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Optimization of University Library Services through Big Data and Multi-source Data Fusion DiYin Zhu Abstract: The advent of the big data era has not only advanced the informatization of libraries but also opened unprecedented opportunities for their sustainable development. Libraries are no longer limited to traditional resource management; instead, they have embraced emerging technologies such as Web 2.0, mobile solutions, cloud computing, resource discovery systems, and big data platforms. While these developments provide a solid technological foundation, libraries must further enhance their ability to conduct data analysis, semantic processing, decision-making, and visualization in order to respond effectively to evolving user demands and complex information environments. This study contributes to that goal by discussing the application of multi-source data fusion in science and technology decision-making. It presents a comprehensive decision support framework that integrates semantic preprocessing techniques including data cleaning, partition segmentation, and synonym merging supported by Python s Pandas library and Jieba s text-cutting functions. Through this approach, the research successfully identified six science and technology text clusters and three mass technology-related clusters, thereby providing a refined view of user information needs and thematic structures within large-scale datasets. The findings demonstrate that a decision support framework based on multi-source data fusion can proactively detect and respond to user needs, moving libraries from passive service providers to active, intelligent participants in knowledge dissemination. This proactive transformation enriches the quality of information services, enables accurate and personalized decision support, and aligns with the demands of the new era defined by innovation-driven and intelligence-first strategies. Ultimately, this work highlights the value of integrating big data technologies into library management and decision-making systems. By bridging semantic analysis with multi-source data fusion, libraries can evolve into dynamic hubs of innovation, offering precise, context-aware services that not only enhance user satisfaction but also strengthen their role in supporting scientific research, technological advancement, and informed decision-making in the digital age. Keywords: Continuous Glucose Monitoring; Diabetes Detection; Fog Computing; IoT; Machine Learning models. |
Deep Neural Watermarking for Robust Copyright Protection in 3D Point Clouds Khandoker Ashik Uz Zaman, Mohammad Zahangir Alam, Mohammed N. M. Ali and Mahdi H. Miraz Abstract: The protection of intellectual property has become critical due to the rapid growth of three-dimensional content in digital media. Unlike traditional images or videos, 3D point clouds present unique challenges for copyright enforcement, as they are especially vulnerable to a range of geometric and non-geometric attacks that can easily degrade or remove conventional watermark signals. In this paper, we address these challenges by proposing a robust deep neural watermarking framework for 3D point cloud copyright protection and ownership verification. Our approach embeds binary watermarks into the singular values of 3D point cloud blocks using spectral decomposition, i.e. Singular Value Decomposition (SVD), and leverages the extraction capabilities of Deep Learning using PointNet++ neural network architecture. The network is trained to reliably extract watermarks even after the data undergoes various attacks such as rotation, scaling, noise, cropping and signal distortions. We validated our method using the publicly available ModelNet40 dataset, demonstrating that deep learning-based extraction significantly outperforms traditional SVD-based techniques under challenging conditions. Our experimental evaluation demonstrates that the deep learning-based extraction approach significantly outperforms existing SVD-based methods with deep learning achieving bitwise accuracy up to 0.83 and Intersection over Union (IoU) of 0.80, compared to SVD achieving a bitwise accuracy of 0.58 and IoU of 0.26 for the Crop (70%) attack, which is the most severe geometric distortion in our experiment. This demonstrates our method's ability to achieve superior watermark recovery and maintain high fidelity even under severe distortions. Through the integration of conventional spectral methods and modern neural architectures, our hybrid approach establishes a new standard for robust and reliable copyright protection in 3D digital environments. Our work provides a promising approach to intellectual property protection in the growing 3D media sector, meeting crucial demands in gaming, virtual reality, medical imaging and digital content creation. Keywords: 3D point cloud; copyright protection; digital watermarking; deep learning; robust watermark extraction; singular value decomposition; spectral decomposition. |
