Browsing by Author "Habib, Md. Tarek"

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A Context-Sensitive Approach to Find Optimum Language Model for Automatic Bangla Spelling Correction
(nternational Journal of Advanced Computer Science and Applications, 2018) Islam, Muhammad Ifte Khairul; Habib, Md. Tarek; Rahman, Md. Sadekur; Rahman, Md. Riazur; Ahmed, Farruk
Automated spelling correction is an important phenomenon in typing that has intense effect on aiding both literate and semi-literate people while using keyboard or other similar devices. Such automated spelling correction technique also helps students significantly in learning process through applying proper words during word processing. A lot of work has been conducted for English language, but for Bangla, it is still not adequate. All work done so far in Bangla is context-free. Bangla is one of the mostly spoken languages (3.05% of world population) and considered seventh language of all languages in the world. In this paper, we propose a context-sensitive approach for automated spelling correction in Bangla. We make combined use of edit distance and stochastic, i.e. N-gram language model. We use six N-gram models in total. A novel approach is deployed in order to find the optimum language model in terms of performance. In addition, for finding out better performance, a large Bangla corpus of different word types is used. We have achieved a satisfactory and promising accuracy of 87.58%.
A Deep Learning Approach to Recognize Bangladeshi Shrimp Species
(Independent University, Bangladesh, 2023-07) Hasan, Md. Mehedi; Nishi, Jubiria Subrin; Habib, Md. Tarek; Islam, Mohammad Monirul; Ahmed, Farruk
Shrimp, the most popular shellfish in Bangladesh, is a good source of protein, minerals, vitamin D, and iodine that promote a healthy body and balanced nutrition. In Bangladesh shrimp is referred to as white gold. It consumes about 70% of exported agricultural food. In our country, about 56 species of shrimp are found. Most people do not know all of the species very well. Ordinary people even the fisherman are sometimes confused about different species because of looking like the same. To solve the problem in this work we introduced an intelligence mahine that can help people to concede Shrimp species accurately. We expect this work also help the export sector to differentiate the shrimp species monitoring. To achieve the goal, we build a custom CNN algorithm for image processing and feature extraction. We build three different CNN architectures and differentiate them by hyperparameter and number of convolutional layers. Model 1 and Model 3 both obtain an accuracy of 99.01%, however Model 3 was chosen as the final model for Computer Vision integration. Though both models generated the best accuracy why do we use model 3 as the final model? In this work, we will also describe with appropriate reason.
A Machine Learning Approach for Driver Identification
(Institute of Electrical and Electronics Engineers Inc., 2023-04-15) Ali Khan, Md. Abbas; Ali, Mohammad Hanif; Haque, Fazlul; Habib, Md. Tarek
Driver identification is a momentous field of modern decorated vehicles in the perspective of the controller area network (CAN-Bus). Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-Bus but there are some difficulties because of the variation of a protocol of different models of vehicle. We aim to identify the driver through supervised learning algorithms based on driving behavior analysis. To identify the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from CAN-Bus sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II drive identification is possible. However, we have gained two types of accuracy on a full data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to a higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm.
A Machine Vision Approach for Recognizing Coastal Fish
(Scopus, 22) Raihan, Afiq; Sharmin, Israt; Khan, B M Marjan; Jabiullah, Md. Ismail; Habib, Md. Tarek
t Coastal fish is one of the prominent marine resources, which takes a necessary role in the economic growth of a country. Because of environmental issues along with other reasons, not only most of the marine resources are diminishing but also many coastal fishes are getting extinct gradually. As a result, the young peoples have insufficient knowledge of coastal fish. This issue can be solved with the use of vision-based technologies. To deal with this situation, a coastal fish recognition system based on machine vision is conceived, which can be approached by the images of coastal fish that are captured with a portable device and identify the fish to recognize fish. Numerous experimental analyses are executed to exhibit the benefit of this proposed expert system. In the beginning, the conversion of a color image into a gray-scale image occurs and the gray-scale histogram is developed. Using the histogram-based method, image segmentation is conducted. After that, a set of sixteen features comprising four classes is extracted to be fed to a classifier. For reducing the number of features, PCA is applied. To recognize coastal fish, five classical machine learning classifiers are performed, where k-NN provides a potential accuracy of up to 98.89%.
