The concept of sustainable tourism development is imposed as an inevitable way of improving the tourism industry as a whole. This study tries to offer an adequate inclusion of sustainable factors in overall tourism development efficiency results. Through the detection and estimation of potential sources of efficiency, the paper will do the efficiency benchmarking of tourism services on the level of countries as destinations. In order to complete the task, data collection was focused on 27 EU countries and five Western Balkan countries over the period from 2011 to 2017. This paper utilized an output-oriented data envelopment analysis (DEA) procedure to estimate efficiency scores for each country, and a panel data Tobit regression model to emphasize the (in)significance of each individual tourism development indicator. The results in the first stage show relatively high-efficiency scores, particularly in the case of EU 15 countries and with room for improvement in the case of the others. The second stage reveals positive and significant effects on relative tourism efficiency by the sustainability of tourism development, the share of GDP, tourist arrivals and inbound receipts, as well as visa requirements and rate of use. Policymakers should gradually take control of the mentioned variables to protect the interests of all relevant stakeholders involved in the tourism development process.

Artificial Intelligence (AI) has made tremendous progress in anomaly detection. However, AI models work as a black-box, making it challenging to provide reasoning behind their judgments in a Log Anomaly Detection (LAD). To the rescue, Explainable Artificial Intelligence (XAI) improves system log analysis. It follows a white-box model for transparency, understandability, trustworthiness, and dependability of Machine Learning (ML) and Deep Learning (DL) Models. In addition, Shapely Additive Explanation (SHAP), added to system dynamics, makes informed judgments and adoptable proactive methods to optimize system functionality and reliability. Therefore, this paper proposed the Shapely eXplainable Anomaly Detection (SXAD) framework to identify different events (features) that impact the models’ interpretability, trustworthiness, and explainability. The framework utilizes the Kernel SHAP approach, which is based on Shapley values principle, providing an innovative approach to event selection and identifying specific events causing abnormal behavior. This study addresses the LAD by transforming it from a black-box model into a white-box one, leveraging XAI to make it transparent, interpretable, explainable, and dependable. It utilizes benchmark data from the Hadoop Distributed File System (HDFS), organized using a Drain parser, and employs several ML models, such as Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). These models achieve impressive accuracy rates of 99.99%, 99.85%, and 99.99%, respectively. Our contribution are novel because no earlier work has been done in the area of Log Anomaly Detection (LAD) with integration of XAI-SHAP.

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm.

Electric Vehicle Charging Station (EVCS) security is a growing concern in today's connected world due to the growing complexity and frequency of cyber threats. Traditional Intrusion Detection Systems (IDS) for EV chargers struggle to detect novel or unexpected attacks due to their usage of predetermined signatures and limited detection capabilities. Existing EV charging station security systems are unable to identify many known and undiscovered threats since they primarily rely on feature selection and categorization accuracy. It is common for these systems to be constructed using conventional machine learning algorithms. So many common signs of attacks are ignored. This paper proposes a Transfer learning (TL) framework for cyber-physical attack detection in EVCS in order to overcome these difficulties and improve both accuracy and scalability. The weights preserved from the Deep Neural Network (DNN) model after implementing data normalization and min-max scaling techniques utilized for training are used to initialize a new model termed Transfer Learning. The study also provides a comparison with different DL models such as Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN), and Gated Recurrent Unit (GRU). The CICEVSE2024 (EVSE-A and EVSE-B) datasets are used to assess the framework, where one dataset is used to train and store weights, and the second is used to evaluate the learned patterns using transfer learning. Several evaluation matrices are used to evaluate the suggested model. The experimental results demonstrate that the TL model attained 93% accuracy. Consequently, the pre-train TL model provides a high degree of symmetry between EVCS security and the detection of malicious attacks.

Adversarial attacks involve manipulating data to trick Artificial Intelligence (AI) models, making false predictions or classifications or even disrupting the normal functions of the smart grid. This can be done by providing the wrong information to the models, hence producing wrong predictions and therefore leading to instabilities, power imbalances and overall operational failure. Thus, employing an AI model is vital for controlling energy usage, identifying and even predicting equipment failures, and accurately determining power availability. Thus, their input dependency does not enable them to withstand cyber-attacks, which compromises the stability of the grid. These attacks affect energy control, cause losses and intrude into the critical infrastructure part of the smart grid, hence the call for enhanced smart grid protection. This study first generates a novel adversarial attack dataset on smart grids with three attacks: adversarial perturbation, backdoor injection, DOS attacks, and one benign class. Next, it provides a fine-tuned Deep Neural Network (DNN) model to improve resistance against adversarial attacks on smart grids significantly. Results from the various Machine Learning (ML) and DNN algorithms showed accuracy varying between 29.10% to 73.9%, with DNN recording the highest levels of accuracy. This shows how the approach can be utilised in the prediction of such attacks and how the grid can be protected and secured.