Browsing by Author "Hasan, Nahid"

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A modified algorithm for motif discovery Based on risotto and projection
(Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh, 2014-11-15) Galib, Marnim; Hasan, Nahid
An important part of gene regulation is mediated by specific proteins, called transcription factors, which influence the transcription of a particular gene by binding to specific sites on DNA sequences, called transcription factor binding sites (TFBS) or, simply, motifs. Such binding sites are relatively short segments of DNA, normally 5 to 25 nucleotides long, overrepresented in a set of co-regulated DNA sequences. There are two different problems in this setup: motif representation, accounting for the model that describes the TFBS’s; and motif discovery, focusing in unraveling TFBS’s from a set of co-regulated DNA sequences. This thesis proposes a discriminative scoring criterion that culminates in a discriminative mixture of Bayesian networks to distinguish TFBS’s from the background DNA. This new probabilistic model supports further evidence in nonadditivity among binding site positions, providing a superior discriminative power in TFBS’s detection. On the other hand, extra knowledge carefully selected from the literature was incorporated in TFBS discovery in order to capture a variety of characteristics of the TFBS’s patterns. This extra knowledge was combined during the process of motif discovery leading to results that are considerably more accurate than those achieved by methods that rely in the DNA sequence alone.
A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms
(Scopus, 2024) Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption
A Novel Approach to Analyzing the Impact of Ai, Chatgpt, and Chatbot on Education Using Machine Learning Algorithms
(Institute of Advanced Engineering and Science (IAES), 2024-08-15) Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
An autoencoder-based decentralized clustering leveraging model aggregation fusion strategy
(BRAC University, 2024-05) Hasan, Nahid; Alam, Md. Golam Rabiul
Unsupervised clustering plays a crucial role in various real-life applications. It works by grouping similar data points together based on certain features or characteristics, without the use of predefined labels. The process generally starts with gathering data in a centralized system that are to be clustered. This data could be in the form of numerical features, text, images, or any other type of information. The exponential expansion of digital transformation, the Internet of Things (IoT), social media, and online platforms has precipitated an unprecedented surge in data generation. This proliferation is characterized by an incessant stream of information flowing from various sources, encompassing user interactions, sensor readings, online transactions, and more. This deluge of data poses both challenges and opportunities for businesses, governments, and individuals alike. The ever-increasing amount of data poses both opportunities and challenges. So, gathering, managing, processing this amount of data in a centralized system requires time and is a very tough process. Additionally, concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. Moreover, it’s important to respect individuals’ privacy rights and adhere to relevant data protection laws and regulations. Federated learning addresses concerns about data volume and privacy by leaving user data on devices. Federated unsupervised representation learning is an architecture that pre-traines deep neural networks utilizing unlabeled input in a federated fashion via unsupervised representation learning. In centralized settings, model-based clustering approaches demonstrate significant effectiveness. These methods rely on statistical models to identify underlying patterns and group data points accordingly. By leveraging sophisticated algorithms, modelbased clustering can efficiently handle complex data structures and accurately partition datasets into meaningful clusters. This approach enables centralized systems to efficiently organize and analyze large volumes of data, facilitating insights and decision-making processes across various domains. Moreover, model-based clustering offers flexibility in accommodating different data distributions and can adapt to diverse clustering requirements, making it a versatile tool for centralized data analysis tasks. In contrast to the centralized setup, this way of clustering in federated settings is still relatively unexplored, maybe because training models in a highly diversified context using the FedAvg method is more difficult. The normalizing flow model is used by the recently announced Unsupervised Iterative Federated Clustering (UIFCA) Algorithm to perform clustering on unlabeled datasets in federated environments. The IFCA framework, which tackles the problem of very varied settings, is the foundation of UIFCA. A novel approach for decentralized clustering utilizing proposed model parameter aggregation strategy FednadamN in conjunction with the deep generative model autoencoder is introduced. FednadamN combines the benefits of two cutting-edge optimization methods for federated learning: Adam and Nadam. Adam optimization offers quick convergence and resilience to noisy data by using adaptive learning rates based on the first and second moments of gradients. Adam is expanded by Nadam with the use of Nesterov accelerated gradients, hence increasing the stability and speed of convergence. The method addresses the challenge of clustering in decentralized settings by leveraging the collective intelligence of distributed nodes while preserving data privacy and minimizing communication overhead. By aggregating model parameters across decentralized nodes and employing Autoencoder-based representations, efficient clustering is enabled efficient clustering without the need for central data storage or coordination. This approach promises to enhance scalability, privacy, and performance in decentralized clustering tasks across various domains. Additionally, a comparison between the tailored approach and the current technique using benchmark datasets is offered. The following four benchmark datasets were used: image segmentation, protein localization, letter image recognition, and vowel deterrence. The suggested technique for clustering letter image recognition data has produced the greatest mutual information score of 1.192 and highest v measure score of 0.373 using the kmeans algorithm. However, FedAvg’s fuzzy k means algorithm yields the highest rand index score of 0.925. The proposed approach for clustering Deterding Vowel Recognition Data has the highest v measure score of 0.264 and the highest rand index score of 0.850 when using the kmeans algorithm; however, it performs less well than FedAdam, which uses the minibatch kmeans algorithm to show a v measure score of 0.258. The proposed approach for clustering Protein Localization Data yields the greatest rand score 0.774 , highest mutual info score 0.908 , and highest v measure score 0.527 while utilizing the minibatch kmeans algorithm. The proposed method for clustering Image Segmentation data yields the greatest mutual information score of 1.084, the highest rand score of 0.849, and the highest v measure score of 0.565 when utilizing the minibatch kmeans algorithm. This result demonstrates the suggested approach’s improved performance and its potential applicability for various clustering goals. The enhanced efficiency of this method makes it a valuable tool for diverse clustering tasks. Its robustness and adaptability underscore its utility in different contexts. Moreover, the approach’s superior outcomes suggest broader relevance across multiple domains.
Effect of Scouring Bleaching on the Physical Characteristics of Woven Fabric
(Daffodil International University, 22-11-27) Rabby, Md.Fazle; Hasan, Nahid
Scouring and Bleaching is done commonly for the woven fabric for improving the surface of the fabric Removal of natural and added impurities using caustic soda and Hydrogen per-oxide from cotton woven fabric .The object of our project is to identify the effect of scouring and bleaching on the characteristics of woven fabric .To conduct this project work three fabrics which are 100% cotton plain woven fabric ,100% cotton satin and 100% cotton twill were taken. Moreover, some tests like GSM, Absorbency test, EPI, PPI, Tensile strength, Tear strength were done to identify the change in the characteristics of woven fabric. It has been observed that scouring and bleaching has significant impact on characteristics of woven fabric.
Enhancing Performance of Heterogeneous Wireless Sensor Network (WSN)-A Modified Approach
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Hasan, Nahid; Habib, Mufti Bin; Salam, MD Abdus
Wireless sensor networks have received considerable attention in recent years. They were initially employed for surveillance by the military. It has now grown into industrial and civilian applications like as weather forecasting, pollution control, traffic control, healthcare, and other sectors. Developing an energy-efficient routing strategy is one of the most difficult challenges in wireless sensor networks. Due to the limited available power of sensor nodes, energy saving is a critical challenge in wireless sensor networks for nodes and network longevity. An application-specific protocol is preferable to layered protocols when dealing with harsh and difficult environments. The primary goal of this study is to provide a way for ensuring more efficient network or data transfer. This study presented a mechanism called as Weighted Balance Energy Efficient Clustering (WBDEEC) to improve network efficiency. MATLAB was used to run the simulations. In terms of factors such as number of living nodes, number of dead nodes, packets to BS transmission, and number of clusterheads at the conclusion of specific rounds, simulation results demonstrate that WBDEEC outperforms LEACH,DEEC,EDEEC, and DDEEC.
