Browsing by Author "Johora, Fatema Tuj"

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A Load Balancing Strategy for Reducing Data Loss Risk on Cloud Using Remodified Throttled Algorithm
(Daffodil International University, 2022-06-03) Johora, Fatema Tuj; Ahmed, Iftakher; Shajal, Md. Ashiqul Islam; Chowdhory, Rony
Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.
A New Chaotic-Based Analysis of Data Encryption and Decryption
(Springer, 2023-05-14) Johora, Fatema Tuj; -Ul-Islam, Alamin; Yesmin, Farzana; Rahman, Md. Mosfikur
Because the amount of exchange hypersensitive data via the Internet is growing at an exponential rate, network and data security have recently been the most pressing worry. On the subject of data security, many approaches are available, including “cryptography.” When data is sent from the sender to the receiver, it is encrypted using an encryption method, and when it is received by the receiver, it is decrypted using a decryption algorithm to see the exact and true data that was sent by the sender previously. Data encryption can be done in a number of ways. With algorithms like AES, DES, and RSA for data encryption and decryption, this research proposes a novel CRSA (chaotic random seed algorithm) technique. CRSA was also compared to other algorithms to see how well they performed. The experimental data presented in this work are used to examine those algorithms as well as our new CRSA algorithm. Cryptography, encryption, decryption, random reed, millisecond, and data security are all terms used in this paper.
A Noble Approach to Develop Dynamically Scalable Namenode in Hadoop Distributed File System Using Secondary Storage
(Scopus, 2020) Shaha, Tumpa Rani; Akhtar, Md. Nasim; Johora, Fatema Tuj; Hossain, Md. Zakir; Rahman, Mostafijur; Ahmad, R. B.
For scalable data storage, Hadoop is widely used nowadays. It provides a distributed file system that stores data on the compute nodes. Basically, it represents a master/slave architecture that consists of a Name Node and copious Data Nodes. Data Nodes contain application data and metadata of application data resides in the Main Memory of Name Node. In cached approach, they fragment the metadata depending on the last access time and move the least frequently used data to secondary memory. If the requested data is not found in main memory then the secondary data will be loaded again on the RAM. So when the secondary data reloads to the primary memory then the NameNode main memory limitation arises again. The focus of this research is to reduce the namespace problem of main memory and to make the system dynamically scalable. A new Metadata Fragmentation Algorithm is proposed that separates the metadata list of NameNode dynamically. The NameNode creates Secondary Memory File in perspective of the threshold value and allocates secondary memory location based on the requirement. According to the proposed algorithm the maximum third, out of fourth of main memory is used at the secondary file caching time. The free space aids in faster operation by Dynamically Scalable NameNode approach. This proposed algorithm shows that the space utilization is increased to 17% and time utilization is increased to 0.0005% with the comparison of the existing fragmentation algorithm.For scalable data storage, Hadoop is widely used nowadays. It provides a distributed file system that stores data on the compute nodes. Basically, it represents a master/slave architecture that consists of a NameNode and copious Data Nodes. Data Nodes contain application data and metadata of application data resides in the Main Memory of NameNode. In cached approach, they fragment the metadata depending on the last access time and move the least frequently used data to secondary memory. If the requested data is not found in main memory then the secondary data will be loaded again on the RAM. So when the secondary data reloads to the primary memory then the NameNode main memory limitation arises again. The focus of this research is to reduce the namespace problem of main memory and to make the system dynamically scalable. A new Metadata Fragmentation Algorithm is proposed that separates the metadata list of NameNode dynamically. The NameNode creates Secondary Memory File in perspective of the threshold value and allocates secondary memory location based on the requirement. According to the proposed algorithm the maximum third, out of fourth of main memory is used at the secondary file caching time. The free space aids in faster operation by Dynamically Scalable NameNode approach. This proposed algorithm shows that the space utilization is increased to 17% and time utilization is increased to 0.0005% with the comparison of the existing fragmentation algorithm.For scalable data storage, Hadoop is widely used nowadays. It provides a distributed file system that stores data on the compute nodes. Basically, it represents a master/slave architecture that consists of a NameNode and copious Data Nodes. Data Nodes contain application data and metadata of application data resides in the Main Memory of NameNode. In cached approach, they fragment the metadata depending on the last access time and move the least frequently used data to secondary memory. If the requested data is not found in main memory then the secondary data will be loaded again on the RAM. So when the secondary data reloads to the primary memory then the NameNode main memory limitation arises again. The focus of this research is to reduce the namespace problem of main memory and to make the system dynamically scalable. A new Metadata Fragmentation Algorithm is proposed that separates the metadata list of NameNode dynamically. The NameNode creates Secondary Memory File in perspective of the threshold value and allocates secondary memory location based on the requirement. According to the proposed algorithm the maximum third, out of fourth of main memory is used at the secondary file caching time. The free space aids in faster operation by Dynamically Scalable NameNode approach. This proposed algorithm shows that the space utilization is increased to 17% and time utilization is increased to 0.0005% with the comparison of the existing fragmentation algorithm.
