Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055636
M. Rahman, Faisal Bin Ashraf, Md Rayhan Kabir
People connect with a plethora of information from many online portals due to the availability and ease of access to the internet and electronic communication devices. However, news portals sometimes abuse press freedom by manipulating facts. Most of the time, people are unable to discriminate between true and false news. It is difficult to avoid the detrimental impact of Bangla fake news from spreading quickly through online channels and influencing people’s judgment. In this work, we investigated many real and false news pieces in Bangla to discover a common pattern for determining if an article is disseminating incorrect information or not. We developed a deep learning model that was trained and validated on our selected dataset. For learning, the dataset contains 48,678 legitimate news and 1,299 fraudulent news. To deal with the imbalanced data, we used random undersampling and then ensemble to achieve the combined output. In terms of Bangla text processing, our proposed model achieved an accuracy of 98.29% and a recall of 99%.
{"title":"An Efficient Deep Learning Technique for Bangla Fake News Detection","authors":"M. Rahman, Faisal Bin Ashraf, Md Rayhan Kabir","doi":"10.1109/ICCIT57492.2022.10055636","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055636","url":null,"abstract":"People connect with a plethora of information from many online portals due to the availability and ease of access to the internet and electronic communication devices. However, news portals sometimes abuse press freedom by manipulating facts. Most of the time, people are unable to discriminate between true and false news. It is difficult to avoid the detrimental impact of Bangla fake news from spreading quickly through online channels and influencing people’s judgment. In this work, we investigated many real and false news pieces in Bangla to discover a common pattern for determining if an article is disseminating incorrect information or not. We developed a deep learning model that was trained and validated on our selected dataset. For learning, the dataset contains 48,678 legitimate news and 1,299 fraudulent news. To deal with the imbalanced data, we used random undersampling and then ensemble to achieve the combined output. In terms of Bangla text processing, our proposed model achieved an accuracy of 98.29% and a recall of 99%.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122547055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thyroid illness is a medical disorder in which the thyroid gland fails to produce enough hormones. Males, females, babies, teenagers, and the elderly are all susceptible to thyroid illness. It could be present from birth (hypothyroidism), or it could develop as you become older (often after menopause in women). People with thyroid diseases suffer from various problems like gaining weight, forgetfulness, anxiety, losing weight, fatigue, sleeping disorder, etc. So, diagnosing thyroid diseases is a vital issue as the diseases can be cured through proper and timely diagnosis. Recently machine learning techniques are used for diagnosing thyroid diseases. The feature selection approach was used to eliminate certain irrelevant characteristics from the thyroid dataset (from the UCI machine learning repository) and to select optimal features. The dataset has three target classes named normal, hypothyroid, and hyperthyroid. The subjects were classified through seven different machine-learning algorithms. Random Forest classifier achieves the highest accuracy 99.58% which is better than the existing state-of-the-art methods.
{"title":"Thyroid Disease Prediction based on Feature Selection and Machine Learning","authors":"Zahrul Jannat Peya, Md. Shymon Islam, Mst. Kamrun Naher Chumki","doi":"10.1109/ICCIT57492.2022.10054746","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054746","url":null,"abstract":"Thyroid illness is a medical disorder in which the thyroid gland fails to produce enough hormones. Males, females, babies, teenagers, and the elderly are all susceptible to thyroid illness. It could be present from birth (hypothyroidism), or it could develop as you become older (often after menopause in women). People with thyroid diseases suffer from various problems like gaining weight, forgetfulness, anxiety, losing weight, fatigue, sleeping disorder, etc. So, diagnosing thyroid diseases is a vital issue as the diseases can be cured through proper and timely diagnosis. Recently machine learning techniques are used for diagnosing thyroid diseases. The feature selection approach was used to eliminate certain irrelevant characteristics from the thyroid dataset (from the UCI machine learning repository) and to select optimal features. The dataset has three target classes named normal, hypothyroid, and hyperthyroid. The subjects were classified through seven different machine-learning algorithms. Random Forest classifier achieves the highest accuracy 99.58% which is better than the existing state-of-the-art methods.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10054644
Nusratul Jannat, Avishek Das, Omar Sharif, M. M. Hoque
Advances in social media platforms led to the widespread adoption of memes, making them a powerful communication tool on the internet. Memes’ visual aspect gives them a remarkable ability to influence users’ opinions. However, individuals misemploy this popularity to foment animosity. The spread of these hostile memes can have a detrimental effect on people, causing depression and suicidal thoughts. Therefore, stopping inappropriate memes from spreading on the internet is crucial. However, identifying memes is di cult due to their multimodal nature. This paper proposes a deep-learning-based framework to classify sentiment (into ‘positive’ or ‘negative’) from multimodal memes in Bengali. Due to the unavailability of standard corpora, a Bengali meme corpus consisting of 1671 memes is developed to perform the memes’ sentiment classification task. Five popular deep learning models (CNN, BiLSTM) and pre-trained models (VGG16, VGG19, InceptionV3) are investigated for textual and visual features. The framework is developed by combining visual and textual models. The comparative analysis confirms that the proposed model (BiLSTM + VGG19) achieved the highest f1-score (0.68) compared to other multimodal methods.
