人工智能技术学报(英文)最新文献

{"title":"Detection of Streaks in Astronomical Images Using Machine Learning","authors":"Charles Jeffries, Ruben Acuña","doi":"10.37965/jait.2023.0413","DOIUrl":"https://doi.org/10.37965/jait.2023.0413","url":null,"abstract":"Satellites in Low Earth Orbit (LEO) pose a challenge to astronomy observations requiring long exposure times or wide observation areas. As the number of satellites in LEO dramatically increases, it motivates an increased need for methods to filter out artifacts caused by satellites crossing into observation fields. This paper develops and evaluates a deep learning model based on U-Net to filter these artifacts from collected data. The proposed model is compared with two existing filtering methods on a dataset generated using the state-of-the-art tool Pyradon. Although the initial application of deep learning does include some unpredictable behavior not found in traditional algorithms, the proposed model outperforms the existing methods in overall accuracy while requiring significantly less computational time. This suggests that the application of deep learning to satellite artifact removal which has previously been underdeveloped in the literature may be an appropriate avenue.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42724704","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}

{"title":"An Optimal BDCNN ML Architecture for Car Make Model Prediction","authors":"Kriti Kashyap, Rohit Miri","doi":"10.37965/jait.2023.0268","DOIUrl":"https://doi.org/10.37965/jait.2023.0268","url":null,"abstract":"We take on the challenge of classifying car photos, from the most general car type to the precise make, model, and year of the vehicle for a given input. Analyzing pre-existing datasets, we find that the CompCars-SV are a great place to begin our classification project. We demonstrate that convolutional neural networks can obtain a classification accuracy of more than 90% on the most difficult task. Due to a skewed mix between training and testing, this impressive result isn't really typical of how people do in the actual world. Using an ML system for car detection, we automatically generate a vehicle-tight bounding box for each picture, which we disseminate to the full dataset together with the existing (but limited) type-level annotation. We have designed and implemented car classification algorithms to analyze this car dataset, two of which take advantage of the hierarchical nature of car annotations. According to our research, a more precise classification of car type at a finer resolution now achieves an accuracy of 99.25%. It serves as a baseline benchmark for future research. Focusing on \"vehicle\" tasks, this work intends to bring attention to the vision community's lack of attention to these tasks compared to other objects. The important reason getting higher accuracy is extraction of binary descriptor (BD) feature using edge detection before training the CNN. This step reduced the size of the car dataset; hence network took less time to get trained. From the result outcomes shown it is clear that the presented network architecture having 31 layers of 2d convolutional layer, batch normalization, maxpool, ReLU, fully connected layer and Softmax classifier layer, has given higher accuracy. Numerous relevant car-related issues and solutions have yet to be carefully examined and researched, according to our findings. Car model categorization, model verification, and attribute prognosis are just a few examples of how the dataset might be put to use.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48919578","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}

{"title":"A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning","authors":"R. Mani, J. Kamalakannan, Y. P. Rangaiah, S. Anand, uppu Venkata Subba Rao","doi":"10.37965/jait.2023.0246","DOIUrl":"https://doi.org/10.37965/jait.2023.0246","url":null,"abstract":"Breast cancer is one of the deadly cancer among the female population, and still a developing area of research in the field of medical imaging. The fatality rate is more in patients who are not early diagnosed and are given delayed treatment. Hence researchers are keeping their lot of efforts in developing breast cancer detection systems that could provide accurate diagnosis in the initial stages which are relied on medical imaging. Deep learning is offering key solutions to overcome many image classification tasks. Though deep learning techniques have extended their root to many medical fields even it suffers from the problem of lack of sufficient data. Convolutional Neural Networks are more preferred for medical image classification tasks. In this paper, we propose a transfer learning method that overcomes the issue of insufficient data. We use a familiar pre-trained network VGG-16 (Visual Geometric Group) + with Logistic Regression as a binary classifier. Since hyper-parameters of every CNN has a closer impact on the performance of the entire deep learning model, our method focus on optimizing hyper-parameters using particle swarm optimization which is a bio-inspired algorithm, The proposed model performs classification of Breast Histopathology images into benign and malignant images and produce better results. We use Break His Dataset to test our method and achieve an accuracy of around 96.9%. The experimental results show that this study has improved performance metrics when compared to other transfer learning methods.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47908982","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}

{"title":"Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images","authors":"S. Rama, Naresh Cherukuri, D. Kumar, R. Jayakarthik, B. Nagarajan, A. Balaram, G. Jyothi, Y. Kumar, Email S. Rama Krishna","doi":"10.37965/jait.2023.0264","DOIUrl":"https://doi.org/10.37965/jait.2023.0264","url":null,"abstract":"Diabetic retinopathy (DR), a long-term complication of diabetes, is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms. Standard diagnostic procedures for DR now include OCT and digital fundus imaging. If digital fundus images alone could provide a reliable diagnosis, then eliminating the costly optical coherence tomography would be beneficial for all parties involved. Optometrists and their patients will find this useful. Using deep convolutional neural networks, we provide a novel approach to this problem. Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones. In order to collect more subtle information for categorisation, two such DCNNs, each with a different number of layers, are trained. To establish these limits, we use deep convolutional neural networks (DCNNs) and features extracted from picture metadata to train a support vector machine classifier. In our experiments, we used information from Kaggle's open DR detection database. We fed our model 34,124 training images, 1,000 validation examples, and 53,572 test images to train and test it. Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4. Experimental results show a higher identification rate (86.17%) than those found in the existing literature, indicating the suggested strategy may be effective. We have jointly developed an algorithm for machine learning and accompanying software, and we've named it Deep Retina. Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology. This technology might be used for self-diagnosis, at-home care, and telemedicine.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46270051","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}

{"title":"Automated Staging and Grading for Retinopathy of Prematurity on Indian Database","authors":"S. Kadge, S. Nalbalwar, A. B. Nandgaokar, P. Shah, V Narendran","doi":"10.37965/jait.2023.0235","DOIUrl":"https://doi.org/10.37965/jait.2023.0235","url":null,"abstract":"Retinopathy of prematurity (ROP) is a disorder of the retina in neonates. If ROP is not treated at early stage, neonates’ vision is affected, leading to blindness. It is necessary to diagnose and treat ROP at earliest. Several ROP assessment techniques based on Image analysis have been introduced in recent years. These studies identify only normal, abnormal and plus disease. This research article explores the identification of distinct ROP stages along with normal and abnormal detection. Detecting the stages will help to expedite the treatment and prevent vision loss. The proposed framework consists of feature extraction using Scale Invariant Feature Transform (SIFT) and Pyramid Histogram of Words (PHOW) techniques. Three efficient supervised machine learning algorithms, namely random forest (RF), support vector machine (SVM) and extreme boosting gradient (XGBoost), are used to classify different stages of ROP. A data set captured by RetCam 3 is used to evaluate the model. Based on rigorous evaluation, the accuracy of different ROP stages is 93.68%, 83.33%, 85.71%, 55.55% and 100% for normal, stage 1, 2, 3 and 4, respectively.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43998017","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}

{"title":"Gender Classification from Fingerprint Using Hybrid CNN-SVM","authors":"Vidhya Keren T, Serin J, Mary Ivy Deepa I S, V.Ebenezer, A.Jenefa","doi":"10.37965/jait.2023.0192","DOIUrl":"https://doi.org/10.37965/jait.2023.0192","url":null,"abstract":"Gender classification is used in numerous applications such as biometrics, criminology, surveillance, HCI, and business profiling. Although biometric factors like gait, face, hand shape, and iris have been used to classify people into genders, the majority of research has focused on facial traits due to their more recognisable qualities. This research employs fingerprints to classify gender, with the intention of being relevant for future studies. Several methods for gender classification utilising fingerprints have been presented in the literature, including ANN, KNN, Naive Bayes, the Gaussian mixture model, and deep learning-based classifiers. Although these classifiers have shown good classification accuracy, gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy, computation, and running time. In this paper, a CNN-SVM hybrid framework for gender classification from fingerprints is proposed, where preprocessing, feature extraction, and classification are the three main components. The main goal of this study is to use CNN to extract fingerprint information. These features are then sent to an SVM classifier to determine gender. The hybrid model's performance measures are examined and compared to those of the conventional CNN model. Using a CNN-SVM hybrid model, the accuracy of gender classification based on fingerprints was 99.25%.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44503822","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}

{"title":"Design of P-FLANN Model for Intelligent Water Fountain Sound Pleasantness Monitoring Using Bio-inspired Computing and Human Speech Perception","authors":"Barnali Brahma, T. Dash, G. Panda, L. V. N. Prasad, R. Kulkarni","doi":"10.37965/jait.2023.0229","DOIUrl":"https://doi.org/10.37965/jait.2023.0229","url":null,"abstract":"Cognitive-inspired Computational Computing systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals. It also helps in early and consistent decision-making for various health issues including human psychological health. Water fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also it provides a relaxing environment to the visitors. These natural sounds have a direct effect on the psychological health of visitors. Very few research works are reported on developing the relationship between water sounds and their corresponding psychological impact. This assessment needs trained manpower and a lot of experimental time which is costly and may not be always available. In this paper to access the human health-friendly water fountain sounds from the pleasantness, a Perceptually Weighted functional link artificial neural network (P-FLANN) model is developed. To reduce the computational complexity of training and for faster convergence, swam intelligence-based optimization algorithm is used for updating the weights. It is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95% accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45663932","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}

