期刊文献+
共找到8篇文章
< 1 >
每页显示 20 50 100
MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves
1
作者 Chirag Chandrashekar K.P.Vijayakumar +1 位作者 K.Pradeep a.balasundaram 《Computers, Materials & Continua》 SCIE EI 2024年第2期2511-2533,共23页
The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accu... The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing costs.MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput. 展开更多
关键词 Leaf disease detection deep convolutional neural networks transfer learning optimization MangoLeafBD Dataset
下载PDF
Deep Learning Based Face Detection and Identification of Criminal Suspects
2
作者 S.Sandhya a.balasundaram Ayesha Shaik 《Computers, Materials & Continua》 SCIE EI 2023年第2期2331-2343,共13页
Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is co... Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their identity.Hence,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the CriminalDatabase.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above. 展开更多
关键词 Deep learning OPENCV deep neural network single shot multi-box detector auto-encoder cosine similarity
下载PDF
Gaussian Blur Masked ResNet2.0 Architecture for Diabetic Retinopathy Detection
3
作者 Swagata Boruah Archit Dehloo +2 位作者 Prajul Gupta Manas Ranjan Prusty a.balasundaram 《Computers, Materials & Continua》 SCIE EI 2023年第4期927-942,共16页
Diabetic Retinopathy (DR) is a serious hazard that can result inirreversible blindness if not addressed in a timely manner. Hence, numeroustechniques have been proposed for the accurate and timely detection ofthis dis... Diabetic Retinopathy (DR) is a serious hazard that can result inirreversible blindness if not addressed in a timely manner. Hence, numeroustechniques have been proposed for the accurate and timely detection ofthis disease. Out of these, Deep Learning (DL) and Computer Vision (CV)methods for multiclass categorization of color fundus images diagnosed withDiabetic Retinopathy have sparked considerable attention. In this paper,we attempt to develop an extended ResNet152V2 architecture-based DeepLearning model, named ResNet2.0 to aid the timely detection of DR. TheAPTOS-2019 datasetwas used to train the model. This consists of 3662 fundusimages belonging to five different stages of DR: no DR (Class 0), mild DR(Class 1), moderate DR (Class 2), severe DR (Class 3), and proliferativeDR (Class 4). The model was gauged based on ability to detect stage-wiseDR. The images were pre-processed using negative and positive weightedGaussian-based masks as feature engineering to further enhance the qualityof the fundus images by removing the noise and normalizing the images. Upsamplingand data augmentation methods were used to address the skewnessof the original dataset. The proposed model achieved an overall accuracyof 91% and an area under the receiver-operating characteristic curve (AUC)score of 95.1%, outperforming existing Deep Learning models by around 10%.Furthermore, the class-wise F1 score for No DR was 92%, Mild DR was 82%,Moderate DR was 66%, Severe was DR 89% and Proliferative DR was 80%. 展开更多
关键词 Diabetic retinopathy deep learning transfer learning image processing image classification
下载PDF
CE-EEN-B0:Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images
4
作者 Abishek Mahesh Deeptimaan Banerjee +2 位作者 Ahona Saha Manas Ranjan Prusty a.balasundaram 《Computers, Materials & Continua》 SCIE EI 2023年第3期5967-5982,共16页
A brain tumor is the uncharacteristic progression of tissues in the brain.These are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life span.Hence,their classificatio... A brain tumor is the uncharacteristic progression of tissues in the brain.These are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life span.Hence,their classification and detection play a critical role in treatment.Traditional Brain tumor detection is done by biopsy which is quite challenging.It is usually not preferred at an early stage of the disease.The detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the tumor.This paper aims to identify and detect brain tumors based on their location in the brain.In order to achieve this,the paper proposes a model that uses an extended deep Convolutional Neural Network(CNN)named Contour Extraction based Extended EfficientNet-B0(CE-EEN-B0)which is a feed-forward neural network with the efficient net layers;three convolutional layers and max-pooling layers;and finally,the global average pooling layer.The site of tumors in the brain is one feature that determines its effect on the functioning of an individual.Thus,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma tumor.This network provides an accuracy of 97.24%,a precision of 96.65%,and an F1 score of 96.86%which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image.This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps. 展开更多
关键词 Brain tumor image preprocessing contour extraction disease classification transfer learning
下载PDF
Energy and Bandwidth Based Link Stability Routing Algorithm for IoT 被引量:1
5
作者 D.Kothandaraman a.balasundaram +4 位作者 R.Dhanalakshmi Arun Kumar Sivaraman S.Ashokkumar Rajiv Vincent M.Rajesh 《Computers, Materials & Continua》 SCIE EI 2022年第2期3875-3890,共16页
Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links.Particularly,some of the nodes in IoT are mobile and dynamic in nature.He... Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links.Particularly,some of the nodes in IoT are mobile and dynamic in nature.Hence maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing.In such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of service.Due to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the nodes.The existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the routing.Link stability helps us to define whether the node is within or out of a coverage range.This paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and throughput.In this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability value.Among the existing routes,the sink node will choose the optimal route which is having less link stability value.Highly stable link is determined by evaluating link stability value using distance and velocity.Residual-energy of the node is estimated using the current energy and the consumed energy.Consumed energy is estimated using transmitted power and the received power.Available bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision. 展开更多
关键词 Link stability internet of things optimal energy optimal bandwidth residual energy
下载PDF
Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network 被引量:1
6
作者 V.Ulagamuthalvi G.Kulanthaivel +1 位作者 a.balasundaram Arun Kumar Sivaraman 《Computer Systems Science & Engineering》 SCIE EI 2022年第10期275-289,共15页
One of the fast-growing disease affecting women’s health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than m... One of the fast-growing disease affecting women’s health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately.Deep learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical images.Existing methods focused on using various conventional and machine learning methods for processing natural and medical images.It is inadequate for the image where the coarse structure matters most.Most of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in classification.Whereas deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate prediction.Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images.The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms. 展开更多
关键词 Medical image processing deep learning convolution neural network breast cancer feature extraction classification
下载PDF
Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors
7
作者 P.Arunachalam N.Janakiraman +5 位作者 Junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman a.balasundaram 《Computers, Materials & Continua》 SCIE EI 2022年第8期2521-2543,共23页
In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are d... In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity. 展开更多
关键词 Principal components analysis independent components analysis linear discriminant analysis support vector machine blockchain technology IoT application industry application
下载PDF
Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction
8
作者 S.Karthik Robin Singh Bhadoria +5 位作者 Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta a.balasundaram Brijesh Kumar Chaurasia S.Ashokkumar 《Computers, Materials & Continua》 SCIE EI 2022年第7期243-259,共17页
Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reduc... Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python. 展开更多
关键词 Bayesian learning model kalman filter machine learning data accuracy prediction
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部