{"title":"An Empirical Framework for Identifying Sentiment from Multimodal Memes using Fusion Approach","authors":"Nusratul Jannat, Avishek Das, Omar Sharif, M. M. Hoque","doi":"10.1109/ICCIT57492.2022.10054644","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054644","url":null,"abstract":"Advances in social media platforms led to the widespread adoption of memes, making them a powerful communication tool on the internet. Memes’ visual aspect gives them a remarkable ability to influence users’ opinions. However, individuals misemploy this popularity to foment animosity. The spread of these hostile memes can have a detrimental effect on people, causing depression and suicidal thoughts. Therefore, stopping inappropriate memes from spreading on the internet is crucial. However, identifying memes is di cult due to their multimodal nature. This paper proposes a deep-learning-based framework to classify sentiment (into ‘positive’ or ‘negative’) from multimodal memes in Bengali. Due to the unavailability of standard corpora, a Bengali meme corpus consisting of 1671 memes is developed to perform the memes’ sentiment classification task. Five popular deep learning models (CNN, BiLSTM) and pre-trained models (VGG16, VGG19, InceptionV3) are investigated for textual and visual features. The framework is developed by combining visual and textual models. The comparative analysis confirms that the proposed model (BiLSTM + VGG19) achieved the highest f1-score (0.68) compared to other multimodal methods.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129736171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10056094
P. Das, Nurul A. Asif, M. Hasan, S. H. Abhi, Mehtar Jahin Tatha, Swarnali Deb Bristi
Nowadays, our home is designed with various technologies which have increased our living comfort and offering more flexibility. Installing various technology in our Home makes it a smart home and we also call this installation process Home Automation. The popularity of Home Automation systems is increasing rapidly and it develops the quality of living. Home automation offers automatic light, fan, temperature, etc. control and also an automatic alarming system to alert the people, etc. Already there are various techniques have been used for implementing Home Automation. Here, in this paper, an intelligent door controller, an application of home automation is presented by using deep learning techniques. An intelligent door basically opens automatically and closes after a predefined time based on the person coming in front of the door. If a person is known then the door will be opened and after his/her entrance the door will be closed automatically. And if the person is not known then the door will remain closed. Here to identify the person, the person’s face is recognized by using deep learning. As well ass, Arduino and Servo motors are used to control the door opening or closing.
{"title":"Intelligent Door Controller Using Deep Learning-Based Network Pruned Face Recognition","authors":"P. Das, Nurul A. Asif, M. Hasan, S. H. Abhi, Mehtar Jahin Tatha, Swarnali Deb Bristi","doi":"10.1109/ICCIT57492.2022.10056094","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10056094","url":null,"abstract":"Nowadays, our home is designed with various technologies which have increased our living comfort and offering more flexibility. Installing various technology in our Home makes it a smart home and we also call this installation process Home Automation. The popularity of Home Automation systems is increasing rapidly and it develops the quality of living. Home automation offers automatic light, fan, temperature, etc. control and also an automatic alarming system to alert the people, etc. Already there are various techniques have been used for implementing Home Automation. Here, in this paper, an intelligent door controller, an application of home automation is presented by using deep learning techniques. An intelligent door basically opens automatically and closes after a predefined time based on the person coming in front of the door. If a person is known then the door will be opened and after his/her entrance the door will be closed automatically. And if the person is not known then the door will remain closed. Here to identify the person, the person’s face is recognized by using deep learning. As well ass, Arduino and Servo motors are used to control the door opening or closing.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128637742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present land document reservation process that is done manually provides a lot of insecurity, unsafely, and many scopes for land deed fraud in terms of storing land documents and maintaining the details of ownership of specific land property. So, the current land deed reservation and verification method don’t seem reliable and efficient. To make it a reliable and safe transaction, we will use blockchain technology. We have created a blockchain system for land deed authentication utilizing the data encryption algorithm SHA-256. Using this system, land deed transactions will be safer and can store, verify, and preserve all of the relevant information of a land deed document. This proposed architecture store retrieves and detects attempts to modify copies using a variety of approaches, including several efficient technologies like Zero Knowledge proof, Public-key cryptography, and IPFS; It generates far more efficient solutions than the other systems. This method has been put out as a potential means of thwarting fraudulent land deeds and bringing delight to the public.
