Browsing by Author "Islam, Saiful"

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A Computer Vision Approach to Classify Local Flower using Convolutional Neural Network
(IEEE, 2020-06-19) Islam, Saiful; Foysal, Md. Ferdouse Ahmed; Jahan, Nusrat
Flower is the most beautiful part of this earth. In our busy lives, many flowers can be seen all over the places. Till now, more than 352,000 flower species in the world. In our country Bangladesh, the total numbers of species are not too much and are getting away from this natural beauty and becoming addressed with city life. Most of us are even unable to tell more than 10 names of local flowers. The problem is addressed and proposed an approach to identify the local flower of Bangladesh. Our proposed approach will be valuable to a botanist as well as people of other fields. With the support of machine learning techniques, object identification from an image is now quite encouraging with some challenges. Recent research has been focused on CNN (Convolutional neural network) model to train a machine with a large dataset to get more accurate results. A model is proposed, where CNN has used to classify the local flower dataset. The "ReLu" acti vation function "Adam optimizer" and the "Softmax" function are used to build the network layer. Our experiments are conducted on eight types of local flowers and considered a total of 5120 training images and 1280 test images to present eight types of flower categories and then applied eight augmentation methods to increase data volume. Finally, our proposed CNN structure provided 85% classification accuracy.
A Design Method for a QW VCSEL for operating at 980 nm using In0.2Ga0.8As/GaAs Materials and its Performance Analysis
(AIUB Office of Research and Publication, 2010-08) Basak, Rinku; Islam, Saiful
In this paper, a method for designing a 980 nm In0.2Ga0.8As/GaAs quantum well (QW) VCSEL has been presented. Using this method the strain induced shift of In0.2Ga0.8As has been computed which has been used in the computation of energy gap for the above mentioned combination of materials in the active region. The material gain for this strained In0.2Ga0.8As/GaAs QW has been computed for analyzing the performance of the VCSEL. The material gain and transparency carrier density for GaAs and In0.2Ga0.8As materials are optimized with the aim of designing a 980 nm In0.2Ga0.8As/GaAs QW VCSEL. A higher material gain with lower transparency carrier density is chosen for designing a 980 nm VCSEL. For the designed VCSEL, appropriate threshold current and modal gain have been computed. Using theses values the plots of output power vs. time as well as modulation performance have been obtained. From the plots of bias voltage vs. injection current it is found that a small voltage of 1.8 volt is required to reach the threshold current of 1.5 mA at 250C. From the plot of output power vs. time at 300K a maximum optical output power of 5 mW is obtained at 7.4 mA (≈ 5Ith) injection current and the corresponding obtained modulation bandwidth is 16.5 GHz which indicates the superior performance of the designed VCSEL compared to the similar results of other research works.
Achieving high reflectivity using GaAs and AlAs semiconductor DBR layers and alternatively by using Amorphous Silicon and SiO2 dielectric DBR layers in a VCSEL
(AIUB Office of Research and Publication, 2007-08) Basak, Rinku; Islam, Saiful
In this paper, the effects of using GaAs and AlAs in alternate Distributed Bragg Reflector (DBR) layers for a VCSEL have been investigated using computer simulation. It has been shown that by using GaAs and AlAs in alternate DBR layers, high reflectivity of 99.9% can be achieved using 20 pairs of layers on a single side. Next, the effects of using Amorphous Silicon and SiO2 in alternate DBR layers for a VCSEL have been investigated. It has been shown that only 4 pairs are needed to achieve 99.9% reflectivity. This is a significant saving of layers (space) compared to using semiconductor DBR layers. However, the semiconductor DBR layers mentioned above are advantageous because absorption of light in each layer of the dielectric DBR layers is high. The result obtained from this comparative study is expected to be valuable in developing new types of structures of VCSEL.