A study on social media addiction analysis on the people of Bangladesh using machine learning algorithms
(Scopus, 2024-10) Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
: Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
A Study on Social Media Addiction Analysis on the People of Bangladesh Using Machine Learning Algorithms
(Institute of Advanced Engineering and Science (IAES), 2024-10-15) Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
A Systematic Analysis for Machine Learning Based Cow Price Prediction
(IEEE, 2023-07-12) Alam, A. K. M. Tasnim; Nirob, Zahid Hasan; Urme, Afrin Jahan; Hridoy, Rashidul Hasan; Habib, Md. Tarek; Ahmed, Farruk
Cow meets a significant number of demands for meats in South Asian countries, and a huge number of cows were sold in Bangladesh on the eve of Eid al-Adha. Cow prices depend on several factors, and determining the price of a cow is a cumbersome task for an inexperienced individual. Nowadays machine learning algorithms are significantly used for accurate price estimation. This study presents an efficient and accurate tool for determining cow prices using several characteristics of cows based on machine learning. Sixteen characteristics of a cow are considered in this study to determine its price. Four different machine learning algorithms were used and evaluated in this study for generating an accurate price prediction model. In this machine learning based systematic analysis, several experimental studies are conducted for evaluating models more precisely. Performance matrices were also used to evaluate machine learning algorithms.
A Systematic Analysis for Machine Learning Based Cow Price Prediction
(Independent University, Bangladesh, 2023-07) Alam, A.K.M. Tasnim; Nirob, Zahid Hasan; Urme, Afrin Jahan; Rahman, Afroza; Hridoy, Rashidul Hasan; Habib, Md. Tarek; Ahmed, Farruk
Cow meets a significant number of demands for meats in South Asian countries, and a huge number of cows were sold in Bangladesh on the eve of Eid al-Adha. Cow prices depend on several factors, and determining the price of a cow is a cumbersome task for an inexperienced individual. Nowadays machine learning algorithms are significantly used for accurate price estimation. This study presents an efficient and accurate tool for determining cow prices using several characteristics of cows based on machine learning. Sixteen characteristics of a cow are considered in this study to determine its price. Four different machine learning algorithms were used and evaluated in this study for generating an accurate price prediction model. In this machine learning based systematic analysis, several experimental studies are conducted for evaluating models more precisely. Performance matrices were also used to evaluate machine learning algorithms.
Achieving Robust Global Bandwidth Along with Bypassing Geo-Restriction for Internet Users
(Scopus, 2019) Islam, Gazi Zahirul; Emran, Al- Nahian Bin; Juman, Aman Ullah; Khan, Md. Abbas Ali; Hossain, Md. Fokhray; Habib, Md. Tarek
Not all Internet Service Providers provide a sufficient amount of bandwidth to their users. Although the amount of local bandwidth is reasonable, global bandwidth is not satisfactory at all. Based on bandwidth allocation, location and price; service providers capped their users’ global bandwidth i.e., reducing global internet speed. As a consequence, we observe severe global bandwidth limitation among Internet users. In this article, we implement a flexible and pragmatic solution for Internet users to bypass global bandwidth restriction. To achieve robust global bandwidth, we utilize a combination of communication technologies and devices namely, Internet Exchange Point, Virtual Private Network, chain VPN technology etc. In this project, we show that internet speed of international route i.e., global bandwidth can enhance significantly if there are multiple ISPs use a common IXP and at least one of those ISPs provides pleasant global bandwidth. Usually, regional ISPs use a common IXP to route their local traffic using local bandwidth within the region without wasting global bandwidth. We show that using our proposed method global internet speed of a user can raise several times effectively utilizing assigned local bandwidth. In addition, we also implement a geo-restriction bypassing technique integrating an offshore ISP with local ISP using VPN. Thus, we enjoy tremendous Internet speed along with unrestricted access to the websites.
An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh
(Daffodil International University, 2021-09-18) Habib, Md. Tarek; Mia, Md. Jueal; Uddin, Mohammad Shorif; Ahmed, Farruk
Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, -means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research.
An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh
(Scopus, 2021) Habib, Md. Tarek; Mia, Md. Jueal; Uddin, Mohammad Shorif; Ahmed, Farruk
Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, K-means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research.
An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction
(Modern Education and computer Science, 2018-02-08) Habib, Md. Tarek; AL-Mamun, Abdullah
Word completion and word prediction are two important phenomena in typing that have intense effect on aiding disable people and students while using keyboard or other similar devices. Such auto completion technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. N-gram based language models are proposed for auto completing a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.
