2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)
Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and c... more Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model.
International Journal of Engineering and Manufacturing, Apr 8, 2022
In contemporary times startup is a very popular and growing entity, especially for the software i... more In contemporary times startup is a very popular and growing entity, especially for the software industry as the technology field continues to grow. However, the failure rate of software startups is extremely high, and many reasons are behind this failure. There are many different variables behind the success or failure of a company. The purpose of this study is to mainly identify the possible reasons behind why the startups can't reach the promise they are presumed to reach. To identify the reasons, company and employees of the company data were collected and analyzed using sentiment analysis and statistical correlation. Based on the analysis, some constructive suggestions were proposed accordingly. The discussion will enable the company employees and employers to bring out a transparent relation to ensure productive and satisfactory work environment. All the collected data for the study and the codes that were used to analyses the findings are attached below [28].
Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we presen... more Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we present several strategies for resolving the imbalance issue for fake news detection in Bangla with a comparative assessment of proposed methodologies. Additionally, we propose a technique for improving performance even when the dataset is imbalanced. We applied our proposed approaches to BanFakeNews [4], a dataset developed for the purpose of detecting fake news in Bangla comprising of 50K instances but is significantly skewed, with 97% of majority instances. We obtained a 93.1% F1-score using data manipulation manipulation techniques such as SMOTE, and a 79.1% F1-score using without data manipulation approaches such as Stacked Generalization. Without implementing these techniques, the F1-score would have been 67.6% for baseline models. We see this work as an important step towards paving the way of fake news detection in Bangla. By implementing these strategies the obstacles of imbalanced dataset can be removed and improvement in the performance can be achieved.
Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kin... more Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using expensive medical tests are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images has been used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&E and IHC. Among all images of the dataset, 3896 and 977 images are applied to train and test the convoHER2 model, respectively. As all the images are in high resolution, we resize them so that we can feed them in our convoHER2 model. The cancerous samples images are classified into four classes based on the stage of the cancer (0+, 1+, 2+, 3+). The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future.
Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we presen... more Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we present several strategies for resolving the imbalance issue for fake news detection in Bangla with a comparative assessment of proposed methodologies. Additionally, we propose a technique for improving performance even when the dataset is imbalanced. We applied our proposed approaches to BanFakeNews [4], a dataset developed for the purpose of detecting fake news in Bangla comprising of 50K instances but is significantly skewed, with 97% of majority instances. We obtained a 93.1% F1-score using data manipulation manipulation techniques such as SMOTE, and a 79.1% F1-score using without data manipulation approaches such as Stacked Generalization. Without implementing these techniques, the F1-score would have been 67.6% for baseline models. We see this work as an important step towards paving the way of fake news detection in Bangla. By implementing these strategies the obstacles of imbalanced da...
Virtual Reality has become a significant element of education throughout the years. To understand... more Virtual Reality has become a significant element of education throughout the years. To understand the quality and advantages of these techniques, it is important to understand how they were developed and evaluated. Since COVID-19, the education system has drastically changed a lot. It has shifted from being in a classroom with a whiteboard and projectors to having your own room in front of your laptop in a virtual meeting. In this respect, virtual reality in the laboratory or Virtual Laboratory is the main focus of this research, which is intended to comprehend the work done in quality education from a distance using VR. As per the findings of the study, adopting virtual reality in education can help students learn more effectively and also help them increase perspective, enthusiasm, and knowledge of complex notions by offering them an interactive experience in which they can engage and learn more effectively. This highlights the importance of a significant expansion of VR use in learning, the majority of which employ scientific comparison approaches to compare students who use VR to those who use the traditional method for learning.
Food is essential for human survival, and people always try to taste different types of delicious... more Food is essential for human survival, and people always try to taste different types of delicious recipes. Frequently, people choose food ingredients without even knowing their names or pick up some food ingredients that are not obvious to them from a grocery store. Knowing which ingredients can be mixed to make a delicious food recipe is essential. Selecting the right recipe by choosing a list of ingredients is very difficult for a beginner cook. However, it can be a problem even for experts. One such example is recognising objects through image processing. Although this process is complex due to different food ingredients, traditional approaches will lead to an inaccuracy rate. These problems can be solved by machine learning and deep learning approaches. In this paper, we implemented a model for food ingredients recognition and designed an algorithm for recommending recipes based on recognised ingredients. We made a custom dataset consisting of 9856 images belonging to 32 different food ingredients classes. Convolution Neural Network (CNN) model was used to identify food ingredients, and for recipe recommendations, we have used machine learning. We achieved an accuracy of 94 percent, which is quite impressive.
