Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074586
Bineesh Jose, K. Pushpalatha
A novel Deep Convolutional Neural Network (DCNN) model is proposed for handwritten Malayalam character recognition using Transfer Learning in this work. Popular Transfer Learning models such as Inception-V4, AlexNet, DenseNet, and VGG are used as a feature extractors. The implementation of popular models like AlexNet, DenseNet-121, DenseNet-201, VGG-11, VGG-16, VGG-19 and Inception-v4 was done with 15 epochs. 99% accuracy was achieved by Inception-V4 with an average epoch time of 16.3 minutes. At the same time, 98% accuracy was achieved by AlexNet with an average training time of 2.2 minutes per epoch, which shows that Inception-V4 performs well. Inception framework that has demonstrated excellent performance at a low computational cost. In this paper, we used residual connections within a traditional Inception architecture, which resulted in state-of-the-art learning performance with the highest accuracy of 99.69% and an average epoch time of 15.1 minutes.
{"title":"Malayalam Handwritten Character Recognition Using Transfer Learning","authors":"Bineesh Jose, K. Pushpalatha","doi":"10.1109/AICAPS57044.2023.10074586","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074586","url":null,"abstract":"A novel Deep Convolutional Neural Network (DCNN) model is proposed for handwritten Malayalam character recognition using Transfer Learning in this work. Popular Transfer Learning models such as Inception-V4, AlexNet, DenseNet, and VGG are used as a feature extractors. The implementation of popular models like AlexNet, DenseNet-121, DenseNet-201, VGG-11, VGG-16, VGG-19 and Inception-v4 was done with 15 epochs. 99% accuracy was achieved by Inception-V4 with an average epoch time of 16.3 minutes. At the same time, 98% accuracy was achieved by AlexNet with an average training time of 2.2 minutes per epoch, which shows that Inception-V4 performs well. Inception framework that has demonstrated excellent performance at a low computational cost. In this paper, we used residual connections within a traditional Inception architecture, which resulted in state-of-the-art learning performance with the highest accuracy of 99.69% and an average epoch time of 15.1 minutes.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130429938","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074180
Aashish Kalra, Aishwarya Salunke, Pooja Majali, Preeti Bhandiwad, Kavita Chachadi, S. Kamath, Sandeep Jana, Rajas Joshi
Hand pose estimation has been playing a major role in many applications such as in Augmented/Virtual reality that is the human-computer interaction and gesture recognition. Among the existing hand datasets, some of them are synthetically generated which do not provide information about the background considering the various lighting conditions where the hand skin tone information would be lost.Hence, the proposed Labelled Hand Dataset in the Wild provides this additional information and also solves a major problem ie occlusion. Since manually annotating a large dataset is a tedious task,hence we propose a novel approach to automate the generation of large dataset using the triangulation method which is also known as multiview Annotation.In this approach two best frames are labelled with the 2D points(21 keypoints) which are then triangulated in 3D space using multiview geometry with the use of fiducial markers.These triangulated points in a 3D space are reprojected onto all other images of a particular pose and this process is repeated for all the other poses thus automating the generation of large labeled dataset in wild.
{"title":"Labeled Hands in Wild","authors":"Aashish Kalra, Aishwarya Salunke, Pooja Majali, Preeti Bhandiwad, Kavita Chachadi, S. Kamath, Sandeep Jana, Rajas Joshi","doi":"10.1109/AICAPS57044.2023.10074180","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074180","url":null,"abstract":"Hand pose estimation has been playing a major role in many applications such as in Augmented/Virtual reality that is the human-computer interaction and gesture recognition. Among the existing hand datasets, some of them are synthetically generated which do not provide information about the background considering the various lighting conditions where the hand skin tone information would be lost.Hence, the proposed Labelled Hand Dataset in the Wild provides this additional information and also solves a major problem ie occlusion. Since manually annotating a large dataset is a tedious task,hence we propose a novel approach to automate the generation of large dataset using the triangulation method which is also known as multiview Annotation.In this approach two best frames are labelled with the 2D points(21 keypoints) which are then triangulated in 3D space using multiview geometry with the use of fiducial markers.These triangulated points in a 3D space are reprojected onto all other images of a particular pose and this process is repeated for all the other poses thus automating the generation of large labeled dataset in wild.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130011886","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074314
Mohammad Sakib Hossain, S. M. Nazmul Hassan, M. Al-Amin, Md. Nakib Rahaman, Rakib Hossain, Muhammad Iqbal Hossain
Chronic kidney disease, often called chronic kidney failure, is a steady decline of renal function. Some of the most common reasons for kidney failure are cysts, stones, and tumors. There may be no symptoms of chronic renal disease in its first stages. However, It’s possible to have kidney disease and not know it until it’s too late. Fortunately, various neural networks have been shown to be beneficial in early disease prediction as machine learning and computer science have progressed. In this paper, we have used 3 CNN classification methods that are based on watershed segmentation and make use of deep neural networks (DNN) to classify 4 types (cyst, normal, stone, tumor) of kidney CT images. There are two stages to our work. We have first segmented the region of choice in CT images by the watershed algorithm. The segmented kidney data was then used to train a variety of classification networks, which includes EAnet and the transfer learning-based pre-trained neural network: ResNet50, and a customized CNN model. The models were trained using the CT Kidney Normal Cyst Tumor and Stone dataset that was made public on Kaggle. Finally, EANet, ResNet50, and the proposed CNN model achieved 83.65%, 87.92%, and 98.66% of accuracy, respectively, on the test set of classification models. We observed that the proposed CNN model had the highest sensitivity and specificity as well as the best overall accuracy.
{"title":"Kidney Disease Detection from CT Images using a customized CNN model and Deep Learning","authors":"Mohammad Sakib Hossain, S. M. Nazmul Hassan, M. Al-Amin, Md. Nakib Rahaman, Rakib Hossain, Muhammad Iqbal Hossain","doi":"10.1109/AICAPS57044.2023.10074314","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074314","url":null,"abstract":"Chronic kidney disease, often called chronic kidney failure, is a steady decline of renal function. Some of the most common reasons for kidney failure are cysts, stones, and tumors. There may be no symptoms of chronic renal disease in its first stages. However, It’s possible to have kidney disease and not know it until it’s too late. Fortunately, various neural networks have been shown to be beneficial in early disease prediction as machine learning and computer science have progressed. In this paper, we have used 3 CNN classification methods that are based on watershed segmentation and make use of deep neural networks (DNN) to classify 4 types (cyst, normal, stone, tumor) of kidney CT images. There are two stages to our work. We have first segmented the region of choice in CT images by the watershed algorithm. The segmented kidney data was then used to train a variety of classification networks, which includes EAnet and the transfer learning-based pre-trained neural network: ResNet50, and a customized CNN model. The models were trained using the CT Kidney Normal Cyst Tumor and Stone dataset that was made public on Kaggle. Finally, EANet, ResNet50, and the proposed CNN model achieved 83.65%, 87.92%, and 98.66% of accuracy, respectively, on the test set of classification models. We observed that the proposed CNN model had the highest sensitivity and specificity as well as the best overall accuracy.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432931","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074505
D. Tayal, Neha Srivastava, Urshi Singh
In recent years, research has focused on developing a deep learning network that could use images of the ear drum identify conditions in the middle ear. Automatic ear problem diagnosis in particular, can be helpful. Even when antibiotics are used to treat it, otitis media still drives hearing impairment, even loss of hearing in almost all age groups. The evaluation of the tympanic membrane and assessment of the potential value of the network during the diagnostic process constitute the initial examination for the diagnosis of ear sickness. The strategy for identifying middle ear disorders using several deep learning models is proposed in this paper. Deep neural learning aid in the analysis of ear condition may increase medical accessibility for persons without access to otolaryngologists by assisting non- specialists in recognizing otitis media. This deep learning network will be able to diagnose the middle ear problems more precisely and help doctors analyse images of the tympanic membrane.
{"title":"Diagnosis of Middle Ear Diseases using Deep Learning Paradigm","authors":"D. Tayal, Neha Srivastava, Urshi Singh","doi":"10.1109/AICAPS57044.2023.10074505","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074505","url":null,"abstract":"In recent years, research has focused on developing a deep learning network that could use images of the ear drum identify conditions in the middle ear. Automatic ear problem diagnosis in particular, can be helpful. Even when antibiotics are used to treat it, otitis media still drives hearing impairment, even loss of hearing in almost all age groups. The evaluation of the tympanic membrane and assessment of the potential value of the network during the diagnostic process constitute the initial examination for the diagnosis of ear sickness. The strategy for identifying middle ear disorders using several deep learning models is proposed in this paper. Deep neural learning aid in the analysis of ear condition may increase medical accessibility for persons without access to otolaryngologists by assisting non- specialists in recognizing otitis media. This deep learning network will be able to diagnose the middle ear problems more precisely and help doctors analyse images of the tympanic membrane.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122782700","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074447
G. S, G. C., Vishnu Vinod
Medical image analysis plays a powerful role in clinical assistance for the diagnosis and treatment of diseases. Image segmentation is an essential part of the medical imaging process as it extracts the region of interest through semi-automated or automated methods. Deep learning approaches have emerged as a fast-growing research field in medical image analysis. Vision transformers (ViT) are deep learning models that came up as a competing substitute for convolutional neural networks. ViT reports breakthroughs in computer vision tasks including object classification, detection, localization, and segmentation. Colon polyp detection and segmentation is a challenging task in the medical diagnosis and prognosis of colorectal cancer. Early detection and segmentation of polyp regions are of the utmost importance in preventing disease in later stages. In this work, we explore a hierarchical vision transformer as the backbone, replacing convolutional neural networks (CNNs) for the segmentation of polyps. The hierarchical vision transformer is composed of several stages, each having a different resolution. Through the use of a convolutional decoder, the patches from various stages are successively combined to produce full pre-dictions. The transformer backbone has a global receptive field at every stage that provide finer-grained and globally relevant predictions. Experimental results indicate that we can fine-tune the architecture to generate promising results on segmentation metrics even on smaller datasets, with mean Dice and mean IoU scores of 74% and 73% on the Kvasir-SEG dataset.
{"title":"Hierarchical vision transformer model for polyp segmentation","authors":"G. S, G. C., Vishnu Vinod","doi":"10.1109/AICAPS57044.2023.10074447","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074447","url":null,"abstract":"Medical image analysis plays a powerful role in clinical assistance for the diagnosis and treatment of diseases. Image segmentation is an essential part of the medical imaging process as it extracts the region of interest through semi-automated or automated methods. Deep learning approaches have emerged as a fast-growing research field in medical image analysis. Vision transformers (ViT) are deep learning models that came up as a competing substitute for convolutional neural networks. ViT reports breakthroughs in computer vision tasks including object classification, detection, localization, and segmentation. Colon polyp detection and segmentation is a challenging task in the medical diagnosis and prognosis of colorectal cancer. Early detection and segmentation of polyp regions are of the utmost importance in preventing disease in later stages. In this work, we explore a hierarchical vision transformer as the backbone, replacing convolutional neural networks (CNNs) for the segmentation of polyps. The hierarchical vision transformer is composed of several stages, each having a different resolution. Through the use of a convolutional decoder, the patches from various stages are successively combined to produce full pre-dictions. The transformer backbone has a global receptive field at every stage that provide finer-grained and globally relevant predictions. Experimental results indicate that we can fine-tune the architecture to generate promising results on segmentation metrics even on smaller datasets, with mean Dice and mean IoU scores of 74% and 73% on the Kvasir-SEG dataset.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123072007","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074211
Aditya D. Joshi, Rajarshi Bhattacharyay, Garvit Luhadia, V. V.
The objective of this research is to demonstrate the performance difference exhibited by the seam carving algorithm when executed sequentially on a conventional CPU as opposed to when it is run parallelly. Multithreading and multiprocessing were two of the parallelization methods used in this project. These execution times were recorded, and an execution time analysis was performed. In the end, the researchers compared the two to determine which parallelization technique performed better. The researchers believe that the applications of this algorithm when parallelized can be significantly expanded. This expansion could potentially allow it to be used in applications such as thumbnail generation, responsive web development, and a complete contentaware alternative to image resizing (skewing and stretching).