An In-Depth Exploration of Automated Jackfruit Disease Recognition
(Daffodil International University, 2022-05-05) Habib, Md. Tarek; Mia, Md. Jueal; Uddin, Mohammad Shorif; Ahmed, Farruk
Bangladesh extensively depends on agriculture for the economy as well as food security owing to its huge population. In this connection, it becomes very important to efficiently grow plants and increase their yields. Quantity and quality of fruits can be degraded having attacked by various diseases. It is a matter of fact that not even a single research work has been conducted for automated recognition of jackfruit diseases to facilitate those distant farmers who need proper cultivation support. Presuming that our context is the recognition of jackfruit diseases, two challenging problems are mainly raised, i.e. detection of diseases and classification of diseases. In this research, we perform an in-depth investigation of an agro-medical expert system, which proceeds with a digital image acquired with a cellphone or other handheld device and recognizes the disease. Exhaustive experiments have been performed to assess the feasibility of our intended expert system. At first, a discriminatory feature set is selected. k-means clustering segmentation is put into action to detect disease-affected regions of an image of a disease-attacked jackfruit and extract the features from these regions. Then classification of the diseases is accomplished by using nine off-the-shelf classification algorithms in order to thoroughly assess the merits of the classifiers in the index of seven prominent performance metrics. Random forest is found outperforming all other classifiers to the amount of all metrics used by attaining an accuracy approaching to 90%. On the contrary, logistic regression shows not only the poorest result of an accuracy approaching to 75% but also some other poorest metric-values.
An in-depth Exploration of Automated Jackfruit Disease Recognition
(Journal of King Saud University - Computer and Information Sciences, 2020) Habib, Md. Tarek; Mia, Md. Jueal; Uddin, Mohammad Shorif; Ahmed, Farruk
Bangladesh extensively depends on agriculture for the economy as well as food security owing to its huge population. In this connection, it becomes very important to efficiently grow plants and increase their yields. Quantity and quality of fruits can be degraded having attacked by various diseases. It is a matter of fact that not even a single research work has been conducted for automated recognition of jackfruit diseases to facilitate those distant farmers who need proper cultivation support. Presuming that our context is the recognition of jackfruit diseases, two challenging problems are mainly raised, i.e. detection of diseases and classification of diseases. In this research, we perform an in-depth investigation of an agro-medical expert system, which proceeds with a digital image acquired with a cellphone or other handheld device and recognizes the disease. Exhaustive experiments have been performed to assess the feasibility of our intended expert system. At first, a discriminatory feature set is selected. k-means clustering segmentation is put into action to detect disease-affected regions of an image of a disease-attacked jackfruit and extract the features from these regions. Then classification of the diseases is accomplished by using nine off-the-shelf classification algorithms in order to thoroughly assess the merits of the classifiers in the index of seven prominent performance metrics. Random forest is found outperforming all other classifiers to the amount of all metrics used by attaining an accuracy approaching to 90%. On the contrary, logistic regression shows not only the poorest result of an accuracy approaching to 75% but also some other poorest metric-values.
An in-depth exploration of Bangla blog post classification
(Bulletin of Electrical Engineering and Informatics, 2021-02) Islam, Tanvirul; Prince, Ashik Iqbal; Zaman Khan, Md. Mehedee; Jabiullah, Md. Ismail; Habib, Md. Tarek
Bangla blog is increasing rapidly in the era of information, and consequently, the blog has a diverse layout and categorization. In such an aptitude, automated blog post classification is a comparatively more efficient solution in order to organize Bangla blog posts in a standard way so that users can easily find their required articles of interest. In this research, nine supervised learning models which are Support Vector Machine (SVM), multinomial naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge classifier and random forest are utilized and compared for classification of Bangla blog post. Moreover, the performance on predicting blog posts against eight categories, three feature extraction techniques are applied, namely unigram TF-IDF (term frequency-inverse document frequency), bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show above 80% accuracy. Other performance evaluation metrics also show good results while comparing the selected classifiers.
An In-Depth Exploration of Bangla Blog Post Classification
(Scopus, 2021) Islam, Tanvirul; Prince, Ashik Iqbal; Khan, Md. Mehedee Zaman; Jabiullah, Md. Ismail; Habib, Md. Tarek
Bangla blog is increasing rapidly in the era of information, and consequently, the blog has a diverse layout and categorization. In such an aptitude, automated blog post classification is a comparatively more efficient solution in order to organize Bangla blog posts in a standard way so that users can easily find their required articles of interest. In this research, nine supervised learning models which are Support Vector Machine (SVM), multinomial naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge classifier and random forest are utilized and compared for classification of Bangla blog post. Moreover, the performance on predicting blog posts against eight categories, three feature extraction techniques are applied, namely unigram TF-IDF (term frequency-inverse document frequency), bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show above 80% accuracy. Other performance evaluation metrics also show good results while comparing the selected classifiers.
