Effective Classification Technique Research Articles

Modified U-Net and Convolutional Networks for Breast Cancer Segmentation and Classification with New Texture Descriptors

Effective techniques for detection and classification are necessary to enhance the results of patients with common fatal diseases like breast cancer. With benefits including increased effectiveness and precision, DL and ML methods are currently being used as effective tools in the breast cancer diagnosis process. However, the efficiency and stability constraints of current approaches highlight the necessity for additional study. Therefore, this work suggested a new model of modified U-Net and convolutional networks for Breast Cancer (BC) segmentation and classification with new texture descriptors and the proposed model is named “U-TexBCNet”. First, mammography images are preprocessed by enhancing the image using the CLAHE filter and after inputting the preprocessed image into the modified U-Net-based segmentation for the segmentation process, the modified U-Net segmentation is named as “HexpNet”. Once the segmentation process is completed, the feature extraction process takes place. Here, the features include shape feature, color feature, VGG-16 feature, Inception V3 feature, GoogLeNet and modified LGTP feature extracted from the segmented image. Then, the extracted features are inputted into the hybrid classification model including modified CNN and LSTM. Lastly, the outcome from the classifiers (Modified CNN and LSTM) is averaged together to get the outcome of the proposed model. Ultimately, the results of the simulation are evaluated using both positive and negative criteria by contrasting them with the conventional models.

An effective and empirical technique for classification using association rule

An effective and empirical technique for classification using association rule

Investigating Effective Data Augmentation Techniques for Accurate Gastric Classification in the Development of a Deep Learning-Based Computer-Aided Diagnosis System

Gastric cancer is a significant health concern, particularly in Korea, and its accurate detection is crucial for effective treatment. However, a gastroscopic biopsy can be time-consuming and may, thus, delay diagnosis and treatment. Thus, this study proposed a gastric cancer diagnostic method, CADx, to facilitate a more efficient image analysis. Owing to the challenges in collecting medical image data, small datasets are often used in this field. To overcome this limitation, we used AutoAugment’s ImageNet policy and applied cut-and-paste techniques using a sliding window algorithm to further increase the size of the dataset. The results showed an accuracy of 0.8317 for T-stage 1 and T-stage 4 image classification and an accuracy of 0.8417 for early gastric cancer and normal image classification, indicating improvements of 7 and 9%, respectively. Furthermore, through the application of test-time augmentation to the early gastric cancer and normal image datasets, the image classification accuracy was improved by 5.8% to 0.9000. Overall, the results of this study demonstrate the effectiveness of the proposed augmentation methods for enhancing gastric cancer classification performance.

Survey on Hinglish to English Translation and Classification Techniques

Code-mixing is the process of using many languages in one sentence and has a widespread occurrence in multilingual communities. It is particularly prevalent in texts on social media. Due to the widespread usage of social networking sites, a substantial amount of unstructured text is produced. Hinglish, i.e. code-mixed Hindi and English, is a frequent occurrence in everyday language use in India. Hence, a translation process is required to help monolingual users and to aid in the comprehension of language processing models. In this paper, we study the effective techniques for classification and translation tasks and also find gaps and challenges in the current research domain. After comparing a few existing methodologies for machine translation, a framework which showed an improvement in task of translation over the previous methods is proposed.

ChatAgri: Exploring potentials of ChatGPT on cross-linguistic agricultural text classification

In the era of sustainable smart agriculture, a vast amount of agricultural news text is posted online, accumulating significant agricultural knowledge. To efficiently access this knowledge, effective text classification techniques are urgently needed. Deep learning approaches, such as fine-tuning strategies on pre-trained language models (PLMs), have shown remarkable performance gains. Nonetheless, these methods face several complex challenges, including limited agricultural training data, poor domain transferability (especially across languages), and complex and expensive deployment of large models. Inspired by the success of recent ChatGPT models (e.g., GPT-3.5, GPT-4), this work explores the potential of applying ChatGPT in the field of agricultural informatization. Various crucial factors, such as prompt construction, answer parsing, and different ChatGPT variants, are thoroughly investigated to maximize its capabilities. A preliminary comparative study is conducted, comparing ChatGPT with PLMs-based fine-tuning methods and PLMs-based prompt-tuning methods. Empirical results demonstrate that ChatGPT effectively addresses the mentioned research challenges and bottlenecks, making it an ideal solution for agricultural text classification. Moreover, ChatGPT achieves comparable performance to existing PLM-based fine-tuning methods, even without fine-tuning on agricultural data samples. We hope this preliminary study could inspire the emergence of a general-purpose AI paradigm for agricultural text processing.

Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset

Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.

IMPROVING CLASSIFICATION PERFORMANCE USING ENSEMBLE LEARNING APPROACH

The data mining techniques are used for evaluation of the data in order to find and represent the data in such manner by which the applications are becomes beneficial. Therefore, different kinds of computational algorithms and modeling’s are incorporated for analyzing the data. These computational algorithms are help to understand the data patterns and their application utility. The data mining algorithms supports supervised as well as unsupervised techniques of data analysis. This work is aimed to investigate about the supervised learning technique specifically performance improvements on classification techniques. The proposed classification model includes the multiple classifiers namely Bayesian classifier, k-nearest neighbor and the c4.5 decision tree algorithm. By nature of the outcomes and the modeling of the data these algorithms are functioning differently from each other. Thus, a weight based classification technique is introduced in this work. The weight is a combination of outcomes provided by the implemented three classifiers in terms of their predicted class labels. Using the weighted outcomes, the final class label for the input data instance is decided. The implementation of the proposed working model is performed with the help of JAVA and WEKA classes. The results obtained by experimentation of the proposed approach with the vehicle data set demonstrate the high accurate classification results. Thus, the proposed model is an effective classification technique as compared to single model implementation for classification task.

Comparison of logistic regression and artificial neural network model for apron allocation assignment

Management of the parking apron is one of the most essential airport ground service operations for flight operations to run smoothly. Effective airport ground service management will have a direct effect on the cost and duration of flights. Therefore, in this paper, we address the issue of using machine learning techniques, such as logistic regression analysis and artificial neural network (ANNs) models, for classified targets of stand locations assignment of an arriving flight. Also, this could assist ground controllers to assign apron allocation and improve the efficiency and predictability of airport operations which reduce the time required for airport ground processing to increase flight capacity. In order to evaluate the performance of the proposed method, simulation results reveal that ANN has the lowest error rate and the highest accuracy. Therefore, ANN is the effective classification technique for this data set.

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides.

Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.

Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images

With recent advanced technologies, various automated diagnosis tools were developed to prevent retinal diseases. The automatic segmentation of blood vessels can help detect various retinal diseases and also assist in reducing doctors’ workload. In existing, numerous techniques have been established to segment RBV automatically. But, they failed to provide better accuracy because of higher computational complexity and lower efficiency. Thus, the proposed work introduces an effective hybrid deep learning technique for RBV segmentation and classification of vessels. Initially, the retinal images are pre-processed to enhance the image quality by performing two steps such as image cropping and colour channel conversion. From the pre-processed images, the most important regions are segmented through a new Enhanced Fuzzy C-Means (EFCM) clustering scheme. During the segmentation process, the retinal images are clustered according to the thickness of the blood vessels, which helps minimize the computational complexity. After segmentation, a hybrid deep learning technique like DenseNet and ShuffleNet is introduced to perform feature extraction and classification. The experimental setup is done by the Python platform using the databases like DRIVE (Digital Retinal Images for Vessel Extraction), STARE (STructured Analysis of the Retina), and HRF (High Resolution fundus). Using the DRIVE dataset, the proposed model achieves an accuracy of 99%, the attained accuracy value of the STARE dataset is 98%, and the HRF dataset obtains an accuracy of 98%. The result analysis proves that the proposed hybrid deep learning technique is more efficient than the state-of-the-art techniques.

Coronavirus disease 2019 patients prognostic stratification based on low complex lung ultrasound video compression

In the last years, efforts have been made towards automating semi-quantitative analysis of lung ultrasound (LUS) data. To this end, several methods have been proposed with a focus on frame-level classification. However, no extensive work has been done to evaluate LUS data directly at the video level. This study proposes an effective video compression and classification technique for assessing LUS data. This technique is based on maximum, mean, and minimum intensity projection (with respect to the temporal dimension) of LUS video data. This compression allows preserving hyper- and hypo-echoic regions and results in compressing a LUS video down to three frames, which are then classified using a convolutional neural network (CNN). Results show that this compression not only preserves visual artifacts appearance in the reduced data, but also achieves a promising agreement of 81.61% at the prognostic level. Conclusively, the suggested method reduces the amount of frames needed to assess LUS video down to 3. Note that on average a LUS videos consists of a few hundreds frames. At the same time, state-of-the-art performance at video and prognostic levels are achieved, while significantly reducing the computational cost.

Handwritten Text Classification Based on Convolutional Neural Network

The convolutional neural network (CNN) is a popular and highly effective deep learning technique for image classification. As the popularity of CNNs grew, the model has become popular in several machine learning problems. This paper utilizes a CNN model and the popular LeNet-5 transfer learned model to classify texts after the words are preprocessed and segmented from an image. The EMNIST database is used to train the models. The paper achieves an 89.36% validation accuracy on the EMNIST Balanced dataset and an 86.64% on the EMNIST By_Class dataset for the CNN model of four convolutional layers and one dense layer. Similarly, the LeNet-5 model obtained a validation accuracy of 85.88% on the EMNIST Balanced dataset and 85.01% accuracy on the EMNIST By_Class dataset. However, despite a higher accuracy in the EMNIST Balanced dataset, the EMNIST By_Class dataset achieves better results in real-world handwritten texts.

Improving intrusion detection in cloud-based healthcare using neural network

This research introduces an efficient smart security solution with the effective healthcare technique for Arrhythmia classification using the cloud computing scenario. Initially, the ECG of the patient in both the static and dynamic environment is collected using sensors and is stored in cloud, in which the clustering of the network and cluster head (CH) selection is devised using the Hybrid tempest optimization algorithm. In order to ensure the security for the collected healthcare data, the intrusion detection model is devised and implemented in the cloud storage for secure data access using the Neural network (NN), where the NN is trained using the Hybrid tempest optimization algorithm. The access is granted to the legitimate users for acquiring the data for diagnosis. The hybrid tempest-NN method is analyzed through accuracy, sensitivity, and specificity and obtained the maximal values of 95.72%, 96.78%, and 95.29% respectively.

Effective Deep Learning Data Augmentation Techniques for Diabetic Retinopathy Classification

Diabetic retinopathy(DR) is an eye disorder that affects 80-85% of diabetic patients, and there is a need to detect it in earlier stages to take precautions. Currently, testing for DR is a time-consuming task and requires qualified human resources. Automating DR detection is time-saving and cost-effective, benefiting both doctors and patients. The proposed work automates the DR image detection using deep learning(DL) by using transfer learning. The proposed work provides better results with multi-class classification and generalization than the previous works. i.e a ResNet-50 DL model is trained with the APTOS dataset and tested using the EyePACS dataset. The low accuracy in multi-class classification and data imbalance problems are overcome by using appropriate image pre-processing and augmentation techniques, which have been found by conducting several experiments. Further, the performance of the DL model is enhanced using Test Time Augmentation(TTA). Finally, the authors are able to achieve a multi-class accuracy of 97.87% and Quadratic weighted kappa(QWK) score of 0.985.

