Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

Electric vehicles (EVs) are drawing increasing attention, given the volatile fuel prices and impending reduction in fuel supplies. EVs are also preferred in the automotive industry as they reduce the environmental impact and fuel consumption while achieving a higher efficiency when compared to internal combustion engine vehicles (ICEVs). Although most of energy and environmental policies worldwide have set targets with the goal to shift from classic fossil fuel driven vehicles to electrified transport, the share of EVs is still rather low. The complexity of changing the human perception of transportation goes beyond technical and economic aspects and very few research activities managed to capture the additional factors. This paper presents a comprehensive review of studies on consumer preferences for EV, aiming to better inform policy-makers and give direction to further research. In addition, this paper discusses the main obstacles that limit the social acceptance in adopting EVs such as driving obstacles, charging obstacles and willingness-to-pay from consumers. Furthermore, mitigation strategies are outlined as the recommendation to promote a better EV deployment.

This paper proposes an effective classification method named Rider Chicken Optimization Algorithm-based Recurrent Neural Network (RCOA-based RNN) to perform big data classification in spark architecture. Initially, the input data are collected from the network by the master node and then forwarded to the slave node. These nodes are responsible for storing the data and performing computations. The features are effectively selected in the slave node using the proposed RCOA. The selected features are forwarded to the master node. The big data classification is achieved in the master node by using the RNN classifier, and the training of the classifier is done using the proposed RCOA algorithm, which is the integration of the Rider optimization algorithm (ROA) with the standard Chicken Swarm Optimization (CSO). The experimentation is done by using the Switzerland dataset, Cleveland dataset, Hungarian dataset and Skin disease dataset, in which the proposed RCOA-based RNN attained better performance based on the quantitative properties, such as sensitivity, accuracy and specificity with the values of 9.3E+01%, 9.4E+01% and 9.3E+01% using Hungarian dataset. The existing learning methods failed to address the complex classification problems at a reasonable time, which is overcome by the proposed method.

Road safety is a critical concern, and unsurfaced roads are a special case where potholes and cracks are among the greatest hazards to human-driven and autonomous vehicles. To follow through with the issue of tackling road hazards in real time, we propose a real-time road hazard detection system that incorporates deep learning, explainable AI (XAI) techniques, and context-aware detection to enhance the Advanced Driver Assistance Systems (ADAS). The YOLOv11 model is used to detect road anomalies quickly and easily available for hazard identification. YOLOv11 was selected after comparative evaluation with YOLOv8, Single Shot Detector (SSD), and Attention based Convolutional Neural Networks due to its higher detection accuracy, faster inference and better performance under occlusion. It outperformed other models in terms of mean Average Precision (mAP), reduced false positives, and maintained superior inference speed under occlusion, shadow, and low-light conditions. Furthermore, its architecture supports efficient edge deployment, crucial for real-time ADAS systems. Edge detection, coupled with occlusion sensitivity analysis, yields context-aware detection, which detects partially exposed potholes and cracks, thereby reducing false negatives and providing the overall robustness of the system under difficult road conditions. Stereo vision with the use of multi- camera fusion methodology is used to estimate depths and effectively handle occlusions caused by other vehicles as well as provide better spatial localizations of hazards. The field of view of the vehicle is used to divide the location of the hazards into left, centre, and right zones. A real-time voice alert mechanism helps in acting ahead of time in a larger perspective of situational awareness. The proposed system is cloud-integrated in order to achieve large-scale deployment and to continuously keep improving the model with model updates and sharing of the hazard data in the cloud at remote nodes for collective intelligence. This research integrates deep learning, explainable AI, and contextual analysis, rooted in knowledge garnered from the intrinsic knowledge of its generation, and builds a future generation of knowledge-enabled intelligent transportation systems for the accurate, interpretable real-time road hazard detection for applications of ADAS and drivers at large, improving road safety for all users.