An Early Warning System of Heart Failure Mortality With Combined Machine Learning Methods
(Institute of Advanced Engineering and Science (IAES), 2023-08-18) Sutradhar, Ananda; Al Rafi, Mustahsin; Alam, Mohammad Jahangir; Islam, Saiful
Heart failure (HF) is currently the leading cause of morbidity and mortality worldwide. Identifying the risk of mortality at the early s tages is crucial to reducing the mortality rate. However, the traditional methods for exploring the signs of mortality are difficult and time - consuming. Whereas, m achine learning (ML) methods are superior in reducing HF’s mortality rate by providing early warnings. This study presents a novel ML classifier called imperial boost - stacked (IBS) that can serve as an effective early warning system for predicting HF mortality. Initially, we performed an efficient data balancing technique named synthetic minority oversampling technique with edited nearest neighbors ( SMOTE - ENN ) to mitigate the imbalance problem. Next, two well - known feature selection techniques , the extra tree (ET) and information gain (IG), are applied to reduce the data dimensions and select the m ost significant features. Following that, the prepared feature sets are trained with our proposed IBS classifier. Simultaneously leveraging the advantages of boosting, stacking, and multiple robust methods, it significantly correlates with the intricate pa tterns of clinical data of HF patients. Finally, the robust outcomes of 92.75% accuracy over existing studies reveal that our proposed study can effectively warn the HF mortality at early stages and reduce the burden on the healthcare sector
An Evaluation of G-33 Proposal of Public Stockholding for Food Security in the Least Developed Countries: A Case Study on Bangladesh
(Faculty of Business Studies, BUFT, 2022-08-01) Islam, Saiful
Purpose: This study evaluates whether food security is a genuine case for public stockholding of rice in Bangladesh and whether the country should make the most of the G-33 proposal as an eligible signatory. Research Methodology: Using a qualitative research approach with descriptive statistics, this study analyses Bangladesh's food security, food self-sufficiency, existing public stockholding policy, and the potential impact of public stockholding of rice on production, market prices, and agricultural trade of Bangladesh. Findings: The findings show that Bangladesh is still positioned at the "serious" hunger level and could not achieve sustainable food self-sufficiency. At various crises, Bangladesh relies on the international market to supplement the required amount of rice, which justifies its rice stockholding for food security. Therefore, this study finds a legitimate ground for Bangladesh to exceed the current de minimis limit set under the AoA and use the provisions of the G-33 proposal only as an interim solution. Practical Implications: This study outlines the legitimate ground for adopting the G-33 proposal of public stockholding for food security in Bangladesh. Originality: This study also extends the theoretical base of the G-33 proposal for Least Developed Countries (LDCs), which are currently non-signatory of this proposal but requires more government support for food security in the country. Limitations: More in-depth research is required to quantify Bangladesh's new de minimis limit if the country wishes to adopt the G-33 proposal as an interim solution.
Anticancer activity of alangium salvifolium flower in ehrlich ascites carcinoma bearing mice
(© 2011 Academic Journals Inc., 2011-03) Zahan, Ronok; Alam, Md. Badrul; Islam, Saiful; S. Chowdhury, Nargis; B. Hosain, Salman; Mosaddik, Ashik; Jesmin, Mele; Haq, M. Ekramul
The research study was conducted to determine the antitumor effect of the flower of Alangium salvifolium (crude extract and diethylether fractions) against Ehrlich Ascites Carcinoma (EAC) in mice at the doses of 10 mg kg-1 body weight intraperitoneally. Extract/fractions was administered for nine consecutive days. Twenty-four hours of last dose and 18 h of fasting, the mice were sacrificed and antitumor effect was assessed by evaluating tumor volume, viable and nonviable tumor cell count, tumor weight and hematological parameters of EAC bearing host. Significant (p<0.001) increases of survival times 30±0.96 and 25±0.40 days for crude extract and diethylether fraction of the A. salvifolium (10 mg kg-1) treated tumor bearing mice, respectively were confirmed with respect to the control group (20±0.13 days). The extract/fraction also decreased the body weight of the EAC tumor bearing mice. Hematological studies reveal that the heamoglobin (Hb) content was decreased in EAC treated mice whereas restoration to near normal levels was observed in extract treated animals. There was a significant (p<0.001) decrease in RBC count and increase in WBC counts in extract/fraction treated animals when compared to EAC treated animals. From the result it was showed that the extract has significant anticancer activity and that is comparable to that of Bleomycin.
Anticancer activity of alangium salvifolium flower in ehrlich ascites carcinoma bearing mice
(© 2011 Academic Journals Inc., 2011-03) Zahan, Ronok; Alam, Md. Badrul; Islam, Saiful; S. Chowdhury, Nargis; B. Hosain, Salman; Mosaddik, Ashik; Jesmin, Mele; Haq, M. Ekramul
The research study was conducted to determine the antitumor effect of the flower of Alangium salvifolium (crude extract and diethylether fractions) against Ehrlich Ascites Carcinoma (EAC) in mice at the doses of 10 mg kg-1 body weight intraperitoneally. Extract/fractions was administered for nine consecutive days. Twenty-four hours of last dose and 18 h of fasting, the mice were sacrificed and antitumor effect was assessed by evaluating tumor volume, viable and nonviable tumor cell count, tumor weight and hematological parameters of EAC bearing host. Significant (p<0.001) increases of survival times 30±0.96 and 25±0.40 days for crude extract and diethylether fraction of the A. salvifolium (10 mg kg-1) treated tumor bearing mice, respectively were confirmed with respect to the control group (20±0.13 days). The extract/fraction also decreased the body weight of the EAC tumor bearing mice. Hematological studies reveal that the heamoglobin (Hb) content was decreased in EAC treated mice whereas restoration to near normal levels was observed in extract treated animals. There was a significant (p<0.001) decrease in RBC count and increase in WBC counts in extract/fraction treated animals when compared to EAC treated animals. From the result it was showed that the extract has significant anticancer activity and that is comparable to that of Bleomycin.
Anticancer activity of alangium salvifolium flower in ehrlich ascites carcinoma bearing mice
(© 2011 Academic Journals Inc., 2011-03) Zahan, Ronok; Alam, Md. Badrul; Islam, Saiful; S. Chowdhury, Nargis; B. Hosain, Salman; Mosaddik, Ashik; Jesmin, Mele; Haq, M. Ekramul
The research study was conducted to determine the antitumor effect of the flower of Alangium salvifolium (crude extract and diethylether fractions) against Ehrlich Ascites Carcinoma (EAC) in mice at the doses of 10 mg kg-1 body weight intraperitoneally. Extract/fractions was administered for nine consecutive days. Twenty-four hours of last dose and 18 h of fasting, the mice were sacrificed and antitumor effect was assessed by evaluating tumor volume, viable and nonviable tumor cell count, tumor weight and hematological parameters of EAC bearing host. Significant (p<0.001) increases of survival times 30±0.96 and 25±0.40 days for crude extract and diethylether fraction of the A. salvifolium (10 mg kg-1) treated tumor bearing mice, respectively were confirmed with respect to the control group (20±0.13 days). The extract/fraction also decreased the body weight of the EAC tumor bearing mice. Hematological studies reveal that the heamoglobin (Hb) content was decreased in EAC treated mice whereas restoration to near normal levels was observed in extract treated animals. There was a significant (p<0.001) decrease in RBC count and increase in WBC counts in extract/fraction treated animals when compared to EAC treated animals. From the result it was showed that the extract has significant anticancer activity and that is comparable to that of Bleomycin.
Anticancer activity of alangium salvifolium flower in ehrlich ascites carcinoma bearing mice
(© 2011 Academic Journals Inc., 2011-03) Zahan, Ronok; Alam, Md. Badrul; Islam, Saiful; S. Chowdhury, Nargis; B. Hosain, Salman; Mosaddik, Ashik; Jesmin, Mele; Haq, M. Ekramul
The research study was conducted to determine the antitumor effect of the flower of Alangium salvifolium (crude extract and diethylether fractions) against Ehrlich Ascites Carcinoma (EAC) in mice at the doses of 10 mg kg-1 body weight intraperitoneally. Extract/fractions was administered for nine consecutive days. Twenty-four hours of last dose and 18 h of fasting, the mice were sacrificed and antitumor effect was assessed by evaluating tumor volume, viable and nonviable tumor cell count, tumor weight and hematological parameters of EAC bearing host. Significant (p<0.001) increases of survival times 30±0.96 and 25±0.40 days for crude extract and diethylether fraction of the A. salvifolium (10 mg kg-1) treated tumor bearing mice, respectively were confirmed with respect to the control group (20±0.13 days). The extract/fraction also decreased the body weight of the EAC tumor bearing mice. Hematological studies reveal that the heamoglobin (Hb) content was decreased in EAC treated mice whereas restoration to near normal levels was observed in extract treated animals. There was a significant (p<0.001) decrease in RBC count and increase in WBC counts in extract/fraction treated animals when compared to EAC treated animals. From the result it was showed that the extract has significant anticancer activity and that is comparable to that of Bleomycin.
