ABSTRACT Privacy‐Preserving Data Sharing (PPDS) masks the individual's collected data (e.g., medical healthcare data) before being disseminated by organizations for analysis and research. Patient data contains sensitive values that must be dealt with while ensuring certain privacy conditions are met. This minimizes the risk of re‐identification of an individual record from the group of privacy‐preserved data. However, with the advancement in technology (i.e., Big Data, the Internet of Things (IoT), and Blockchain), the existing classical privacy‐preserving techniques are becoming obsolete. In this paper, we propose a blockchain‐based secure data sharing technique named “SecureChain”, which preserves the privacy of an individual record using local differential privacy (LDP). The three distinguished features of the proposed approach are lower latency, higher throughput, and improved privacy. The proposed model outperforms the benchmarks in terms of both latency and throughput. In terms of precision, the proposed method improves the accuracy to 88.53% compared to its counterparts, which achieved 49% and 85% accuracy. The results of the experiment verify that the proposed approach outperforms its counterparts.

Ensuring secure and efficient intrusion detection in Software-Defined Networking (SDN) within network virtualization is crucial for modern cybersecurity. In this context, this work presents an AI-powered hybrid deep learning model integrating CNN, LSTM, GRU, and a Transformer Encoder for feature selection. SMOTE is used to balance class distributions, therefore strengthening the model. With ROC-AUC values of 0.9628, and accuracy of 82%, therefore attesting to improved classification performance. For virtualized SDN settings, this method presents an adaptive intrusion detection, hence improving network security and dependability for useful cyber-defense purposes.

With the exponential growth of IoT networks, ensuring robust security has become increasingly critical, as these systems are vulnerable to various cyberattacks. Traditional methods often struggle to handle the massive data generated by IoT devices. This paper introduces an AI-driven, big data approach to intelligent attack detection for IoT networks. Utilizing the NSLKDD dataset, we employed PySpark for preprocessing and chi-square-based feature selection to identify the 15 most significant features, optimizing performance and reducing computational costs. The proposed model, based on XGBoost, achieved outstanding classification results with 98.93% accuracy, and precision, recall, and F1-score approaching 99 %. Comparative analysis against models like Random Forest and LightGBM confirmed its effectiveness, providing a scalable, accurate solution for IoT security.

Human emotion recognition (HER) has rapidly advanced, with applications in intelligent customer service, adaptive system training, human–robot interaction (HRI), and mental health monitoring. HER’s primary goal is to accurately recognize and classify emotions from digital inputs. Emotion recognition (ER) and feature extraction have long been core elements of HER, with deep neural networks (DNNs), particularly convolutional neural networks (CNNs), playing a critical role due to their superior visual feature extraction capabilities. This study proposes improving HER by integrating EfficientNet with transfer learning (TL) to train CNNs. Initially, an efficient R‐CNN accurately recognizes faces in online and offline videos. The ensemble classification model is trained by combining features from four CNN models using feature pooling. The novel VGG‐19 block is used to enhance the Faster R‐CNN learning block, boosting face recognition efficiency and accuracy. The model benefits from fully connected mean pooling, dense pooling, and global dropout layers, solving the evanescent gradient issue. Tested on CK+, FER‐2013, and the custom novel HER dataset (HERD), the approach shows significant accuracy improvements, reaching 89.23% (CK+), 94.36% (FER‐2013), and 97.01% (HERD), proving its robustness and effectiveness.

Medical images, particularly chest radiographs and computed tomography (CT), are essential noninvasive diagnostic tools for detecting chest diseases, such as long-term obstructive pulmonary disease, and other respiratory diseases. However, the manual process of detecting diseases and writing their radiology reports is labor-intensive and time-consuming for radiologists. As a result, there has been a growing interest in the development of automated systems for generating radiological chest X-ray (CXR) reports. Although previous research has concentrated on improving the quantitative performance of these automated reports, the quality of the reports has often been neglected. Radiology reports are crucial for communicating with physicians and patients. Therefore, developing automated systems to generate these reports can significantly reduce radiologists’ workload and enhance efficiency in clinical practice by automating the diagnosis of CXR through artificial intelligence (AI) and generating CXR reports. This paper aims to ease the burden especially on radiologists. In this paper, we propose a novel approach using the long short-term memory (LSTM) based model and gated recurrent unit (GRU) model as a decoder in combination with five different convolutional neural network (CNN) models, in which the DenseNet169 model shows the most promising results. Our LSTM-based decoder with the DenseNet169 model achieves the highest results in terms of the BLEU score B1 at 0.5856, B2 at 0.4982, B3 at 0.3470, B4 at 0.1269, ROUGE at 0.4534, and CIDEr at 0.463. Although our GRU-based model achieves the results in terms of the BLEU score (B1: 0.5619, B2: 0.4682, B3: 0.3370, B4: 0.1169), ROUGE (0.4401), and CIDEr (0.4320), the empirical evaluations demonstrate that our proposed approach, especially when combined with DenseNet169, achieves more accurate disease identification and generates reports of fluent and accurate radiological findings. The approach also compared with existing baseline methods.

Web traffic analysis is crucial for optimising user experience and engagement. This research explores a hybrid approach combining traditional statistical methods, like the autoregressive integrated moving average (ARIMA) model, with advanced techniques such as long short-term memory (LSTM) neural networks and the Prophet model. ARIMA effectively captures linear trends, seasonal effects, and cyclic behaviours, while LSTM handles complex non-linear patterns, and Prophet addresses seasonal variations and missing data. The hybrid model demonstrated 93% accuracy in predicting web traffic, highlighting the benefits of integrating these methodologies. This approach enables businesses to better manage resources, boost user engagement, and improve revenue. Future research will focus on refining hybrid models by incorporating new data features and ensemble methods to further enhance prediction accuracy, ultimately advancing the understanding of web traffic trends and user behaviour.

This paper explores the role of AI and database management in organizational transformation using insights from Twitter data. By analyzing 30,000 English-language tweets with methods such as word analysis, topic modeling, network analysis, sentiment analysis, and emotion analysis, the study reveals a strong correlation between AI and digital transformation. The findings show positive sentiment and optimism about AI's potential. This research highlights the importance of social influence, perceived trust, and awareness in AI adoption, offering valuable insights for researchers and practitioners. Despite relying on Twitter data, the study provides practical guidance for leveraging AI in digital transformation efforts.

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.