A New Enhanced Learning Approach to Automatic Image Classification Based on Salp Swarm Algorithm

In the field of supervised machine learning, a transfer learning environment is defined as the training data having different distribution characteristics than the testing data. This is due to the lack of available labeled data for the domain of interest, which prompts an alternate domain to be used as the training data. Because there is insufficient labeled data from the domain of interest, validation techniques cannot be reliably used for the algorithm selection process in a transfer learning environment. A transfer learning algorithm is typically comprised of a domain adaptation step followed by a learning step. The learning step is usually implemented using a traditional machine learning algorithm. In this paper, we examine and analyze the impact that the traditional machine learning algorithm (the learning step) has on the overall performance of a transfer learning algorithm. Using the transfer learning test framework, we test five state-of-the-art transfer learning algorithms coupled with seven different traditional learning algorithms for a total of 35 unique transfer learning algorithms. For our experiment, no labeled data from the domain of interest is available for the training process. Since validation techniques cannot be reliably used for the algorithm selection process in a transfer learning environment, it is important for machine learning researchers and practitioners to understand the impact of a traditional machine learner on the overall performance of a transfer learning algorithm.

Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS (DLIDIC-SCPS) model for healthcare sector. The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector. Primarily, data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network (FDNN) technique. Besides, Multiple Share Creation (MSC) approach is used to create several shares of medical image so as to accomplish security. Also, blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client. For image classification, Inception v3 with Fuzzy Wavelet Neural Network (FWNN) is utilized that diagnose the disease from the applied medical image. Finally, Salp Swarm Algorithm (SSA) is utilized to fine tune the parameters involved in WNN model, thereby boosting its classification performance. A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique. The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security, privacy, and image classification outcomes over recent state-of-the-art techniques.

Support Vector Machine (SVM) is typically a supervised learning algorithm that carefully examines input and identifies distinct patterns. The function of SVM classifier relies on adjusting or controlling of kernel and penalty parameter values. Nature Inspired Algorithm helps to solve the natural problems and has been attracting considerable attention due to their better performance. Salp Swarm Algorithm (SSA) is a Nature Inspired Algorithm (NIA) which is used to control the finest SVM parameters value. To improve exploration capability of SSA, mutation method is developed to find the optimal value for kernel parameter and penalty parameter. The preliminary result indicates Mutated SSA with SVM increases classification accuracy than simple SSA with SVM.

The era in which we live has become the era of big data, particularly with the spread of the concept of Internet-of-Things (IoT) and artificial-intelligence (AI). Images constitute a large portion of this data that is created and handled. Image classification is the process of identifying and labeling groups of pixels or vectors inside an image using certain criteria. Major image classification techniques divided into two categories which are supervised and unsupervised techniques. This study looks at various research, techniques, and issues of image classification. The focus is on summarizing the main advanced classification strategies and methods such as (K-Mean clustering, Fuzzy measure, Artificial Neural Networks (ANN), Decision-Tree (DT), Support-Vector-Machines (SVMs), Naive Bayes (NB), K-Nearest-Neighbor (KNN), Random-Forest (RF), etc.) that can be utilized after some update to enhance classification accuracy. Also, this study shows the common challenges and solutions in this area.

Image classification experiments face several problems related to image specifications, size of samples, and classification accuracy. The image classification related issues motivated the researchers to use Reinforcement Learning (RL) with image classification experiments to enhance it. RL is a self-learning approach where machines can learn from experience. This paper aims to study the influence of RL on image classification trials. Besides, exploring the main issues in RL-based image classification. The authors reviewed and analyzed 15 papers related to applying RL techniques on image classification. Major studies used RL techniques to solve image classification issues, e.g., high dimensionality and misclassifications. Whereas other studies developed RL-based image modification methods used for processing images, such as rotating and cropping. This work concludes that the RL has a beneficial impact on solving classification issues and modifying images. However, some areas should be further considered, such as applying RL techniques in image classification in real-world automation projects.

