Advance driver assistance systems (ADAS) swiftly and effectively detect oncoming cars’ lanes-changing intentions in intelligent transportation, supporting decision support and safety. Current techniques fail to account for vehicle interactions and trajectory data temporal dependencies; hence this research proposes a multi-model fusion-based lane-changing intention recognition framework for intelligent transportation. Using actual vehicle trajectory data from a dataset, the suggested model is verified and contrasted with several well used baseline models. According to the experimental findings, the lane change intention detection technique can greatly increase prediction accuracy by fusing attention processes, reinforcement learning-based CRF, and vehicle interaction data. The system’s main components are input processing and lane-changing intention recognition. Vehicle trajectory data is cleaned, labelled, sliced, and one-hot encoded during input processing BiLSTM-F model detects driver lane-change intent, enhanced by incorporating attention mechanism to the Bidirectional Long Short-Term Memory (BiLSTM) network, the model may give changing weights to input processing section output. This lets the model focus on lane-changing intention-affecting factors. Finally, a Reinforcement Learning-based Conditional Random Field (CRF) efficiently determines the globally optimal lane-changing intention. This field fully represents input data temporal interdependence. The model was trained and tested on the public NGSIM dataset. Validation results show it can achieve up to 97.19% accuracy and predict a vehicle’s lane change intention with 94.16% accuracy, two seconds before the actual maneuver occurs. The suggested model outperforms baseline lane-changing intention recognition models in terms of accuracy, loss performance, F1 score, and stability.

Deep Learning (DL) has proven to be an efficient methodology for yellow fever determination, yet privacy concerns from restricted data sharing by medical institutions compromise its performance. While existing literature has explored Federated Learning (FL) for privacy-preserving model training, this research introduces a novel FL-inspired methodology that integrates multimodal data sources of audio and visual data to identify yellow fever symptoms. This approach employs the Dynamic Integration Approach to estimate health severity, marking a significant advancement over traditional methods that typically rely on single data modalities. Comparative assessments with state-of-the-art methods demonstrate superior performance, with statistical measures showing Accuracy (98.57%), Precision (99.85%), F-Score (96.53%), and Recall (94.72%). Additionally, improved results in delay latency (11.80s) and stability (71%) highlight the approach’s efficiency. This work has broader implications for healthcare and public health systems, offering a scalable and privacy-preserving solution for epidemic monitoring.

Analyzing groundwater is crucial for minimizing contamination and preventing health risks. Real-time water quality assessment requires evaluating multiple parameters, yet many existing methods rely on time-invariant models, which can lead to inaccuracies and inconsistencies in impurity detection. To address this, the current research proposes a hybrid system based on a Two-Actor Game-Theoretic decision model for time-sensitive water quality evaluation. A novel metric,Water Quality Measure (WQM), is introduced to quantify risk and vulnerability to human health. The framework was validated using real-time data acquired from a remote location. Experimental results demonstrate improved efficiency with Sensitivity of 94.75%, Specificity of 95.45%, and Precision of 95.56%, indicating strong predictive reliability. Additionally, it recorded low computational latency (15.62s), and high Reliability (90.18%) and Stability (75%), supporting its effectiveness for accurate and real-time water quality forecasting.

The importance of vehicular networking technology has grown significantly with advancements in autonomous driving and intelligent transportation systems over cyber-physical systems (CPS). Due to its open-access environment, vehicular networking presents significant security problems in assuring message reliability and vehicle trustworthiness. To overcome this problem, this study proposes a framework for managing trust in cars for safe transportation in cyber-physical systems (T-CPS). This framework builds on existing blockchain-based trust management systems to address issues with scalability and ineffective consensus approaches. The framework is divided into three modules: message trust evaluation, vehicle trust update, and trust block generation and agreement. The message trust evaluation module evaluates message reliability by taking into account both direct and indirect vehicle trust over T-CPS. This assessment leads to the identification of misleading messages originating from malicious nodes. Within the T-CPS trust update module, the aim is to mitigate any malicious actions vehicles perform successfully by adjusting the level of trust assigned to each vehicle in T-CPS, which is determined by evaluating the messages received and considering the vehicle’s past behaviour. Ultimately, the effectiveness of the framework is confirmed through simulation studies. The experimental results show clear enhancements in scalability and resilience using the proposed algorithm.

Unmanned Aerial Vehicles (UAVs) offer the immense capability for allowing novel applications in a variety of domains including security, military, surveillance, medicine, and traffic monitoring. The prevalence of UAV systems is due to the collaboration and accomplishment of tasks efficiently and effectively. UAVs embedded with camcorders, GPS receivers, and wireless sensors propose enormous promise in realizing the Internet of Things (IoT) service delivery in vast domains. It results in establishing an airborne field of the IoT when empowered with communication protocols of LTE, 4G, and 5G/6G networks. However, numerous difficulties must be addressed before UAVs may be used effectively namely privacy, security, and administration. Conspicuously, in the current article, novel UAV-specific domains enabled by IoT and 5G/6G technology are explored. Moreover, the presented technique assesses sensor requirements and provides an overview of fleet management systems that address aerial networking, privacy, and security concerns. Furthermore, a framework based on the IoT-5G/6G aspect is proposed which can be deployed over UAVs. Finally, in a heterogeneous computational platform, the proposed framework provides a complete IoT architecture that enables secure UAVs.

The integration of various computational paradigms including the Internet of Things (IoT), and Edge-Cloud platforms, have the potential to enhance the efficiency of evacuation during emergencies. Conspicuously, this study presents an intelligent evacuation framework by integrating the IoT-Edge-Cloud (IEC) computing paradigm. The proposed framework utilizes IoT technology to collect ambient data and track occupant movement based on the location. Edge computing involves the incorporation of a Support Vector Machine (SVM) to identify emergency events. Additionally, it facilitates real-time Spatio-temporal data processing. Furthermore, cloud computing enables the implementation of an evacuation algorithm for efficiently computing a secure and expeditious path based on environmental and occupant data. The presented algorithm generates a comprehensive evacuation map, which serves as a guidance tool to direct individuals toward the designated exit point. Based on experimental simulations, enhanced results were obtained for performance enhancement in terms of Temporal delay (5.23s), Decision-making Efficiency (Precision (95.56%), Sensitivity (96.44%), Specificity (96.97%), F-Measure (96.69%)), Energy Efficiency (4.56mJ), Reliability (92.69%) and Stability (73%).

Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research introduces a method for managing electricity consumption in smart residences using IoT-Fog technology, focusing on efficient energy allocation and real-time energy needs. The study specifically examines the effectiveness of electricity grid sub-stations in distributing energy using fog computing technology. By utilizing a quantum computing-assisted approach, optimal energy distribution is achieved by calculating a novel Electricity Usage Measure (EUM) based on actual energy usage patterns of smart homes. Furthermore, the Quantumized Neural Network (QiM-NN) technique is developed to forecast the electricity distribution over grid substations. For performance assessment, 4-month data are collected using four smart houses. Comparative analysis with existing data assessment techniques illustrates the effectiveness in terms of Temporal Delay (6.33 ms), Optimization Performance (Specificity (93.00%), Sensitivity (90.86%), Precision (96.66%), Coverage (96.66 %), Reliability (93.76%), and Stability (71%).

With smart electricity distribution and dependable electric appliances, the revolutionary impact of the Internet of Things (IoT) technology has considerably improved the service-oriented features of the power grid industry. In the current study, a methodology for IoT-based electricity distribution for intelligent homes is described to detect power consumption efficiently. Although effective power resource allocation remains a primary issue for every power grid house, poor energy distribution has significantly influenced everyday living. The current study focuses on the effective distribution of electricity resources by power grid houses over a spatial–temporal basis. Specifically, the spatial–temporal consumption index is calculated for each home in a geographical region based on electricity usage, which enables the effective allocation of power resources. Additionally, an automated game-theoretic decision-making model is proposed to assist power grid house managers in optimizing the spatial–temporal distribution of electricity resources. For validation purposes, a simulated environment is used to monitor four smart houses for 60 days. A comparative analysis with state-of-the-art data assessment methodologies shows that the presented approach is significantly better in terms of statistical parameters of temporal delay (113.24 s), classification efficacy [precision (93.23%), sensitivity (92.34%), and specificity (92.34%)], decision-making efficiency, reliability (88.45%), and stability (72%).

Healthcare is the most pivotal domain of every nation. With the sudden upraise of the COVID-19 pandemic, there has been a major concern for the healthcare industry to provide quality medical services to the common people. Vulnerable healthcare conditions have proven to be fatal for the patients. Conspicuously, it has become indispensable to assess the quality of healthcare services provided by the hospitals. The current article focuses on analyzing healthcare service quality delivered by the hospitals and healthcare centers. Specifically, the presented framework utilizes Internet of Things (IoT) technology to acquire real-time ambient data inside smart hospitals. The quantification of the healthcare service is performed using the Probability of Health Grade (PoHG) to classify data segments using the probabilistic Bayesian belief model. Furthermore, the temporal data abstraction is performed for the numerical analysis of healthcare service quality in terms of the health quality index (HQI). Finally, a 2-player game theory-inspired decision modeling is performed to analyze healthcare quality in a time-sensitive manner. The proposed framework is assessed using a simulated environment where 225325 data segments are analyzed. Results are compared with state-of-the-art techniques in which enhanced performance measures are registered in terms of classification efficacy (93.74%), decision-making efficiency [coefficient of determination (95%), accuracy (97.53%), mean square error (2.01%), and root mean square error (1.95%)], temporal delay (96.62 s), and reliability (91.58%).

With the advent of computers and their widespread use, cloud computing has been identified as one of the major emerging components of computer technology. The benefits of cloud computing, in the form of processing power and computing resources connected via the internet, have not only bolstered business and personal operations of users but have also led to severe security and privacy threats that require adaptation into cloud computing systems. In light of this, the present study explores the various threats to cloud computing, in addition to outlining defence mechanisms against these threats. It was found that there is a major threat concerning data breaches because of the lack of management understanding of the use of cloud computing services and their defence mechanisms. Furthermore, there can be an abuse of cloud computing services that, in turn, affect not only sensitive data pertaining to the organisation but also the personal identity and information of the user.

Blockchain is a distributed operation and information supervision technology programmed initially for Bitcoin cryptocurrency. The awareness in Blockchain technology is rapidly growing since the notion was invented in the year 2008. The motivation for the concentration in Blockchain is its significant characteristics that deliver security, privacy, and information reliability devoid of any additional system regulating the communications, and consequently it generates fascinating research domains, specifically from the viewpoint of methodological difficulties and restrictions. This study discovers the wide‐ranging Blockchain technology and studies it's perspective with respect to ‘ internet‐of‐things ’ controlled nodes. A resilient prototype method has been programmed that reveals a basic system exhausting Blockchain. The outcome illustrates that the established method is functional in test‐bed environment.