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This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

Pakistan's present war against extremists has many folds and sheds. The country's initial participation in the Afghan War in 1979 later gave birth to different extremist trends in the country. State patronage of the extremist Wahabi Islamists during the Afghan jihad opened another conflict in Pakistan, and things became more complicated. The combination of external and internal factors gave birth to the worst kind of conflict, which now has not only become dangerous for the country's own existence but also a major threat for global peace. The Afghan jihad initially started as a war against Soviet occupation and later became the hub of global jihad-war against infidels.

This chapter analyzes how external factors promoted internal contradictions in Pakistan due to which the country became not only an exporter of jihadis for the world but also the worst kind of sectarian conflicts, including. Shia–Sunni, Deobandi–Wahabi clashes, entered into in the past two decades. Such a strong link exists with Pakistan's official support to global jihad. Draft sectarian groups now head to head with their opponents have killed thousands of members of rival sectors, have strong support from external sympathizers, and have spread in the country. The well planned terrorist activities of these groups reflect the fact that support to these groups in the past is now leading to a severe crisis in Pakistan. The nexuses of these indigenous extremists like Lashkar-e-Jhangvi, Lashkar-e-Taiba, Tehrik-e-Taliban Pakistan, and Hizb-ul-Mujahideen with external terrorist organizations like Al-Qaeda, the Taliban, and Islamic Movement of Uzbekistan of Tahir Yuldasher Chechen Guerilla War has led to several bloody clashes in the country and outside.