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Classification of Breast Invasive Ductal Carcinomas Using Histopathological Images Based on Deep Learning Techniques
Women suffer from cancer, which is the main reason for death for females around the world. With the use of artificial intelligence, it is possible to predict and detect all types of cancers in the near future. It is not just women who can heal, and most breast cancers are caused by the most vulnerable type of breast. Eighty percent of all diagnoses of carcinoma are invasive ductal carcinomas (IDCs). In this paper, deep learning techniques are extended to support visible semantic evaluation of tumor areas, using convolutional neural networks (CNNs).A CNN is skilled ended a large number of photo covers (tissue areas) after Whole Slide Images (WSI) to study ranked part-based total image. About 600 normal image patches and 200 breast invasive ductal carcinomas are selected for the experiment. It was intended to amount classifier correctness in the detection of IDC tissue areas in Whole Slide Images. We achieved excellent measurable outcomes for an automated finding of IDC areas with our technique. The results are evaluated based on performance measures and compared with a different number of neurons, and the results are highlighted. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification of countries based on development indices by using K-means and grey relational analysis
Clustering countries based on their development profile is important, as it helps in the efficient allocation and use of resources for institutions like the World Bank, IMF and many others. However, measuring the status of development in each country is challenging, as development encompasses several facets such as economic, social, environmental and institutional aspects. These dimensions should be captured and aggregated appropriately before attempting to classify countries based on development. In this context, this paper attempts to measure various dimensions of development through four indices namely, Economic Index (EI), Social Index (SI), Sustainability Index (SUI) and Institutional Index (II) for the period between 1996 through 2015 for 102 countries. And then we categorize the countries based on these development indices using the grey relational analysis and K-means clustering method. Our study classifies countries into four clusters with twelve countries in the first cluster, fifty in second, twenty-seven and thirteen countries in third and fourth clusters respectively. Having taken each of the dimensions of development independently, our results show that no cluster has performed poorly in all four aspects. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Classification of Disaster Tweets using Machine Learning and Deep Learning Techniques
Social networks provide a plethora of information for gathering extra data on people's behavior, trends, opinions, and feelings during human-affecting occurrences, such as natural catastrophes. Twitter is an inevitable communication medium during calamities. People mainly depend on Twitter to announce real-time emergencies. However, it is rarely straightforward if someone is declaring a tragedy. Sentiment analysis of disaster tweets aid in situational awareness and realizing the disaster dynamics. In our paper, we perform a sentimental analysis of disaster tweets using techniques based on machine learning and deep learning. The tweets are pre-processed before being converted into a structured form using Natural Language Processing (NLP) methods. Supervised learning techniques such as the Support Vector Machine and the Naive Bayes Classifier algorithm are used to develop the Classifier, which categorizes tweets into distinct catastrophes and selects the most appropriate algorithm. The chosen algorithm is further enriched with an emoticon detection algorithm for explicit elucidation. Our research would help disaster relief organizations and news agencies to conclude about the state of affairs and do the needful. 2022 IEEE. -
Classification of Diseased Leaves in Plants Using Convolutional Neural Networks
The article focuses on the classification of diseased leaves using a machine learning algorithm. The main focus in agriculture is controlling pests and weeds, for which farmers spray chemical pesticides to get a good yield. The issue here is over-usage and under-usage of pesticides, which might harm the end consumer. To achieve the goal of reducing pesticide use and detecting pests in the crop early, the machine learning algorithm is deployed on the leaf image. The image data of the leaf of the cauliflower plant is collected for 40days. The data was collected from the day the plant was seeded in a pot until the day it was ready to be planted in the soil. From this data, the pest attack on the plants is tracked without the application of pesticides. To achieve this, the CNN algorithm is used on the collected image data. The outcome of the study would be to classify the diseased leaves based on the pest attack and know the right time to spray the pesticides to reduce the damage to the plant. This also reduces the use of pesticides and costs to the farmer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50Hz from raw EEG recordings. Raw EEGs were segmented into 1s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70% for normal-pre-ictal, 99.70% for normal-epileptic and 99.85% for pre-ictal-epileptic. 2016, Springer Science+Business Media Dordrecht. -
Classification of extragalactic point sources and flux variability characteristics of blazars