An Investigative Design of Optimum Stochastic Language Model for Bangla Autocomplete
(Indonesian Journal of Electrical Engineering and Computer Science, 2019) Eyamin, Md.Iftakher Alam; Habib, Md. Tarek; Muhammad Ifte Khairul Islam; Rahman, Md. Sadekur; Khan, Md. Abbas Ali
Word completion and word prediction are two important phenomena in typing that have extreme effect on aiding disable people and students while using keyboard or other similar devices. Such autocomplete technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. N-gram based language models are proposed for autocomplete a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.
An LSTM-Based Word Prediction in Bengali
(Springer, 2022-11-14) Hasan, Mustahid; Sakib, Nazmus; Hridoy, Rashidul Hasan; Ananto, Nazmul Hossain; Akhter, Sonia; Habib, Md. Tarek
"In this paper, Bengali text information has been utilized for predicting the next word contingent based on the previous one. To do that, one should consider two key aspects such as the natural language processing (NLP) stage and the word predicting stage. When both work together, the system gets a new predicted word that is relevant to the previous word. For achieving such correct predicted words, long short-term memory (LSTM) has been used which is best known for its memory management. LSTM embeds the input words and fits them into the model, then after successful training of the model, it can predict the next word from a given sentence. The user can also initialize the number of predicted words. This paper gives an overview of word prediction for the Bengali language based on LSTM and describes the database integration and proposed approach obtained 97.60% accuracy."
Analysis of Recognition Performance of Plant Leaf Diseases Based on Machine Vision Techniques
(Daffodil International University, 2022-03-01) Haque, Imdadul; Alim, Mohsin; Alam, Mahbub; Nawshin, Samia; Noori, Sheak Rashed Haider; Habib, Md. Tarek
Agriculture is the primary source of income for the majority of the population in Bangladesh. Agriculture is also a big part of the economy of the country. Therefore, it's more necessary to grow our crops and fruits and boost their harvests. Fruits are adored by the people of this country, and farmers love growing fruits. Owing to numerous diseases, both the quality and quantity of fruits are not meeting expectations. Native fruits are contracting many types of new diseases, and the magnitude of the problem is increasing alarmingly. To deal with this issue, quick detection of the disease and correct treatment or recuperation is required. In many cases, locals fail to even detect rare diseases. Thanks to the hug e advancement in technology, rare diseases can now be detected with the use of the right technologies. A good plant's growth is dependent on its leaves. Early leaf disease detection can help in keeping the leaves disease-free, as well as the plants and fruits. Our research focuses on identifying litchi leaf diseases by employing sophisticated image processing technologies to ensure the freshness of the leaves. A machine-vision-based technique, i.e., the Convolutional Neural Network (CNN), has been used in this research work.
Betel Nut Addiction Detection Using Machine Learning
(IEEE, 2020-11) Khatun, Johura; Azad, Tamanna; Seaum, Samin Yeasar; Jabiullah, Md. Ismail; Habib, Md. Tarek
Betel nut addiction has become a psychoactive addiction to hundreds and millions of people around the world. Among the four common hallucinatory addictions in the world, betel nut addiction is one of them after tobacco, alcohol, and caffeine. It is more acute in the countries like Bangladesh, India, Pakistan and Taiwan. This ancient habit is much harmful to the human body as it contains substances like arecoline, which is more similar to nicotine. Consuming it on a repetitive basis causes the deformation of dental structure and change in the oral mucosa. Also, people have a risk of facing several diseases such as oral cancer, neck, and head cancer, submucous fibrosis, etc. by consuming it. Unfortunately, in Bangladesh, rural women are more addicted to this harmful practice. The purpose of this research is to detect betel nut addiction so that its disadvantages can be spread among the people. In this paper, the machine learning algorithms detect whether a person is addicted or not addicted to betel nut. Nowadays, machine learning is the most popular tool for using algorithms that compute data and learn from it for decision making or prediction about something. Thus, machine learning approach was chosen. The methodology has required data, which was collected manually by analyzing some factors from previous research papers on betel nut addiction. The k-nearest neighbor (kNN), Support Vector Machine (SVM), decision tree, logistic regression, random forest and Naive Bayes algorithm was used for classification and machine learning technique have been performed and evaluated them regarding performance matrices with a featured dataset. Among all the algorithms, random forest performs the best with the highest accuracy of about 99.0% with less training time.