This paper introduces a Quantum and Blockchain Integrated Legal Intelligence Framework (QBLIF) for the probabilistic assessment of evidence and the optimization of sentencing in computational jurisprudence. The proposed Quantum Probabilistic Sentencing Optimization Algorithm (QPSOA) employs quantum amplitude estimation and variational quantum circuits to enhance Bayesian evidence aggregation and decision uncertainty models. Quantum subroutines can compute likelihoods faster than classical Bayesian networks and reduce inference time by up to 65 %. To ensure data integrity, the framework incorporates a permissioned Hyperledger blockchain layer to securely store evidential data, algorithm parameters, and decision outputs as tamper-evident, using cryptographically signed smart contracts. This blockchain implementation ensures nonrepudiation, transparency, and decentralized verification of all steps involved in judicial inference. The system is optimized at the system level to perform multi-objective functions that balance fairness constraints, moral compliance, and consistency in sentencing, leveraging quantum-enhanced probabilistic reasoning. Simulated legal data experiments demonstrate that the system improves accuracy (92.4 %), bias mitigation, and auditability while maintaining a low latency of 2.7 ms per transaction. With the help of quantum computing and clear blockchain records, QBLIF offers a new way for AI to make legal decisions that guarantee fairness, accountability, and ethical handling of digital justice processes

The broad acceptance of the Internet of Things (IoT) has transformed diverse industries, yet the system produced major security issues, which made the network prone to cyberattacks. Besides, the various traditional approaches have been introduced for intrusion detection, however the frameworks struggled with major challenges including high computational complexity, a lack of generalizability, flexibility issues, a lack of scalability, and higher processing time, respectively. Therefore, to overcome these limitations, the research proposes the Drift Aware Quantum-enabled Support Vector Machine and Light Gradient Boosting Machine ensembled model based on the Multi-head Learning (DQSLGM) model for performing effective detection of intrusions in IoT networks. In addition, the development of DQSLGM model offers more accurate, resilient, and effective defense against emerging threats in resource-constrained IoT environments. Moreover, the findings of the model demonstrate a greater accuracy of 98.72%, 98.30% sensitivity, and 99.23% specificity utilizing the CICIDS2017 dataset in accordance with a training percentage of 90, respectively.

In an era where artificial intelligence (AI) is evolving at an unprecedented rate, the role of strategic leadership has undergone a significant transformation. This paper explores the interplay between human intuition and data-driven insights in enhancing management decision-making. AI offers unparalleled capabilities in processing data, predictive analytics, and optimization. However, the human elements of creativity, empathy, and ethical judgment remain irreplaceable. By integrating AI technologies into leadership frameworks, organizations can achieve a symbiotic relationship that balances objective analytics with subjective reasoning. This study highlights key challenges leaders face in adopting AI, including ethical concerns, bias in algorithms, and over-reliance on automated systems. It also emphasizes strategies for fostering an AI-ready leadership culture, focusing on upskilling, ethical AI practices, and fostering collaboration between human teams and AI systems. Our proposed Neural networks achieved the highest accuracy (90%), followed by reinforcement learning (88%), random forest (85%), decision trees (75%), and fuzzy logic (70%). This paper presents actionable insights for leaders to understand the complex nature of AI and its integration. The findings of this study bring the importance of aligning AI tools with organizational values and human-centric approaches into the limelight, enabling leaders to harness AI's potential without compromising their core decision-making principles. This balance is crucial for sustainable growth and innovation in the digital age.

This paper provides a comprehensive analysis of Wireless Body Area Networks (WBANs), which are a specialized category of Wireless Sensor Networks designed primarily for healthcare monitoring and interactive gaming. It outlines the three-tier architecture of WBANs, including sensor nodes for data collection, wireless communication for data transmission, and a Decision Control Unit for processing and storing data. The study addresses key challenges in WBAN routing such as energy efficiency, node temperature regulation, and Quality of Service (QoS) requirements. It also categorizes routing protocols into temperature-sensitive, QoS-sensitive, energy-aware, and hybrid protocols, each designed to optimize WBAN performance under unique constraints like limited bandwidth, dynamic network topology due to human movement, and power limitations. This research underscores the significance of WBANs in modern healthcare.

This paper introduces a methodical strategy for creating and executing IoT designs that are enhanced by edge computing. The goal is to meet the changing requirements of future devices. With the rise of the Internet of Things (IoT), there is a significant increase in the amount of data being generated due to the large number of interconnected devices. Conventional cloud-based processing models frequently encounter difficulties related to latency, bandwidth, and privacy problems. In order to address these difficulties, we provide a pioneering framework that incorporates edge computing into Internet of Things (IoT) systems. Our methodology disperses data processing, relocating computing in proximity to the data origin at the periphery of the network. By implementing this, the latency is reduced, the bandwidth utilization is minimized, and the data privacy is enhanced. In this document, we delineate the fundamental concepts that govern the design of this architecture, with a specific emphasis on modularity, scalability, and security. The architecture is intentionally built to be flexible, capable of accommodating a diverse array of IoT devices and applications, spanning from smart home systems to industrial automation. We showcase a preliminary version of our framework, illustrating its efficacy in several settings. Performance assessments demonstrate substantial enhancements in reaction time and bandwidth economy when compared to conventional cloud-based models. Our architecture additionally integrates sophisticated security protocols to protect against potential risks in IoT contexts. We analyze the consequences of our methodology on the future of IoT, emphasizing how architectures improved by edge computing can enable innovative opportunities for intelligent, effective, and protected IoT systems. This study establishes the foundation for future research and development in this potential subject, providing a detailed plan for the creation of the next generation of IoT devices.

Introduction:The rapid growth of the global population and intensive agricultural activities has posed serious environmental challenges. In response, there is an increasing demand for sustainable agricultural solutions that ensure efficient resource utilization while maintaining ecological balance. Among these, intercropping has gained prominence as a viable method, promoting enhanced land use efficiency and fostering environment for crop development. However, disease management in intercropping systems remains complex due to the potential for cross-infection and overlapping disease symptoms among crops. Early and precise illness recognition is, therefore, critical for sustaining crop condition and efficiency. Methods:This study introduces an intelligent intercropping framework for early leaf disease detection, utilizing hyperspectral imaging and hybrid deep learning models for precision agriculture. Hyperspectral imaging captures intricate biochemical and structural variations in crops like maize, soybean, pea, and cucumber-subtle markers of disease that are otherwise imperceptible. These images enable accurate identification of diseases such as rust, leaf spot, and complex co-infections. To refine disease region segmentation and improve detection accuracy, the proposed model employs the synergistic swarm optimization (SSO) algorithm. A phase attention fusion network (PANet) is utilized for deep feature extraction, minimizing false detection rates. Furthermore, a dual-stage Kepler optimization (DSKO) algorithm addresses the challenge of high-dimensional data by choosing the most applicable landscapes. The disease classification is performed using a random deep convolutional neural network (R-DCNN). Results and discussion:Experimental evaluations were conducted using publicly available hyperspectral datasets for maize-soybean and pea-cucumber intercropping systems. The suggested ideal attained remarkable organization accuracies of 99.676% and 99.538% for the respective intercropping systems, demonstrating its potential as a robust, non-invasive tool for smart, sustainable agriculture.

The surveillance of symptoms related to pneumonia is increasingly crucial due to its widespread occurrence and similarity to symptoms exhibited in other contagious diseases such as influenza, respiratory syncytial virus (RSV), and COVID-19. The timely identification of pneumonia can significantly diminish mortality rates. To tackle this issue, a pioneering non-contact technique for monitoring pneumonia symptoms has been developed. This investigation mainly concentrates on the early detection of pneumonia, which can serve as an indicator of the onset of other persistent ailments. The technique entails an analytical approach to scrutinize symptoms such as cold, cough, chills, sore throat, altered respiratory rates, and elevated body temperature by employing depth imaging methods. The crux of this exploration lies in the utilization of a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks to classify video images in order to identify symptoms associated with pneumonia. The proposed model has showcased an impressive overall accuracy rate of 98.02% along with a significantly optimized prediction time of a mere 8.63 ms. Moreover, the study encompasses a comprehensive evaluation of various deep learning techniques in the detection of diseases exhibiting symptoms akin to pneumonia. The study introduces a pivotal advancement in medical diagnostics, emphasizing the importance and effectiveness of a fusion-based, profound learning system in the non-contact identification of pneumonia symptoms. This innovative approach has the potential to revolutionize the way pneumonia and similar diseases are diagnosed and monitored.

The evolving landscape of wireless communication is heralding the era of 6G, characterized by hyper-connectivity, extremely low latencies, and ubiquitous coverage. In this context, Unmanned Aerial Vehicles (UAVs) equipped with artificial intelligence (AI) capabilities present an innovative solution for advanced network management and service provisioning. This paper offers a com-prehensive overview of the synergistic integration of AI-enabled UAVs in 6G networks. We first elucidate the fundamental challenges of 6G networks, including the demand for denser deployments, dynamic spectrum sharing, and real-time network optimization. AI-powered UAVs address these challenges by serving as aerial base stations, facilitating three-dimensional (3D) network coverage, and enabling adaptive beamforming and interference management. Subsequently, we delve into the operational roles of AI-UAVs in 6G networks. Emphasis is placed on predictive network analytics, where UAVs forecast user demand and potential bottlenecks, autonomous fault detection and self-healing, reducing net-work downtimes, and (iii) dynamic resource allocation, ensuring optimal quality of service (QoS) for users. Finally, we discuss the practical considerations and potential limitations of deploying AI-enabled UAVs, including regulatory constraints, power consumption issues, and UAV-to-UAV coordination. Through this paper, we underscore the paramount importance of AI-UAVs in realizing the full potential of 6G communications, proposing future research directions and emphasizing the need for holistic frameworks that seamlessly integrate terrestrial and aerial network components.