Comparative Analysis of the Prices of Various Garment Types
(Daffodil International University, 2024-03-31) Ayon, Nazmus Sadat; Johora, Fatema Tuj
This research looks at the differences in price for 13 shirts, 3 pants, and 5 shorts within the same brands. It aims to compare the costs of these clothing items across different sizes, styles, and materials offered by the same manufacturers or retailers. The objective of this comparative study is to understand the pricing structure within a single brand for similar clothing items. By analyzing the cost differentials, consumers can evaluate whether the pricing is consistent across different products or if there are disparities that warrant consideration. The study involves collecting pricing data for the specified clothing items from a single brand. Researchers gather information on various sizes, styles, and materials available for each item, comparing their respective costs. Factors such as production costs, material quality, and brand positioning are analyzed to determine the pricing rationale behind each product variant.
DIU Student Help Portal
(Daffodil International University, 23-01-29) Quamer, MD Saad; Hossain, Meharaz; Johora, Fatema Tuj
Students struggle a lot in their university life and they face various problems due to lack of faculty information, proper guidance, prefect system, losing project, finding supervisor, skill development course information, cv create etc in their different semester which we have already faced. So as a solution to their problems we wanted to do a project where students can find solutions to all their problems together. Our "Diu Student Help Portal" initiative is primarily focused on providing solutions to issues that students have while attending university. Our main objective is to simplify the university experience for students. Without these, we would want to create a system that automatically creates resumes to assist students in finding information about all faculties. The projects from all of their classes can stay together. Students can locate their courses for skill improvement. We'll create a system for student prefects that makes it easier for students to identify personal prefects for their classes. All notices will be distributed to students through the notice board. All pupils will find this approach to be very useful because it is more adaptable and simple to utilize. once they graduate, whenever they want. When seeking employment after graduation, individuals might display all of their completed projects as examples of their prior work. Students may readily seek support from a teacher when they need assistance with a task or further aid with a problem, and they can address that issue quickly. All of the students may benefit from this initiative, which will increase their academic flexibility. Our project ‘Diu Student Help Portal’ is a web-based application, we use html, css, javascript, and bootstrap use for front-end development on our project, and raw php for back-end development to make our website dynamic.
Heart Disease Prediction Using Machine Learning
(IEEE, 2023-05-24) Bilgaiyan, Saurabh; Ayon, Tajul Islam; Khan, Aliza Ahmed; Johora, Fatema Tuj; Parvin, Masuma; Alam, Mohammad Jahangir
Mainly related to the cardiovascular system, brain, kidney, and peripheral arteries, the disease is called heart disease. Heart disease can have many causes, but high blood pressure and atherosclerosis are the main ones. Additionally, structural and physiological changes in the heart with age are largely responsible for heart disease, which can occur even in healthy individuals. They are not put to use or employed in any way. If these data were investigated and examined, diseases may be predicted or perhaps prevented. By using images of cancer cells to train a dataset, diseases like cancer may be identified and their stage can be forecasted. Similarly, to that, factors like cholesterol, diabetes, heart rate, etc. can be used to predict heart disease. It is difficult and dangerous to predict cardiac disorders. We noticed that sometimes there are multiple approaches used to solve a problem. It varies depending on the circumstances. The fact that most of the data are sparse or absent since they weren't recorded with the intention of analysis presents another difficulty. With data from four hospitals in four distinct locations, we, therefore, set out to determine which strategy would be best for forecasting the diseases. This study compares the effectiveness of various data mining methods for predicting cardiac disease, including, K-nearest neighbors, Random Forest, and Multi-layer Perceptron, Logistic Regression. The effectiveness of prediction for each approach utilized is reported after an analysis of the Data Mining methodologies. The outcome demonstrated that heart problems can be predicted with greater than 97 percent accuracy.