Balanced clustering approach to enhance lifetime and throughput of wireless sensor networks
(Institute of Information and Communication Technology, 2019-02-12) Islam, Saiful; Islam, Dr. Md. Saiful
Wireless sensor networks (WSN) is a special type of Micro Electro-Mechanical System (MEMS) which is composed of large number of small, inexpensive and low powered sensor nodes. Lifetime is one of the crucial challenges of WSN. Balanced clustering may be a suitable solution of this challenge. Proper selection of cluster head (CH), cluster formation and a suitable intra-cluster communication technique can create a balanced clustering. Balanced clustering will extend lifetime as well as throughput. Many protocol exist for clustering. They used various matrices like residual energy, position or distance from base station (BS), neighbor set, neighbor information etc., but none of them can create balanced clustering. Moreover, many of them create back transmission which consume further energy and degrade lifetime. In this research, we offered a balanced clustering by selecting CH, cluster formation and a suitable intra-cluster communication technique. The parameters residual energy (RE), number of neighbor nodes (NNN), one-hop neighbor information (ONI) and distance from nodes to BS (DNB) have been used for selecting a CH. RE information helps to select comparatively higher energetic node as CH, NNN helps to select CH from better density area of nodes of the network, ONI will restrict to select one CH from one cluster and DNB will reduce back transmission path. In cluster formation, energy may be wasted due to too high or too low cluster size. We have restricted it by using central border, internal and balancing nodes. We have used two threshold value (maximum and minimum) for ensuring suitable cluster size. Most of the clustering approach used TDMA for intra-cluster communication. But huge TDMA time slots may be unused due to data un-availability or lower trafic of slots owner’s node. We have used three steps in intra-cluster communication (ICC) technique for sending more packets. At first, we used “power level adjustment” for sending non-owner node’s data to CH by exploiting capture effect. Secondly, we used “time slot adjustment” by adjusting window size which ensured whether a non-owner node should send data or not. Lastly, we used “Preamble based CSMA” with waiting time adjusting according to power level of data for sensing channel, it will reduce collision. These three steps, ensure more packets sending to CH and BS, hence throughput increased more and more compare to previous works. The proposed methods are evaluated by OMNeT++ simulator and compared with LEACH-C, LEACH-MAC and an energy efficient and balanced clustering approach for improving throughput (EEBCAIT). It is found that major improvement of performance in terms of First Node Death (FND), Tenth Node Death (TND), End Node Death (END), Consumption of Energy vs Rounds. Remaining Energy vs Rounds, Alive Nodes vs Rounds, Dead Nodes vs Rounds, CHs vs Rounds, Total Packets vs Rounds, Packets sending per round and improvement using of idle slots per round. We also found great improvement of lifetime and throughput.
Bangladesh Times
(Daffodil International University, 2021-01) Islam, Saiful
We know that having a university degree may not enough for candidates to get job in today‟s market. Students must have additional qualification to prove themselves. After our long academic learning, internship gives us the opportunity to learn from hand in hand, which gives us the flavor of real job experience. So we can say that internship is the investment for our future which set the foundation for career. During internship we have to work with various people in field and office, thus we connected with new people of different level and profession, and our network with various professions has grown day by day. Those Networks will help us to get references and find new job opportunities in future.
Cardiovascular Disease Forecast Using Machine Learning Paradigms
(Proceedings of the 4th International Conference on Computing Methodologies and Communication, ICCMC 2020, IEEE, 2020-04-23) Islam, Saiful; Jahan, Nusrat; Khatun, Mst. Eshita
In this recent era, Cardiovascular disease (CVD) propagation rate has been intensifying the cause of death worldwide among the non-communicable disease. In particular the south asian countries have a tremendous risk of cardiovascular disease at an early age than any other ethnic group. Most often it's challenging for medical practitioners to predict cardiovascular disease as it requires experience and knowledge which is a complex task to accomplish. This health industry has enormous amounts of data which is useful for making effective conclusions using their hidden information. So, using appropriate results and making effective decisions on data, some superior data analysis techniques are used, for example Naive Bayes, Decision Tree. By using some properties like (age, gender, bp, stress, etc) it can be predicted the chances of cardiovascular disease. In this study, we collected 301 sample data with 12 clinical attributes. Logistic regression, Decision tree, SVM, and Naive bayes classification algorithms have been applied to predict heart disease. In this case, logistic regression provided 86.25% accuracy. However, we also compared the UCI dataset based results with our model.
