Social networks, particularly Twitter, significantly influence public emotions during health crises, often amplifying distress and misinformation. Effective sentiment analysis is crucial for mitigating social unrest and enabling timely interventions. This study introduces a novel two-stage framework, the fuzzy-integrated case-based and adaptive deep-belief neural network (2-SCBAADBNN) model, for real-time sentiment classification in public health emergencies. The framework integrates neural architecture search (NAS) and large machine learning models (LMMs) such as bidirectional encoder representations from transformers (BERT) to optimize fuzzy logic components and enhance feature extraction, improving sentiment detection accuracy. The two-stage classification first distinguishes between personal and news-related tweets using a fuzzy clue-based method, followed by sentiment classification of personal tweets into positive or negative categories. By combining fuzzy logic with deep learning, this multimodal approach aligns with advancements in emotion AI, offering greater scalability, adaptability, and precision in sentiment analysis. Comparative evaluations show that 2-SCBAADBNN outperforms existing models, providing a robust solution to monitor emotional distress and combat misinformation during crises. This research advances emotion AI by integrating NAS and LMM, allowing more context-sensitive real-time sentiment analysis. It contributes to developing AI-driven empathy-based systems capable of understanding and responding to public sentiment more effectively in critical social scenarios.

With the growing availability of multimodal health data including patient behavior signals, clinical text, and medical images, AI systems have an increasing potential to support early disease detection and decision-making in aging populations. As healthcare systems evolve into complex socio-technical environments, integrating explainable AI with human-centered design is essential to improve transparency, trust, and adoption. In this study, we propose a multimodal human-centered deep learning framework (MDLHC) to support breast cancer diagnosis by integrating patient data, imaging, and explainable attribution (XAI) methods. The framework is designed with the social and cognitive needs of elderly patients and clinicians in mind and uses demographic data and mammography images for personalized diagnostics. To bridge structured and unstructured data, we incorporate large language models (LLMs) for natural language alignment and summarization of clinical insights. For improved image understanding, a CNN-based residual integrated attention (RIAC) module is applied for noise reduction, followed by optimized feature selection using evolutionary PSO with Laplacian centrality (EPSO-LC). Classification is performed via a deep backpropagation CNN (DL-BP-CNN), enhanced with two explainability modules: ensemble random SHAP (ERS) and submodular selection-based LIME (SMS-LIME) for both localized and global transparency. This framework contributes to the development of a transparent, accurate, and personalized AI system that aligns with the scope of computational social systems in healthcare care, particularly for aging populations that require reliable diagnostic support.

The distributed network utilizing the integrity-based resilient machine learning (DRML) approach offers a robust methodology to address challenges associated with complex Internet of Things (IoT) applications, computational resource limitations, and environmental sustainability. In heterogeneous cloud computing environments, where edge and central clouds collaborate to meet diverse demands in various sectors, significant challenges arise in task offloading. This research proposes an IoT and cloud computing framework integrated with a distributed and resilient deep learning (DLRTO) model to optimize system utility and bandwidth allocation for local controller devices (LCDs). The DLRTO model is designed to generate near-optimal offloading decisions for LCDs, edge cloud servers, and central cloud servers, thus maximizing both system utility and bandwidth allocation. Additionally, we introduce the RL-NN integrated round robin-based dynamic task scheduling (RNRRDTS) algorithm on edge and cloud servers to address job scheduling issues, with a focus on renewable energy generation and job migration. Extensive simulations were conducted using real-world IoT application tasks, renewable energy data, and grid electricity pricing data to evaluate the proposed model. The experimental results demonstrate the superior performance of the DLRTO model compared with existing methods. This research provides valuable insights and advances toward the creation of sustainable, resilient, and optimized IoT systems applicable across various domains.

Social networking websites are now considered to be the best platforms for the dissemination of news articles. However, information sharing in social media platforms leads to explosion of fake news. Traditional detection methods were focusing on content analysis, while the current researchers examining social features of the news. In this work, we proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model. The proposed work is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer. In this work, four steps were carried out: 1) data acquisition; 2) information retrieval (IR); 3) NLP-based data processing and feature extraction; and 4) deep learning-based classification model that classifies news articles as fake or real using credibility score of publishers, users, messages, headlines, and so on. Three datasets, such as Buzzface, FakeNewsNet, and Twitter, were used for evaluation of the proposed model, and simulation results were computed. This proposed model obtained an average accuracy of 99.72% and an$F1$score of 98.33%, which outperforms other existing methods.

In this article, the concept of cryptographic algorithms is used as an efficient access control mechanism for Internet of Medical Things-based health care system. The algorithms, such as Rivest Cipher (RC6), are used to generate the key value, and elliptic curve digital signature algorithm will encrypt the key value from RC6 and the encrypted output is send to secure hash algorithm (SHA256) for hashing process based on cipher value which improves data integrity. Furthermore, these high-security algorithms are used to provide availability and confidentiality to protect sensitive information from implantable devices and strengthen the health care systems through enhanced services. Comprehensive experimental analysis and simulation results indicate that the proposed scheme is more secure against various known attacks, such as denial of service, router attack, and sensor attacks. This proposed system has better resistance protocols in analyzing the safety of patients.