This article investigates the robust offloading performance of an unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA) incorporating mobile-edge computing (MEC) with the wireless power transfer (WPT)-enabled in Internet of Things (IoT) networks. To assess the system efficacy, we derive the closed-formed expressions of outage successful computation probability (OSCP) under Nakagami-m fading channel. Subsequently, we formulate a system optimization problem of maximizing OSCP by utilizing particle swarm optimization (PSO) algorithm. Numerical findings are implemented with a variety of parameters, thereby validating the precision of our work.
This paper investigates unmanned aerial vehicle (UAV) and nonorthogonal multiple access (NOMA)-mobile-edge computing (MEC) in the Internet of Things (IoT) network, where a UAV acts as a relay. To evaluate offloading performance, we derived closed-form formulas for the successful computation probability (SCP) using the Nakagami-m fading channel model. Furthermore, we also propose an optimization problem to maximize SCP by optimizing the UAV deployment location using the genetic algorithm (GA) method. Finally, numerical results are presented to demonstrate the validity of our analysis.
Optimizing device-to-device (D2D) topologies is pivotal for enhancing the performance and efficiency of 6G networks. This paper introduces a novel approach for forming optimal subnet trees within the 6G networks using BDIx agents and advanced minimum-weight spanning tree (MWST/MST) algorithms augmented by graph neural networks (GNNs), and feedforward neural networks (FFNNs). Our solution aims to significantly boost network performance, particularly in highdemand scenarios such as urban areas, large-scale events, and remote locations. Our approach dynamically adapts to changing network conditions, user movements, and traffic patterns by minimizing the power consumption and maximizing the throughput. We implement various MWST algorithms, including Kruskal’s, Prim’s, and Boruvka’s algorithms, and introduce a GNN model to predict edge weights combined with FFNNs to select parent nodes (called GNN-FFNN model), aiding in the construction of minimum-weight spanning trees (MWST). Additionally, a “weighted distance” metric is proposed to analyze network performance comprehensively. The proposed AI/MLdriven solution integrates BDIx agents with MWST algorithms, focusing on optimizing subnets under gNodeB in 6G networks, enhancing data transmission efficiency, reducing latency, and increasing throughput. This research contributes to developing scalable and flexible network management solutions suitable for diverse configurations and architectures.
The integration of Internet of Things (IoT) technology into poultry farming has revolutionized the industry, offering new possibilities for automation, real-time monitoring, and data-driven decision-making. This paper explores the innovative applications of IoT in poultry farming, highlighting how these technologies enhance operational efficiency, improve animal welfare, and increase productivity. By examining IoTenabled devices, systems, and their implementation, we present an overview of the current advancements and future potential in smart poultry farming.
The development of analytics has revolutionized human resource management by enhancing data-driven decisionmaking. However, human resources analytics (HRA) adoption remains limited, and this is where HR professionals play a crucial role. This study examines the determinants of HR professionals, intention to adopt HRA in addition to their subsequent usage behaviour, utilizing the Unified Theory of Acceptance and Use of Technology model. The study also examines the moderating role of organizational culture in this relationship. Data was gathered through a structured questionnaire from 73 Human Resources professionals in Jakarta. Structural equation modelling-partial least squares was used for the analysis process. The results expose that performance expectancy and social influence significantly impact HRA usage intention, while effort expectancy and facilitating conditions do not. Furthermore, HRA Adoption Intention significantly influences HR analytics usage behaviour. Particularly, organizational culture strengthens the connection between HRA usage intention and usage behaviour. These results emphasize HR professionals’ importance in driving HRA’s adoption, highlighting its performance gains and leveraging social influence. Organizations should adopt a supportive culture to improve the transition from intention to actual usage. The results contribute to the literature by addressing gap in the current understanding of the factors influencing HRA adoption while providing practical implications for organizations targeting to connect the value of HR analytics.
Phishing attacks are the most common cyberattacks nowadays. Phishing attacks rely on social engineering concepts to trick victims into reaching the goals of malicious attackers. In addition, phishing attacks are the largest vector for various cyberattacks. However, URLs are a fulcrum for phishing attacks. The difficulty distinguishing between legitimate and phishing URLs is the reason for the increased success rates of these attacks. An integrated framework is proposed in this study to detect phishing attacks based on classifying URLs into phishing or legitimate URLs through machine learning models such as decision tree (DT) and random forest (RF), which have high power and prediction accuracy in binary classification tasks. The RF model, using the cross validation (CV) technique, achieved an accuracy score of $\mathbf{9 8. 2}$. This methodology is embedded in a web application with a graphical user interface to provide ease of handling and show alerts in real time and visually. This contributes to providing the field of cybersecurity with a highly accurate verification system to reduce users falling victim to these dangerous attacks.
Recently, 3D Gaussian, as an explicit 3D representation paradigm, has demonstrated strong competitiveness over NeRF (neural radiance fields) in terms of expressing complex scenes and training duration. These advantages signal a wide range of applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the segmentation of 3D Gaussians is still in its infancy. The existing segmentation methods are not only cumbersome but also incapable of segmenting multiple objects simultaneously in a short amount of time. In response, this paper introduces a 3D Gaussian segmentation method implemented with 2D segmentation as supervision. This approach uses input 2D segmentation maps to guide the learning of the added 3D Gaussian semantic information, while nearest neighbor clustering and statistical filtering refine the segmentation results. Experiments show that our concise method can achieve comparable performances on mIOU and mAcc for multi-object segmentation as previous single-object segmentation methods.
Wheat is one of the crucial cereal crops globally, and its productivity is affected by various diseases. Therefore, the timely and precise detection and classification of these diseases are important. The advancements in deep learning (DL), especially convolutional neural networks, have observed significant evolution in wheat disease identification. This article provides a comprehensive overview of research works, which effectively utilized DL for wheat disease prediction and classification. The paper begins by introducing the importance of wheat disease management and the challenges associated with traditional disease identification methods. The survey further explores the general architecture of the DL-based wheat disease prediction framework the study highlights the significance of DL models in achieving accurate and effective disease classification. In addition, it demonstrates the challenges associated with different DL models relative to wheat disease identification. Furthermore, it examines the research works related to wheat disease detection using DL models and highlights the limitations of those methods. Finally, the survey provides the future research scope for an improved disease identification model.
In this comprehensive documentation, we embark on an enlightening journey into the dynamic realm of Internet of Things (IoT) home automation. From the seamless integration of smart devices to the complex orchestration of automated processes, IoT home automation redefines the very essence of domesticity. We explore the landscape of cutting-edge advancements, delving into the intricate interplay of sensors, actuators and connectivity protocols that underpin the functionality of smart homes. Furthermore, we delve into the practical applications of IoT in various aspects of home management. This includes optimizing the energy efficiency, monitoring the environment, controlling personalized comfort and enhancing security measures. As we navigate through real-world case studies and exemplary implementations, we gain invaluable insights into the practical challenges and opportunities inherent in deploying IoT solutions within residential settings. In our proposed work, we have developed a prototype for a smart IoT-based smart home to control and monitor various systems such as light, lamp, socket and fan using ESP32 controller. The various controls are performed through relays. The data are sent to the firebase cloud through Wi-Fi and stored in the cloud. Later, the appliances are switched ON/OFF through virtual key using mobile APP. By using virtual keys, we address pertinent ethical and privacy considerations, shedding light on the importance of responsible data stewardship and robust cybersecurity measures. These are crucial in safeguarding the integrity of smart home ecosystems. By combining theoretical frameworks with empirical observations, we aim to provide a comprehensive understanding of IoT home automation.
