Multilingual Semantic Object Identification Using Computer Vision and NLP

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

In the digital era, Customer Relationship Management (CRM) systems have become central to sustaining competitive advantage through enhanced customer satisfaction and operational efficiency. With the exponential rise in digital interactions, chatbot interfaces powered by Natural Language Processing (NLP) models have emerged as transformative tools in automating and personalizing customer service. This research explores the design, integration, and evaluation of chatbot user interfaces (UIs) tailored for CRM using advanced NLP techniques. The study begins with a comprehensive review of existing literature, highlighting the evolution of chatbots from rule-based systems to intelligent, context-aware virtual assistants. Key NLP components—such as intent recognition, entity extraction, sentiment analysis, and context management—are discussed in the context of enhancing user interaction and personalization. We then present a modular architecture for chatbot-CRM integration, emphasizing user interface design principles that prioritize accessibility, trust, and conversational clarity. A comparative analysis of popular chatbot platforms (Salesforce Einstein Bot, Azure Bot, Rasa, and Dialogflow) is conducted based on criteria such as NLP capabilities, CRM integration, analytics support, and cost-effectiveness. Real-world performance metrics—including customer satisfaction (CSAT), average resolution time, and escalation rates—are analyzed to assess the effectiveness of chatbotdriven CRM solutions. Furthermore, we identify key challenges such as language ambiguity, handling complex queries, and ethical concerns like data privacy and AI bias. The paper concludes with strategic recommendations for hybrid models combining AI and human support, and future research directions including emotion-aware chatbots and multilingual support. This study provides a roadmap for businesses and developers to implement effective, scalable chatbot UIs that optimize customer relationship management using NLP.

In view of the low accuracy of the current English teaching quality evaluation, a teaching quality evaluation method based on neural network and natural language processing is proposed. First, principal component analysis is used to select the teaching quality evaluation indicators, and then the RBF neural network teaching is designed. The evaluation model is used to optimize the initial weights of the RBF neural network. The experimental results show that the method can effectively evaluate the quality of English teaching, and the accuracy and real-time performance are high.

Transformers are becoming the state-of-the-art in multiple Computer vision (CV) and Natural language processing (NLP) tasks. As for hyperspectral target detection, a Transformer architecture named SpectralFormer2 has been developed and demonstrated improved performance over the previously state-of-the-art Convolutional neural network (CNN) architecture on widely studied classification tasks. The SpectralFormer was adapted from a CV architecture, the Vision Transformer (ViT). Concurrently, still in CV, a hierarchical and multi-scale version of the ViT, named Shifted windows (Swin) Transformer, is gaining momentum and is already the stateof-the-art on multiple tasks. In this paper, we adapt the Swin Transformer for hyperspectral classification and rare sub-pixel target detection. We apply this new architecture to commonly studied classification benchmarks public datasets such as Pavia University and Centre datasets, and to a new, large-scale airborne sub-pixel target detection dataset we developed. This new dataset is composed of over 100 M pixels taken over three collect days at three locations thousands of kilometers apart and in different climates. The proposed model reaches competitive performance on all of those tasks while reducing memory usage by 63 %.

Machine learning (ML) has become the de-facto approach for various scientific domains such as computer vision and natural language processing. Despite recent breakthroughs, machine learning has only made its way into the fundamental challenges in computer systems and networking recently. This article attempts to shed light on recent literature that appeals for machine learning-based solutions to traditional problems in computer systems and networking. To this end, we first introduce a taxonomy based on a set of major research problem domains. Then, we present a comprehensive review per domain, where we compare the traditional approaches against the machine learning-based ones. Finally, we discuss the general limitations of machine learning for computer systems and networking, including lack of training data, training overhead, real-time performance, and explainability, and reveal future research directions targeting these limitations.

In the era of digitalization, the interaction between humans and machines, particularly in Natural Language Processing, has gained crucial importance. This study focuses on improving the effectiveness and accuracy of chatbots based on Natural Language Processing. Challenges such as the variability of human language and high user expectations are addressed, analyzing critical aspects such as grammatical structure, keywords, and contextual factors, with a particular emphasis on syntactic structure. An optimized chatbot model that considers explicit content and the user’s underlying context and intentions is proposed using machine learning techniques. This approach reveals that specific features, such as syntactic structure and keywords, are critical to the accuracy of chatbots. The results show that the proposed model adapts to different linguistic contexts and offers coherent and relevant answers in real-world situations. Furthermore, user satisfaction with this advanced model exceeds traditional models, aligning with expectations of more natural and humanized interactions. This study demonstrates the feasibility of improving chatbot–user interaction through advanced syntactic analysis. It highlights the need for continued research and development in this field to achieve significant advances in human–computer interaction.

Enhancing Cross-Linguistic Image Caption Generation with Indian Multilingual Voice Interfaces using Deep Learning Techniques

Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getting a handle on the various phenomena that regulate the meaning of linguistic utterances can pave the way for solving many compelling and ambitious tasks in the field, from Machine Translation to Question Answering and Information Retrieval. A complete semantic model of language, however, needs first of all reliable building blocks. In the last two decades, research in lexical semantics (which focuses on the meaning of individual linguistic elements, i.e., words and expressions), has produced increasingly comprehensive and effective machine-readable dictionaries in multiple languages: like humans, NLP systems can now leverage these sources of lexical knowledge to discriminate among various senses of a given lexeme, thereby improving their performances on downstream tasks and applications. In this paper, we focus on the case study of BabelNet, a large multilingual encyclopedic dictionary and semantic network, to describe in detail how such knowledge resources are built, improved and exploited for crucial NLP tasks such as Word Sense Disambiguation, Entity Linking and Semantic Similarity.