Cardiovascular Disease Forecast Using Machine Learning Paradigms
(Proceedings of the 4th International Conference on Computing Methodologies and Communication, ICCMC 2020, IEEE, 2020-04-23) Islam, Saiful; Jahan, Nusrat; Khatun, Mst. Eshita
In this recent era, Cardiovascular disease (CVD) propagation rate has been intensifying the cause of death worldwide among the non-communicable disease. In particular the south asian countries have a tremendous risk of cardiovascular disease at an early age than any other ethnic group. Most often it's challenging for medical practitioners to predict cardiovascular disease as it requires experience and knowledge which is a complex task to accomplish. This health industry has enormous amounts of data which is useful for making effective conclusions using their hidden information. So, using appropriate results and making effective decisions on data, some superior data analysis techniques are used, for example Naive Bayes, Decision Tree. By using some properties like (age, gender, bp, stress, etc) it can be predicted the chances of cardiovascular disease. In this study, we collected 301 sample data with 12 clinical attributes. Logistic regression, Decision tree, SVM, and Naive bayes classification algorithms have been applied to predict heart disease. In this case, logistic regression provided 86.25% accuracy. However, we also compared the UCI dataset based results with our model.
Comparative simulation of nonlinear radiative nano casson and maxwell fluids with periodic magnetic force and sensitivity analysis
(Scopus, 2024-04-15) Islam, Saiful; Ali, Md Yousuf; Rabbi, Sk Reza-E-
This study investigated cyclic magneto-hydrodynamic radiative effects in Casson and Maxwell fluids, including nonlinear radiation and Arrhenius activation energy. It promotes non-Newtonian fluid use in diverse fields like industry, manufacturing, sciences, medicine, and engineering. Using boundary layer approximations, non-dimensional equations are formulated. For numerical solutions, widely recognized explicit finite difference method (EFDM) has been utilized. To ensure the robustness of EFDM results, stability and convergence tests are performed. Exploration involve a detailed sensitivity analysis by using RSM, offering a thorough understanding of influential parameters. These analyses explore complex interactions among physical parameters, affecting Nusselt number, skin friction, and Sherwood number. Maxwell fluid's velocity is more affected by periodic magnetic force than Casson fluid, during the presence of nonlinear radiation. Additionally, nonlinear thermal radiation has a greater impact on temperature and concentration profiles compared to linear radiation for both fluids. Moreover, Casson fluid has a stronger influence on the average heat transfer rate compared to Maxwell fluid with nonlinear thermal radiation which is 8.6 % greater than the Maxwell fluid. On the other hand, at constant thermal radiation (Ra), due to decrease of Brownian motion (Nb), the rate of heat transfer is reduced by 1.2 % and 0.3 % respectively for Maxwell and Casson fluid. Also, for thermophoresis parameter (Nt), this rate is reduced by 2 % and 1.6 % respectively. The investigation also revealed that the Ra exhibits a positive sensitivity towards average Nusselt number, while Nb and Nt are displayed a negative sensitivity.
Context-based News Headlines Analysis Using Machine Learning Approach
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2019-08-09) Rahman, Shadikur; Hossain, Syeda Sumbul; Islam, Saiful; Chowdhury, Mazharul Islam; Rafiq, Fatama Binta; Badruzzaman, Khalid Been Md.
An increasing number of people are changing their way of thinking by reading news headlines. The interactivity and sincerity present in online news headlines are becoming influential to society. Apart from that, news websites build efficient policies to catch people’s awareness and attract their clicks. In that case, it is a must to identify the sentiment polarity of the news headlines for avoiding misconception. In this paper, we analyze 3383 news headlines generated by five major global newspapers during a minimum of four consecutive months. In order to identify the sentiment polarity (or sentiment orientation) of news headlines, we use 7 machine learning algorithms and compare those results to find the better ones. Among those Bernoulli Naïve Bayes technique achieves higher accuracy than others. This study will help the public to make any decision based on news headlines by avoiding misconception against any leader or governance and will help to identify the most neutral newspaper or news blogs.
