This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-...This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.展开更多
Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in ...Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in mortality rates associated with cardiovascular diseases.Consequently,the development of a robust and continuous blood pressure monitoring system holds paramount significance.In the context of this research paper,we introduce an innovative deep learning regression model that harnesses phonocardiogram(PCG)data to achieve precise blood pressure estimation.Our novel approach incorporates a convolutional neural network(CNN)-based regression model,which not only enhances its adaptability to spatial variations but also empowers it to capture intricate patterns within the PCG signals.These advancements contribute significantly to the overall accuracy of blood pressure estimation.To substantiate the effectiveness of our proposed method,we meticulously gathered PCG signal data from 78 volunteers,adhering to the ethical guidelines of Suranaree University of Technology(Human Research Ethics number EC-65-78).Subsequently,we rigorously preprocessed the dataset to ensure its integrity.We further employed a K-fold cross-validation procedure for data division and alignment,combining the resulting datasets with a CNNfor blood pressure estimation.The experimental results are highly promising,yielding aMeanAbsolute Error(MAE)and standard deviation(STD)of approximately 10.69±7.23 mmHg for systolic pressure and 6.89±5.22 mmHg for diastolic pressure.Our study underscores the potential for precise blood pressure estimation,particularly using PCG signals,paving the way for a practical,non-invasive method with broad applicability in the healthcare domain.Early detection of abnormal blood pressure levels can facilitate timely medical interventions,ultimately reducing cardiovascular disease-related mortality rates.展开更多
Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates dise...Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.展开更多
Consider the geo-localization task of finding the pose of a camera in a large 3 D scene from a single image.Most existing CNN-based methods use as input textured images.We aim to experimentally explore whether texture...Consider the geo-localization task of finding the pose of a camera in a large 3 D scene from a single image.Most existing CNN-based methods use as input textured images.We aim to experimentally explore whether texture and correlation between nearby images are necessary in a CNN-based solution for the geo-localization task.To do so,we consider lean images,textureless projections of a simple 3 D model of a city.They only contain information related to the geometry of the scene viewed(edges,faces,and relative depth).The main contributions of this paper are:(i)to demonstrate the ability of CNNs to recover camera pose using lean images;and(ii)to provide insight into the role of geometry in the CNN learning process.展开更多
基金This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number:IMSIU-RP23008).
文摘This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.
基金Suranaree University of Technology,Thailand Science Research and Innovation(TSRI)National Science,Research,and Innovation Fund(NSRF)(NRIIS Number 179292).
文摘Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in mortality rates associated with cardiovascular diseases.Consequently,the development of a robust and continuous blood pressure monitoring system holds paramount significance.In the context of this research paper,we introduce an innovative deep learning regression model that harnesses phonocardiogram(PCG)data to achieve precise blood pressure estimation.Our novel approach incorporates a convolutional neural network(CNN)-based regression model,which not only enhances its adaptability to spatial variations but also empowers it to capture intricate patterns within the PCG signals.These advancements contribute significantly to the overall accuracy of blood pressure estimation.To substantiate the effectiveness of our proposed method,we meticulously gathered PCG signal data from 78 volunteers,adhering to the ethical guidelines of Suranaree University of Technology(Human Research Ethics number EC-65-78).Subsequently,we rigorously preprocessed the dataset to ensure its integrity.We further employed a K-fold cross-validation procedure for data division and alignment,combining the resulting datasets with a CNNfor blood pressure estimation.The experimental results are highly promising,yielding aMeanAbsolute Error(MAE)and standard deviation(STD)of approximately 10.69±7.23 mmHg for systolic pressure and 6.89±5.22 mmHg for diastolic pressure.Our study underscores the potential for precise blood pressure estimation,particularly using PCG signals,paving the way for a practical,non-invasive method with broad applicability in the healthcare domain.Early detection of abnormal blood pressure levels can facilitate timely medical interventions,ultimately reducing cardiovascular disease-related mortality rates.
基金the Begum Rokeya University,Rangpur,and the United Arab Emirates University,UAE for partially supporting this work。
文摘Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.
文摘Consider the geo-localization task of finding the pose of a camera in a large 3 D scene from a single image.Most existing CNN-based methods use as input textured images.We aim to experimentally explore whether texture and correlation between nearby images are necessary in a CNN-based solution for the geo-localization task.To do so,we consider lean images,textureless projections of a simple 3 D model of a city.They only contain information related to the geometry of the scene viewed(edges,faces,and relative depth).The main contributions of this paper are:(i)to demonstrate the ability of CNNs to recover camera pose using lean images;and(ii)to provide insight into the role of geometry in the CNN learning process.
