bluuebunny/crossref_metadata_2025 huggingface dataset:
The Austrian school of economics and interpretive sociology
Authors: Robitaille Christian
Published: December 2024

During the second half of the nineteenth century, Carl Menger, the founder of the Austrian school of economics, was involved in a debate with members of the German historical school on the role of theory in social inquiries as opposed to that of history. This debate had a profound impact on the future development of economics and sociology. Various sociologists proposing an interpretive approach have positioned their work in the light of the Austrian school’s discussion of theory and its role in social investigations. Although this clearly indicates that there are links between interpretive sociology and the Austrian school, few studies have sought to delineate their similarities and divergences throughout their evolution in the history of ideas. This article aims to contribute to the history of intellectual thought by showing how interpretive sociology has often evolved in dialogue with the Austrian school.
Parallelization and Scaling of Deep Learning and HPC
Authors: Ravikumar Aswathy , Sriraman Harini
Published: December 2024

Optimal high-performance computing (HPC) use for deep learning is a difficult task that requires progress in a variety of research domains. Complex systems and events are represented using a combination of science-based and data-driven models. Additionally, there is a growing demand for real-time data analytics, that necessitates the relocation of large-scale computations nearer to data and data infrastructures, as well as the adaptation of HPC-like modes. Parallel deep learning tries to maximize the performance of complex neural network models by executing them concurrently on recent hardware platforms. Considerable time has been invested in integrating HPC technology into deep learning frameworks that are both reliable and highly functional. In this chapter, distributed deep neural network model designing using parsl, tensor flow, Keras, Horovod libraries, and the implementation of these models using Hadoop, SPARK clusters on local-area networks (LAN) as well as on cloud services like Amazon web services (AWS) and Google cloud platform (GCP) are discussed.
Accelerating Scientific Discovery With Machine Learning and HPC-Based Simulations
Authors: Gunda Sai Krishna
Published: December 2024

An integration of machine learning (ML) with high-performance computing (HPC) is radically transforming research in the scientific field. ML algorithms can span large-scale simulations and predict a great deal in materials science, climate modeling, and drug discovery by optimizing resource allocation and finding novel insights. HPC has accelerated complex data-sets processing, and ML models allow automation in pattern recognition and advanced analytics. The synergy of these technologies accelerates the experimentation rate, shortens time-to-discovery, and improves the scalability to tackle complex scientific challenges. It explores different key methodologies, case studies, and directions with future impacts that illustrate how advances in ML and HPC can revolutionize scientific innovation across disciplines. It demonstrates improved problem-solving capacity and accuracy in prediction as it addresses some of the most profound global challenges.
Advances in Computational Visual Information Processing for Neuroscience and Healthcare Applications
Authors: Banu E. Afreen , Suresh Chalumuru , Nyemeesha V. , Rao Mannepalli Venkata Krishna , Muthuraj B. , Muthuvel S.
Published: December 2024

Advances in visual information processing have revolutionized computational neuroscience and healthcare by enhancing our understanding of neural mechanisms and developing new medical solutions. Techniques like deep learning algorithms, convolutional neural networks, and computer vision applications have provided unprecedented insights into brain activity and visual organ function. These technologies also enhance our ability to model and simulate cognitive processes, making them crucial in understanding and improving medical outcomes. Visual information processing enhances healthcare diagnostics, medical imaging, and treatment personalization, ranging from radiology to pathology. It integrates advanced technology with neural interfaces and AI, revolutionizing patient care and neurological research. Further, ethical considerations, data privacy, and the challenge of transforming complex visual data into actionable insights for healthcare professionals and neuroscientists are discussed in this chapter.
High-Performance Computer-Integrated Microcontrollers for Enhancing Food Quality Monitoring
Authors: Salve Prachi Rajendra , Jemila M. R. Mano , Sridharan S. , Meti Vinod Kumar V. , Roy Bhaskar
Published: December 2024

Application of high-performance computer systems and microcontrollers in food quality monitoring pays extra attention to their improvements in accuracy, efficiency, and real-time analysis. Advanced microcontrollers allow the accomplishment of more precise monitoring of critical parameters such as temperature, humidity, and gas composition in order to ensure food safety and quality from supply chain outlets. Innovations in sensor technology, data processing, and wireless communication gave birth to smart systems for detecting contamination, spoilage, or adulteration. These systems feed real-time data that enables instantaneous decisions and reduces food waste. Microcontroller-based solutions allow for IoT connectivity and machine learning algorithms with a better possibility of predictive analytics and automation in the management of food quality. The chapter is focused on the key technologies, applications, and challenges, giving insight into the future regarding food quality monitoring in this very connected world, particularly about food industries.
High-Performance Computing for Electric Vehicle Performance Analysis and Simulation
Authors: Babu D. Venkata Srihari , Maharana Ramaprasad , Ponrekha M. , Ramesh S. , Kirubakaran D.
Published: December 2024

High Performance Computing (HPC) has established an important place in the performance analysis and simulation of electric vehicles. An enhanced study built on HPC answers to the high computational requirements of performance evaluation of electric vehicles, namely battery management, energy efficiency, and thermal dynamics. HPC, with parallel processing and improved algorithms, can intensively simulate powertrain systems, aerodynamics, and vehicle dynamics, keeping a stream of avenues to improve the design and functionality of electric vehicles. Further, it optimizes battery life and energy consumption as well as charging systems. This is useful for manufacturers and researchers to work toward enhancing the efficiency of EVs. The current chapter also deals with real-time simulations of HPC that allow prototyping, reduce development costs, and enable sustaining innovations in the electric vehicle business.
Architectures of High-Performance Computing Systems for Machine Learning Workloads
Authors: Raju M. , Gulhane Kimsy , A. Gulshan Banu , Chandra Saurabh , Dwaramwar Pravin A. , Boopathi Sampath
Published: December 2024

This chapter explores the architectures of high-performance computing (HPC) systems designed for machine learning (ML) workloads with special attention paid to advanced hardware and software optimizations intended to accelerate computational efficiency. It discusses the state-of-the-art use of GPUs, TPUs, and FPGAs in parallelizing operations, optimizing deep learning models during training and inference periods. The final section covers distributed computing frameworks, such as Apache Spark and Hadoop, that also provide support for big data processing in clusters. It also examines the challenges arising from the handling of data, distribution of resources, and scalability aspects as well as emerging solutions, such as memory-centric architectures and quantum computing, respectively. This chapter provides a comprehensive understanding of the evolution of HPC systems to meet the requirements of modern ML workloads, faster model development, reduced energy consumption, and better scalability, along with some of the trends and future research directions in HPC for ML.
Fostering Inclusivity and Empowerment
Authors: Yingsoon Goh Ying , Suyan Zhang , Chua Nurul Ain , Chen Yiming
Published: December 2024

In today's increasingly diverse educational landscape, culturally responsive literacy practices are not just beneficial—they are essential. This chapter examines strategies for creating literacy ecosystems that honor and reflect learners' cultural, experiential, and linguistic diversity. We argue that adopting a culturally responsive approach strengthens literacy skills, validates students' identities, and promotes social equity. The chapter begins with exploring the theoretical foundations of culturally responsive literacy, emphasizing the importance of recognizing and valuing students' cultural wealth. Following this, a comprehensive framework is provided for building a culturally responsive literacy ecosystem, covering curriculum design, instructional methods, assessment strategies, and classroom management. Ultimately, we demonstrate how these practices foster inclusive environments that empower all learners and encourage active, meaningful engagement with literacy.
Integrating Artificial Intelligence to Foster Inclusivity in Social Work and Community Development Education
Authors: Lanham Jennifer , Smith Garreth
Published: December 2024

This chapter explores the integration of artificial intelligence (AI) into social work and community work education to foster an inclusive learning environment. By aligning with both the Council on Social Work Education's mandates, and the All-Ireland Standards for Community Work Education, educators leverage AI tools to design competency-based educational frameworks that emphasize anti-racism, diversity, equity, and inclusion. Incorporating AI elevates education quality, empowering a diverse student body to flourish in nurturing learning communities. Through a transatlantic collaboration between Campbellsville University in the United States and Technological University Dublin in Ireland, this chapter examines the practical application of these technologies and their impact on social work and community work education.
Comparative Study on Implementation of PID Control Architecture and Sliding Mode Control With IoT Integration on Inverted Pendulum Systems
Authors: Dawane Mahendra K. , Malwatkar G. M.
Published: December 2024

In this chapter, the issue of using sliding mode control to stabilize an inverted pendulum system is examined. The goal is to keep the pendulum in an upright position by giving the cart the proper control inputs. To achieve system stabilization, sliding mode control, a type of variable structure control, is used. This control strategy entails creating control systems with the intention of maintaining the system state within a restricted area close to a switching function. Numerous real-world situations, including aerial landing pads over water, walking balance systems for people, and cart inverted pendulum systems, make use of the dynamics of inverted pendulum systems. Due to its inherent instability and nonlinearity, designing a controller for this underactuated, non-minimum phase system is difficult.
Data Reduction Techniques in Wireless Sensor Networks With Internet of Things
Authors: Gunturu Vijaya , Goswami Chandrshekhar , Nassa Vinay Kumar , Mandapuram Mounika , Tiwari Tripti , Tiwari Mohit
Published: December 2024

