Browsing by Author "Ahmed, Nasim"

Now showing 1 - 8 of 8

An interactive knowledge based recommender system for tourism
(BRAC University, 2017-04) Ahmed, Wakil; Nur, Farah; Hema, Nabila Bhuyan; Ahmed, Nasim; Tairin, Suraiya; Islam, Md. Saiful
The purpose of this project was to construct a centralized tourist management system that would serve as a consolidated platform to provide an effective and efficient mechanism for tourist management and means for availing various travels and booking related services. Providing optimal solution using interactive knowledge-based system using dynamic information of the users, e.g. compare different options for traveling within a budget of the user and providing recommendations. The entire system is designed to streamline the travel management system by primarily targeting all the districts and division of Bangladesh and then centralizing the process maintaining schedules, queues and confirming hotel and transport bookings. Providing optimal solution using interactive knowledge-based system using dynamic information of the users, e.g. compare different options for traveling within a budget of the user and providing recommendations. Currently operations of these procedures are of an erratic where due to delays and other factors of inefficiency management services can neither be properly availed nor be found online. Using this system, the tourism can avail services which are best suited for their need, based on different criteria including specialty, current location, queue and other aspects which are deemed to be of convenience.
Computational Intelligence Approaches for Prediction of Chronic Kidney Disease
(Springer, 2022-01-01) Ahmed, Md. Razu; Ali, Md. Asraf; Ahmed, Nasim; Bhuiyan, Touhid
Over the past few decades, it has been observed that there is a growing interest in the area of intelligence systems, such as Machine Learning. Machine learning has been extensively used in order to support medical specialists and clinicians in the help of forecast and diagnosis of various diseases. The aim of this study is to compare the performance of six supervision-based Machine Learning techniques, which are used in the prediction and detection of the chronic kidney disease outbreak. Machine learning techniques are used to solve clinical problems and medical diagnosis’ which have recently been developed. Hence, it is essential to have a framework that can instantly recognize the prevalence of kidney disease in thousands of samples. This research uses the chronic kidney disease dataset that contains 400 Kidney patient’s data including 25 parameters. Moreover, we evaluated the performance of six supervision-based machine learning classification techniques, which are: KNN, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes and Logistics Regression. The performance of the supervised machine learning classification techniques was validated with sensitivity, specificity, f1 measure and accuracy. In this experiment, NB and RF outperformed, they were found to be at 100% accuracy, whereas the DT achieved 98% accuracy. Moreover, the KNN, SVM, LR classification techniques achieved 96% accuracy. Our findings showed that both the Random Forest and Naïve Bayes classification techniques outperformed as compared to other classification techniques used to predict kidney disease of the patients tested. In summary, our study has emphasized the research trends and scope in relation to Chronic Kidney Disease and as well as clinical research areas by machine learning techniques, which have had an effective impact in biomedical fields.
Effects of Triangular Core Rotation of a Hybrid Porous Core Terahertz Waveguide
(SCIENDO, 2017-02-10) Ali, Sharafat; Ahmed, Nasim; Alwee, Syed; Islam, Monirul; Rana, Sohel; Bhuiyan, Touhid
In this paper, we investigate the effects for rotating the triangular core air hole arrangements of a hybrid design porous core fiber. The triangular core has been rotated in anticlockwise direction to evaluate the impact on different waveguide properties. Effective Material Loss (EML), confinement loss, bending loss, dispersion characteristics and fraction of power flow are calculated to determine the impacts for rotating the triangular core. The porous fiber represented here has a hybrid design in the core area which includes circular rings with central triangular air hole arrangement. The cladding of the investigated fiber has a hexagonal array of air hole distribution. For optimum parameters the reported hybrid porous core fiber shows a flat EML of ±0.000416 cm-1 from 1.5 to 5 terahertz (THz) range and a near zero dispersion of 0.4±0.042 ps/THz/cm from 1.25 to 5.0 THz. Negligible confinement and bending losses are reported for this new type of hybrid porous core design. With improved concept of air hole distribution and exceptional waveguide properties, the reported porous core fiber can be considered as a vital forwarding step in this field of research. Full Text Link: https://doi.org/10.1515/eletel-2017-0004
Ensemble-based Machine Learning Algorithms for Classifying Breast Tissue Based on Electrical Impedance Spectroscopy
(Advances in Intelligent Systems and Computing, Springer, 2019-06-19) Rahman, Sam Matiur; Ali, Md Asraf; Altwijri, Omar; Alqahtani, Mahdi; Ahmed, Nasim; Ahamed, Nizam U.
