Ali Kashif Bashir – författare

Digital Twin for Smart Manufacturing: Emerging Approaches and Applications provides detailed descriptions on how to integrate and optimize novel digital technologies for smart manufacturing. The book discusses digital twins, which combine the industrial internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. In addition, they provide an effective way to integrate technologies like cyber-physical systems into a smart manufacturing system, potentially optimizing the entire business process and operating procedure of the manufacturing firm.

Drawing on the latest research, the book addresses the topics and technologies key to successful implementation of a smart manufacturing system, including augmented and virtual reality, big data and energy management. Broader subjects such as additive manufacturing and robotics are also covered in this context, covering every aspect of production.

Includes detailed case studies that show how digital twins have been successfully implemented Shows how digital twins can be used to improve sustainability through superior energy usage management Outlines potential future uses of the digital twin, thus pointing the way for future research directions

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users.FEATURES Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures Addresses emerging trends and issues on IoT systems and services across various application domains Investigates the challenges posed by the implementation of deep learning on IoT networking models and services Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT Explores new functions and technologies to provide adaptive services and intelligent applications for different end usersUttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia.Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom.Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.

Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. This book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 11 chapters, this book provides insights into the fundamentals of the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases, it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies speci cally applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.Provides a succinct overview of the cutting-edge technologies that are altering disease diagnosis, patient monitoring, and medical researchBridges the gap between biomedical engineering and deep learning by providing a comprehensive resource for comprehending the intersection of these disciplinesInvestigates how deep learning may change healthcare by providing new insights, diagnostics, and treatments via intelligent biomedical systems

Applied Mathematical Modelling for Biomedical Robotics and Wearable Devices delves into the innovative convergence of mathematical frameworks and biomedical engineering. The book begins by exploring how advanced mathematical modelling underpins the development and optimization of robotic systems and wearable technologies tailored for medical applications. With a strong emphasis on practical implementation, it serves as a bridge between theoretical concepts and real-world engineering challenges in the healthcare sector. Readers will gain insights into the transformative role of mathematical techniques that drive precision, functionality, and human-centric design in cutting-edge medical technologies.The book also covers interdisciplinary applications, integrating domains like biomechanics, sensor technology, and data analytics. By highlighting case studies and real-world scenarios, it showcases practical advancements in wearable devices that monitor health metrics and robotic systems that assist in surgical procedures.Examines the role of applied mathematical modeling in the design, analysis, and optimization of biomedical robots and wearable devicesProvides an insightful exploration of cutting-edge developments in robotics and wearable devicesBridges the gap between the areas of mathematics, engineering, and healthcare

AI-Enabled Sustainable Healthcare: Demystifying Secure Quantum Blockchain explores the convergence of quantum computing, blockchain technology, and artificial intelligence in the context of healthcare. In today's rapidly evolving technological landscape, traditional healthcare systems face challenges related to data security, interoperability, and the efficient utilization of patient information. The emergence of quantum computing, blockchain, and AI presents unprecedented opportunities to overcome these challenges and revolutionize healthcare delivery. This book provides a clear and accessible overview of these complex technologies and their potential applications in healthcare. By demystifying these concepts and illustrating their real-world implications, the book equips readers with the knowledge and insights needed to navigate the intersection of technology and healthcare effectively. Furthermore, as the demand for secure, transparent, and sustainable healthcare solutions continues to grow, there is an urgent need for informed discussions and innovative approaches in this field.Allows readers to grasp the interconnectedness of the technologies and their potential to revolutionize various aspects of healthcare delivery, from data security to personalized treatmentExplores the ethical and sustainability implications of integrating emerging technologies into healthcare systems Includes real-world case studies and examples, demonstrating how emerging technologies can be applied to improve healthcare outcomes and efficiency

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users.FEATURES Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures Addresses emerging trends and issues on IoT systems and services across various application domains Investigates the challenges posed by the implementation of deep learning on IoT networking models and services Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT Explores new functions and technologies to provide adaptive services and intelligent applications for different end usersUttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia.Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom.Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.

RESOURCE MANAGEMENT IN ADVANCED WIRELESS NETWORKS Written and edited by a team of experts in the field, this exciting new volume provides a comprehensive exploration of cutting-edge technologies and trends in managing resources in advanced wireless networks. This groundbreaking new volume from Wiley-Scrivener discusses the challenges that are emerging while managing the resources in various wireless networking technologies. Initially, the evolution of wireless networking technologies is presented, focusing on the advantages of improving data rates and data reliability. The book then goes through the various architecture designs based on the network paradigms, along with the evolution of networks based on the trends in the telecommunication industry. Various salient features are highlighted in managing resources, and the role of routing strategies is addressed with regard to real-time applications. Covering resource management in wireless networks, various industries are covered, such as healthcare and financial services, but the ideas are useful across many industries. Whether for the veteran engineer, industry professional, or student, this is a must- have for any library.

This book is essential for anyone looking to understand how hyperautomation can revolutionize businesses by simplifying operations, reducing errors, and creating more intelligent and adaptable workplaces through the use of automation technologies such as artificial intelligence, machine learning, and robotic process automation. The use of automation technologies to simplify any and every activity conceivable in a business, allowing repeated operations to operate without manual intervention, is known as hyperautomation. Hyperautomation transforms current and old processes and equipment by utilizing artificial intelligence, machine learning, and robotic process automation. This digital transformation may assist a business in gaining cost and resource efficiency, allowing it to prosper in a more competitive environment. With the advancement of automation technologies, hyperautomation is becoming more prevalent. Companies are shifting their methods to create more human-centered and intelligent workplaces. This change has ushered in a new era for organizations that rely on technology and automation tools to stay competitive. Businesses may move beyond technology’s distinct advantages to genuine digital agility and scale adaptability when all forms of automation operate together in close partnership. Automation tools must be simple to incorporate into the current technological stack while not requiring too much effort from IT. A platform must be able to plug and play with a wide range of technologies to achieve hyperautomation. The interdependence of automation technologies is a property that is connected to hyperautomation. Hyperautomation saves individuals time and money by reducing errors. Hyperautomation has the potential to create a workplace that is intelligent, adaptable, and capable of making quick, accurate decisions based on data and insights. Model recognition is used to determine what to do next and to optimize processes with the least amount of human engagement possible.

This book is essential for anyone interested in understanding how smart agriculture, utilizing information and technology such as computer vision and deep learning, can revolutionize agriculture productivity, resolve ongoing concerns, and enhance economic and general effectiveness in farming. The need for a reliable food supply has driven the development of smart agriculture, which leverages technology to assist farmers, especially in remote areas. A key component is computer vision (CV) technology, which, combined with deep learning, can manage agricultural productivity and enhance automation systems for improved efficiency and cost-effectiveness. Automation in agriculture ensures benefits like reduced costs, high performance, and accuracy. Aerial imaging and high-throughput research enable effective crop monitoring and management. Computer vision and AI models aid in detecting plant health, impurities, and pests, supporting sustainable farming. This book explores using CV and AI to develop smart agriculture through deep learning, data mining, and intelligent applications.

Comprehensive reference exploring the benefits and implementation of digital twins in industrial production and manufacturing Digital Twins in Industrial Production and Smart Manufacturing provides an overview of digital twin theoretical concepts, techniques, and recent trends used to meet the requirements and challenges of industrial production and smart manufacturing. The text describes how to achieve industrial excellence through virtual factory simulation and digital modeling innovations for next-generation manufacturing system design. The contributing authors address the many possible technical advantages of major Industry 5.0 technological advancements, using illustrations to aid readers in practical implementation of concepts, along with existing scenarios, potential research gaps, adoption difficulties, case studies, and future research objectives. The text also presents many applications and use cases of Industry 5.0 and digital twins in a variety of industries, including the aerospace industry, pharmaceutical manufacturing and biotech, augmented reality, virtual reality, edge computing and blockchain-based Internet of Things (IoT), cobots, intelligent logistics and supply chain management, and more. Edited by a group of highly qualified academics with significant experience in the field, Digital Twins in Industrial Production and Smart Manufacturing covers additional topics such as: Hyper-automation technology, including specialized workflow procedures and particular sectors of solicitations linked to hyper-automationDigital twins in the context of smart cities, with attempts to draw comparisons with the use of digital twins in industrial IoTVirtual factories based on digital twins and corresponding architecture to facilitate modeling, simulation, and assessment of manufacturing systemsCognitive, interactive, and standardization aspects of digital twins, and the proper implementation of digital twin technology for safety critical systemsDigital Twins in Industrial Production and Smart Manufacturing is a must-have reference for researchers, scholars, and professionals in fields related to digital twins in industrial production and manufacturing. It is also suitable as a hands-on resource for students interested in the fields of digital twins and smart manufacturing.

Environmental Monitoring Using Artificial Intelligence is a vital resource for anyone looking to leverage cutting-edge technologies in artificial intelligence and sensor systems to effectively address environmental challenges, offering innovative solutions and insights essential for creating a sustainable future. Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability. From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you’re exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.

Advancements in sensor technology have enabled autonomous systems to operate efficiently and safely in the Internet of Vehicles environment. Multisensor image fusion is a crucial component in enhancing the capabilities of these autonomous systems by combining information from multiple sensors such as cameras, LiDAR, radar, and ultrasonic sensors. This book delves into the role of multisensor image fusion in the Internet of Vehicles for autonomous systems. It will cover the fundamental concepts of multisensor image fusion, different fusion methods, and their applications in autonomous systems for the IoV. It will also address the challenges associated with multisensor fusion, such as sensor calibration, synchronization, and noise reduction and discuss the benefits of multisensor fusion in improving object detection, tracking, and decision-making processes in autonomous vehicles operating in the IoV. This book is a comprehensive overview of multisensor image fusion in the context of IoV for autonomous systems, highlighting its importance in achieving reliable and robust autonomous navigation in dynamic and complex environments.

Medical imaging informatics play an important role in the effectiveness of present-day healthcare systems. Advancement of artificial intelligence, big data analytics, and internet of things technologies contribute greatly to various healthcare applications. Artificial intelligence techniques are contributing to improvements with traditionally human-based systems and ensuring that the accuracy of prediction and diagnosis is being continually enhanced. The development of reliable and accurate healthcare models is becoming ever more possible with the help of machine learning and deep learning technologies. Artificial intelligence has the power to solve many complex problems in medical imaging and is a technology that will help to design the future of many healthcare systems.This edited book highlights and addresses various issues in medical imaging and provides viable solutions utilising artificial intelligence and big data tools. This book discusses techniques, algorithms, and tools which help build and develop research practices, platforms, and applications in medical image informatics.Medical image enhancement, big data analytics and artificial intelligence models are discussed with relation to applications in the detection of cancer, autism, allergies and diabetes. The design and development of internet of medical things and virtual reality tools for mental health disorders are also explored.This book is suitable reading for researchers and scientists, in both academia and industry, working in computer science and engineering, machine learning, image processing, and healthcare technologies. Those in aligned professions, such as healthcare practitioners, administrators, designers and developers may also find the subject matter of interest.

This book constitutes selected papers presented during the First International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2022, held in Chennai, India, in March 2022. The 27 papers presented were thoroughly reviewed and selected from 97 submissions. deep learning and vision computing;

This two-volume set, CCIS 2176-2177, constitutes the proceedings from the Second International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023, held in Chennai, India, in April 2023. The 74 full papers and 8 short papers presented here were thoroughly reviewed and selected from 252 submissions.

This two-volume set, CCIS 2176-2177, constitutes the proceedings from the Second International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023, held in Chennai, India, in April 2023. The 74 full papers and 8 short papers presented here were thoroughly reviewed and selected from 252 submissions.

Beyond 5G and 6G Networks, the AI-Native Revolution explores the transformative convergence of generative artificial intelligence (GenAI), large language models (LLMs), and next-generation telecommunications. This book delves into the architectural innovations enabling AI-driven connectivity, from intelligent edge devices to adaptive radio access networks. It covers the integration of GenAI for real-time network optimization, generative modeling of wireless channels, and advanced analytics for actionable insights. Security frameworks for AI-native systems, orchestration of autonomous networks, and the fusion of AI with signal processing are thoroughly examined, offering practical solutions for researchers, engineers, and policymakers.The book also addresses the challenges of computational constraints, ethical dilemmas, and regulatory complexities in deploying AI-driven networks. It provides a technical yet accessible roadmap for building equitable, trustworthy, and efficient systems, balancing innovation with responsibility. With insights grounded in rigorous research and real-world applications, this work is a guide for students, practitioners, and visionaries shaping the future of connectivity. It invites readers to reimagine networks as intelligent ecosystems that anticipate needs and empower humanity’s highest aspirations.

The new paradigm "Industry 5.0" promises great shifts not only in industry, but also in business and consumption models. With the help of data science and internet of things, manufacturers focus on delivering in real time, and customers will benefit from personalized products. Robots and cobots will collaborate with the humans. This book explains various facets of Industry 5.0, focusing on its applications on medical research and manufacturing.

The book covers topics of network and computing technologies, artificial intelligence and machine learning, security and privacy, communication systems, cyber physical systems, data analytics, cyber security for Industry 4.0, and smart and sustainable environmental systems.

This book includes peer-reviewed articles from the 1st International Conference on Intelligent Healthcare and Computational Neural Modelling (ICIHCNN—2022), held on November 30, 2022, at Dehradun, India.