Since they are used in an array of real-life uses, wireless sensor equipment in Internet of Things (IoT) systems will be among the biggest prolific sources of big data on the connection. The huge quantity of data collected from sensing equipment increases transmission overhead, reducing the short lifespan of IoT sensing equipment. As a result, it is required to cleanse and reduce the sensed information in order to cut transmission costs and conserve power on sensing equipment. A Data Reduction and Cleansing Technique (DRCT) for power consumption in IoT-based wireless sensor networks (WSNs) is suggested in this research. This method depends on 2 levels of data cleansing and reduction: sensing and aggregation.
Role of Neural Network, Fuzzy, and IoT in Integrating Artificial Intelligence as a Cybersecurity System
Authors: Sharma M. K. , Dodamani Abeda , Chakravarthi M. Kalyan , Magendiran N. , Tiwari Mohit , Tiwari Tripti
Published: December 2024

Issues regarding cybersecurity have grown in tandem with the prompt expansion of the IoT-internet of technology in the past decade. Artificial intelligence (AI) technology is employed to develop advanced methods to protect networking and structures, such as IoTs innovations, are at the top of cybersecurity. Nevertheless, attackers have found ways to exploit the AI, and thus they are starting to use it upon their competitors to carry out security breaches. Because of the minimal computational analysis and storing capabilities of IoT systems, traditional advanced cybersecurity methods are insufficient to safeguard an IoT platform.
Smart Security System Using Hybrid System With IoT and Machine Learning
Authors: M. Kandan. , Jayaganesh J. , Sarkar Prithu , Syed Karimulla , Balasubramanyam Kalangi , Tiwari Mohit
Published: December 2024

Smart security systems (SSS) in houses are growing increasingly prevalent as technological advances become more prevalent in everyday life. Lighting, temperature, security cameras, and gadgets are all controlled by a SSS in houses. The widespread adoption of Internet of Things (IoT) technologies in smart houses has altered the environment of connected devices, online activity, and data transfers in household broadband connections and the web overall. Current cyber assaults and hazards against IoT-based SSS in houses, on the other hand, have shown widespread hazards and dangers in IoT devices that encompass the layer of data connection protocols to service providers.
Encrypted Negative Password (ENP) Authentication System
Authors: Barua Namrata , Saha Tanusree , Pattanayak Jui , Ghosh Prolay
Published: December 2024

Password authentication is among the most effective ways to authenticate for security resiliency. The challenge of securely keeping credentials is one of the most important ones. Based on the Negative Database (NDB), a cryptographic hash function, and symmetric encryption, our study suggests the Encrypted Negative Password (ENP) password protection approach. Methods: This paper suggests a password verification structure for protected password storage that may be effectively incorporated into existing authentication systems. A hybrid method is used to encrypt the client's plain password. Additionally, a password authentication system built on the ENP is presented. Because it is the hash value of each user's password, the secret key in the ENP is almost always unique and does not have to be separately produced and maintained. As a result, passwords can be protected using symmetric encryption.
AI-Driven Predictive Maintenance in Asset Management
Authors: Virmani Deepali , K. L. Meera , Selvi G. Pandi , Javed Sarfaraz , M. Raja Ambethkar , Palav Manesh R.
Published: December 2024

Contemporary asset management methodologies are increasingly reliant on AI-enabled predictive maintenance. By employing data analytics and machine learning algorithms, businesses can anticipate equipment malfunctions in advance, thereby reducing unexpected periods of inactivity and streamlining the scheduling of preventative maintenance. This method results in significant cost savings, as well as improvements in operational efficiency and asset longevity. The integration of IoT devices with AI-driven predictive maintenance systems enables the collection and analysis of real-time data, thereby enabling a comprehensive assessment of asset health. This article investigates the methods, advantages, and disadvantages of AI-driven predictive maintenance in asset management, with a particular focus on prospective market trends and case studies.
Smart Resource Allocation in Enterprises Using AI and IoE
Authors: Sethumadhavan R. , Sharma Sonal , Raha Shrinwantu , Maaliw III Renato R. , Gupta Alok , Bagale Girish
Published: December 2024

Artificial Intelligence (AI) and the Internet of Everything (IoE) are being utilized in ultramodern businesses to maximize the utilization of available resources. This article is to investigate how artificial intelligence algorithms and Internet of Everything technologies can be used to allocate resources such as labor force, equipment, and installations based on real-time data and predictive analytics. Businesses can attain lower levels of efficacy, lower costs, and improved decision-making processes by utilizing the ability of artificial intelligence to analyze massive datasets and the connectedness among biases that the Internet of Things provides. The research investigates several applications of artificial intelligence, such as machine learning and optimization techniques, and these applications are implemented in resource allocation scripts. Also, it investigates the role that the Internet of Everything plays in improving data collecting and connectivity across corporate systems, which in turn makes it possible to implement forward-thinking resource management techniques.
Integrating Classic Optimization Methods With Machine Learning for Enhanced Predictive Analytics in Healthcare
Authors: Jain Vishal , Mitra Archan
Published: December 2024

