Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL‐based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML‐based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL‐based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI‐based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in‐depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs.

Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings, which necessitates energy optimization and increased user comfort. To address the issue of energy management, many researchers have developed various frameworks; while the objective of each framework was to sustain a balance between user comfort and energy consumption, this problem hasn’t been fully solved because of how difficult it is to solve it. An inclusive and Intelligent Energy Management System (IEMS) aims to provide overall energy efficiency regarding increased power generation, increase flexibility, increase renewable generation systems, improve energy consumption, reduce carbon dioxide emissions, improve stability, and reduce energy costs. Machine Learning (ML) is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy (IoE) network. The IoE network is playing a vital role in the energy sector for collecting effective data and usage, resulting in smart resource management. In this research work, an IEMS is proposed for Smart Cities (SC) using the ML technique to better resolve the energy management problem. The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy, and 7.89% miss-rate.

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

Web Services (WSs) are gaining worldwide popularity due to reliable and fast intercommunication services for the development of web and mobile applications. WSs are provided to client application developers through web Application Programming Interfaces (APIs), such as YouTube API, Twitter API, Facebook API, etc. Due to the popularity of WSs, the developers frequently discuss various WSs-based application’ issues on online forums, such as Stack Overflow (SO). The comprehension of such conversations on SO can give insight into the frequency, difficulty, and popularity of different WSs-related problems of developers. We downloaded 12,746 posts from SO relevant to WSs-related issues for this article. We used the topic modeling technique (LDA) to extract various topics from the SO dataset. The topics are labeled and organized into categories and sub-categories according to relationships among them. The difficulty and popularity of each topic have been analyzed. Our investigation yield several findings. First, developers focus on six topics related to WSs on SO: Client APIs development, Data Processing,Web services Authorization, Framework Support,Web APIs, and Mobile Applications. Secondly, the advantages and disadvantages of web applications topic (Fused_Popularity=0.39), from the Clients APIs development category have the highest prevalence, followed by Database (DB) and Data Processing in Applications topic (Fused_Popularity=0.38) from the Data Processing category. Third, most WSs-related topics in all categories are evolving promptly on SO, i.e., new questions are added daily about WSs development, deployment, and authorization. Fourth, the questions of type “how” are primarily asked in Framework support, Client APIs development, and Web APIs categories. Although, many questions in other categories are of the kind “What”. It is also observed that WSs developers not only used SO to ask How and What types of questions but they also used SO to ask information-seeking questions (i.e., in Data processing and Client APIs development categories). Fifth, the topics relevant to Web APIs (Fused_Popularity=10.8) and Client API Development ((Fused_Popularity=9.35) categories of WSs are very popular on SO. Sixth, the questions relevant to the Web APIs (Fused_Difficulty =3) & Client APIs development (Fused_Difficulty=2.25) categories are more difficult than the other four categories. The results of our research may be helpful for the following WSs stakeholders: WSs Client application developers, WSs Educators, and WSs researchers. The WSs Educators and investigators can get more knowledge of new methods and discover novel techniques to make challenging WSs topics easy to understand. WSs framework developers can utilize our extracted WSs topics and categories to know the preferences of WSs developers that may support them in upgrading existing frameworks or developing new ones.

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically. Thus, our proposed methodology aims to detect DR using a hybrid quantum transfer learning approach. To build our model we extract the APTOS 2019 Blindness Detection dataset from Kaggle and used inception-V3 pre-trained classical neural network for feature extraction and Variational Quantum classifier for stratification and trained our model on Penny Lane default device, IBM Qiskit BasicAer device and Google Cirq Simulator device. Both models are built based on PyTorch machine learning library. We bring about a contrast performance rate between classical and quantum models. Our proposed model achieves an accuracy of 93%–96% on the quantum hybrid model and 85% accuracy rate on the classical model. So, quantum computing can harness quantum machine learning to do work with power and efficiency that is not possible for classical computers.

Vision Transformers (ViTs) have proved to be a more powerful substitute for Convolutional Neural Networks (CNNs) in various computer vision tasks, using the self-attention approach to gain remarkable results and observations. However, the adversarial robustness of ViTs against adversarial attack methods raises critical questions, and the issues of using these models in security-related applications remain under discussion. This paper presents a novel and systematic approach to evaluate and compare the adversarial robustness of ViTs with CNNs, explicitly concentrating on the image classification problem. We have performed extensive experiments using state-of-the-art adversarial example attacks, such as the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool Attack (DFA). The findings of this research study represent that CNNs are more robust against more straightforward attacks such as FGSM. Still, ViTs show excellent resistance against more dangerous attacks like PGD and DFA attack methods. This work provides useful outcomes revealing the advantages and limitations of CNNs and ViTs, which are helpful for further study and applications regarding safer and more effective use of deep learning models of CNNs and ViTs.