The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

This study’s main goal is to create a useful software application for finding and classifying fine art photos in museums and art galleries. There is an increasing need for tools to swiftly analyze and arrange art collections based on their artistic styles as a result of the digitization of art collections. To increase the accuracy of the style categorization, the suggested technique involves two parts. The input image is split into five sub-patches in the first stage. A DCNN that has been particularly trained for this task is then used to classify each patch individually. A decision-making module using a shallow neural network is part of the second phase. Probability vectors acquired from the first-phase classifier are used to train this network. The results from each of the five patches are combined in this phase to deduce the final style classification for the input image. One key advantage of this approach is employing probability vectors rather than images, and the second phase is trained separately from the first. This helps compensate for any potential errors made during the first phase, improving accuracy in the final classification. To evaluate the proposed method, six various already-trained CNN models, namely AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and InceptionV3, were employed as the first-phase classifiers. The second-phase classifier was implemented as a shallow neural network. By using four representative art datasets, experimental trials were conducted using the Australian Native Art dataset, the WikiArt dataset, ILSVRC, and Pandora 18k. The findings showed that the recommended strategy greatly surpassed existing methods in terms of style categorization accuracy and precision. Overall, the study assists in creating efficient software systems for analyzing and categorizing fine art images, making them more accessible to the general public through digital platforms. Using pre-trained models, we were able to attain an accuracy of 90.7. Our model performed better with a higher accuracy of 96.5 as a result of fine-tuning and transfer learning.

The suspension of institutions around the world in early 2020 due to the COVID-19 virus did not stop the learning process. E-learning concepts and digital technologies enable students to learn from a safe distance while continuing their educational pursuits. Currently, the Internet of Things (IoT) is one of the most rapidly increasing technologies in today’s digital world; and e-learning is one of the most powerful learning methods available. In today’s world, smart devices and new technologies assist teachers in concentrating on new models of student learning while avoiding time wastage. By examining the characteristics of the Internet of Things and the challenges that exist in the field of e-learning, the potential functions, benefits, and advancements of utilizing the Internet of Things in online education are identified and discussed. This article examines the existing and future condition of the Internet of Things world as it pertains to the topic of education and sophisticated capabilities available through the Internet of Things that enable the application of e-learning after an architecture has been designed. Students’ pulse rates, brain waves, and skin resistance are measured in real time by a collection of IoT sensors, including cameras, microphones, and wearable gadgets. By utilizing the proposed architecture, universities can change their distance learning tactics to maximize resources and boost efficiency without changing their overall academic activities. According to the study’s findings, e-learning has a favorable and statistically significant impact on students’ flexibility, learning experience, educational productivity, and overall quality of education.

The growth of the internet and big data has spurred the demand for more extensive information hoarding to store and distribute information. In today’s digital era, ensuring the security of data transmission is paramount. Advancements in digital technology have facilitated the proliferation of high-resolution graphics over the Internet, raising security concerns and enabling unauthorized access to sensitive data. Researchers have increasingly explored steganography as a reliable method for secure communication because it plays a crucial role in concealing and safeguarding sensitive information. This study introduces a novel and comprehensive steganography framework using the discrete cosine transform (DCT) and the deep learning algorithm, generative adversarial network. By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. While conventional steganography methods are typically classified into spatial and transform domains, extensive research and analysis demonstrate that the hybrid approach surpasses individual techniques in performance. The experimental results validate the effectiveness of the proposed steganography approach, showcasing superior visual image quality with a mean square error (MSE) of 93.30%, peak signal-to-noise ratio (PSNR) of 58.27%, root mean squared error (RMSE) of 96.10%, and structural similarity index measure (SSIM) of 94.20%, in comparison to existing leading methodologies. The proposed model achieved reconstruction accuracies of 96.2% using Xu Net and 95.7% with SR Net. By combining DCT with deep learning algorithms, the proposed approach overcomes the limitations of spatial domain methods, offering a more flexible and effective steganography solution. Furthermore, simulation results confirm that the proposed technique outperforms state-of-the-art methods across key performance metrics, including MSE, PSNR, SSIM, and RMSE.

In text analysis, identifying plagiarism is a crucial area of study that looks for copied information in a document and determines whether or not the same author writes portions of the text. With the emergence of publicly available tools for content generation based on large language models, the problem of inherent plagiarism has grown in importance across various industries. Students are increasingly committing plagiarism as a result of the availability and use of computers in the classroom and the generally extensive accessibility of electronic information found on the internet. As a result, there is a rising need for reliable and precise detection techniques to deal with this changing environment. This paper compares several plagiarism detection techniques and looks into how well different detection systems can distinguish between content created by humans and content created by Artificial Intelligence (AI). This article systematically evaluates 189 research papers published between 2019 and 2024 to provide an overview of the research on computational approaches for plagiarism detection (PD). We suggest a new technically focused structure for efforts to prevent and identify plagiarism, types of plagiarism, and computational techniques for detecting plagiarism to organize the way the research contributions are presented. We demonstrated that the field of plagiarism detection is rife with ongoing research. Significant progress has been made in the field throughout the time we reviewed in terms of automatically identifying plagiarism that is highly obscured and hence difficult to recognize. The exploration of nontextual contents, the use of machine learning, and improved semantic text analysis techniques are the key sources of these advancements. Based on our analysis, we concluded that the combination of several analytical methodologies for textual and nontextual content features is the most promising subject for future research contributions to further improve the detection of plagiarism.