False Smut Disease Detection in Paddy Using Convolutional Neural Network
(Springer Nature Limited, 2023-06-11) Hasan, Nahid; Hasan, Tanzila; Hossain, Shahadat; Hasan, Md. Manzurul
Rice false smut (RFS) is the most severe grain disease affecting rice agriculture worldwide. Because of the various mycotoxins produced by the causal pathogen, Villosiclava virens, epidemics result in yield loss and poor grain quality (anamorph: Ustilaginoidea virens). As a result, the farmers’ main concern is disease management measures that are effective, simple, and practical. Because of this, we look at the image of the RFS to understand and predict this severe grain disease. This research proposes a model based on the Convolutional Neural Network (CNN), widely used for image classification and identification due to its high accuracy. First, we acquire data from actual rice farming fields with high-resolution RFS images. Then, we train and test our model’s performance using actual images to compare and validate it. As a result, our model provides 90.90% accurate results for detecting the RFS in actual photos. Finally, we evaluate and record all of the data for subsequent studies.
Financial Performance Analysis of Trust Bank Limited
(Daffodil International University, 22-10-10) Hasan, Nahid
Trust Bank Limited is one of the leading private commercial banks having a spread network of 113 branches & SME centers, 5 sub-branches, 245 ATM Booths and above 500 POS across Bangladesh and plans to open more branches to cover the important commercial areas in Dhaka, Chittagong, Sylhet and other areas in 2021. The bank, sponsored by the Army Welfare Trust (AWT), is first of its kind in the country. With a wide range of modern corporate and consumer financial products Trust Bank has been operating in Bangladesh since 1999 and has achieved public confidence as a sound and stable bank. In 2001, the bank introduced automated branch banking system to increase efficiency and improve customer service. In the year 2005, the bank moved one step further and introduced ATM services for its customers. Since bank’s business volume increased over the years and the demands of the customers enlarged in manifold, our technology has been upgraded to manage the growth of the bank and meet the demands of our customers. In January 2007, Trust Bank successfully launched Online Banking Services which facilitate Any Branch Banking, ATM Banking, Phone Banking, SMS Banking, & Internet Banking to all customers. Customers can now deposit or withdraw money from any Branch of Trust Bank nationwide without needing to open multiple accounts in multiple Branches.Via Online Services and Visa Electron (Debit Card), ATMs now allow customers to retrieve 24x7 hours Account information such as account balance checkup through mini-statements and cash withdrawals. Trust Bank has successfully introduced Visa Credit Cards to serve its existing and potential valued customers. Credits cards can now be used at shops & restaurants all around Bangladesh and even internationally. Trust Bank is a customer oriented financial institution. It remains dedicated to meet up with the ever growing expectations of the customer because at Trust Bank, customer is always at the center.
Helping Hand BD: A Web Based Application
(Daffodil International University, 2019-05) Hasan, Nahid; Hossain, MD Nasim; Akhter, Moriom
Presently the season of web innovation. This time web is the most exotic correspondence media on the planet. We lead this innovation to digitalize an urgent piece of genuine that is gathering pledges crusade. There are individuals who are holding magnanimously outlook to help humankind by methods for making gift support for the weak and impecunious individuals. In this unique situation, we conceptualized venture on "Assistance Helping System: (Give so others can live)" to encourage these assets from various classification giver to the last recipient, the general population. In this framework contributor searcher or client can join as a part. When he/she turn into a part after then he/she make a crusade for gift. The battle was pending and no one but administrator can affirm the crusade with satisfy terms and condition. At the point when a part post any crusade, the part should present his/her profile subtleties and battle subtleties by contact mail or contact specifically "Assistance Helping System's" office/administrator board / supervisory group. In this undertaking, we distinguish prerequisite dependent on functional associations with various partners. The necessity is changed into a web based application for fitting precise cooperation. The framework is actualized on PHP and MySQL stage. The framework is tried and surveyed for agreeable execution. Later on, the framework will be additionally expanded dependent on changes in prerequisites.