Classification of different types of astronomical objects in large surveys usually done through spectroscopy requires enormous amounts of time. Hence, many attempts have been made using broad band photometric magnitudes and spectroscopic observations to classify the sources, particularly extragalactic sources such as active galactic nuclei (AGNs), starburst galaxies and normal galaxies. However, a method which does not involve spectroscopic data would be ideal. -
Classification of extragalactic point sources and flux variability characteristics of blazars
Classification of different types of astronomical objects in large surveys usually done through spectroscopy requires enormous amounts of time. Hence, many attempts have been made using broad band photometric magnitudes and spectroscopic observations to classify the sources, particularly extragalactic sources such as active galactic nuclei (AGNs), starburst galaxies and newlinenormal galaxies. However, a method which does not involve spectroscopic data would be ideal. With this in view, in this work we have made an effort to classify a sample of 37,492 point sources into Quasi-Stellar Objects (QSOs), galaxies and stars using template fitting technique and multiwavelength photometric magnitudes from the Sloan Digital Sky Survey (SDSS) and newlinethe Galaxy Evolution Explorer (GALEX) with coverage from the optical (z: 8931 to the far ultraviolet (FUV: 1516 . Templates for QSOs, galaxies and stars were used to fit the data of the objects to the seven photometric bands of SDSS and GALEX. The results were compared with SDSS spectroscopic classification. Two UV bands (NUV and FUV) were included to remove the possible degeneracies in the classification based only on optical bands or in color-color method. UV bands play a crucial role in the classification and characterization of astronomical objects that emit over a wide range of wavelengths, especially for those that are bright at UV. Classification using template fitting method is consistent with spectroscopic methods, provided UV information of the objects is available. UV bands are particularly important for separating quasars and stars, as well as spiral and starburst galaxies. We have achieved the efficiency of 89% for QSOs, 63% for galaxies and 84% for stars. Objects for which spectroscopic data is not available can also be classified using this method which does not require spectroscopic information. -
Classification of fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s). -
Classification of financial news articles using machine learning algorithms
The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection. 2023, Modern Education and Computer Science Press. All rights reserved. -
Classification of Hypothyroid Disorder using Optimized SVM Method
Hypothyroidism is an endocrine disorder where the thyroid organ doesn't provide the enough amount of thyroid hormones. It is one of the common diseases found in women. Detection of hypothyroidism needs suitable diagnostic tests to encourage prompt analysis and medication. Accurate and early detection of a disease is more important and compulsory in healthcare domain to facilitate correct and prompt diagnosis and timely treatment. The information generated in healthcare domain is on large scale, crucial and complex with multiple parameters. To interpret and understand such a huge data and retrieve the accurate and relevant information from it is a tedious as well as challenging task. However, there is a need and importance to facilitate the patients with better medical solutions. This will help to reduce the cost, time and give more relief to users by applying advanced and upgraded knowledge. It will also assist to prevent the further complications. The proposed study gains the knowledge from the hypothyroid dataset to predict the level of disease. To identify the level of hypothyroid disorder, we used four classification machine learning techniques, namely KNN (K-Nearest Neighbour), SVM (Support Vector Machines), LR (Logistic Regression) and NN (Artificial Neural Network). The Experimental results compared the classification accuracy of four methods. Logistic Regression method achieved 96.08% accuracy among other three classifiers. But, SVM is found the best classifier after standardizing the data and parameter tuning with accuracy of 99.08%. 2019 IEEE. -
Classification of myocardial ischemia in delayed contrast enhancement using machine learning
This chapter addresses the classification of myocardial ischemia in delayed contrast enhancement using machine-learning techniques for magnetic resonance imaging which solves the social issue of a sudden cardiac death. To automate the classification of myocardial ischemia, the computer-aided design has a crucial path on the mixture ensemble of machine learning. The mixture ensemble of machine learning can partition a high-dimensional image in a simultaneous and competitive way. The detection and the segmentation processes are carried out through Fuzzy C-Means multispectral and single-channel algorithms along with a morphological filtering technique for feature extraction. Furthermore, the feed forward neural network (FFNN) technique is trained through the Levenberg-Marquardt Back Propagation algorithm for the classification of myocardial ischemia in delayed contrast enhancement. The proposed classification model performs well for the classification of myocardial ischemia. The rigorous process of the proposed result reveals that the FFNN classifier produces 99.9% accuracy on the classification of myocardial ischemia and also shows that the given classifier is considered one of the best methods in classifying medical myocardial ischemia. 2019 Elsevier Inc. All rights reserved. -
Classification of Psychological Disorders by Feature Ranking and Fusion using Gradient Boosting Classification of Psychological Disorders
Negative emotional regulation is a defining element of psychological disorders. Our goal was to create a machine-learning model to classify psychological disorders based on negative emotions. EEG brainwave dataset displaying positive, negative, and neutral emotions. However, negative emotions are responsible for psychological health. In this paper, research focused solely on negative emotional state characteristics for which the divide-and-conquer approach has been applied to the feature extraction process. Features are grouped into four equal subsets and feature selection has been done for each subset by feature ranking approach based on their feature importance determined by the Random Forest-Recursive Feature Elimination with Cross-validation (RF-RFECV) method. After feature ranking, the fusion of the feature subset is employed to obtain a new potential dataset. 10-fold cross-validation is performed with a grid search created using a set of predetermined model parameters that are important to achieving the greatest possible accuracy. Experimental results demonstrated that the proposed model has achieved 97.71% accuracy in predicting psychological disorders 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved. -
Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images. 2022 Rajesh Doss et al. -
Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing
Skin diseases are a major global health concern for which prompt and precise diagnosis is necessary for effective treatment. Convolutional neural networks (CNN), one of the deep learning techniques, have shown potential in automating the diagnostic procedure. The goal of this research is to enhance the effectiveness of skin disease categorization by fusing the capabilities of CNNs - particularly the VGG architecture - with the histogram equalization preprocessing method. In image processing, histogram equalization is a commonly used approach to enhance the contrast and general quality of medical photographs, which include photos of skin conditions. In order to improve the characteristics and details of dermatological pictures for this study, we employed histogram equalization as a preprocessing step. This allowed CNN to extract pertinent features more quickly. 2024 IEEE. -
Classification of Soil Images using Convolution Neural Networks
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE. -
Classification of supply chain knowledge: A morphological approach
Purpose The purpose of the article is to create a knowledge classification model that can be used by knowledge management (KM) practitioners for establishing a knowledge management framework (KMF) in a supply chain (SC) network. Epistemological and ontological aspects of knowledge have been examined. SC networks provide a more generic setting for managing knowledge due to the additional issues concerning flow of knowledge across the boundaries of organizations. Design/methodology/approach Morphological analysis has been used to build the knowledge classification model. Morphological approach is particularly useful in exploratory research on concepts/ entities having multiple dimensions. Knowledge itself has been shown in literature to have many characteristics, and the methodology used has enabled a comprehensive classification scheme based on such characteristics. Findings A single comprehensive classification model for knowledge that exists in SC networks has been proposed. Nine characteristics, each possessing two or more value options, have been finally included in the model. Research limitations/implications Knowledge characteristics have been mostly derived from past research with the exception of three which have been introduced without empirical evidence. Although the article is primarily about SC knowledge, the results are fairly generic. Practical implications The proposed model would be of use in developing KM policies, procedures and establishing knowledge management systems in SC networks. The model will cater to both system and people aspects of a KMF. Originality/value The proposed knowledge classification model based on morphological analysis fills a gap in a vital area of research in KM as well as SC management. No similar classification model of knowledge with all its dimensions has been found in literature. Emerald Group Publishing Limited. -
Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background
Vehicle detection and recognition is an important task in the area of advanced infrastructure and movement administration. Many researchers are working on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusion, shadow and illumination. These attributes will make the problem very challenging. In the proposed work, system will be trained with different samples of vehicles belongs to the different make. Classify those samples into different classes of models belongs to same make using Neural Network Classifier. Exploratory outcomes display promising possibilities efficiently. 2019, Springer Nature Singapore Pte Ltd. -
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning
In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories. 2021, Springer Nature Singapore Pte Ltd. -
Classification of Vitiligo using CNN Autoencoder
Precise recognition of skin ailment is a time-consuming procedure even for Professionals. With the invention of deep learning and medical image processing, the identification of skin disease is possible in a time-efficient manner and accurately. Autoencoder is the generative algorithm but in the proposed work it is used as a generator and as well as a classifier. In this work, a Convolutional (CNN) autoencoder was used to classify the skin disease Vitiligo. In this work encoding and decoding layers were used but in the last layer in place of reproducing the original image, the classification layer was used to classify the image. The proposed work gave training accuracy of 87.71 % whereas validation accuracy was 90.16%. 2022 IEEE.