There is very little notable research on generating descriptions of the Bengali language. About 2... more There is very little notable research on generating descriptions of the Bengali language. About 243 million people speak in Bengali, and it is the 7th most spoken language on the planet. The purpose of this research is to propose a CNN and Bidirectional GRU based architecture model that generates natural language captions in the Bengali language from an image. Bengali people can use this research to break the language barrier and better understand each other's perspectives. It will also help many blind people with their everyday lives. This paper used an encoder-decoder approach to generate captions. We used a pre-trained Deep convolutional neural network (DCNN) called InceptonV3image embedding model as the encoder for analysis, classification, and annotation of the dataset's images Bidirectional Gated Recurrent unit (BGRU) layer as the decoder to generate captions. Argmax and Beam search is used to produce the highest possible quality of the captions. A new dataset called B...
Proceedings of the International Conference on Computing Advancements, 2020
Student's feedback on academics can provide valuable information for an institution for findi... more Student's feedback on academics can provide valuable information for an institution for finding their current teaching practices quality and, also can provide suggestions for improving the overall teaching process. There are various kinds of student feedback systems used in different institutions and are mostly manual based process. Therefore, the study proposes a concept of computerized student feedback system (SFS) for academics. Student generally provides their feedback using open ended sentences. SFS will classify all feedback into several categories such as positive, negative, neutral. The proposed SFS uses a combination of machine learning's rule-based and lexicon based custom corpus datastore. This system will be implemented and evaluated in various educational institutions to test the effectiveness of SFS in the academic's context.
2020 23rd International Conference on Computer and Information Technology (ICCIT), 2020
There is not more research on the linguistic characteristics of the Bengali language. Bengali is ... more There is not more research on the linguistic characteristics of the Bengali language. Bengali is spoken by about 193 million people globally, and it is one of the top ten spoken languages worldwide. In this paper, a CNN and Bidirectional GRU architecture is proposed for producing a natural language caption from an image in the Bengali language. Bangladeshi people may use this study to grasp one another better and crack language barriers and increase their cultural understanding. This study would immensely help several blind people in their daily lives. The encoder-decoder approach was used in this paper for captioning. We used a pre-trained Deep CNN named InceptionV3 image encoder to interpret, identify, and annotate the dataset’s images and used a Bidirectional GRU architecture as the decoder to produce captions. In order to deliver the finest and subtle Bengali captions from our model, argmax search and beam search are included. We proposed a new dataset named BNATURE that contain...
2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)
Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and c... more Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model.
International Journal of Engineering and Manufacturing, Apr 8, 2022
In contemporary times startup is a very popular and growing entity, especially for the software i... more In contemporary times startup is a very popular and growing entity, especially for the software industry as the technology field continues to grow. However, the failure rate of software startups is extremely high, and many reasons are behind this failure. There are many different variables behind the success or failure of a company. The purpose of this study is to mainly identify the possible reasons behind why the startups can't reach the promise they are presumed to reach. To identify the reasons, company and employees of the company data were collected and analyzed using sentiment analysis and statistical correlation. Based on the analysis, some constructive suggestions were proposed accordingly. The discussion will enable the company employees and employers to bring out a transparent relation to ensure productive and satisfactory work environment. All the collected data for the study and the codes that were used to analyses the findings are attached below [28].
Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we presen... more Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we present several strategies for resolving the imbalance issue for fake news detection in Bangla with a comparative assessment of proposed methodologies. Additionally, we propose a technique for improving performance even when the dataset is imbalanced. We applied our proposed approaches to BanFakeNews [4], a dataset developed for the purpose of detecting fake news in Bangla comprising of 50K instances but is significantly skewed, with 97% of majority instances. We obtained a 93.1% F1-score using data manipulation manipulation techniques such as SMOTE, and a 79.1% F1-score using without data manipulation approaches such as Stacked Generalization. Without implementing these techniques, the F1-score would have been 67.6% for baseline models. We see this work as an important step towards paving the way of fake news detection in Bangla. By implementing these strategies the obstacles of imbalanced dataset can be removed and improvement in the performance can be achieved.
Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kin... more Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using expensive medical tests are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images has been used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&E and IHC. Among all images of the dataset, 3896 and 977 images are applied to train and test the convoHER2 model, respectively. As all the images are in high resolution, we resize them so that we can feed them in our convoHER2 model. The cancerous samples images are classified into four classes based on the stage of the cancer (0+, 1+, 2+, 3+). The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future.
Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we presen... more Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we present several strategies for resolving the imbalance issue for fake news detection in Bangla with a comparative assessment of proposed methodologies. Additionally, we propose a technique for improving performance even when the dataset is imbalanced. We applied our proposed approaches to BanFakeNews [4], a dataset developed for the purpose of detecting fake news in Bangla comprising of 50K instances but is significantly skewed, with 97% of majority instances. We obtained a 93.1% F1-score using data manipulation manipulation techniques such as SMOTE, and a 79.1% F1-score using without data manipulation approaches such as Stacked Generalization. Without implementing these techniques, the F1-score would have been 67.6% for baseline models. We see this work as an important step towards paving the way of fake news detection in Bangla. By implementing these strategies the obstacles of imbalanced da...
Virtual Reality has become a significant element of education throughout the years. To understand... more Virtual Reality has become a significant element of education throughout the years. To understand the quality and advantages of these techniques, it is important to understand how they were developed and evaluated. Since COVID-19, the education system has drastically changed a lot. It has shifted from being in a classroom with a whiteboard and projectors to having your own room in front of your laptop in a virtual meeting. In this respect, virtual reality in the laboratory or Virtual Laboratory is the main focus of this research, which is intended to comprehend the work done in quality education from a distance using VR. As per the findings of the study, adopting virtual reality in education can help students learn more effectively and also help them increase perspective, enthusiasm, and knowledge of complex notions by offering them an interactive experience in which they can engage and learn more effectively. This highlights the importance of a significant expansion of VR use in learning, the majority of which employ scientific comparison approaches to compare students who use VR to those who use the traditional method for learning.
Food is essential for human survival, and people always try to taste different types of delicious... more Food is essential for human survival, and people always try to taste different types of delicious recipes. Frequently, people choose food ingredients without even knowing their names or pick up some food ingredients that are not obvious to them from a grocery store. Knowing which ingredients can be mixed to make a delicious food recipe is essential. Selecting the right recipe by choosing a list of ingredients is very difficult for a beginner cook. However, it can be a problem even for experts. One such example is recognising objects through image processing. Although this process is complex due to different food ingredients, traditional approaches will lead to an inaccuracy rate. These problems can be solved by machine learning and deep learning approaches. In this paper, we implemented a model for food ingredients recognition and designed an algorithm for recommending recipes based on recognised ingredients. We made a custom dataset consisting of 9856 images belonging to 32 different food ingredients classes. Convolution Neural Network (CNN) model was used to identify food ingredients, and for recipe recommendations, we have used machine learning. We achieved an accuracy of 94 percent, which is quite impressive.
There is very little notable research on generating descriptions of the Bengali language. About 2... more There is very little notable research on generating descriptions of the Bengali language. About 243 million people speak in Bengali, and it is the 7th most spoken language on the planet. The purpose of this research is to propose a CNN and Bidirectional GRU based architecture model that generates natural language captions in the Bengali language from an image. Bengali people can use this research to break the language barrier and better understand each other's perspectives. It will also help many blind people with their everyday lives. This paper used an encoder-decoder approach to generate captions. We used a pre-trained Deep convolutional neural network (DCNN) called InceptonV3image embedding model as the encoder for analysis, classification, and annotation of the dataset's images Bidirectional Gated Recurrent unit (BGRU) layer as the decoder to generate captions. Argmax and Beam search is used to produce the highest possible quality of the captions. A new dataset called B...
Proceedings of the International Conference on Computing Advancements, 2020
Student's feedback on academics can provide valuable information for an institution for findi... more Student's feedback on academics can provide valuable information for an institution for finding their current teaching practices quality and, also can provide suggestions for improving the overall teaching process. There are various kinds of student feedback systems used in different institutions and are mostly manual based process. Therefore, the study proposes a concept of computerized student feedback system (SFS) for academics. Student generally provides their feedback using open ended sentences. SFS will classify all feedback into several categories such as positive, negative, neutral. The proposed SFS uses a combination of machine learning's rule-based and lexicon based custom corpus datastore. This system will be implemented and evaluated in various educational institutions to test the effectiveness of SFS in the academic's context.
2020 23rd International Conference on Computer and Information Technology (ICCIT), 2020
There is not more research on the linguistic characteristics of the Bengali language. Bengali is ... more There is not more research on the linguistic characteristics of the Bengali language. Bengali is spoken by about 193 million people globally, and it is one of the top ten spoken languages worldwide. In this paper, a CNN and Bidirectional GRU architecture is proposed for producing a natural language caption from an image in the Bengali language. Bangladeshi people may use this study to grasp one another better and crack language barriers and increase their cultural understanding. This study would immensely help several blind people in their daily lives. The encoder-decoder approach was used in this paper for captioning. We used a pre-trained Deep CNN named InceptionV3 image encoder to interpret, identify, and annotate the dataset’s images and used a Bidirectional GRU architecture as the decoder to produce captions. In order to deliver the finest and subtle Bengali captions from our model, argmax search and beam search are included. We proposed a new dataset named BNATURE that contain...
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