Ozturk, Seren DuzenliHunerli, DuyguAykan, SimgeIsbitiren, Yagmur Ozbek2025-09-252025-09-25202597810328305139781040439401https://doi.org/10.1201/9781003531449-12https://hdl.handle.net/20.500.14365/6442Artificial Intelligence (AI) is a branch of computer science that focuses on replicating human intelligence in machines (Malik & Solanki, 2021), allowing them to possess problem-solving (Zeigler, Muzy, & Yilmaz, 2009), and decision-making abilities akin to the human brain (Malik & Solanki, 2021). AI methods undergo training using extensive datasets, enabling them to perform specific tasks. Subsequently, they use this acquired knowledge to evaluate unfamiliar data and generate targeted outcomes. One of the remarkable aspects of AI is its capacity to swiftly process massive datasets without human intervention. Advancements in hardware technologies have facilitated a progression from conventional machine learning to deep learning within the field of AI, resulting in the emergence of widely used applications such as natural language processing, speech recognition, computer vision, and image classification parameters (Rana, Rawat, Bijalwan, & Bahuguna, 2018). Moreover, ongoing advancements in hardware aim to move towards neuromorphic hardware, which would lower the energy consumption of AI systems, emulating the energy efficiency of the human brain (Berggren et al., 2020). In essence, AI empowers machines to intelligently and intuitively tackle complex problems and make informed decisions. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessBrainComputer HardwareDecision MakingDeep LearningEnergy EfficiencyEnergy UtilizationGreen ComputingImage ClassificationLearning SystemsNatural Language Processing SystemsArtificial Intelligence MethodsClinical NeuroscienceDecisions MakingsHuman BrainHuman IntelligenceHuman InterventionMassive Data SetsProblem-SolvingSpecific TasksZeiglerSpeech RecognitionBook Part10.1201/9781003531449-122-s2.0-105013215128