The travel mode preference exists in both culture and theenvironment. The wide scale of people's mobility makesour cities more polluted and congested, eventually affecting urban assets.Understanding people’s mode choice is important to develop urbantransportation planning policies effectively. This study aims to model andpredict the commuter’s mode choice behaviour in Lahore, Pakistan. A surveywas conducted, and the data was used for model validation. The comparative study was further done among multinomial logit model (MNL),Random Forest (RF), and K-Nearest Neighbor (KNN) classification approaches. It’s common in existing studies that vehicle ownership is rankedas the most important among all features impacting commuters’ travel modechoice. Since many commuters in Lahore own no vehicle, it’s unclear whatthe rank of factors impacting non-vehicle owners is. Other than thecomparison of predicting the performance of the methods, our contributionis to do more analysis of the rank of factors impacting the different types ofcommuters. It was observed that occupation is ranked as the most importantamong all features for non-vehicle owners.

Time series analysis and decomposition are crucial in examining economic data as they uncover elements such as trends, and seasonal influences, within the data. However, some approaches have difficulty in accommodating complex, high-dimensional data. In this research, we investigate the possibilities of utilizing artificial intelligence (AI) tools, specifically, machine learning (ML) and deep learning (DL) for better timeliness and accuracy of economic forecasting. In some instances, it was shown how recent AI models can improve the data analysis of economic indicators (GDP, inflation, stock indices) through the accurate depiction of non-linear trends and changing seasonals. Model enhancements using AI also result in significant improvement in the accuracy of economic forecasts and provide more detailed and useful time series decomposition for economists and policymakers. This paper is a step towards more extensive use of artificial intelligence in econometric analysis and provides evidence on the feasibility of such in practical econometric studies.

Artificial intelligence(AI) is a well and indeed done deal, and now the AI economy is not only keeping itself alive but also being regarded as a force for transformation to transform fighting and its prediction and prevention into a global endeavor. Since it can use the power of massive data sources and machine learning and pattern recognition algorithms, AI systems can detect and warn of early signs of conflict so that decision-makers can get a head start. Much more specifically, an emphasis on data can amplify bias or produce incorrect predictions, undermining the trust in the results that AI promises to provide. There are significant ethical, political, and technical challenges to integrating AI into peacekeeping frameworks that need to be carefully walked along to use AI responsibly. It is this paper that studies those dimensions and looks to the future to analyze how AI might be distributed in conflict management.

Corporate climate disclosures have come to the fore of measuring environmental responsibility, but worries about greenwashing of exaggeration or parts of the environmental performance of exaggerating or overselling environmental performance remain. This paper fulfills this crucial gap in establishing the validity of such revelations by offering the machine learning method of identifying possible greenwashing. It is probable that the mixed-methods design has been used, where the textual analysis of the composed corporate sustainability reports and supervised learning algorithms trained on labeled examples of misleading statements are supplemented. Through the implementation of natural language processing and classification algorithms, the model will recognise patterns that are suggestive of a lack or even exaggeration of commitment with regard to climate pledges. The findings can be used to illustrate industry-related patterns and important language indications linked to greenwashing.