Using machine learning and conventional optimisation methods improves healthcare predictive analytics accuracy, interpretability, and clinical relevance. This study suggests combining machine learning's predictive power with optimization's decision-making rigour. Healthcare situations like patient readmission prediction and treatment planning use the paradigm. Incorporating limitations and objectives directly into prediction models ensures accurate, realistic, and clinically relevant outcomes. These strategies improve healthcare decision-making by addressing the constraints of existing machine learning models. This study introduces a new predictive model paradigm for patient outcomes and healthcare resource optimisation in machine learning, optimisation, and healthcare analytics. The results show that this integrated strategy could improve healthcare analytics and improve efficiency, effectiveness, and personalisation.
A Hybrid EfficientNetB0-XGBoost Framework for Efficient Brain Tumor Classification Using MRI Images
Authors: Kumar Hemant , Pandey Digvijay , Yadav Amit , Agarwal Rashi , Tripathi Shivneet , Malhotra Somesh Kumar
Published: December 2024

Brain tumors pose significant challenges in medical diagnosis due to their diverse characteristics and critical impact on human health. This study proposes an advanced framework for brain tumor classification, integrating EfficientNet-B0 for feature extraction and XGBoost for classification. The preprocessing stage involves resizing, normalization, and data augmentation of brain MRI images to ensure high-quality input for the model. EfficientNet-B0's parameter efficiency and computational speed are leveraged to extract discriminative features, which are then classified using the XGBoost algorithm. Techniques such as Global Average Pooling and a dropout ratio of 0.3 are employed to enhance the model's robustness and mitigate overfitting. Experimental results on the Kaggle brain tumor dataset demonstrate the superiority of our approach, achieving an overall accuracy of 99.21%, precision of 99.18%, recall of 98.96%, and F1-score of 98.92%. This research shows that deep learning and machine learning can accurately and efficiently classify brain tumours, improving clinical diagnostic.
Anxiety, Depression, and Stress Prediction in Modern Life Using Machine Learning, Taxonomy, Applications, and Challenges
Authors: Ghosh Prolay , Saha Tanusree , Chowdhury Ritadrik , Barua Siddhartha
Published: December 2024

The rise of psychological complication and the importance for fruitful medical treatment have opened the area of an research analysis of machine learning that can be practiced in psychological complication. In this paper we have offered a thorough analysis of techniques of machine learning in Anxiety, Depression and Stress prediction. We have also concentrated on the difficulties, restrictions, and potential future prospects for the use of machine learning in this area. We have collected research journals and studies that enlights on different machine learning techniques in prediction of mental health problems. While doing this review, we adhered to the PRISMA process.. We have listed a total of 15 research studies and articles in this review after comprehensive identification and screening procedures. Then, we classify the research studies based on the different mental health complication such as anxiety, stress and depression etc.
Integration of IoT With Robotics and Drones
Authors: Gupta Brij B. , Wu Jinsong
Published: December 2024

This chapter explores the synergistic integration of the internet of things (IoT) with robotics and drones, highlighting how this convergence is revolutionizing industrial operations and capabilities. It delves into the mechanisms through which IoT devices and sensors enhance the autonomy, efficiency, and intelligence of robotic systems and drones, enabling real-time data exchange and analysis. The chapter discusses the implementation of IoT for advanced monitoring, predictive maintenance, and seamless operational control, illustrating its impact through practical examples across various sectors. It also addresses the challenges of scalability, security, and interoperability, presenting forward-looking strategies to navigate these hurdles. By emphasizing the transformative potential of IoT in augmenting the capabilities of robotics and drones, the chapter underscores the pivotal role of IoT in driving innovation and operational excellence in the digital age.
Innovations Focusing on Artificial Intelligence in Computer Science Education
Authors: Erdağı Ertürk
Published: December 2024

Computer science education is a field that needs constant development due to technological developments. Especially in recent years, the widespread use of the concept of artificial intelligence in all scientific fields has affected the field of computer science education. Today, the concept of artificial intelligence is used on a very large scale in many fields other than technical fields. This has enabled the integration of the artificial intelligence universe with all fields with an interdisciplinary approach. Artificial intelligence is capable of directing the entire educational process rather than being a stand-alone educational content during computer science education. Students can use this field to solve all problems in their field of expertise during and after their education. In this section, artificial intelligence techniques and subfields used in computer science education, artificial intelligence trends, challenges related to artificial intelligence, and the interaction of the curriculum with artificial intelligence are discussed.
Low-Code Programming for K-12 Education
Authors: Özdemir İbrahim Halil , Sarsar Fırat , Calandra Brendan
Published: December 2024

Computer science is crucial in the information age, driving an international movement towards digital literacy. The rapid development of digital technologies, especially data science and AI, necessitates a deeper understanding of human-machine interactions. CS literacy is essential for individual and societal success, leading to increased research on programming instruction from an early age. Programming is now integral to K-12 curricula worldwide. To enhance computer programming education, strategies must address both cognitive and affective aspects. Low-code programming, which enables application creation with minimal manual coding, is a promising approach. This book chapter introduces low-code programming as a valuable toolset for K-12 Computer Science Education.
Methods for Accessible Computer Science and Computer Programming Education for Visually Impaired and Blind Students
Authors: Güneş Habibe
Published: December 2024