The increasing linguistic diversity in higher education has led to a growing need for inclusive and effective multilingual instructional strategies. This conceptual paper examines the potential of Natural Language Processing (NLP) to support multilingual instruction by addressing the communication barriers faced by non-native speakers in diverse classroom settings. Drawing on current theoretical perspectives and technological developments, the paper explores how NLP tools—such as real-time translation, speech recognition, and automated feedback systems—can enhance comprehension, accessibility, and collaborative learning in higher education. These technologies offer opportunities to bridge language gaps, enabling students to engage more effectively with content delivered in multiple languages. The paper also discusses challenges related to language diversity, technological limitations, and cultural sensitivity in implementing NLP in educational contexts. While acknowledging these limitations, the paper argues that integrating NLP into multilingual instruction represents a promising direction for fostering inclusivity and equity in higher education. Ultimately, this theoretical exploration urges institutions to embrace innovative language technologies to create more dynamic and supportive learning environments for students of all linguistic backgrounds.

As two core fields of machine learning, Natural Language Processing and Computer Vision have derived a variety of cross research topics, and image captioning as a key topic of the current cross field has an important relationship with the development of both. Image captioning has been attached importance to by researchers for a long time, and has formed a relatively perfect theoretical model system. At present, this topic has made great progress in accuracy, but this is only one aspect of the evaluation of image content description algorithm, which has new requirements in diversity, stylization and controllability. Based on this, this paper studies the technical routes and modeling methods of various auto-coding models, variational models and attention mechanisms, proposing a new way to solve the inherent contradiction between diversity and accuracy, that is to increase the diversity of each sample while ensuring the upper bound of accuracy index scores in multiple samples. The test results on MS COCO data set show that the model implemented in this paper has good performance in accuracy and diversity.

This research proposes an advanced approach that integrates multiple image detection techniques and natural language processing (NLP) methodologies for English-to-Spanish language translation. The developed software accepts an image as input, which undergoes preprocessing using adaptive thresholding, morphological transformations, and edge detection algorithms such as Canny and Sobel operators to enhance text clarity. Text detection and localization are achieved using the EfficientDet and EAST (Efficient and Accurate Scene Text) detector frameworks, followed by Optical Character Recognition (OCR) using PyTesseract, a wrapper for Google’s Tesseract OCR. The detected text is passed to an NLP system for translation, which employs a sequence-to-sequence transformer model implemented with Keras, TensorFlow, and NumPy. Additional techniques, such as Byte Pair Encoding (BPE) for text tokenization and positional encoding for transformer-based attention, improve translation efficiency. An English-Spanish dictionary from Anki and a large parallel corpus dataset were used for training. The NLP pipeline leverages semantic analysis, part-of-speech tagging, and dependency parsing to preserve grammatical structure and context. Fine-tuning the transformer model parameters, including learning rate scheduling and gradient clipping, further optimized system performance. The research demonstrates a 93.7% translation accuracy, achieved by combining state-of-the-art image processing algorithms, advanced transformer architectures, and a robust training dataset. This hybrid approach significantly improves the accuracy of English-to-Spanish translations, validating the effectiveness of integrating computer vision and NLP technologies.

This chapter examines the evolving landscape of translation pedagogy, highlighting the shift from traditional methodologies to innovative practices shaped by technological advancements and a deeper understanding of multilingual contexts. Through a systematic literature review, we analyze trends from 2018 to 2025, with a particular emphasis on how translation technology is reshaping teaching methodologies, the challenges related to trainer preparedness, curriculum adaptability, learner engagement, and attitudes toward translation. Additionally, we offer recommendations for the meaningful integration of technology within educational frameworks. The chapter culminates in a call for culturally inclusive and ethically grounded pedagogical practices, urging a transition from an emphasis on linguistic accuracy to one focused on critical engagement with translation tools. This approach aims to foster a comprehensive educational experience that equips students for success in a diverse, multilingual world.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.

Inclusive education is a fundamental principle that aims to provide equal learning opportunities for all students, regardless of their background, abilities, or disabilities. With the rise of digital technologies, the landscape of education has transformed, presenting both challenges and opportunities for creating more inclusive learning environments. This paper explores the role of digital tools and technologies in addressing the needs of diverse learners within the framework of inclusive education. The paper critically examines how technology can be leveraged to enhance learning experiences for students with various learning styles, disabilities, and cultural backgrounds. It highlights the potential of digital platforms, such as online learning systems, assistive technologies, and adaptive learning software, to personalize education and provide tailored support to students, thus promoting accessibility and equity in educational settings. Furthermore, the paper delves into the challenges that educators, policymakers, and institutions face in ensuring that digital inclusion is achieved, emphasizing the need for digital literacy, infrastructure, and teacher training. The review also discusses the social and ethical implications of digital inclusion, addressing concerns related to data privacy, inequality in access to technology, and the potential risks of over-reliance on digital tools. By exploring both the potential benefits and challenges, this paper offers insights into how the integration of digital tools can support the creation of truly inclusive educational environments that cater to the diverse needs of learners in the digital era. The findings of this paper highlight the importance of collaboration between educators, technologists, and policymakers to overcome barriers and promote digital inclusion in education.