A cloud computing environment is a computer environment that provides services and stores data as virtualized a data warehouse and information that is accessed

The GreenGuard: CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring initiative will create multiple future-oriented results. The processing of agricultural imagery becomes revolutionized through the combination of median filtering and Exponential Tsallis entropy Gaussian Mixture model (ExTS-GMM) advanced techniques initially. The essential preprocessing operation delivers better quality data to the Convolutional Neural Network (CNN) classifier which results in optimal performance outcomes. The simple integration of CNN classifiers will launch an innovative age that delivers more accurate and efficient paddy leaf detection for agricultural images. Deep learning features of a CNN enable it to uncover complex structural details found in both normal and sick paddy leaf specimens. The classifier's aptitude creates an efficient pathway to execute precise assessment and group data into appropriate categories while processing extended agricultural database information rapidly. Effective implementation of "GreenGuard" will reshape conventional paddy field crop health monitoring systems into modern standards. Modern agricultural stakeholders can make precise choices about pest management along with disease control and irrigation schedules because of timely crop health assessments from the implemented system. The new capabilities generated from this empowerment system will create major crop yield growth and enhance food safety protocols as well as promote sustainable farming throughout paddy farms globally.

The rapid evolution in types of cyber threats and anticipated threats through quantum computing requires a change in cybersecurity strategy. The traditional Threat Intelligence Platforms (TIPs) usually use static rule-based systems and reactive measures to provide real-time solutions to address attacks that are either emerging or sophisticated. This paper proposes a next-generation Threat Intelligence Platform (TIP) incorporating Generative Artificial Intelligence (GenAI) to enable predictive threat scenario modelling, anomaly detection, and autonomous mitigation strategies using quantum-security mechanisms to provide very-long-term cryptographic resilience. The GenAI engine provides live threat simulation, adequate situational awareness, and improved anomaly detection accuracy by large-scale cyber datasets. At the same time, the Quantum-Security Module consists of post-quantum cryptographic (PQC) algorithms, such as lattice-based, hash-based, and multivariate cryptosystems. All of these seek to plug holes caused by quantum adversaries. The suggested TIP architecture is a multi-layered one that possesses a data ingestion layer, an AI-driven threat analytics, a quantum-resilient cryptographic framework, and an interactive visualization dashboard. It delineates an architectural framework, important points of innovation, and potential applications across the main sectors of finance, healthcare, defence, and enterprise cybersecurity. Additionally, the research presents a high-level strategic roadmap for the deployment of GenAI-intensified TIPs with quantum-resilient security, scalable and adaptable for future-proof data integrity. Addressing AI-enabled predictive security features and post-quantum cryptographic resilience, the research offers future-proof cybersecurity as an answer to continuous changes in digital threats.

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings.