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Intricate Plane of Adversarial Attacks in Sustainable Territory and the Perils faced Machine Intelligent Models
The issue of model security and reliability in Artificial Intelligence (AI) is a concern due to adversarial attacks. In order to tackle this issue, researchers have developed sustainable defense strategies, but certain challenges remain. These challenges involve transferability, higher computing costs, and adaptability. Striking a balance between accuracy and robustness is difficult, as defense mechanisms often come with trade-offs between the two. Real-world situations demonstrate the practical implications of sustainable adversarial AI. For example, it improves the security of self-driving vehicles, enhances the accuracy of medical imaging diagnoses, and incorporates AI-driven defenses into network intrusion detection and phishing detection systems. It is crucial to consider ethical aspects throughout this process. Future trends in adversarial AI research for cybersecurity will involve ensemble defense mechanisms, adversarial learning from limited data, and hybrid attacks. By embracing the evolving landscape, researchers and practitioners can develop sustainable AI systems that are more secure and resilient, effectively countering adversarial threats. 2023 IEEE. -
Intrinsic betterness and reason based extrinsic preference towards social shopping: A study among college students in Bangalore
The main purpose of this study is to analyze the Intrinsic and Extrinsic factors and its impact on the social shopping of student community in the city of Bangalore. Bangalore is one of the most popular cities in the down south. This city attracts a mix of various cultures from various countries of the world. Thus, this city and population would be apt to study the social shopping pattern considering the Intrinsic and Extrinsic factors. The data was collected from the student community, perusing their college education in the city of Bangalore. Using convenient sampling method, a sample size of 225 was drawn from educational institutions in the city of Bangalore. The research work makes use of both first hand and second-hand data. The reliability of the data is acceptable as the Cronbach's alphas value is more than. 6. The drafted questionnaire was subjected to expert opinion before the data collection process. The study results make it clear that both intrinsic and extrinsic values motivate the consumers to get involved in the social shopping. But comparatively consumers are more influenced by those factors present in the external environment. It can be concluded by saying that, youngsters are quite smart before putting themselves into the purchase behavior, as they are in a group of friends, they get influenced by various experiences and comments shared by many. In a way social shopping is better, as too many minds generate ideas for a single purchase. 2018 Transilvanian Association for the Literarure and Culture of Romanian People (ASTRA). All rights reserved. -
Introduction
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Introduction
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Introduction
In the digital era, signal processing has found application in daily life from medical diagnosis to social networking. The digital domain has evolved as the preferred choice in communication system design due to its advantages over analog systems, such as high-speed transmission, improved quality, and effortless copying with high precision. The advancement of digital media brings new opportunities. The internet boom of this millennium has allowed digital data to move around the world in real time. Efficient segmentation, recognition, and analysis of multidimensional data such as hyperspectral images, medical imaging, data analysis in social media, and audio signals are still challenging issues. Digital data produced through data-processing algorithms has the fundamental advantages of transportability, proficiency, and accuracy of information content; but such data is also at significant risk because perfect illegal replicas can be made in unlimited numbers. 2021 John Wiley & Sons Ltd. -
INTRODUCTION
World politics in the twenty-first century represents a complex arena characterised by a diversity of paradigms. These paradigms entail a dynamic interplay of conventional and newer ways of engagement. Today, the globalising world witnesses newer imaginations of space and interactions where state actors continue to enjoy a preeminent status, adopting policies based on imaginations of space in terms of connectivity and gateways while maintaining their territorial integrity. They devise a whole array of mechanisms to define, redefine and secure their interests as well as elevate their aspirations of assuming newer responsibilities and bigger roles. What we witness today is dynamic endeavours by the states to hold on to and amplify their traditional roles and carving out newer contours of forging and consolidating relationships in the global framework of international relations. This also leads to the construction of new geo-strategic and economic hotspots. This complex interplay of the traditional and the newer interactions creates both synergies and discord. The Indo-Pacific represents such a hotspot in contemporary world politics, and Indias engagement with Southeast Asia is a significant area of interest therein. 2024 Taylor & Francis. -
INTRODUCTION
The twenty-first century has been witnessing a global federal resurgence, distinguished by conversations focusing on interdependencies, multiculturalism, overlapping jurisdictions, multilateralism, multiple centres of policy-making and multiple notions of citizenship. Any assessment of cooperative federalism needs to go beyond institutional structures by incorporating images of diversity, pluralism, identity, issues of empowerment and democratization. Cooperative federalism facilitates cooperation among the national, state and local governments. It perceives the federation and the states as complementary parts of an arrangement where sovereignty is shared. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
INTRODUCTION
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Introduction
Beginning The Rashtriya Uchchatar Shiksha Abhiyan (RUSA) in 2013, a significant development in higher education, in addition to several other initiatives such as the introduction of the New Methodology by the National Assessment and Accreditation Council (NAAC), the New Education Policy (NEP2020) and technological challenges of the COVID pandemic in the current times, Indian higher education has been undergoing a profound transformation, especially intending to revamp, through policy changes, upgrading and enhancing quality, ranking, research, bridging the skill gap, technological innovations and global perspectives. There is an urgent need to understand the evolving dynamics and emerging perspectives that appear to challenge the higher education system in India. The conference intends to draw attention to those key stakeholders in the present context to realise the anticipated drive towards overhauling the higher education system in India. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Introduction
This book Education and Pedagogical Experiences: Coping with Human Emergencies and Exploring Resilience Strategies is an array of chapters contributed by a panel of experts that explains different human emergencies and the role of education and pedagogy in addressing these crises. The contributions illustrate how educational practices during emergencies have evolved, persisted, and impacted communities globally. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Introduction to blockchain for internet of things
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Introduction to brand management in the digital age
The rapid development of digital technology has introduced a new order of large-scale change within brand management in the fast-changing landscape of the digital marketplace. This chapter provides the fundamental principles of brand management within the digital era and demonstrates the transformation of building brands via online platforms, data analytics, and digital technologies. Others might state that social media, e-commerce, and AI integration offer brands a new horizon for managing brand equity, improving customer engagement, and creating experiences to adapt to changing tastes. Adjustment towards this change can prove challenging for the brands, but on the other hand, they are opening new possibilities that drive innovation in performance and strategy about their brands. 2025, IGI Global Scientific Publishing. All rights reserved. -
Introduction to connecting coalitions
A connecting coalition in a graph G(V;E) consists of two disjoint vertex subsets V1 and V2 of V (G), where neither G[V1] nor G[V2] is a connected graph but G[V1 [V2] is a con- nected graph. A connecting coalition partition is a vertex partition ? = fV1; V2; : : : ; Vkg, and the maximum cardinality of all possible ? is called the connecting coalition number, ?(G). Some bounds on the coalition number ?(G) are found, and graphs having ?(G) = 2 are characterized. Further, the existence of connecting coalition partitions in graphs is explored. 2025 World Scientific Publishing Company. -
Introduction to connecting coalitions
A connecting coalition in a graph G(V;E) consists of two disjoint vertex subsets V1 and V2 of V (G), where neither G[V1] nor G[V2] is a connected graph but G[V1 [V2] is a con- nected graph. A connecting coalition partition is a vertex partition ? = fV1; V2; : : : ; Vkg, and the maximum cardinality of all possible ? is called the connecting coalition number, ?(G). Some bounds on the coalition number ?(G) are found, and graphs having ?(G) = 2 are characterized. Further, the existence of connecting coalition partitions in graphs is explored. 2025 World Scientific Publishing Company. -
Introduction to Data Mining and Knowledge Discovery
Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining system consists of a data warehouse, a database server, a data mining engine, a pattern analysis module, and a graphical user interface. Data mining techniques include mining the frequent patterns and association learning rules with analysis, sequence analysis. Data mining technique is applicable on the top of various kinds of intelligent data storage systems such as data warehouses. It provides some analysis processes to make some useful strategic decisions. There are various issues and challenges faced by a data mining system in large databases. It provides a great place to work for data researchers and developers. Data mining is the process of classification, which can be executed based on the examination of training data (i.e., objects whose class label is predefined). With the help of an expert set of previous class objects with known class labels, it can find a model that can predict a class object with an unknown class label. These classification models can be classified into a variety of categories, including nearest neighbor, neural network, and others. Bayesian model, decision tree, neural network Random forest, decision trees Support vector machine, random forest SVM (support vector machine), for example. By analyzing the most common class among k closest samples, the K-Nearest Neighbor (KNN) technique aids in predicting of the class object with the unknown class label. Its an easy-to-use strategy that yields a solid classification result from any distribution. The Naive Bayes theory helps to perform the classification. It is one of the fastest classification algorithms, capable of efficiently handling real-world discrete data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Introduction to International Financial Economics and Banking
The global financial economics and banking is emerging to be a very important element in an economical setting that becomes increasingly global. The primary objective is to deliberate how the capital flows, foreign exchange markets, interest rate parity and banking regulations have affected financial stability across nations and the economic growth. Background and theoretical structures that define the field of international finance and international banking that interconnect is outlined in the chapter. The chapter sheds some light on the dynamic entanglements between global financial institutions multinationals, central banks, and the overall global capital finance markets, cross-border risk evaluation set ups, and international liquidity indicators for macro-financial connections. The given work can contribute to the main part of the scholarly work by providing a complete image that combines the classical theory with the modern developments, hence, can be of use to scholars, policy makers, and financial professionals dealing with global financial markets. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Introduction to multimodal learning and heterogenous data
With the rising advancements in computation, technology, and many innovative evolutions coming into play, multimodal learning is one of the most rapidly growing fields within the domain of artificial intelligence and Machine Learning. It mainly focuses on integrating information from multiple sources called "modalities," allowing the systems to utilize the varieties of data to furtherenhance their understanding and performance. These so-called modalities make use of various types of data in the form of text, images, audio, and sensor readings. They are able to process complex information due to these modalities and thus provide more insightful results for the tasks that they are assigned. Another important aspect of multimodal learning is heterogeneous datadata that differs significantly in structure, format, and origin. This type of data falls mainly into three categories, comprising structured data, which is quite organized and therefore easy to locate or search, as in the case of the database records. Then comes unstructured data characterized by the free form, which comprises mainly social media posts, videos, and images. In addition, it has been possible to separate semistructured data. It incorporates some features of being ordered, like the metadata included in XML or JSON files; however, a fixed schema does not apply. The understanding of the kind is important because each type calls for a different problem, and each type poses new opportunities in analysis. Handling the heterogeneous data effectively is all the more important because the said system will be fed heterogeneous data, and if its combination and analysis go reasonably logically, it is expected to be a source for multimodal systems. The ability to merge structured, unstructured, and semistructured data improves performance across a wide range of tasks, including but not limited to common applications like image recognition, sentiment analysis, and decision-making processes in autonomous systems. For example, in the multimodal learning case, it would be beneficial for the system that learns customer feedback to merge textual reviews, audio recordings of customer interactions, and visual data from product images. It has been known to yield a much clearer picture of what customers really want and how they actually behave. This chapter introduces notions of multimodal learning as well as heterogeneous datatheir characteristics, types, sources, and practical usage. It will attempt to establish a basic understanding of these two concepts in relation to each other in order to support more advanced applications through machine learning. In a review of the possible compositions between multimodal learning as well as heterogeneous data, the chapter will introduce their importance regarding the creation of intelligent systems that can address complex, intricate tasks across differing fields. As we enter the data age, with multiple sources churning out data at unprecedented rates that appear to have no bounds, integration of multimodal learning with heterogeneous data cannot be ignored. This will be vital for coming up with flexible yet useful applications to real-world problems. This region is promising for systems that perceive, interpret, and respond to the variability of information in a fashion similar to human reasoning and decision-making. Future application of artificial intelligence in the life of man will result from continuous research in the areas of multimodal learning and heterogeneous data. 2026 Elsevier Inc. All rights reserved. -
Introduction to natural language processing: Exploring techniques, applications, and challenges
This book provides a thorough and comprehensive introduction to natural language processing (NLP), a critical field at the intersection of artificial intelligence and computational linguistics. It explores key techniques such as sentiment analysis, which enables the detection of emotional tone in text, machine translation, facilitating the conversion of text between languages, and named entity recognition (NER), which identifies and classifies entities like names, dates, and locations within text data. The book delves into deep learning advancements, particularly the use of neural networks such as transformers and recurrent models, which have revolutionized NLP applications. Readers will gain insights into how these models drive innovations in areas such as text classification, language generation, and speech recognition. In addition to technical concepts, the book also addresses the ethical considerations surrounding NLP, emphasizing the responsible use of AI technologies to mitigate issues like bias, misinformation, and privacy concerns. Practical case studies and real-world examples are included to illustrate how NLP is applied in various sectors, including healthcare, finance, and customer service. This book is an invaluable resource for students, researchers, and industry professionals seeking to understand the foundational concepts, cutting-edge advancements, and broader implications of NLP, equipping them with the knowledge to innovate and apply these technologies effectively in their respective fields. 2025 River Publishers. All rights reserved. -
INTRODUCTION TO NEUROCOGNITIVE REHABILITATION
This chapter offers a comprehensive examination of the historical development, theoretical foundations, and contemporary directions in neurocognitive rehabilitation. Beginning with early clinical observations, including the cases of Phineas Gage and Alexander Lurias wartime studies, the narrative traces the evolution of the field from anecdotal case reports to empirically validated interventions. The discussion delineates core principles that underpin effective rehabilitation practice, including individualization and person-centered planning, goal-directed and functionally relevant interventions, evidence-based methodologies, interdisciplinary collaboration, and ecological validity. Established frameworks such as the cognitive neuropsychological model and the information processing model are critically appraised alongside the biopsychosocial perspective and holistic neuropsychological rehabilitation approaches. Particular attention is given to emerging trends, including the integration of advanced technologies such as virtual reality, tele-rehabilitation, and adaptive computerized training as well as their implications for accessibility, scalability, and equity in service delivery. The chapter further considers the relevance of these paradigms to forensic psychology and legal scholarship, highlighting their role in capacity assessment and the determination of criminal responsibility. Drawing upon recent systematic reviews and high-quality empirical studies, this synthesis underscores the necessity of combining scientific rigor with ethically grounded, person-centered care. While artificial intelligence tools supported aspects of drafting, the content has been critically curated and adapted to reflect current scholarship and clinical expertise. The chapter concludes by emphasizing the imperative for rehabilitation professionals to engage in lifelong learning and innovation to meet the evolving needs of individuals with acquired brain injury. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Introduction to optimization: Techniques and applications in engineering
A key idea in computer science, engineering, economics, and mathematics is optimization, which seeks to identify the optimal option from a range of workable options. An overview of optimization, its importance, and its many uses are given in this chapter. It highlights various forms of optimization, such as linear, nonlinear, convex, and combinatorial optimization, and examines the fundamental concepts of optimization, such as objective functions, constraints, and viable regions. In addition to contemporary strategies such as evolutionary algorithms, machine learning-based optimization, and metaheuristic techniques like genetic algorithms and simulated annealing, the chapter explores few traditional optimization techniques. Real-world applications in banking, logistics, AI, and industrial process optimization are also covered. This chapter offers insights into issue formulation, solution approaches, and efficiency concerns, with a focus on both theoretical underpinnings and real-world applications. It also presents important optimization tools and software that are frequently used in both industry and academics. By the end of this chapter, readers will have a basic understanding of optimization concepts that will allow them to use these ideas to effectively tackle challenging issues. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors.