Hezam Gawbah

Abstract Climate change and socio-economic factors have made predicting energy demand and assessing sustainability important problems in today’s energy systems. This work proposes an integrated framework for multi-country energy forecasting and sustainability assessment using deep learning and explainable artificial intelligence (XAI). A multi-country dataset for the years 2020–2024 is used, including climate, socio-economic, environmental and energy-system variables.

Abstract Exchange rate forecasting is an important and well-studied problem in finance. However, most existing approaches rely only on historical price data and overlook the role of public sentiment, which can be a strong signal during periods of political and economic instability. This paper presents a hybrid forecasting framework for the USD/PKR currency pair that combines historical exchange rate time series with daily sentiment scores extracted from Urdu-language tweets.

Abstract Asthma and chronic obstructive pulmonary disease (COPD) are significant global health burdens with conventional diagnosis relying on resource-intensive spirometry. This paper presents a reproducible multimodal respiratory sound screening model combining complementary acoustic and clinical representations.