{"title":"Decentralized Blockchain Based Land Deed Verification and Reservation System in Bangladesh","authors":"Md Musleh Uddin Hasan, Md. Mahinur Alam, Kanita Jerin Tanha","doi":"10.1109/ICCIT57492.2022.10054857","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054857","url":null,"abstract":"The present land document reservation process that is done manually provides a lot of insecurity, unsafely, and many scopes for land deed fraud in terms of storing land documents and maintaining the details of ownership of specific land property. So, the current land deed reservation and verification method don’t seem reliable and efficient. To make it a reliable and safe transaction, we will use blockchain technology. We have created a blockchain system for land deed authentication utilizing the data encryption algorithm SHA-256. Using this system, land deed transactions will be safer and can store, verify, and preserve all of the relevant information of a land deed document. This proposed architecture store retrieves and detects attempts to modify copies using a variety of approaches, including several efficient technologies like Zero Knowledge proof, Public-key cryptography, and IPFS; It generates far more efficient solutions than the other systems. This method has been put out as a potential means of thwarting fraudulent land deeds and bringing delight to the public.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124564308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055090
S. M. Taslim Uddin Raju, M. Hashem
Diabetes is a perpetual metabolic issue that can prompt severe complications. Blood glucose level (BGL) is usually monitored by collecting a blood sample and assessing the results. This type of measurement is extremely unpleasant and inconvenient for the patient, who must undergo it frequently. This paper proposes a novel real-time, non-invasive technique for estimating BGL with smartphone photoplethysmogram (PPG) signal extracted from fingertip video and deep neural networks (DNN). Fingertip videos are collected from 93 subjects using a smartphone camera and a lighting source, and subsequently the frames are converted into PPG signal. The PPG signals have been preprocessed with Butterworth bandpass filter to eliminate high frequency noise, and motion artifact. Therefore, there are 34 features that are derived from the PPG signal and its derivatives and Fourier transformed form. In addition, age and gender are also included as features due to their considerable influence on glucose. Maximal information coefficient (MIC) feature selection technique has been applied for selecting the best feature set for obtaining good accuracy. Finally, the DNN model has been established to determine BGL non-invasively. DNN model along with the MIC feature selection technique outperformed in estimating BGL with the coefficient of determination (R2) of 0.96, implying a good relationship between glucose level and selected features. The results of the experiments suggest that the proposed method can be used clinically to determine BGL without drawing blood.
{"title":"DNN Based Blood Glucose Level Estimation Using PPG Characteristic Features of Smartphone Videos","authors":"S. M. Taslim Uddin Raju, M. Hashem","doi":"10.1109/ICCIT57492.2022.10055090","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055090","url":null,"abstract":"Diabetes is a perpetual metabolic issue that can prompt severe complications. Blood glucose level (BGL) is usually monitored by collecting a blood sample and assessing the results. This type of measurement is extremely unpleasant and inconvenient for the patient, who must undergo it frequently. This paper proposes a novel real-time, non-invasive technique for estimating BGL with smartphone photoplethysmogram (PPG) signal extracted from fingertip video and deep neural networks (DNN). Fingertip videos are collected from 93 subjects using a smartphone camera and a lighting source, and subsequently the frames are converted into PPG signal. The PPG signals have been preprocessed with Butterworth bandpass filter to eliminate high frequency noise, and motion artifact. Therefore, there are 34 features that are derived from the PPG signal and its derivatives and Fourier transformed form. In addition, age and gender are also included as features due to their considerable influence on glucose. Maximal information coefficient (MIC) feature selection technique has been applied for selecting the best feature set for obtaining good accuracy. Finally, the DNN model has been established to determine BGL non-invasively. DNN model along with the MIC feature selection technique outperformed in estimating BGL with the coefficient of determination (R2) of 0.96, implying a good relationship between glucose level and selected features. The results of the experiments suggest that the proposed method can be used clinically to determine BGL without drawing blood.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055860
Nasehatul Mustakim, Avishek Das, Omar Sharif, M. M. Hoque
People usually express their emotions, views, or sentiment in textual form. The textual sentiment analysis (TSA) identifies or classifies opinions or feelings from texts in a predefined class. The TSA is complicated or infeasible manually due to its voluminous nature and unstructured or messy conditions. Therefore, the automatic sentiment analysis method quickly paves the way to identify the hidden sentiment polarity from the textual content. Although a few studies on sentiment analysis were conducted on a single or specific domain, developing the TSA method concerning multi-domains is unexplored in Bengali. This paper presents a deep learning-based framework called DeepSen to detect textual sentiment from Bengali texts into three polarities: positive, negative and neutral. Four benchmark corpora from available domains, Book, Restaurant, Drama and Cricket, have been used to analyze sentiment from multi-domain heterogeneous data. This work investigates six popular machine learning (LR, DT, MNB, SVM, RF, AdaBoost) and five deep learning (CNN, LSTM, GRU, BiGRU, BiLSTM) techniques using four benchmark Bengali corpora to perform TSA tasks. The evaluation result reveals that the BiLSTM method obtained the highest weighted f1-score (0.85) among all models.