Helping Hand BD: A Web Based Application
(Daffodil International University, 2019-05) Hasan, Nahid; Hossain, Md. Nasim; Akhter, Moriom
Presently the season of web innovation. This time web is the most exoteric correspondence media on the planet. We lead this innovation to digitalize an urgent piece of genuine that is gathering pledges crusade. There are individuals who are holding magnanimously outlook to help humankind by methods for making gift support for the weak and impecunious individuals. In this unique situation, we conceptualized venture on "Assistance Helping System: (Give so others can live)" to encourage these assets from various classification giver to the last recipient, the general population. In this framework contributor searcher or client can join as a part. When he/she turn into a part after then he/she make a crusade for gift. The battle was pending and no one but administrator can affirm the crusade with satisfy terms and condition. At the point when a part post any crusade, the part should present his/her profile subtleties and battle subtleties by contact mail or contact specifically "Assistance Helping System's" office/administrator board/supervisory group. In this undertaking, we distinguish prerequisite dependent on functional associations with various partners. The necessity is changed into a web based application for fitting precise cooperation. The framework is actualized on PHP and MySQL stage. The framework is tried and surveyed for agreeable execution. Later on, the framework will be additionally expanded dependent on changes in prerequisites.
Marketing Mix strategy of Gowala Online Shop
(Daffodil International University, 2019-05-19) Hasan, Nahid
Web based shopping or Web based retailing is a type of electronic business, which enables purchasers to straightforwardly purchase products or administrations from a merchant over the Internet utilizing an internet browser. Elective names are: e-shop, e-store, Internet shop, webshop, web-store, online store, and virtual store. Like other arrangement destinations Gowala online works like a connector between general clients and traders. Gowala online offers its clients a rebate in the interest of the Traders dependent on earlier understanding which drives its clients to the dealers' entryway. The fundamental advantage for clients is the markdown and for dealers it is expanding client base and brand advancement. The topic of my research is “Marketing Mix strategy of Gowala Online Shop” This study attempted to understand the customer satisfactions on product quality. All items of Gowala is best, Due to some reason, product delivery is delay. The study was conducted based on both the primary and secondary sources of information. The nature of the research is a descriptive research. I made a detailed questionnaire for my survey. For conducting my research total sample size was 50. I took the use of descriptive statistics. I input the data into SPSS and got the output of those data. I had use SPSS and word to get my desired outputs. To satisfy these product and service, the management can take some attempts. I have come up with some points, which can improve the efficiency as well as quality of product and Price to retain their customers and make them loyal. Gowala online out late give some exclusive offering such as Gowala Doi 10%, Ghe 5%, Milk10%, Gowala oil 5%. By considering the customer recommendations the Out late improve their product quality and satisfied their customers with effectively and efficiency and makes them loyal customers.
MediShare – A Web-Based Charity System Management
(Daffodil International University, 2021-09-11) Hasan, Nahid
Medication Shop is a phase for every single level customer, this undertaking is utilized fundamentally for drug store stores to keep up with the subtleties of the drug store, for example, medication stock. Every day we deal with numerous issues for discover our medication and we run one drug store to another pharmacy. Medicine shop project assists individuals with discovering medication easily. If anyone need any kinds of medication the person visits my Medication Shop web application and effectively discover their medicine. There are many sorts of medication accessible in my medication shop project like Ayurvedic medication, Antiinfection medicine, syrup, drops, tablet etc. We serve our client 24 hours. Client can look through medication and effectively discover his/her medication if accessible in stock. All the supply of medication is update day by day by admin. My medication shop web application is client friendly.
Predicting the Effects of Microcredit on Women’s Empowerment in Rural Bangladesh
(Institute of Advanced Engineering and Science (IAES), 2024-08-15) Polin, Johora Akter; Sarker, Md.Fouad Hossain; Dolon, Mst Dilruba Khanom; Hasan, Nahid; Rahman, Md. Mahafuzur; Vasha, Zannatun Nayem
This study aimed to predict the impact of microcredit on women’s empowerment in Bangladesh using machine learning (ML) algorithms. In rural Bangladesh, where microcredit programs are not significantly employed, data for the study was gathered through a survey. The study gathered data on a range of socioeconomic, demographic, and women’s empowerment indicators. The Naive Bayes (NB), sequential minimal optimization (SMO), k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) ML techniques were used in the investigation. In terms of the prediction of women’s empowerment, the findings indicated that all five algorithms performed well, with the DT having the highest level of accuracy (83.72%). The results of this study have significant consequences for Bangladesh’s microcredit programs and those in nations that are developing. Microcredit programs can focus their efforts on women who, based on their socioeconomic and demographic features, are most likely to benefit from the program by employing ML algorithms. This may result in more successful microcredit projects that support the empowerment of women and general socioeconomic growth.
Predicting the effects of microcredit on women’s empowerment in rural Bangladesh: using machine learning algorithms
(Scopus, 2024) Polin, Johora Akter; Sarke, Md. Fouad Hossain; Dolon, Mst Dilruba Khanom; Hasan, Nahid; Rahman, Md. Mahafuzur; Vasha, Zannatun Nayem
This study aimed to predict the impact of microcredit on women’s empowerment in Bangladesh using machine learning (ML) algorithms. In rural Bangladesh, where microcredit programs are not significantly employed, data for the study was gathered through a survey. The study gathered data on a range of socioeconomic, demographic, and women’s empowerment indicators. The Naive Bayes (NB), sequential minimal optimization (SMO), k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) ML techniques were used in the investigation. In terms of the prediction of women’s empowerment, the findings indicated that all five algorithms performed well, with the DT having the highest level of accuracy (83.72%). The results of this study have significant consequences for Bangladesh’s microcredit programs and those in nations that are developing. Microcredit programs can focus their efforts on women who, based on their socioeconomic and demographic features, are most likely to benefit from the program by employing ML algorithms. This may result in more successful microcredit projects that support the empowerment of women and general socioeconomic growth.
Real Time Human Activity Detection using Improved DCNN Based on Transfer Learning
(Daffodil International University, 2022-01-30) Hasan, Nahid; Tabassum, Nafisa
Nowadays the world become a digitalization. That's why now security problems are rising every day. We have too much concern about the security system. So human activity detection’s significance is also increased argent. Human activity detection (HAD) is a significant series of data in the computer vision community. Nowadays we are utilizing CCTV cameras, Smartphone cameras for reliability plan. Human action acknowledgment is worried about recognizing various kinds of human developments and activities utilizing information accumulated from different ways. In this project report, we are proposing an improved DCNN that can recognize eating, walking, working, playing, fighting, and other types of real-time human activity are being from images. Using the Deep Convolutional Neural Network model (DCNN) images were fed for image classification. Then, by means of joining the DCNN model with a custom human action identification dataset, limits and new attaching is one great step. The benefit of an improved Deep Convolution Neural Network or (DCNN) is its capacity to separate attributes from the data. Transfer Learning was used to feature extract the images and also the methodology we used is transfer learning. Used Keras framework to train the images. In our Project, we used Keras and TensorFlow as a framework. Moreover, we have contrasted the further improved DCNN model and other conventional techniques, and here the improved DCNN model accomplished an accuracy pace of 98.82% and outperforms different models.
Tomato Pest Recognition Using Convolutional Neural Network in Bangladesh
(Institute of Electrical and Electronics Engineers Inc., 2024-02-01) Polin, Johora Akter; Hasan, Nahid; Habib, Md. Tarek; Rahman, Atiqur; Vasha, Zannatun Nayem; Sharma, Bidyut
The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.
Tomato pest recognition using convolutional neural network in Bangladesh
(Scopus, 2024-02) Polin, Johora Akter; Hasan, Nahid; Habib, Md. Tarek
The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.