CSK Modulation for Secure Wireless Communication Networks Rana H. A. Zubo, Walid A. Al-Hussaibi and Raed Abd-Alhameed Abstract: Physical layer security (PLS) has been considered as a key technology to fulfill the information confidentiality request of modern and future communication networks. Therefore, diverse chaos-based wireless communication (CBWC) systems have been developed as low complexity and cost-effective PLS approaches when compared with the upper layer secrecy protocols. In particular, chaos-shift-keying (CSK) modulation schemes have attracted significant research efforts owing to the simple signal generation techniques and enhanced secrecy. However, the practical implementation of CSK for secure data transmission over realistic CBWC channels still needs further investigation. In this paper, the application of CSK based on multiple chaotic basis functions is examined over a band-limited CBWC channel with Rayleigh fading process. Lorenz and Chua chaotic oscillator circuits are used as basis signal functions for CSK modulation at the transmit side and chaos demodulation/synchronization at the receiver end. The impact of channel bandwidth and requisites of the front-end receiver is modeled as a low pass filter process. Performance results show that chaos filtering can greatly affects the physical features of employed signals at different levels. The achieved results confirmed that inadequate filter bandwidth can remarkably distort the state-space, signature waveform, and spectral components of CSK signals in disparate extents regardless of high SNR level. For target error rate and worst-case eavesdropping secrecy, this issue has a direct impact on decreasing the error security gap of CBWC system compared with the reference CSK schemes based on a single chaotic base function, even at a high received signal-to-noise ratio. As a feasible solution to mitigate the degradation in system reliability and secrecy, it is demonstrated that the designed filter bandwidth must include the effective spectral components of utilized chaotic signals. Keywords: Chaotic circuits; chaotic communications; CSK; error security gap; physical layer security; wireless channels. |
Analysing Public Perception of Solar Energy: An Explainable AI Sentiment Analysis Approach Japhne Anbarasan and Murugeswari Rathinam Abstract: Addressing the contemporary climate crisis is the need of the hour to protect both people and the planet. As countries embark on green energy revolution, focussing on achieving the United Nations (UN) 2030 agenda for Sustainable Development, guaranteeing universal access to affordable, reliable, and modern energy services stands out as an important goal. As part of the implementation of this goal, solar panel installation scheme has been undertaken by the government of India to encourage widespread adoption of green energy. This research work proposes an effective method to assess the acceptance of this scheme among users and the broader audience. User comments/ feedback from various social networking sites are analysed in this research work using Machine Learning techniques along with Explainable Artificial Intelligence (XAI) to make the machine learning models’ predictions more transparent. OpenAI Generative Pre-trained Transformer (GPT) language model is also used to automatically identify key implementation challenges of the scheme by creating a concise summary of the feedback shared by the users. This insight, based on the pain points of the users, can further help in providing recommendations and suggestions to appropriate stakeholders to improve the success rate of this scheme. Five machine learning models- Logistic Regression, Random Forest, Decision Tree, Extreme Gradient Boosting, and Stochastic Gradient Descent- were compared to choose the right technique for sentiment analysis. Among them, Logistic Regression and Stochastic Gradient Descent achieved an accuracy of 93% in predicting the sentiment. Our analysis showed around 63% of user feedback was positive indicating the public acceptance of green energy projects in India despite higher initial investments. The methodology and framework developed during this research work have immense reusability across similar government schemes (where transparency in sentiment analysis and sensitivity of public data are critical) in assessing their effectiveness and identifying areas where improvements are required. Keywords: Explainable Artificial Intelligence; Opinion Mining; Panel Scheme; PM Surya Ghar Muft Bijli Yojana; Rooftop Solar Sentiment Analysis; Sustainable Development Goals. |
Design of Enterprise Data Security Management Based on IoT and CNN Fan Gao Abstract: In the era of rapid digital transformation, enterprise data security faces increasingly complex and dynamic threats. Traditional defense mechanisms are complicated to effectively respond to real-time risks, mainly when enterprises rely extensively on Internet of Things (IoT) devices. To address this problem, this paper proposes and implements a dynamic intelligent security assessment and early warning system based on ResNet-50 architecture and IoT technology. The system builds a distributed IoT data collection platform to collect multi-source data such as network traffic, device status changes, and user behavior in real time. It uses the optimized ResNet-50 model to analyze high-dimensional heterogeneous data streams accurately. The system is deployed in a cloud computing environment and can process large-scale data with low latency. It can instantly detect abnormal activities, conduct threat assessment, and issue alerts based on contextual information. Experimental results show that the system has an accuracy rate of 98.6% for distributed denial of service (DDoS) attacks and 96.2% for malware data leaks, with an average response time of 1.03 seconds, significantly better than traditional detection methods. This study provides an efficient and scalable solution for enterprise data security protection and lays a foundation for further integrating AI-driven models with IoT infrastructure. Keywords: Convolutional Neural Network; Data Security Detection; Enterprise Data Security; IoT; Residual Network. |
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