E-Book, Englisch, 314 Seiten
Ahamed / Thirunavukarasu / Nagarajan Recent Advancements in Computational Intelligence: Concepts, Methodologies and Applications (Part 1)
1. Auflage 2025
ISBN: 979-8-89881-033-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
E-Book, Englisch, 314 Seiten
ISBN: 979-8-89881-033-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
Recent Advancements in Computational Intelligence: Concepts, Methodologies and Applications (Part 1) is an in-depth exploration of computational intelligence (CI), a rapidly evolving field blending artificial intelligence, machine learning, and data-driven problem-solving. The book focuses on biologically inspired methods such as neural networks, fuzzy logic, swarm intelligence, and evolutionary algorithms; it showcases how CI addresses complex real-world problems marked by uncertainty and incomplete data. Integrating theory with Industry 4.0 applications, the book spans diverse domains including healthcare, autonomous systems, cybersecurity, and smart computing. Structured across thematic sections like Social Computing, High-Performance Computing, Network Science, Smart Computing, and Intelligent Communications, it covers topics like deep learning for autonomous vehicles, AI-driven healthcare diagnostics, graph theory in cybersecurity, and reinforcement learning for generative AI. With contributions from international experts, it bridges foundational principles with applied research and case studies. Key Features: • Integrates emerging trends such as generative AI, edge computing, and reinforcement learning. • Demonstrates practical relevance through real-world case studies and industrial applications. • Promotes interdisciplinary understanding across computer science, engineering, and healthcare. • Provides actionable methodologies for researchers and practitioners advancing CI innovation. • Highlights the fusion of theory and practice in intelligent, adaptive systems.
Autoren/Hrsg.
Weitere Infos & Material
Recent Advancements and Applications of Computational Intelligence
Sharon Femi. P.1, *, Kala A.1, V. Rajalakshmi.1, K. Ashwini2
Abstract
Computational Intelligence (CI) is a subfield of Artificial Intelligence (AI) that represents the application of algorithms designed to address specific problems by automating tasks that traditionally require human intelligence. Utilizing algorithms capable of learning and recognizing patterns within data, AI achieves quicker results. With the rapid advancement of CI, its applications have become essential components across diverse domains, transforming industries and reshaping task execution methodologies. This research conducts a thorough examination of the varied techniques and applications of CI, aiming to provide a nuanced understanding of their current status and future potential. The initial segment of the study delves into the foundational tools pivotal for AI development, forming its backbone. Subsequent sections concentrate on the widespread applications of CI in sectors such as healthcare, finance, education, manufacturing, and autonomous systems. The study delivers a comprehensive overview of CI advancements and applications, offering valuable insights for researchers, practitioners, and policymakers. By comprehending the present landscape and anticipating forthcoming developments, stakeholders can navigate the evolving AI ecosystem with informed decision-making, ensuring the responsible and ethical progression of this transformative technology.
* Corresponding author Sharon Femi P.: Sri Venkateswara College of Engineering, Sriperumbudur, India; E-mail: sharon@svce.ac.in
INTRODUCTION
With the advancement of technology, computational intelligence (CI) has expanded significantly in recent years. CI is gradually taking over as the primary force that is constantly changing the virtual environment. Information consumption is evolving as a result of the digital age. With more and more people
turning to digital devices for their day-to-day needs, businesses have started utilizing various models for content creation, social media, education, recruitment, sales, marketing, and decision-making [1, 2].
AI tools encompass a diverse range of software and hardware technologies designed to empower machines to execute tasks traditionally associated with human intelligence. By harnessing algorithms, data, and computational capabilities, these tools emulate cognitive functions like learning, reasoning, problem-solving, perception, and language comprehension. Their pivotal role extends to process automation, extensive data analysis, and the facilitation of intelligent decision-making.
The key components of CI techniques include machine learning, Natural Language Processing (NLP) tools that facilitate the understanding, interpretation, and generation of human language, Computer Vision tools for enabling machines to interpret and decide based on visual data, and Explainable AI (XAI) tools aiming to enhance the interpretability of AI models for human understanding. The rapid evolution of CI techniques is playing a significant role in driving progress across diverse industries. As technology advances further, the prevalence and impact of CI techniques in development and integration are expected to grow substantially.
Machine Learning Tools
Machine learning (ML) plays a vital role in computational intelligence by enabling systems to learn and enhance their performance through experience, eliminating the need for explicit programming. The spectrum of machine learning tools is extensive, encompassing traditional algorithms to deep learning architectures. Their application in AI is diverse and depends on the specific problem at hand. The iterative process of the machine learning pipeline empowers AI systems to continually learn and adapt to evolving conditions, making them potent instruments across a broad array of applications [3].
These tools find utility in various stages of model development, including data collection, preprocessing, feature engineering, model selection, and beyond. Numerous tools serve these purposes, each contributing to the robust construction and deployment of effective models.
Scikit-learn
Scikit-learn (Sklearn) is the most usable and robust machine-learning library. It utilizes a consistency interface to provide a set of proficient tools for machine learning and statistical modeling, which includes classification, clustering, regression, and dimensionality reduction.
Tensorflow
TensorFlow is a differentiable programming infrastructure layer. TensorFlow can use GPUs and TPUs as hardware accelerators and can compute the gradient of any differentiable tensor expression automatically. TensorFlow computation can be distributed to a large number of devices, and a large number of machines can be distributed to a single machine [4].
Keras
TensorFlow's high-level API is called Keras. Deep learning employs keras as its user interface to deal with layers, models, optimizers, loss functions, and metrics. In Keras, the Layer class is the most basic abstraction. A Layer includes weights and few computations [5, 6].
Pytorch
PyTorch provides Tensors that may run on either the CPU or the GPU, greatly speeding up the computation. Slicing, indexing, math operations, linear algebra, and reductions are a few of the tensor routines offered to help improve speed and match scientific computation needs.
NLP Tools
Natural Language Processing (NLP) tools assume a pivotal role, empowering machines to comprehend, interpret, and generate human language. The implementation of these tools frequently involves the utilization of machine learning and deep learning techniques, with their efficacy hinging on the quality and volume of the training data. The evolution of NLP tools remains dynamic, propelled by ongoing advancements in research, thereby enhancing language understanding and generation capabilities. CI is applied in diverse NLP tasks such as tokenization, POS tagging, stemming, lemmatization, and more.
Content Creation
Utilizing natural language processing (NLP) and machine learning algorithms, AI-powered content generators create articles, blog posts, and written content that are coherent, contextually relevant, and aligned with specific styles and tones. The effectiveness of content writing is maximized when it serves as a supportive tool for human writers rather than a substitute. AI contributes to content creation by improving efficiency, providing insights, and assisting writers in various capacities.
Grammar and style checkers, such as Grammarly, play a crucial role in refining written work. These tools analyze content for grammatical errors, and style inconsistencies, and offer suggestions to enhance the overall quality of writing. Additionally, AI is leveraged to analyze user behavior and preferences, enabling the personalization of content recommendations. This customization tailors content to specific audience segments, fostering increased engagement and relevance.
Chatbot
A chatbot stands out as a tool, employing natural language processing (NLP) and machine learning to interact with users through conversations. Its applicability spans diverse domains and industries, where it undertakes tasks, delivers information, and extends assistance. Operating as effective instruments, chatbots streamline repetitive tasks, elevate user interactions, and furnish immediate access to information. Their flexibility and perpetual learning capacities position them as valuable resources across industries and in customer-centric applications.
Computer Vision Tools
Computer vision tools powered by Artificial Intelligence employ advanced algorithms to enable machines to comprehend and interpret visual information from their surroundings. These tools have diverse applications across multiple industries for image classification, facial recognition, gesture recognition, object tracking, optical character recognition, emotion recognition, and so on.