Covid-19 detection using dominant SMOTE in imbalance classification
(Center for Research and Publication (CRP), 2024-12) Islam, Saiful
Global healthcare systems have faced difficulties since the start of the COVID-19 epidemic. For overburdened hospitals, identifying positive patients is a simple and effective fix. The disproportionate distribution of classes poses a significant challenge in identifying the positive case of COVID-19, leading to biased prediction outcomes favoring dominant classes. Consequently, classifiers struggle to learn from imbalanced datasets, resulting in reduced performance. Various techniques, such as oversampling, undersampling, and hybrid sampling, have been proposed to mitigate this issue. However, the Synthetic Minority Oversampling Technique (SMOTE) remains a commonly utilized resampling method despite its limitations, including class mixture. To address these shortcomings, I introduce Dominant SMOTE, a modified version of SMOTE. The proposed method comprises of developing a dominant sample selection approach based on numerical attribute values from the minority class, and selecting the nearest neighbors from the majority class for each minority class sample based on dominance values to achieve balanced dataset. The proposed method is compared with traditional SMOTE and Out-Layer SMOTE, evaluating accuracy, precision, recall, and F1-score on two benchmark datasets. The results indicate that the proposed model outperforms than both the traditional SMOTE and Out-Layer SMOTE.
Customer Feedback Prioritization Technique
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2019-06-29) Hossain, Syeda Sumbul; Jubayer, S. A. M.; Rahman, Shadikur; Bhuiyan, Touhid; Rawshan, Lamisha; Islam, Saiful
Nowadays, a startup is being very popular and entrepreneurs are increasing day by day. Though we are watching many successful startups e.g. Dropbox, Amazon, Viber and so on, the list of unsuccessful startups is very long. Who is being successful they must have their own strategy, which they apply in their startup and get success. In lean startup strategy, the customers give feedbacks about the startup and the owner understands the demand of customers by collecting feedback from customers and provides service according to the feedback. On the other hand, all the feedbacks from the customers are not important for a startup project. So it is needed to separate or prioritize feedbacks which are needed to execute the startup project. But there are not sufficient techniques for prioritizing the feedbacks collected from customers. By conducting a systematic mapping study and a case study (interview and observation is used), we propose a technique which will be used to prioritize customer feedback in lean startup. This technique will be helpful for the startup projects to become successful.
DCNN Based Disease Prediction of Lychee Tree
(Springer, 2023-04-17) Islam, Saiful; Akter, Shornaly; Islam, Mirajul; Rahman, Md. Arifur
Tree disease classification is needed to determine the affected leaves as it controls the economic importance of the trees and their products and decreases their eco-friendly eminence. The lychee tree is affected by some of the diseases named Leaf Necrosis, Stem Canker and leaf spots. Therefore, classifying the Lychee tree is essential to find the good and affected leaves. Our economic growth will be very high if we can adequately do the Lychee tree classification. In this paper, we tried to do a Lychee tree disease classification to make things easier for the farmers as they cannot correctly distinguish the good and bad leaves in an earlier stage. We have created a new data set for training the architectures. We have collected about 1400 images with three categories of pre-harvest diseases “Leaf Necrosis”, “Leaf Spots”, and “Stem Canker”. There are 1400 images in total, and out of those, 80% of the data is for training and 20% is for testing, this dataset has fresh and affected leaves and stems. For Lychee tree disease classification, we have chosen pre-trained CNN and Transfer Learning based approach to classify the layer of the 2D image by layer. This method can classify images efficiently from the images of disease leaves and stems. It will address disease from the images of the leaves and trees and determine specific preharvest diseases.
Design and Development of an Android Application for Medical Service
(Daffodil International University, 2022-01-04) Easin, Md Showkat Osman Gony; Niloy, Shartaj Islam; Islam, Saiful
We had an intention to make an online-based health care application and our final year project gave us the opportunity to do it. By using this application users will get the information of various medicines, ambulances, and diagnostics. Besides this, a number of doctors’ information will also be added here. Introducing people to online-based health care is the main purpose behind making this App. It will make users’ life more spontaneous as it will be facile for them to find the information. People don’t need to be anxious as they are getting four kinds of information from a single app. Our application will be very users friendly so users can easily operate the apps. Ambulance information is a feature of our apps where users can collect the information of ambulances in time of emergency.
Detecting financial fraud using Rule-Based Techniques.
(CUET, 13-Feb-2024) Islam, Saiful
Financial fraud is a growing problem that poses a significant threat to the banking industry,