BIOFORMERS: A SCALABLE FRAMEWORK FOR EXPLORING BIOSTATES USING TRANSFORMERS

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically selected relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves superior performance on 7 graph benchmark datasets with 2.5 $\sim$∼ 449 times less computational cost compared to transformer-based graph models with full attention.

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

Inspired by the tremendous success of the transformer-based model in natural language processing (NLP), many efforts introduce the transformer-based model into the image processing tasks. However, naive transformer models have to down-sample the image resolution to satisfy computational restrictions, thus discarding the local information, which is catastrophic for high-performance remote sensing image segmentation. Hence, this paper proposes a novel trainable boundary-aware bias loss function to enhance transformer-based models of extracting local information. On the Challenging ISPRS Potsdam dataset, two representative transformer-based models achieve remarkable performance improvements, proving the effectiveness of the proposed method.

The advent of powerful transformer-based discriminative language models and, more recently, generative GPT-family models, has led to notable advancements in natural language processing (NLP), particularly in commonsense reasoning tasks. One such task is commonsense reasoning, where performance is usually evaluated through multiple-choice question-answering benchmarks. Till date, many such benchmarks have been proposed and `leaderboards' tracking state-of-the-art performance on those benchmarks suggest that transformer-based models are approaching human-like performance. However, due to documented problems such as hallucination and bias, the research focus is shifting from merely quantifying accuracy on the task to an in-depth, context-sensitive probing of LLMs' generalization and robustness. To gain deeper insight into diagnosing these models' performance in commonsense reasoning scenarios, this thesis addresses three main studies: the generalization ability of transformer-based language models on commonsense reasoning, the trend in confidence distribution of these language models confronted with ambiguous inference tasks, and a proposed risk-centric evaluation framework for both discriminative and generative language models.

As we enter the Industry 5.0 era, enormous volumes of data are being created across digital systems. Machine learning techniques have recently achieved immense success in areas such as intelligent control, decision-making, speech recognition, natural language processing, computer graphics, and computer vision. This despite the significant challenge of analyzing and interpreting massive datasets. Owing to their strong performance, deep learning and machine learning algorithms have become widely deployed across various real-time engineering applications. Developing working knowledge of machine learning is now critical for building automated, smart systems that can process data in domains like healthcare, cybersecurity, and intelligent transportation. There exist multiple strategies in machine learning, including reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning algorithms. This research provides a comprehensive examination of leveraging machine learning for managing real-time engineering systems, with the goal of augmenting their capabilities and intelligence. It contributes to the understanding of how different machine learning approaches can be applied in real-world use cases like cybersecurity, healthcare, and intelligent transportation. Additionally, it highlights ongoing research objectives and difficulties that machine learning techniques encounter while tackling real-world systems. This research serves both industry professionals and academics as a reference, while technically benchmarking decision-making across different application areas and real-world scenarios.

Transformers have achieved remarkable success in natural language processing and computer vision, motivating their extensive research into intelligent fault diagnosis. The multi-head self-attention mechanism in Transformers effectively extracts global features and emphasizes important feature maps. However, directly applying Transformers to vibration signals can incur substantial computation and training effort, which often makes convolutional neural network (CNN)-based approaches more practical. This paper, thus, proposes a new architecture, called Multi-scale Dilated Contracted sub-Transformer (MDCSformer), specifically designed to efficiently process noisy vibration signals for fault diagnosis. First, multi-scale dilated convolution layers capture features at various scales, extracting both local and global characteristics from the signals. Next, a contracted multi-head sub-self-attention module refines these features by selectively emphasizing relevant information while maintaining computational cost. The proposed method is validated with two gearbox vibration datasets and a bearing vibration dataset, demonstrating that MDCSformer outperforms comparable CNN-based, Transformer-based, and hybrid models by achieving higher accuracy and robustness in fault diagnosis. This superior performance stems from its integration of rich multi-scale feature extraction with contracted subspace attention.

Transformer architectures are highly expressive because they use self-attention mechanisms to encode long-range dependencies in the input sequences. In this paper, we present a literature review on Transformer-based (TB) models, providing a detailed overview of each model in comparison to the Transformer’s standard architecture. This survey focuses on TB models used in the field of Natural Language Processing (NLP) for textual-based tasks. We begin with an overview of the fundamental concepts at the heart of the success of these models. Then, we classify them based on their architecture and training mode. We compare the advantages and disadvantages of popular techniques in terms of architectural design and experimental value. Finally, we discuss open research, directions, and potential future work to help solve current TB application challenges in 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.

A survey of recent methods for addressing AI fairness and bias in biomedicine

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

Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals.

Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care

Transformer neural networks have become widely used in a variety of AI applications, enabling significant advances in Natural Language Processing (NLP) and computer vision. Here we demonstrate the use of transformer neural networks in the de novo design of architected materials using a unique approach based on text input that enables the design to be directed by descriptive text, such as “a regular lattice of steel”. Since transformer neural nets enable the conversion of data from distinct forms into one another, including text into images, such methods have the potential to be used as a natural-language-driven tool to develop complex materials designs. In this study we use the Contrastive Language-Image Pre-Training (CLIP) and VQGAN neural networks in an iterative process to generate images that reflect text prompt driven materials designs. We then use the resulting images to generate three-dimensional models that can be realized using additive manufacturing, resulting in physical samples of these text-based materials. We present several such word-to-matter examples, and analyze 3D printed material specimen through associated additional finite element analysis, especially focused on mechanical properties including mechanism design. As an emerging new field, such language-based design approaches can have profound impact, including the use of transformer neural nets to generate machine code for 3D printing, optimization of processing conditions, and other end-to-end design environments that intersect directly with human language.

ABSTRACT: In today’s highly competitive job market, effective interview preparation plays a pivotal role in achieving career success. However, conventional preparation methods often fail to provide personalization, real-time feedback, and comprehensive performance assessment. To address these challenges, this research presents the AI-Driven Interview Assistant — an intelligent, adaptive, and comprehensive AI-powered interview preparation and assessment platform. The proposed system leverages Natural Language Processing (NLP), speech analysis, and computer vision to simulate realistic interview environments while delivering actionable feedback and personalized improvement recommendations. The platform employs Large Language Models (LLMs) such as OpenAI’s GPT and Google’s Gemini for intelligent question generation and response evaluation. Real-time speech-to-text conversion using Google Speech-to-Text API or Whisper AI enables seamless voice interaction, while sentiment analysis with Hugging Face transformers evaluates emotional tone, confidence, and engagement. The core evaluation engine analyzes responses based on content relevance, communication clarity, technical accuracy, and behavioral competence. Optional computer vision modules assess facial expressions and body language for holistic performance insights. From a technical standpoint, the system is built with a React.js frontend and Node.js/Express backend, ensuring a smooth, interactive user experience. WebRTC integration enables live audio-video interview simulations, while MongoDB or PostgreSQL store detailed performance analytics for adaptive learning. The platform also integrates with job portals and company databases to deliver role-specific and industry-oriented interview preparation. Keywords – Artificial Intelligence (AI), Interview Preparation, Natural Language Processing (NLP), Large Language Models (LLMs), Speech Recognition, Whisper AI, Sentiment Analysis, Hugging Face Transformers, Computer Vision, Adaptive Learning, Real-time Feedback, Emotion Detection, React.js, Node.js, WebRTC, MongoDB, PostgreSQL, Interview Simulation, Candidate Assessment, Skill Enhancement