Impact Analysis of Rooftop Solar Photovoltaic Systems in Academic Buildings
(Springer Nature, 2023-12-19) Nayan, Pranta Nath; Ahammed, Amir Khabbab; Rahman, Abdur; Johora, Fatema Tuj; Reza, Ahmed Wasif; Arefin, Mohammad Shamsul
Solar energy is a non-depleting and eco-friendly source of renewable energy that is generated through the use of solar panels, which convert the energy from the sun into electricity. In the current world, we need electricity supply constantly but produced electricity cannot meet the world’s demand. Even in our country, the situation is much more crucial. The government attempts to handle this situation by shedding loads, which has a detrimental impact on industrial, commercial, and educational institutions. In this paper, we provided a solution to produce renewable energy through photovoltaic solar panels that are environmentally friendly. The paper also provides the working process of solar panels, how solar panels meet load demand and reduce the cost of electricity bills, and future trends and aspects of solar energy.
Intergenerational identity formation: scars of displacement and trauma in Susan Abulhawa’s Mornings in Jenin and Hala Alyan’s Salt Houses
(BRAC University, 2024) Johora, Fatema Tuj; Noman, Abu Sayeed Mohammad
Displacement and trauma in the formulation of identity is one of the major areas of discussion in this study with the reference of the primary texts, Mornings in Jenin and Salt Houses. To emphasize the definitive position of the authentic identity and, at the same time, the dialogical relation of protagonists due to the displacement along with the individual and collective trauma, the dilemma could be apprehended from epistemological representations. The epistemological representations imply unbiased convictions where the cultural enunciation in different landscapes and the trauma of both the people who lived inside the catastrophe and outside brought significant details of affecting them in latent or in active conditions. To provide this research with a conventional framework, Cathay Caruth’s Unclaimed Experience, Homi Bhaba’s Location of Culture, and other essays are used, as these have brought the key aspects of discussions, such as trauma in denial and the escape of the moment; then hybridity, discursive ambivalence, cultural difference, and diversity, and so on. The selected primary texts pertain to the theoretical framework; it elaborated the reconciliation of each generation, especially those who have been living in diaspora, from different perspectives, and that it surmized the trauma and collective losses instinctively.
Peripheral Blood Smear Image-Based Blood Cancer Detection Using Transfer Learning
(Springer Nature, 2024-03-30) Shaha, Sonjoy Prosad; Datta, Sajeeb; Mahmud, Md. Nadim; Ahmad, Md. Hassan; Johora, Fatema Tuj; Rahman, Md. Atiqur
The lymphatic, bone marrow, and blood systems are all affected by hematological malignancy, also known as blood cancer. Early detection is essential for improved blood medical care and better patient outcomes. In the recent past, deep learning algorithms have developed as useful tools for the analysis and diagnosis of medical images. Deep learning algorithms are used in this study to introduce a cutting-edge technique for identifying blood cancer. Our method involves training a convolutional neural network (CNN) with a large dataset of blood cell data to identify the presence of cancerous cells. We compare our CNN-based approach’s effectiveness with some other CNN-based approaches and demonstrate that it is more effective at detecting blood cancer. We used a variety of preprocessing techniques to provide highly trainable data for our algorithms in order to do this. Five CNN-based algorithms—VGG-19, VGG-16, MobileNet, InceptionV3, and ResNet50—as well as healthy and fragmented data are used in this study. With an accuracy of 95%, ResNet50 achieved the highest accuracy. Our study’s results suggest that deep learning algorithms could be helpful in detecting blood cancer early, leading to a more precise diagnosis and course of treatment for this fatal condition.
Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test
(IEEE, 2023-05-24) Bhuiyan, Md. Atik; Rad, Sharaf Shahariare; Johora, Fatema Tuj; Islam, Abdullah; Hossain, Md Ismail; Khan, Aliza Ahmed
One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy.