In today’s digital era, the media landscape has seamlessly transitioned from print to online platforms, leading to an unprecedented increase in information accessibility and exchange. However, this transformation has also intensified a major challenge, the rapid proliferation of fake news, which refers to fabricated or misleading information that can be easily produced and disseminated. This paper addresses the growing global concern of misinformation and explores potential solutions through machine learning techniques. The proposed study develops a model designed to assess the authenticity of news articles by evaluating multiple text vectorization methods, specifically the Bag-of-Words approach using both Count Vectorizer and TF-IDF Vectorizer. Two classification algorithms, namely the Multinomial Naive Bayes Classifier and the Passive Aggressive Classifier, are employed to detect fake news. The study further investigates how text pre-processing influences overall model performance. The dataset chosen for training is comprised of 67.7% curated information, while the remaining 33.3% remains untrained raw data. Notably, the model demonstrates a noteworthy efficiency rate of 93.78% under optimum conditions. This strong result demonstrates how well the suggested methodology works to differentiate between real and fake news.
Indian roads present one of the world's most heterogeneous and unstructured traffic environments, simultaneously accommodating motorized vehicles, e-rickshaws, bicycles, pedestrians, and free-roaming animals without strict lane discipline. Autonomous vehicle (AV) perception systems trained primarily on Western datasets fail catastrophically in such mixed-traffic scenarios. This paper proposes HOPNet (Heterogeneous Object Priority Network), a multi-modal real-time object detection framework integrating YOLOv8-based convolutional neural networks with Swin Transformer attention, camera-LiDAR fusion, and domain-adaptive transfer learning. HOPNet is trained and evaluated on the Indian Traffic Dataset (ITD), a novel dataset of 180,000+ annotated images collected from twelve Indian cities. Experimental results demonstrate a mean Average Precision (mAP@0.5) of 91.4%, surpassing the best baseline (YOLOv7) by 11.1%, with a detection latency of 28 ms on NVIDIA Jetson AGX Xavier edge hardware. Per-class analysis shows particularly significant improvements for underrepresented categories: monkey (+27.6%), goat/sheep (+26.2%), street dog (+21.9%), and loaded bicycle (+18.5%). The proposed Contextual Risk Scoring Module (CRSM) enables priority-based AV decision-making for safety-critical unusual objects. DOI: DOI : https://doi.org/10.52783/pst.3468
In this paper, an Artificial Intelligence based methodology for solar radiation forecast in areas where data is limited is presented, based on satellite images and deep learning approaches. Reliable ground-based solar measurements are still limited, especially in developing countries, which is still a significant challenge in the deployment of solar energy. The proposed solution is to use widely available satellite data that can be derived from data on cloud cover images and land surface indicators as proxies for solar resource assessment. To enhance forecasting accuracy over different geographical regions, the use of various deep-learning architectures such as convolutional neural networks, transformers, and spatiotemporal models is explored. Transfer learning and domain-adaptation techniques are also incorporated into the framework allowing transfer of models to areas with lower historical data. In addition, pre-processing methods like normalization, interpolation, augmentation and feature engineering are used to improve the robustness of the model. Methods to aid reliable decision-making and investment planning, such as probabilistic forecasting, uncertainty quantification and calibration methods are included. The proposed approach has promising prospects for enhancing solar-resource assessment, planning for renewable-energy expansion, and for sustainable energy development in areas with a significant data deficiency.
Solar energy is critical in the mitigation of climate change and this helps in reduction of carbon emissions into the atmosphere caused by fossil fuels. Nonetheless, the fluctuation in the generation of solar energy presents difficulties in the effectiveness of combining and functioning of solar technologies in the market. Correct solar resource forecasts are therefore essential in the proper management and strategic planning in power systems. Numerous methodologies of forecasting have appeared, primarily they are classified into physical models, statistical methods and machine learning methods. The first and second categories use familiar physical phenomena, and need less data to train the model. It is worth noting that simply classifying methods by machine learning methods are not comprehensive enough; even the duration of the forecast horizon is a decisive factor. Machine learning has largely influenced solar photovoltaic (PV) forecasting by making the use of solutions that take into account time-series data by now possible, especially since the availability of regional data is increasing. The choice of a model depends on the application and system characteristics, i.e. there is no single application-system characteristics that will be universally applicable. With the energy transition on the verge of happening, it is necessary to consider hybrid renewable and distributed generation systems since individual technology-related limitations are more evident with an increasing market integration. A solution is offered through task-oriented coordinated optimization, which will exclude the necessity of a strictly delineated hybridization framework. DOI : https://doi.org/10.52783/pst.3434
The changes in climate and environmental degradation have exacerbated the demand for more sustainable patterns of household consumption. In the current study, we investigate the role that conscious consumption behaviours can play in lowering carbon footprints by reducing the energy consumption of households and their food waste. It discusses the link between environmental awareness, choices of action and everyday consumption habits that affect GHG emissions. The methodology used was a mixed methods study, which included qualitative interviews, surveys and a behavioral analysis in various cultural and national settings. The results show that actions at the household level, including planning meals, tracking food stored, using less unnecessary appliances and taking the recommended actions in response to energy consumption feedback, can have a substantial impact on carbon emissions. Additionally, the research underscores the significance of social norms, educational initiatives, behavioral nudges, and institutional frameworks in fostering long-term sustainable practices. Besides, cultural differences were revealed to affect the effectiveness of conscious-consumption practices and environmental attitude. Overall, the paper suggests that by raising awareness about the importance of conscious consumption at the household level, it can contribute to climate-change mitigation measures, resource efficiency, waste reduction and a more sustainable way of living for future generations. DOI : https://doi.org/10.52783/pst.3441
