also there was an application of image processing to evaluate the circularity as it varies widely with the change of machining parameters. However, there is a successful prediction of surface roughness and the optimum cutting condition is found out. And this investigation to reduce roughness by producing a mathematical model for a CNC drilling machine is proved to be very much accurate by experimental validation which might be reference for further investigation on surface roughness for another operating conditions .
Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning
(Springer Nature, 2024-03-30) Komol, Md. Mostafizur Rahman; Hasan, Md. Sabid; Md. Razon Hossain; Arafat, Md. Eaysir; Arefin, Mohammad Shamsul; Rahman, Md. Mahfujur
Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for autonomous vehicles. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.
Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning
(Scopus, 2024-03-30) Komol, Md. Mostafizur Rahman; Hasan, Md. Sabid; Hossain, Md. Razon; Arafat, Md. Eaysir; Arefin, Mohammad Shamsul; Rahman, Md. Mahfujur
Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for autonomous vehicles. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.
Road Object Detection in Bangladesh Using Faster R-CNN
(Scopus, 2020) Datta, Anik; Meghla, Tamara Islam; Khatun, Tania; Bhuiya, Mehedi Hasan; Shuvo, Shakilur Rahman; Rahman, Md. Mahfujur
The importance of object detection in our lives is increasing day by day. The role of object detection is very important in autonomous cars, intelligent driving assistance, and advanced traffic analysis. In the case of traffic analysis and intelligent driving assistance in Bangladesh, it is very important to properly identify all the objects from real-time video. Because in both cases the main responsibility of the system is to give the driver or authority a clear idea about the road or the environment around the vehicle. And for this, we need to use modern algorithms and architecture based neural network models with much better object detection accuracy such as Faster R-CNN. There are currently a couple of algorithms that work faster than Faster R-CNN but cannot detect objects accurately, as is the case with small-to-medium objects. We used Faster R-CNN on our data to analyze the environment around the road and the environment around the car. We trained the network for 19 object classes and tested its ability to detect objects with real-time video analysis with an accuracy of 86.42%. Moreover, FPR(false positive rate) and FNR(false negative rate) is calculated to evaluate the proposed model from confusion matrices. In this study, the FPR of the Faster R-CNN model is 15.97% and the FNR of the Faster R-CNN model is 12.2%
The Present Scenario of English Language Teaching In a Bangladeshi School
(Daffodil International University, 2018-11-20) Rahman, Md. Mahfujur
The internship report aims to work on The Present Scenario of English Language Teaching in a Bangladeshi school. For the completion of this research, a three-day-field visit is required. All the data are collected from observation of three different classes conducted by three different English teachers of the school. After keen observation and collecting necessary information regarding the topic, three classes have been conducted with the help of teachers, students, staff and the principal of the institute. The report carries a crystal clear picture of English language teaching in school specifically for students of class 7, 9 and 10. A note of class size and ambience is written in the report. The institute has a bunch of shortcomings in the language teaching area. Besides, the possible scopes for improvement in the very area are also included in the report. In short, the paper conveys a scenario of English Language Teaching in Bangladeshi a school.