{"title":"DeepSen: A Deep Learning-based Framework for Sentiment Analysis from Multi-Domain Heterogeneous Data","authors":"Nasehatul Mustakim, Avishek Das, Omar Sharif, M. M. Hoque","doi":"10.1109/ICCIT57492.2022.10055860","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055860","url":null,"abstract":"People usually express their emotions, views, or sentiment in textual form. The textual sentiment analysis (TSA) identifies or classifies opinions or feelings from texts in a predefined class. The TSA is complicated or infeasible manually due to its voluminous nature and unstructured or messy conditions. Therefore, the automatic sentiment analysis method quickly paves the way to identify the hidden sentiment polarity from the textual content. Although a few studies on sentiment analysis were conducted on a single or specific domain, developing the TSA method concerning multi-domains is unexplored in Bengali. This paper presents a deep learning-based framework called DeepSen to detect textual sentiment from Bengali texts into three polarities: positive, negative and neutral. Four benchmark corpora from available domains, Book, Restaurant, Drama and Cricket, have been used to analyze sentiment from multi-domain heterogeneous data. This work investigates six popular machine learning (LR, DT, MNB, SVM, RF, AdaBoost) and five deep learning (CNN, LSTM, GRU, BiGRU, BiLSTM) techniques using four benchmark Bengali corpora to perform TSA tasks. The evaluation result reveals that the BiLSTM method obtained the highest weighted f1-score (0.85) among all models.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131357812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Properly taking care of us becomes difficult when there is a risk of spreading disease while receiving health care, and the health of many others is threatened by this type of pandemic situation. If a project is designed to avoid such a situation, it can perform the necessary steps for first aid without human contact, such as automatically sanitizing and checking the patient's oxygen saturation level, heart rate or temperature measurement and be able to provide this service to many people at a time without a man-to-man contact. To implement this prototype project, line-following the IR sensor and creating its movement step with fuzzy logic. BPM, SpO2, and temperature sensors are utilized to take data from the patient. All data is processed in NodeMCU, and it’s shown to a web server or app through the Internet of Things (IoT). With its autonomous management system, many service recipients will benefit from it at home or in the hospital. As a result, they can use IoT to monitor their current health state and condition. All the data is stored on the server, allowing any decision-making to play an effective role as the patient's history is known even during the next treatment. However, this reduces the chance of disease spreading and allows many patients to complete the steps before receiving their demanding services.
{"title":"Fuzzy Logic Controlled an Autonomous Patient's Health Monitoring System through the Internet of Things","authors":"Thohidul Islam, Md. Jasim Uddin Qureshi, Md. Farhan Nasir, R. Chowdhury, Hrishin Palit, Papri Mitra","doi":"10.1109/ICCIT57492.2022.10055115","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055115","url":null,"abstract":"Properly taking care of us becomes difficult when there is a risk of spreading disease while receiving health care, and the health of many others is threatened by this type of pandemic situation. If a project is designed to avoid such a situation, it can perform the necessary steps for first aid without human contact, such as automatically sanitizing and checking the patient's oxygen saturation level, heart rate or temperature measurement and be able to provide this service to many people at a time without a man-to-man contact. To implement this prototype project, line-following the IR sensor and creating its movement step with fuzzy logic. BPM, SpO2, and temperature sensors are utilized to take data from the patient. All data is processed in NodeMCU, and it’s shown to a web server or app through the Internet of Things (IoT). With its autonomous management system, many service recipients will benefit from it at home or in the hospital. As a result, they can use IoT to monitor their current health state and condition. All the data is stored on the server, allowing any decision-making to play an effective role as the patient's history is known even during the next treatment. However, this reduces the chance of disease spreading and allows many patients to complete the steps before receiving their demanding services.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125447303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055778
M. Uddin, Mohammad Khairul Islam, Md. Rakib Hassan, Aysha Siddika Ratna, Farah Jahan
The amount of DNA data is growing exponentially because of enormous applications including gene therapy, new variety development, and evolutionary history tracking. Recently, chaos, kmer count, histogram, and deep learning-based alignment-free (AF) algorithms are widely used for DNA sequence analysis. However, these methods have either high time complexity, memory consumption, or low precision rate. Hence, an optimal solution is needed. Therefore, in this research, a part-wise template matching-based novel similarity feature vector is extracted. Based on this vector, a phylogenetic tree is generated. The method is tested on two benchmark and four standard datasets and compared with recent existing studies. The method achieves 100% accuracy, consumes 10 to 70 times less memory than existing studies, and achieves top-rank benchmark results. Moreover, the required time of this method is very close to the existing best methods. Therefore, in real-time scenarios, industries can use this method with a great level of reliability.
{"title":"A novel part-wise template matching technique for DNA sequence similarity identification","authors":"M. Uddin, Mohammad Khairul Islam, Md. Rakib Hassan, Aysha Siddika Ratna, Farah Jahan","doi":"10.1109/ICCIT57492.2022.10055778","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055778","url":null,"abstract":"The amount of DNA data is growing exponentially because of enormous applications including gene therapy, new variety development, and evolutionary history tracking. Recently, chaos, kmer count, histogram, and deep learning-based alignment-free (AF) algorithms are widely used for DNA sequence analysis. However, these methods have either high time complexity, memory consumption, or low precision rate. Hence, an optimal solution is needed. Therefore, in this research, a part-wise template matching-based novel similarity feature vector is extracted. Based on this vector, a phylogenetic tree is generated. The method is tested on two benchmark and four standard datasets and compared with recent existing studies. The method achieves 100% accuracy, consumes 10 to 70 times less memory than existing studies, and achieves top-rank benchmark results. Moreover, the required time of this method is very close to the existing best methods. Therefore, in real-time scenarios, industries can use this method with a great level of reliability.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125508594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10056025
Syed Muaz Ali, Md. Ashraful Alam
In medical application, deep learning-based biomedical semantic segmentation has provided state-of-the-art results and proven to be more efficient than manual segmentation by human interaction in various cases. One of the most popular architectures for biomedical segmentation is U-Net. In this paper, a convolutional neural architecture based on 3D U-Net but with fewer parameters and lower computational cost is used for the segmentation of brain tumors. The proposed model is able to maintain a very efficient performance and provides better results in some cases compared to conventional U-Net, while reducing memory usage, training time and inference time. The model is trained on the BraTS 2021 dataset and is able to achieve Dice scores of 0.9105, 0.884 and 0.8254 on Whole Tumor, Tumor Core and Enhancing-Tumor on the testing dataset.
{"title":"An Efficient Deep Learning Approach for Brain Tumor Segmentation using 3D Convolutional Neural Network","authors":"Syed Muaz Ali, Md. Ashraful Alam","doi":"10.1109/ICCIT57492.2022.10056025","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10056025","url":null,"abstract":"In medical application, deep learning-based biomedical semantic segmentation has provided state-of-the-art results and proven to be more efficient than manual segmentation by human interaction in various cases. One of the most popular architectures for biomedical segmentation is U-Net. In this paper, a convolutional neural architecture based on 3D U-Net but with fewer parameters and lower computational cost is used for the segmentation of brain tumors. The proposed model is able to maintain a very efficient performance and provides better results in some cases compared to conventional U-Net, while reducing memory usage, training time and inference time. The model is trained on the BraTS 2021 dataset and is able to achieve Dice scores of 0.9105, 0.884 and 0.8254 on Whole Tumor, Tumor Core and Enhancing-Tumor on the testing dataset.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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