{"title":"Execution Time Analysis of Multithreading and Multiprocessing on Seam Carving Algorithm","authors":"Aditya D. Joshi, Rajarshi Bhattacharyay, Garvit Luhadia, V. V.","doi":"10.1109/AICAPS57044.2023.10074211","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074211","url":null,"abstract":"The objective of this research is to demonstrate the performance difference exhibited by the seam carving algorithm when executed sequentially on a conventional CPU as opposed to when it is run parallelly. Multithreading and multiprocessing were two of the parallelization methods used in this project. These execution times were recorded, and an execution time analysis was performed. In the end, the researchers compared the two to determine which parallelization technique performed better. The researchers believe that the applications of this algorithm when parallelized can be significantly expanded. This expansion could potentially allow it to be used in applications such as thumbnail generation, responsive web development, and a complete contentaware alternative to image resizing (skewing and stretching).","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122099384","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074000
Dwitrisha Saha, Shwetha Prabhu, Ananya Thapliyal, Manohara M M Pai
Plantar pressure measurements in the upright position and ambulation provide comprehensive statistics for analysis of diseases or foot abnormalities and this can be used to track disease progression. Crucial details on the mechanical aspect of the human foot in both static and dynamic states are also obtained by plantar pressure. In diabetic patients, estimation of the pressure of the plantar region of the foot is important for the prevention of foot abnormalities that occur as a result of loss of sensation in the plantar region due to increased mechanical stress, finally leading to the formation of ulcers on the foot. In this paper, the plantar pressure of various subjects is collected and analysed to detect abnormalities. The plantar pressure at different regions of various subjects is collected through the RSscan foot scanner. The plantar pressure data of 30 subjects, diabetic as well as non diabetic, both male and female of different age groups were considered for experimentation. The data obtained from the foot scanner is in the form of an excel file. For the analysis, the excel file of static plantar pressure data of different subjects is being converted into CSV format for ease of analysis. The analysis is performed through programming in python and the results are compared with the ground truth which indicates that the method is effective.
{"title":"Analysis of Plantar Pressure to detect Foot Abnormalities among various subjects","authors":"Dwitrisha Saha, Shwetha Prabhu, Ananya Thapliyal, Manohara M M Pai","doi":"10.1109/AICAPS57044.2023.10074000","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074000","url":null,"abstract":"Plantar pressure measurements in the upright position and ambulation provide comprehensive statistics for analysis of diseases or foot abnormalities and this can be used to track disease progression. Crucial details on the mechanical aspect of the human foot in both static and dynamic states are also obtained by plantar pressure. In diabetic patients, estimation of the pressure of the plantar region of the foot is important for the prevention of foot abnormalities that occur as a result of loss of sensation in the plantar region due to increased mechanical stress, finally leading to the formation of ulcers on the foot. In this paper, the plantar pressure of various subjects is collected and analysed to detect abnormalities. The plantar pressure at different regions of various subjects is collected through the RSscan foot scanner. The plantar pressure data of 30 subjects, diabetic as well as non diabetic, both male and female of different age groups were considered for experimentation. The data obtained from the foot scanner is in the form of an excel file. For the analysis, the excel file of static plantar pressure data of different subjects is being converted into CSV format for ease of analysis. The analysis is performed through programming in python and the results are compared with the ground truth which indicates that the method is effective.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144238","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074267
Shyam Sundar Ramaswami, Gandharba Swain
E-mails have become an inevitable part of everyone's internet lives today. Be it business, commercial, be it entertainment, e-mails are the crux of marketing and communication e-mail is the primary entry point for many malware-based attacks. Malspam is another form of e-mail delivered with malicious attachments. Macro-based malware is very common these days where the threat actor plans a malicious script that executes or downloads the actual malware when the document is opened. This is more towards a Microsoft Office document. In this paper, we are discussing a technique where the threat actors went un-detected for months by anti-virus vendors and how we ended up detecting the malicious elements inside a Microsoft Office document. This paper also proposes a solution using Anergy Scoring Methodology to flag a good Microsoft Office document vs a bad Microsoft Office Document in a swift and convincing manner.
{"title":"Detecting Macro less and Anti-evasive Malware in Malspam Word Attachments Using Anergy Scoring Methodology","authors":"Shyam Sundar Ramaswami, Gandharba Swain","doi":"10.1109/AICAPS57044.2023.10074267","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074267","url":null,"abstract":"E-mails have become an inevitable part of everyone's internet lives today. Be it business, commercial, be it entertainment, e-mails are the crux of marketing and communication e-mail is the primary entry point for many malware-based attacks. Malspam is another form of e-mail delivered with malicious attachments. Macro-based malware is very common these days where the threat actor plans a malicious script that executes or downloads the actual malware when the document is opened. This is more towards a Microsoft Office document. In this paper, we are discussing a technique where the threat actors went un-detected for months by anti-virus vendors and how we ended up detecting the malicious elements inside a Microsoft Office document. This paper also proposes a solution using Anergy Scoring Methodology to flag a good Microsoft Office document vs a bad Microsoft Office Document in a swift and convincing manner.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128837510","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 : 2022-12-31DOI: 10.1109/AICAPS57044.2023.10074399
Aysha Khan, R. Ali
Social media has become a great platform for users to communicate and share their opinions, photos, and videos that contemplate their moods, feelings, and emotions. This wide variety of data provides multiple possibilities for exploring social media data to investigate feelings and sentiments based on their moods and attitudes. With the enormous increase in mental health disorders among individuals, there is a massive loss in productivity and quality of life. Social media platforms like Reddit are used to seek medical advice on mental health issues. The structure and the content on various subreddits can be employed to interpret and connect the posts for mental health diagnostic problems. In this work, we have focused on mental health disorders from subreddits, namely Anxiety, Depression, Bipolar, Autism, Borderline personality disorder, Schizophrenia, and mental health, which are posted by users on the Reddit social media platform. In this work, we have measured the effectiveness of topic modeling using Latent Dirichlet Allocation on these social media posts to identify the most used words and discover the hidden topics in their posts and also analyzed the results on evaluation metrics based on perplexity and coherence scores on unigrams, bigrams, and trigrams.
{"title":"Measuring the Effectiveness of LDA-Based Clustering for Social Media Data","authors":"Aysha Khan, R. Ali","doi":"10.1109/AICAPS57044.2023.10074399","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074399","url":null,"abstract":"Social media has become a great platform for users to communicate and share their opinions, photos, and videos that contemplate their moods, feelings, and emotions. This wide variety of data provides multiple possibilities for exploring social media data to investigate feelings and sentiments based on their moods and attitudes. With the enormous increase in mental health disorders among individuals, there is a massive loss in productivity and quality of life. Social media platforms like Reddit are used to seek medical advice on mental health issues. The structure and the content on various subreddits can be employed to interpret and connect the posts for mental health diagnostic problems. In this work, we have focused on mental health disorders from subreddits, namely Anxiety, Depression, Bipolar, Autism, Borderline personality disorder, Schizophrenia, and mental health, which are posted by users on the Reddit social media platform. In this work, we have measured the effectiveness of topic modeling using Latent Dirichlet Allocation on these social media posts to identify the most used words and discover the hidden topics in their posts and also analyzed the results on evaluation metrics based on perplexity and coherence scores on unigrams, bigrams, and trigrams.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134292412","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}
Brain tumor is a deadly disease since it spreads to various parts and affects their functioning. Cells grow abnormally and form as tumors. Various techniques have been implemented for the identification but image processing is quite complicated. The dataset used is brain tumor dataset which consists of MRI scans of the brain. The groundwork presents a technique in detecting this ailment of brain tumor from the provided MRI images with approving accuracy. The dataset named brain tumor dataset is utilized for proposed work. Our methodology consists of various steps such as pre-processing to enhance the image by reducing noise through filters. This is followed by threshold segmentation strategy. Later, the morphological operations are involved in the further stage. In the end, the tumor region is inferred employing image subtraction method.
{"title":"Detection of Brain Tumor Using Image Processing Techniques","authors":"Venkata Ratna Prabha K, Ravikiran Gujjarlapudi, Sravya Ravi, Yasaswini Satuluri, Chandini Nekkanti, Ramesh P","doi":"10.1109/AICAPS57044.2023.10074053","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074053","url":null,"abstract":"Brain tumor is a deadly disease since it spreads to various parts and affects their functioning. Cells grow abnormally and form as tumors. Various techniques have been implemented for the identification but image processing is quite complicated. The dataset used is brain tumor dataset which consists of MRI scans of the brain. The groundwork presents a technique in detecting this ailment of brain tumor from the provided MRI images with approving accuracy. The dataset named brain tumor dataset is utilized for proposed work. Our methodology consists of various steps such as pre-processing to enhance the image by reducing noise through filters. This is followed by threshold segmentation strategy. Later, the morphological operations are involved in the further stage. In the end, the tumor region is inferred employing image subtraction method.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075162","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：