One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.

Cloud computing is considered to be the best technique for storing data online instead of using a hard drive. It includes three different types of computing services that are provided to remote users via the Internet. Cloud computing offers its end users a variety of options, such as cost savings, access to online resources and performance, but as the number of users in cloud computing grows, so does the likelihood of an attack. Various researchers have researched and provided many solutions to prevent these attacks. One of the best ways to detect an attack is through an Intrusion Detection System. This article will develop an efficient framework in which will use and discuss various security solutions for a network. Every device on the network will be attacked and the attack rate of the entire network will be monitored. After that, various solutions will be provided to protect the cloud server from attacks. Different principles will be used at the end of the article to test the accuracy of the results and from each conclusion it will be concluded to what extent the results of this paper are better than others.

Semi-supervised learning has been successfully connected in the research fields of machine learning such as data mining and dynamic data analysis. Imbalance class learning is one of the most challenging issues for classification. In recent years, the core focal point of numerous researchers has been on data classification of multi-class imbalanced datasets. In this paper, we proposed semi-supervised Deep Fuzzy C-Mean clustering for imbalanced Multi-Class classification (DFCM-MC). In our paper “Deep” word is used to show how decomposition strategy is applied deeply, first, decomposed the original semi-supervised data into supervised (labeled) and unsupervised (unlabeled) data. For training the model, we used unlabeled data along with labeled data to extract discriminative information which is useful for classification. Second, further decomposed the supervised and unsupervised data into multi intra-cluster that to address the problem of multi-class imbalance data which tends to maximize intra-cluster classes and intra-cluster features. We propose a novel approach DFCM-MC by utilizing multi-intra clusters to extract new features to control redundancy for multi-class imbalance classification, which associates the maximum similarity of features between multi-intra clusters. We further to improve the classification performance of DFCM-MC, apply re-sampling technique to handle the imbalance data for classification. We conduct our experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed approach with four state-of-the-art learning algorithms for multi-class imbalance data with three performance measure (Mean of Accuracy, Mean of F-Measure, and Mean of AUC (Area under the curve). Experiment results demonstrate that our proposed approach perform better due to their capacity to recognize and consolidation fundamental information from unsupervised data.

Software fault prediction is a consequential research area in software quality promise. In this paper, we propose a semi-supervised deep fuzzy C-mean (DFCM) clustering for software fault prediction, which is the cumulation of semi-supervised DFCM clustering and feature compression techniques. Deep is utilized for the feature-based multi clusters of unlabeled and labeled data sets along with their labeled classes. In our approach, for the training model, we simultaneously deal with the unsupervised data and supervised data to exploit the obnubilated information from unlabeled data to labeled data to support the construction of the precise model. We utilize DFCM clustering to handle the class imbalance problem and withal fuzzy theory logic is very akin to human logic and it is facile to comprehend. We further ameliorate the prediction performance with the coalescence of feature learning techniques-feature extraction and feature selection (using random-under sampling) to generate good features and remove irrelevant and redundant features to reduce the noisy data for classification. However, by the performance of the model results, the amalgamation of deep multi clusters and feature techniques work better due to their ability to identify and amalgamation essential information in data feature. The classification model is predicted on the maximum homogeneous between the features of labeled and unlabeled data, the model is trained on the un-noisy data set obtained by the deep coalescence of multi clusters and feature techniques. To check the efficacy of our approach, we chose data sets from real-world software project (NASA & Eclipse), and then we compared our approach with a number of latest classical base-line methods, and investigate the performance by using performance measures such as probability of detection, F-measure, and area under the curve.

Software fault prediction is the very important research topic for software quality assurance. Data-driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of the data set. Many software data sets suffer from the problem of class imbalance. In this regard, undersampling is a popular data pre-processing method in dealing with the class imbalance problem; easy ensemble presents a robust approach to achieve a high classification rate and address the biases toward majority class samples. However, imbalance class is not the only issue that harms the performance of classifiers. Some noisy data and irrelevant and redundant features may also reduce the performance of predictive accuracy of the classifier. In this paper, we propose two-stage data pre-processing, which incorporates feature selection and rough set-based K nearest neighbour rule (KNN) noise filter afore executing easy ensemble rough-KNN noise-filtered easy ensemble (RKEE). In the first stage, we eliminate the irrelevant and redundant features by the feature ranking algorithm, and in the second stage, we handle the imbalance class problem by using rough-KNN noise filter to eliminate noisy samples from both the minority and the majority class and also handle the uncertainty and the overlapping problem from both the minority and the majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse data set, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach, such as the impact of the rough set theory on noise-filter, the relationship between model performance and imbalance ratio, and so on. Comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve.

Convolutional Neural Networks (CNNs) are a class of deep neural networks that have proven highly effective in areas such as image and video recognition. CNNs typically include several types of layers, such as convolutional layers, activation layers, pooling layers, and fully connected layers, all of which contribute to the network’s ability to recognize patterns and features. The pooling layer, which often follows the convolutional layer, is crucial for reducing computational complexity by performing down-sampling while maintaining essential features. This layer’s role in balancing the symmetry of information across the network is vital for optimal performance. However, the choice of pooling method is often based on intuition, which can lead to less accurate or efficient results. This research compares various standard pooling methods (MAX and AVERAGE pooling) on standard datasets (MNIST, CIFAR-10, and CIFAR-100) to determine the most effective approach in preserving detail, performance, and overall computational efficiency while maintaining the symmetry necessary for robust CNN performance.