A Distributed Denial of Service (DDoS) attack occurs when an attacker tries to disrupt a network, service or website by flooding huge numbers of packets on the internet traffic. Detecting DDoS attacks serves the goal of spotting and addressing them promptly to reduce their effects on the network, system or service being targeted. Detecting Distributed Denial of Service (DDoS) attacks is crucial, for people, companies and network managers. The detection of DDoS attacks has ranging uses in industries such as network security safeguarding websites, managing cloud services ensuring the security of online systems and services. Detecting DDoS attacks is essential for safeguarding infrastructure upholding service availability and guaranteeing the security of online systems and services. To achieve this objective, we proposed a framework to detect DDoS attacks including six steps. In step one, we start by gathering information, which includes network activity and system records, for operations as well as instances of DDoS attacks. Step two, we identify characteristics of the data collected such as patterns in network traffic, packet details, IP addresses, types of protocols used and more. Step three, we utilize algorithms for feature selection such as Salp Swarm Algorithm (SSA), Gray Wolf Algorithm (GWA), Particle Swarm Algorithm (PSO) to pinpoint the features that can distinguish between normal activities and DDoS attack patterns. After that in step four, we divide the processed dataset into sections for training and testing purposes to develop and assess the machine learning models such as SVM (support vector machine), and KNN (K-nearest neighbor). Step five we develop a classification model using machine learning techniques like decision trees, forests, support vector machines (SVM) logistic regression models or neural networks. Finally, we assess the effectiveness of models through metrics such as accuracy rates, precision levels, recall rates, and F1 scores. The results show that the proposed models achieve high results (99.9%). In summary detecting DDoS attacks is crucial for protecting networks, systems and online services against disruptions.

Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

This paper proposed a diagnostic supporting tool - i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> DiaKAW (intelligent and interactive knowledge acquisition workbench), which automatically extracts useful knowledge from massive medical data by applying various data mining techniques for supporting real medical diagnosis. To fulfill this effort, i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> DiaKAW has been developed to provide a novel dynamic on-line decision tree learning scheme - i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> Learning (intelligent and incremental learning) methodology that is able to incrementally incorporate the new incoming data with an existing decision tree without relearning the entire tree from scratch. i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> Learning strategy makes up for the traditional incremental decision tree learning algorithms by concerning the new available features in addition to the new coming instances. Such theory is a new attempt and is designed specifically for bridging the current gaps. The empirical results demonstrate a comparison of performance of various classification algorithms on several real-life datasets from UCI repository.

The accurate prediction of stock prices has a significant role to play in the present economic world. However, stocks have an ever-changing nature coupled with a risk factor that always seems much of a gamble, making it worthwhile for researchers to analyze them with machine learning algorithms. We thus seek to develop a machine-based model replacing the traditional yet unreliable time-series forecasting to predict the stocks from the past historical data of a firm and to uncover the underlying patterns to improve the accuracy of the prediction of the model. As a beginner, we have decided to use a couple of conventional machine learning algorithms to study the behavior of learning techniques for stock prediction. This paper presents an empirical study to study and analyze the behavior of decision Tree, Linear Regression, K-Nearest Neighbours, and LSTM learning algorithms to bet on the algorithm that best predicts the stock prices.

Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers

Decision trees belong to the most effective classification methods. The main advantage of decision trees is a simple and user-friendly interpretation of the results obtained. But despite its well-known advantages the method has some disadvantages as well. One of them is that decision tree learning algorithm build an “almost optimal” tree. The paper considers the way to improve the efficiency of decision trees. The paper proposes a modification of decision tree learning algorithms by retraining the part of tree at every node training. The classification problems were solved to compare standard decision tree learning algorithms with the modified ones. Algorithm efficiency refers to the percentage of correctly classified test sample objects. Statistical analysis based on Student’s t-test was carried out to compare the efficiency of the algorithms.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing research community, and there have been a series of attempts to apply them to verbal case frame acquisition. However, a DTLA cannot handle structured attributes like nouns, which are classified under a thesaurus. In this paper, we present a new DTLA that can rationally handle the structured attributes. In the process of tree generation, the algorithm generalizes each attribute optimally using a given thesaurus. We apply this algorithm to a bilingual corpus and show that it successfully learned a generalized decision tree for classifying the verb take and that the tree was smaller with more prediction power on the open data than the tree learned by the conventional DTLA.

The advancements in deep learning (DL) and transfer learning (TL) have transformed artificial intelligence, particularly in image classification. This research examines the theoretical foundations of DL and TL, focusing on their applications in medical image classification, specifically distinguishing between COVID-19, viral pneumonia, and normal lung conditions. By leveraging GPU-enabled high-performance computing and large labeled datasets, DL models particularly Convolutional Neural Networks (CNNs) such as ResNet50 and VGG16 have achieved superior accuracy compared to traditional machine learning methods. This study explores feature extraction using pre-trained models, the implementation of classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and the integration of multi-view learning techniques, such as early fusion. The results demonstrate the effectiveness of DL and TL in improving classification performance, highlighting their significant potential to advance global healthcare diagnostics.