{"title":"Rectal Cancer Prediction and Performance Based on Intelligent Variational Autoencoders Machine Using Deep Learning on CDAS Dataset","authors":"Gaganpreet Kaur, I. Keshta, Mohammad Shabaz, H. S. Batra, Bhupesh T. Vijaya Sagar, Kumar Singh, B. Singh, Vaddempudi Sujatha, Lakshmi","doi":"10.37965/jait.2023.0241","DOIUrl":"https://doi.org/10.37965/jait.2023.0241","url":null,"abstract":"A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival. However, there isn't yet a valid prediction model available. An efficient feature extraction technique is also required to increase a prediction model's precision. CDAS (cancer data access system) program is a great place to look for cancer along with images or biospecimens. In this study, we look at data from the CDAS system, specifically Bowel cancer (colorectal cancer) datasets. This study suggested a survival prediction method for rectal cancer. In addition, determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy. The initial job that leads to correct findings is corpus cleansing. Moving forward, the data pre-processing activity will be performed, which will comprise \"exploratory data analysis and pruning and normalization or experimental study of data, which is required to obtain data features to design the model for cancer detection at an early stage.\" Aside from that, the data corpus is separated into two sub-corpora: training data and test data, which will be utilized to assess the correctness of the constructed model. This study will compare our auto-encoder accuracy to that of other deep learning algorithms, such as ANN, CNN, and RBM, before implementing the suggested methodology and displaying the model's accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer. Various criteria, including true positive rate, ROC curve, and accuracy scores, are used in the experiments to determine the model's high accuracy. In the end, we determine the accuracy score for each model. The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models. It is shown that variational deep encoders have excellent accuracy of 94% in this cancer prediction and 95% for ROC curve regions. The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’ chances of survival. The best results, with 95% accuracy, were generated by deep autoencoders.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45031065","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}

{"title":"Multi-Objective Reptile Search Algorithm Based Effective Image Deblurring and Restoration","authors":"G. S. Yogananda, A. Babu","doi":"10.37965/jait.2023.0204","DOIUrl":"https://doi.org/10.37965/jait.2023.0204","url":null,"abstract":"Images are frequently affected because of blurring, data loss occurred by sampling and noise occurrence. The images are getting blurred because of object movement in the scenario, atmospheric misrepresentations and optical aberrations. The main objective of image restoration is to evaluate the original image from the corrupted data. To overcome this issue, the Multi-Objective Reptile Search Algorithm is proposed for performing an effective Image Deblurring and Restoration (MORSA-IDR). The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation. In that, threshold values are used for detecting and replacing the noisy pixel removal using Deep Residual Network (DRN), and estimation of kernel is performed for deblurring the images. The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information. The MORSA-IDR is evaluated using Peak SignalNoise Ratio (PSNR) and structural similarity index (SSIM). The existing research such as Enhanced Local Maximum Intensity (ELMI) prior and Deep Unrolling for Blind Deblurring (DUBLID) are used to evaluate the MORSA-IDR.The PSNR of MORSA-IDR for image 6 is 30.98 dB which is high when compared to the ELMI and DUBLID.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48506392","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}

{"title":"A Hybrid Method for Magnetic Resonance Brain Images Classification and Segmentation Using Soft Computing Techniques","authors":"Baireddy Sreenivasa Reddy, A. Sathish","doi":"10.37965/jait.2023.0206","DOIUrl":"https://doi.org/10.37965/jait.2023.0206","url":null,"abstract":"Nowadays, Brain tumor is a serious life-threatening disease that can often be treated with risky surgeries. Various classification and segmentation methods for MR (Magnetic Resonance) brain images have been proposed but the expected accuracy value could not be reached so far. In this paper, we proposed a hybrid approach that includes modified fuzzy C-means and ANN classifier. It consists of five stages (a) Noise removal (b) Feature extraction (c) Feature selection  (d) Classification (e) Segmentation. Initially, a genetic optimized median filter (GOMF) is used to remove noise present in the input image, and then the essential features are extracted and selected using Discrete Wavelet Transform (DWT) & Principle Component Analysis (PCA) algorithms respectively. The normal and abnormal images are classified using the ANN classifier. Finally, it is processed through a Modified fuzzy C-means algorithm to segment the tumor portion separately. The proposed segmentation technique has been tested on the BRATS dataset and produces a sensitivity of 98%, Jaccard index of 97%, specificity of 98%, and accuracy of 95%.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49176700","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}