IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.

Fine-tuning a BERT-Base model for specific tasks, such as sentiment analysis, has become resource-intensive and often requires high computational power and memory. This paper introduces SCALE, a novel resource-efficient fine-tuning method that targets the most critical transformer layers, which reduces computational costs without sacrificing performance. By dynamically profiling transformer layers via activation magnitudes and attention entropy, SCALE selects and adapts only the most influential layers with lightweight adapter modules. The proposed method outperforms traditional fine-tuning techniques, achieving a 2.3% improvement in accuracy on the IMDB dataset and reducing training time by 56.3% compared to full-model fine-tuning. Experiments across various sentiment analysis benchmarks demonstrate SCALE’s effectiveness in optimizing fine-tuning for the BERT-base model in resource-constrained environments, achieving up to 99% of the performance of full-model fine-tuning while using only 40% of the parameters. The empirical validation in this study is restricted to binary and multi-class sentiment classification. The evaluation specifically reflects effectiveness in sentiment analysis text classification tasks.

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers.

The world is witnessing rapid and significant advancements in modern technologies. After the Fourth Industrial Revolution, which focused on the Internet of Things (IoT), artificial intelligence (AI), automation, big data, and other digital technologies, the Fifth Industrial Revolution emerged, emphasizing personalization, human-centered design, sustainability, creating human value, and strengthening human–machine collaboration. However, this move toward personalization and creating human value has refocused attention on one of the biggest challenges facing technology: ensuring the security and privacy of user data. As modern devices increasingly function like continuous monitoring tools, vast amounts of data are collected about users. Meanwhile, attackers have more resources and tools with greater computational capabilities to analyze users’ data and extract private and sensitive information. This chapter discusses this critical issue, outlining the main concerns and threats related to the industrial revolutions and their technologies. It also reviews current security and privacy solutions, highlighting their strengths and weaknesses, and finally proposes more adaptive solutions to address this issue in the new reality.

Arabic question-answering systems in high-sensitivity domains require not only accurate retrieval but also reliable evidence grounding and effective hallucination mitigation, as incorrect or unsupported responses may have serious consequences. Existing retrieval and generation approaches do not fully integrate reliable lexical retrieval, semantic understanding, and evidence-based answer verification within a unified framework for these domains. To address this limitation, this study proposes an Enhanced Hybrid Retrieval-Augmented model that combines BM25-based lexical retrieval, dense semantic scoring, semi-structured metadata, domain-aware classification, source-trust and freshness indicators, and evidence-based answer verification. The model was evaluated on a corpus of 128,297 Arabic documents using 200 expert-validated questions and compared against BM25, Dense, and Classical Hybrid retrieval configurations. Expert assessment showed that the Enhanced Hybrid Model produced 198 fully grounded correct answers out of 200, achieving a grounded-correctness rate of 99.0%, compared with 96.5% for Classical Hybrid, 87.0% for BM25, and 86.5% for Dense. It also achieved the strongest answer-level Hit@5 performance, the best golden-answer ordering, and the highest dynamic mixture-based F1-score. These findings demonstrate that integrating reliable retrieval, metadata-aware ranking, and evidence-grounded answer verification can substantially improve the reliability and trustworthiness of Arabic question-answering systems in high-sensitivity domains, providing a practical foundation for future evidence-based intelligent information systems.

Protecting the privacy of users’ data while maintaining reliability and accuracy in crowded events remains an open issue, especially with the growing capabilities and resources of attackers. This challenge becomes more difficult in dynamic environments with moving users/devices. Unfortunately, the current privacy-preserving methods suffer from several drawbacks that include reliability and accuracy of results, the need to fully trust a third party, or the incurrence of heavy overheads. This research presents a novel approach that is enhanced by peer cooperation, which is one of the most suitable techniques for crowded environments. The proposed approach is called “Smart Monikers with Multi-Peer Cooperation (SM2Peer)”. The SM2Peer addresses all the drawbacks of the traditional peer cooperation approach through two scenarios. In addition, the SM2Peer exploits the fog computing layer to control the cooperation among peers effectively, where each fog node manages several peers with smart moniker management. Moreover, SM2Peer provides multiple caches to relax the total overhead. The simulation and comparison with other common privacy approaches show the superiority of the SM2Peer in many aspects and metrics of privacy without a significant effect on performance.