Just like our fingerprints, we are born different from each other with many characteristics. What is expected from education is to respect these differences, to highlight individuals' unique and special characteristics, and to create experiences that will enable them to maximize their talents. However, when structuring education for individuals with special needs, it should be tailored to their individual needs and provide accessibility that allows them to develop skills relevant to the current technological age. In this book chapter, suggestions are presented for developing the basic skills of visually impaired individuals in computer science and coding education.
Teaching Programming Through Metaphors
Authors: Geçitli Erkan , Yurdakul Bünyamin
Published: December 2024

This chapter aims to provide computer science stakeholders with a perspective on teaching programming through metaphors. The chapter emphasises that the difficulties encountered in learning programming stem from the structure of programming as a learning task, individual traits, and many factors, including curriculum and teaching approaches as interactional setting variables. First, the need for pedagogical approach to teaching programming is emphasized, briefly introducing the teaching approaches used in programming. Teaching programming through metaphor is introduced as an alternative approach to teaching programming concepts. It explains metaphor as a concept, conceptual metaphor theory, and the use of metaphor in teaching programming concepts. Then, the need to reveal the pattern of the teaching process through in-depth qualitative studies is highlighted as a future research direction. Finally, it is argued that using metaphor is not limited to teaching of concepts, but can also improve thinking skills, differentiate teaching and support teachers' professional development.
Mathematical Approaches to Real-Time System Identification
Authors: Singh Hambeer , Chinnasamy Karthik , Lakshmi R. Pichai , Akilandeswari K. , Chandrashekara A. C. , Ranganayaki S. , Vyas Vikas
Published: December 2024

Real-time system identification is an essential element in controlling and monitoring dynamic systems. It enables the accurate description of behaviour using observable data. This study explores the application of mathematical techniques such as Kalman filtering, adaptive filtering, and recursive least squares for the identification of real-time systems. The essay examines the fundamental principles and real-world uses of robots, telecommunications, and aviation. The algorithms' capacity to adjust to dynamic system changes and their computational efficiency are emphasised. These techniques improve the ability to control and make decisions in real-time by creating accurate models of the systems, as proven by simulations and case studies.
Real-Time Market Analysis With AI-Enhanced Signal Processing
Authors: Fardale Shyam , Goyal Akhil , Palav Manesh R. , Palav Sharada Manesh , Kalaiarasan C. , Namdeo Avinash Kumar
Published: December 2024

The purpose of this investigation is to examine how artificial intelligence can efficiently reuse and comprehend complicated request signals, which will allow for more accurate forecasting and fast decision-making in dynamic financial environmental conditions. The purpose of this project is to construct a robust framework that is capable of gaining meaningful perceptivity from a variety of data sources, such as request patterns, news sentiment, and profitable pointers. This will be accomplished through the utilization of machine literacy algorithms and signal-processing models. By providing dealers and judges with information that is both understandable and applicable promptly, the solution that has been developed aims to mitigate the risks that are connected with request volatility and questions. The findings highlight the possibility that artificial intelligence-enhanced signal processing may revolutionize request analysis procedures, hence clearing the way for further strategies that are informed and adaptive in the context of financial demands.
AI-Based Real-Time Financial Risk Management
Authors: Swadia Bhavik U. , Anand J. , Meera K. L. , Khan Azeem Ahmad , Javed Sarfaraz , Palav Manesh R.
Published: December 2024

Conventional methods of risk management frequently prove insufficient due to the unpredictable behaviour of the market and the perpetually increasing intricacy of financial products. This investigation concentrates on the application of artificial intelligence in real-time financial risk management, with a particular emphasis on big data analytics, natural language processing, and machine learning. AI systems are capable of efficiently analysing vast quantities of structured and unstructured data in order to anticipate market trends, identify emergent threats, and provide valuable insights. In addition to enhancing the accuracy and efficacy of risk assessment, AI-powered solutions also assist in the implementation of proactive risk mitigation strategies. This article offers a thorough examination of the current methods, challenges, and forthcoming advancements in financial risk management, with a particular emphasis on the potential of artificial intelligence to enhance this field.
Real-Time Fraud Detection Using AI and Signal Processing
Authors: Kodmalwar Pratik , Maheshwari Amitabha , Palav Manesh R. , Priya Sweta , Purusothaman N. , Namdeo Avinash Kumar
Published: December 2024

Scripts that run in real-time and offer a novel approach to the fight against fraud. This work makes use of advanced algorithms for artificial intelligence in conjunction with signal processing techniques to enhance the speed and precision of finding. The objective of this work is to develop a robust system that is capable of relating fake conditioning in the same manner that they do. This will be accomplished by utilizing the capabilities of artificial intelligence (AI) for pattern recognition and signal processing (SP) for extracting useful information from data aqueducts. The method involves preprocessing raw data to discover key characteristics, employing machine literacy models for bracketing, and continuously simplifying models adapt to evolving fraud trends. All of these steps are undertaken to get the desired results. The efficacy of this strategy has been determined by conducting exhaustive tests on datasets that were collected from the actual world. The results of these tests have revealed significant advancements in discovery rates in compared to more standard approach.
AI-Driven Robotics for Real-Time Manufacturing Processes
Authors: Agrawal Banshi Prasad , Sahoo Bidyutlata , Harini V. , Lavanya J. Ananda , Jindal Vaibhav , Namdeo Avinash Kumar , George A. Shaji
Published: December 2024

We investigate the most recent developments in artificial intelligence-driven robots within the industrial industry, with a particular emphasis on real-time operations. Among the most important advancements are machine literacy algorithms for prophetic conservation, autonomous robots for the operation of dynamic tasks, and vision systems that are augmented by artificial intelligence for quality control. These robotic systems can analyze huge amounts of data, optimize workflows, and adapt to changing situations in real time thanks to the application of artificial intelligence. They can execute with less time-out and boost their productivity. In addition to this, the investigation investigates case studies that demonstrate successful implementations across a variety of different types of diligence, highlighting both the benefits and the obstacles connected with the incorporation of AI. Based on the findings, artificial intelligence-driven robotics not only improve functional performance but also pave the road for technologically advanced and more adaptable manufacturing environments.
IoE and AI for Real-Time Social Behavior Analysis
Authors: Vikas Sona , Mathur Ashish , Sharma Saumya , Yadav Shikha , Ghanghas Seema , Sangwan Anjana
Published: December 2024

The possibility of utilizing Artificial Intelligence (AI) and the Internet of Everything (IoE) to evaluate social gestures in real time. Through the utilization of linked bias and sophisticated artificial intelligence algorithms, the system can collect and process enormous quantities of data derived from diverse social relationships. On the other hand, artificial intelligence methods, such as machine literacy and natural language processing, analyze this data to recognize patterns, trends, and anomalies in social gestures. The Internet of Everything frame delivers continuous data aqueducts from detectors, wearables, and other smart bias solutions. The research highlights the implicit operations of this technology in domains such as public safety, internal health, marketing. It demonstrates how real-time perceptivity can be used to inform decision-making and aid in the resolution of challenges. In addition, the discusses the difficulties associated with data sequestration, the ethical issues that must be taken into account, and the requirement for robust data operating procedures.
Real-Time Human Motion Monitoring Using VLSI Solutions
Authors: Hussain Ahmed , Alassedi Zainab , Zouzu Tawfik Mohamad , Alkhayyat Ahmad
Published: December 2024

That is designed to track the movement of the human body in real-time. This technology is known as Veritably Large Integration (VLSI). The design and implementation of efficient tackling infrastructures that are capable of recycling complex stir data in real-time are being researched as part of this investigation. This investigation is being carried out within the scope of this investigation. The study emphasizes the incorporation of detectors, signal processing techniques, and low-power consumption methods into VLSI circuits to build high-performance stir monitoring systems. This is done specifically to produce stir monitoring systems. The optimization of computing efficiency, the development of detector data details, and the enhancement of icing robustness across a variety of environmental conditions are among the most important focuses that are being worked on. The subjects that are discussed in this paper include those that pertain to new VLSI designs and the effects that these designs have on operations such as healthcare, sports analytics, and mortal-computer commerce.
Framework Towards Detection of Stress Level Through Classifying Physiological Signals Using Blockchain Technology
Authors: Hemlathadhevi A. , Disney D. Anu , Behar Nishant , Pant Lalit Mohan , Kumar C. M. Naveen , Tahreem Madiha
Published: December 2024

Stress is a common aspect of everyday existence that everyone encounters at some point. Chronic stress, on the other hand, jeopardizes our well-being and disrupts our usual life. As a consequence, the ability to function and handle critical circumstances is significantly diminished. As a result, comprehending stresses and designing procedures with stress in mind is essential. This study introduces us to stress level detection through physiological signal classification using the blockchain technique. The present study proposes a framework for evaluating sleeping patterns by continually tracking the physiological signals that happen during the rapid eye motions and non-rapid eye motion phases of sleep. Aside from physiological variable shifts, length of sleep, snoring spectrum, eye motion, and limb motions are additionally examined.
IoE Integration in Real-Time Pharmaceutical Supply Chains
Authors: Uprikar Vijay , Karumuri Venkateswarlu , Nargunde Amarja Satish , Bhatia Ambika , Gorkhe Monika , Palav Manesh R. , George A. Shaji
Published: December 2024

Improve the effectiveness, transparency, and safety of the product. The abandonment of Internet of Everything technology presents a disruptive strategy for managing force chains, which is particularly relevant given the increasing complexity and nonsupervisory demands of pharmaceutical logistics. The purpose of this project is to evaluate the operation of linked bias, detectors, and data analytics to cover and optimise the flow of pharmaceutical products from manufacturers to end druggies. Real-time shadowing of force, ambient conditions, and conveyance routes is made feasible through the utilisation of IoE, which helps to ensure the maintenance of the integrity and compliance of sensitive medicinal applications. Case examples that demonstrate improved force chain adaptability, decreased functional costs, and enhanced response times to dislocations are highlighted in this investigation. The purpose of this article is to underline the potential for the Internet of Everything (IoE) to update pharmaceutical logistics by providing a solid framework
Introduction to Neuro-Marketing
Authors: Ghosh Oindrilla , Kumar Binod
Published: December 2024

Neuromarketing is an interdisciplinary field that merges neuroscience, psychology, and marketing to explore how consumers make decisions and how brands can influence these choices. At its foundation, neuromarketing studies the brain's response to marketing stimuli, utilizing techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and eye-tracking to glean insights into emotional and cognitive processes. Pioneering studies demonstrated how consumers react to various marketing stimuli, revealing that emotional experiences often drive purchasing decisions more than rational thought. As the digital landscape has evolved, so too has neuromarketing. Data analytics and machine learning now complement traditional neuroscience methods, enabling more nuanced understanding of consumer behavior in online environments. By leveraging insights from neuromarketing, businesses can develop targeted strategies that resonate with consumers on a deeper psychological level, ultimately fostering stronger brand loyalty and enhancing overall customer experiences.
Overview of Neuromarketing
Authors: Devendran A. , Kumaran S. , Tanguturi Rama Chaithanya , Neeraja B.
Published: December 2024

Neuromarketing combines neuroscience, psychology, and marketing to explore consumer behavior at a subconscious level. By using tools like fMRI, EEG, eye-tracking, and biometrics, it measures consumers' emotional and cognitive responses to marketing stimuli, offering deeper insights than traditional methods. Neuromarketing helps marketers create more personalized campaigns by understanding what drives consumer choices. Key methodologies include fMRI for identifying brain areas linked to emotions and decision-making, EEG for capturing real-time reactions, and eye-tracking to analyze visual attention. Brands like Coca-Cola and Nike use these insights to enhance emotional connections and boost loyalty. However, ethical concerns arise regarding privacy, manipulation, and informed consent, as these techniques may exploit subconscious triggers. Ensuring transparency and ethical use is crucial as neuromarketing continues to shape the future of advertising and consumer engagement.
Error Correction Methods for Protecting Quantum Information
Authors: Mummadi Swathi , Udari Gnaneshwara Chary , Achyuth C. , Karthik C.
Published: December 2024

The field of quantum computing holds immense promise for solving complex problems that are currently intractable for classical computers. However, the practical realization of quantum computers faces a critical challenge: the inherent fragility of qubits, the fundamental units of quantum information. Quantum systems are susceptible to various types of errors and decoherence due to their interaction with the surrounding environment, which can lead to a rapid degradation of quantum information. This chapter delves into the pivotal aspect of quantum error correction techniques as a means to mitigate the adverse effects of errors and decoherence in quantum computing systems. Begins by elucidating the theoretical foundations of quantum error correction, exploring the principles that underlie the encoding of quantum information into larger, redundant codes that are resilient to errors. A comprehensive overview of prominent quantum error correction codes, such as the stabilizer codes and topological codes, is provided, along with a discussion of their respective strengths and limitations.
Integrating EEG Analysis to Illuminate Consumer Behavior and Optimize Marketing Strategies
Authors: Vardireddy Monishwar Reddy , Gajbhiye Pranjali
Published: December 2024

Electroencephalography (EEG) is crucial for understanding how music affects cognitive and emotional processes by providing real-time insights into neural activity. It helps measure emotional states like happiness and sadness in response to music, revealing how music influences brain regions such as the amygdala and prefrontal cortex. Advancements in EEG technology have enabled sophisticated analysis of brain waves (delta, theta, alpha, beta, gamma) and their correlation with music's tempo and rhythm, impacting memory and emotional processing. EEG's application extends to therapeutic settings, enhancing neuroplasticity and cognitive functions in neurological rehabilitation, and in marketing, where it offers insights into consumer reactions and preferences, optimizing ads and product designs. It helps optimize content to evoke desired emotional responses, improve attention and memory encoding, and enhance brand loyalty by integrating EEG insights with traditional market research methods.
Quantum Algorithms and Predictive Analytics
Authors: Tariq Muhammad Usman
Published: December 2024

This chapter examines how quantum computing has impacted predictive analytics in neuromarketing in a significant way. It talks about how marketers understanding, and prediction of consumer behaviour is changing as a result of quantum algorithms' capacity to process large and complex datasets. The chapter explores how quantum AI is being applied in the real world and highlights businesses that have effectively used this technology to get more precise predictive insights. Furthermore, it provides guidelines for responsible implementation and discusses ethical issues related to the use of quantum AI such as privacy and data transparency. In order to improve consumer engagement strategies, the chapter also looks at interdisciplinary approaches that combine quantum AI with marketing and neuroscience. With an eye toward the future the chapter projects how quantum-enhanced predictive analytics will impact neuromarketing over the next ten years by identifying emerging trends possible obstacles and opportunities.
Quantum AI-Enhanced Virtual Reality
Authors: Babu C. V. Suresh , Logambigai R. , Senthamilan R. , Jeswin Franics A. , Mohamed Shajith N.
Published: December 2024

This study explores the impact of Quantum AI and Virtual Reality (VR) on neuromarketing practices, aiming to understand their influence on consumer behavior and enhance marketing strategies. Utilizing a mixed-methods approach, the research incorporates qualitative analysis of case studies and quantitative assessments of consumer responses to VR-enhanced marketing campaigns. Key findings indicate that integrating Quantum AI and VR can significantly improve consumer engagement and satisfaction by creating immersive experiences tailored to individual preferences. The study concludes that these technologies not only offer innovative methods for marketers but also raise ethical considerations regarding consumer privacy and data security. The implications underscore the need for a structured framework to guide the ethical implementation of these technologies in marketing. This research contributes valuable insights into the future of neuromarketing and prepares marketers for emerging trends in the digital landscape.
Quantum Control of Networked Robotic Systems
Authors: Sharma Vishal Kumar , Joshi Shyam Krishan , Bora Avnish
Published: December 2024

In the present work we propose a scheme to control the network of wheeled mobile robots using Quantum computing. This scheme provides a fast and efficient way to navigate a system of networked robots from one position to another using a PID Controllers. It is a well established fact that the robotic systems aredefined using Lagrangian dynamics and PID control is an efficient way to control the movement of robotic systems . In the present work we propose a framework to use Quantum Computers and Photic couplings among robots to form a consensus and move a the coupled robotic system from one place to another. We propose that all the computations must be carried out on quantum computers for efficient control and fast execution. We propose an algorithm that enables the proposed scheme. Numerical Simulations that show synchronized motion of coupled robots have been reported.
Looking Ahead
Authors: Singh Vineet , Gajbhiye Pranjali
Published: December 2024

The next decade promises transformative advancements in neuromarketing and quantum artificial intelligence (AI), reshaping how businesses understand and engage with consumers. Neuromarketing employs neuroscience techniques, such as fMRI and EEG, to analyze emotional and cognitive responses to marketing stimuli, providing deeper insights into consumer behavior. Meanwhile, quantum AI leverages the principles of quantum computing to enhance data analysis capabilities, enabling real-time insights and complex problem-solving. The integration of these fields will allow marketers to create highly personalized campaigns, optimize strategies through predictive analytics, and gain comprehensive consumer profiles. However, ethical considerations regarding data privacy and transparency will be paramount as these technologies evolve. This article explores the potential applications, challenges, and future trends at the intersection of neuromarketing and quantum AI, highlighting their implications for marketing strategies and consumer engagement in an increasingly data-driven landscape.
Ethical Responsibilities of Hospitality Providers in Prenatal Tourism and Birthright Citizenship
Authors: Shukla Ankit , Yadav Nagendra , Berry Karan , Sharma Neha , Uniyal Mahesh Chandra
Published: December 2024

Hospitality providers in prenatal tourism bear significant ethical responsibilities, particularly regarding birthright citizenship. As more pregnant individuals travel abroad to give birth, complex legal and ethical issues arise. Providers must ensure informed consent, helping expectant mothers understand the legal implications of birthright citizenship. Cultural sensitivity is vital, respecting diverse values while offering high-quality care. Privacy and confidentiality, especially with cross-border medical data, must be safeguarded. Providers also need to comply with both international and domestic legal frameworks, including immigration and healthcare laws, while ensuring equitable access to services without discrimination. Ethical hospitality in prenatal tourism demands transparency, legal compliance, and a commitment to protecting the rights and well-being of mothers and their children in the context of cross-border births and citizenship.
Global Mobility and Economic Incentives in Prenatal Tourism
Authors: Singh Ram , Singh Vinay Pal
Published: December 2024

Prenatal tourism involves travelling to another country to give birth and securing citizenship for the child. This practice has surged due to socio-economic and legal factors, making it crucial for policymakers to understand its economic impact on sending and receiving countries. This chapter explores the economic drivers behind prenatal tourism and its effects on global mobility. Using a mixed-methods approach, it analyzes trends, challenges and opportunities and highlights economic incentives like better healthcare and long-term benefits for the child. It also discusses challenges for host countries, including healthcare revenue and resource strain, while examining the impact on origin countries. The findings are expected to reveal motivations such as dual citizenship and socio-economic mobility, offering suggestions for balanced immigration policies and sustainable healthcare financing and concluding, that prenatal tourism presents economic benefits but also complex challenges, requiring careful policy management to address both the incentives and impacts on global economies.