Multiclass classification of Ethiopian coffee bean using deep learning

Ethiopia is the homeland of Coffee Arabica. Coffee is the major export commodity and a high-income source of foreign currency for the country. In addition to this, coffee has a great role in social interaction between people and is also a source of income for the coffee-producing farmers. Several types of coffee beans grow in Ethiopia. These beans are distinct from each other in terms of quality, color, shape etc. based on their geographical origins. Classification of these coffee beans are based on growing origin, altitude, bean shape and color, preparation method and others. However, the quality of the coffee beans is determined by visual inspection, which is subjective, laborious, and prone to error. This creates the necessity for the development of an automatic method that is precise, non-destructive and objective. Thus, this research aims to develop a model that classifies coffee beans of six different origins of Ethiopia (Jimma, Limmu, Nekemte, Yirgacheffe, Bebeka, and Sidama) in to nine classes. The dataset for this research is collected from the Ethiopian Coffee Quality Inspection and Auction Center (ecqiac). This research followed design science research (dsr) to investigate the problem. Image processing and the state-of-the-art deep-learning techniques were employed to automatically classify coffee bean images into nine different classes grown in six different regions of Ethiopia. A total of 8646 coffee bean images were collected and 1190 images were added using augmentation to make the total dataset 9836. The model is trained and tested by tuning the hyper-parameters of the cnn algorithm. When 80% of the dataset is used for training, 10% for validation, and the remaining 10% for testing, the proposed model achieved a 99.89% overall classification accuracy with 0.92% generalization log-loss. In conclusion, the result of this research shows that deep learning is an effective technique for classification of Ethiopian coffee beans and can be implemented in the coffee industry.

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

D2TS: a dual diversity tree selection approach to pruning of random forests

Random Forest is one of the most effective classification techniques. It is an ensemble technique with typically decision trees as its classifiers. Each tree votes for an outcome when a new instance is being classified, and a majority vote is taken to decide the final output. Two main factors play an essential role in Random Forests performance, namely diversity among trees in the forest and their number. Higher diversity increases prediction accuracy, whereas lower numbers of trees result in faster predictions. This paper aims at optimizing these two factors by using clustering analysis of trees in order to prune correlated trees while keeping outlier trees to maintain diversity. We group the trees into clusters and only take a number of representatives from each cluster while also keeping some or all of the outliers to preserve diversity. The resulting subset of trees will constitute a random forest of a reduced size. We will use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering. DBSCAN is one of the most commonly used clustering techniques and is immune to outliers. We will use DBSCAN to (i) group trees in clusters based on their prediction behaviour and (ii) identify outliers. Each of the clustered and outlier trees bring an element of diversity into the pruned random forest, thus giving our approach its dual diversity aspect. Our approach achieved up to a 99% pruning level while resulting in similar, or even better, accuracy compared to the original forests for 19 public datasets with varying properties. Our source code is publicly available on GitHub.

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

Deep transfer learning-based vehicle classification by asphalt pavement vibration

Deep transfer learning (TL) has great potential for a wide range of applications in civil engineering. This work aims to propose a deep transfer learning-based method for vehicle classification by asphalt pavement vibration. This work first used the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. The vibration signals were then represented in two different ways, including the time-domain graph and the time-frequency graph. Finally, two deep transfer learning-based methods, namely Method Ⅰ (Time-domain & TL) and Method Ⅱ (Time-frequency & TL), were applied for vehicle classification according to the two different representations of vibration signals. The results show that the CNN model had a satisfactory performance in both methods with the accuracy of Method Ⅰ exceeding 0.94 and Method Ⅱ exceeding 0.95. The CNN model in Method Ⅱ performed better in the accuracy metrics with considering label imbalance, but worse in the accuracy metrics without considering label imbalance than that in Method Ⅰ. The differences between these two methods have been investigated and discussed in detail in terms of input types, accuracy metrics, and application prospects. The CNN model with deep transfer learning could be an effective, accurate, and reliable technique for vehicle classification based on asphalt pavement vibration.