The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility.
PC (personal computing) banking system to Core Banking System (CBS) : a case study based on United Commercial Bank Ltd.
(BRAC University, 2015-06) Ahmed, Nasim; Hossain, Noman
United Commercial Bank is the oldest private commercial bank operating in Bangladesh. It has over the years created one of the largest networks among all the other banks in Bangladesh. Although a trendsetter in offering a various ranges of products in the market, the product offers by United Commercial Bank are quickly imitated by competitors. As a pioneer of Private Banking System in Bangladesh, United Commercial Bank ltd. adopted some state of art banking system that helps bank to reduce banking risk similar to Hall marks and others. In this report I tried to analyze the core banking activities of United Commercial Bank Limited. Behind the success of the bank they efficiently analyze the credit risk and the other risk and handle the risk in such a way that brings them the success. The first part of the report I discuss about the background of the study, the literature review and the research methodology of the report. In the background of the study there is statement of the problem, rationale of the study, scope of the project and the objective of the project. Second part of the report I discuss about the organization overview, mission and vision of the organization, goals and objectives, its operations and performance of the bank at a glance etc. The third part of the report I will describe the common feature of Core Banking System and PC Banking system. In the fourth part I will try to analyze both CBS and Pc Banking System from different aspect. The Fifth part I would like to talk about different IT project taken by United Commercial bank which made them technological advanced than other banks. The Sixth part of the report I tries to analyze SWOT and tried to give some suggestion about the findings of the report and I conclude the report with the conclusion part.
The Experimental Significance of Isorhamnetin as an Effective Therapeutic Option for Cancer: A Comprehensive Analysis
(IEEE, 2024-06-10) Biswas, Partha; Kaium, Md. Abu; Tareq, Md. Mohaimenul Islam; Tauhida, Sadia Jannat; Hossain, Md Ridoy; Siam, Labib Shahriar; Parvez, Anwar; Bibi, Shabana; Hasan, Md Hasibul; Rahman, Md. Moshiur; Hosen, Delwar; Siddiquee, Md. Ariful Islam; Ahmed, Nasim; Sohel, Md.; Al Azad, Salauddin; Alhadrami, Albaraa H.; Kamel, Mohamed; Alamoudi, Mariam K.; Hasan, Md. Nazmul; Abdel-Daim, Mohamed M.
Isorhamnetin (C16H12O7), a 3'-O-methylated derivative of quercetin from the class of flavonoids, is predominantly present in the leaves and fruits of several plants, many of which have traditionally been employed as remedies due to its diverse therapeutic activities. The objective of this in-depth analysis is to concentrate on Isorhamnetin by addressing its molecular insights as an effective anticancer compound and its synergistic activity with other anticancer drugs. The main contributors to Isorhamnetin's anti-malignant activities at the molecular level have been identified as alterations of a variety of signal transduction processes and transcriptional agents. These include ROS-mediated cell cycle arrest and apoptosis, inhibition of mTOR and P13K pathway, suppression of MEK1, PI3K, NF-κB, and Akt/ERK pathways, and inhibition of Hypoxia Inducible Factor (HIF)-1α expression. A significant number of in vitro and in vivo research studies have confirmed that it destroys cancerous cells by arresting cell cycle at the G2/M phase and S-phase, down-regulating COX-2 protein expression, PI3K, Akt, mTOR, MEK1, ERKs, and PI3K signaling pathways, and up-regulating apoptosis-induced genes (Casp3, Casp9, and Apaf1), Bax, Caspase-3, P53 gene expression and mitochondrial-dependent apoptosis pathway. Its ability to suppress malignant cells, evidence of synergistic effects, and design of drugs based on nanomedicine are also well supported to treat cancer patients effectively. Together, our findings establish a crucial foundation for understanding Isorhamnetin's underlying anti-cancer mechanism in cancer cells and reinforce the case for the requirement to assess more exact molecular signaling pathways relating to specific cancer and in vivo anti-cancer activities.
The Experimental Significance of Isorhamnetin as an Effective Therapeutic Option for Cancer: A Comprehensive Analysis
(Elsevier, 2024-06-10) Biswas, Partha; Kaium, Md. Abu; Tareq, Md. Mohaimenul Islam; Tauhid, Sadia Jannat; Hossain, Md Ridoy; Siam, Labib Shahriar; Parvez, Anwar; Bibi, Shabana; Hasan, Md Hasibul; Rahman, Md. Moshiur; Hosen, Delwar; Siddiquee, Md. Ariful Islam; Ahmed, Nasim; Sohel, Md.; Azad, Salauddin Al; Alhadrami, Albaraa H.; Kamel, Mohamed; Alamoudi, Mariam K.; Hasan, Md. Nazmul; Abdel-Daim, Mohamed M.
Isorhamnetin (C16H12O7), a 3'-O-methylated derivative of quercetin from the class of flavonoids, is predominantly present in the leaves and fruits of several plants, many of which have traditionally been employed as remedies due to its diverse therapeutic activities. The objective of this in-depth analysis is to concentrate on Isorhamnetin by addressing its molecular insights as an effective anticancer compound and its synergistic activity with other anticancer drugs. The main contributors to Isorhamnetin's anti-malignant activities at the molecular level have been identified as alterations of a variety of signal transduction processes and transcriptional agents. These include ROS-mediated cell cycle arrest and apoptosis, inhibition of mTOR and P13K pathway, suppression of MEK1, PI3K, NF-κB, and Akt/ERK pathways, and inhibition of Hypoxia Inducible Factor (HIF)-1α expression. A significant number of in vitro and in vivo research studies have confirmed that it destroys cancerous cells by arresting cell cycle at the G2/M phase and S-phase, down-regulating COX-2 protein expression, PI3K, Akt, mTOR, MEK1, ERKs, and PI3K signaling pathways, and up-regulating apoptosis-induced genes (Casp3, Casp9, and Apaf1), Bax, Caspase-3, P53 gene expression and mitochondrial-dependent apoptosis pathway. Its ability to suppress malignant cells, evidence of synergistic effects, and design of drugs based on nanomedicine are also well supported to treat cancer patients effectively. Together, our findings establish a crucial foundation for understanding Isorhamnetin's underlying anti-cancer mechanism in cancer cells and reinforce the case for the requirement to assess more exact molecular signaling pathways relating to specific cancer and in vivo anti-cancer activities.
The Impact of Software Fault Prediction in Real-World Application
(Scopus, 2020) Ahmed, Md. Razu; Ali, Md. Asraf; Ahmed, Nasim; Zamal, Md. Fahad Bin; Shamrat, F.M. Javed Mehedi
Software fault prediction and proneness has long been considered as a critical issue for the tech industry and software professionals. In the traditional techniques, it requires previous experience of faults or a faulty module while detecting the software faults inside an application. An automated software fault recovery models enable the software to significantly predict and recover software faults using machine learning techniques. Such ability of the feature makes the software to run more effectively and reduce the faults, time and cost. In this paper, we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task. Moreover, we used different prominent evaluation benchmark to evaluate the model's performance such as ten-fold cross-validation techniques, precision, recall, specificity, f 1 measure, and accuracy. This study reports a significant classification performance of 98-100% using SVM on three defect datasets in terms of f1 measure. However, software practitioners and researchers can attain independent understanding from this study while selecting automated task for their intended application.