User Query Research Articles

GEAR-Up: Generative AI and External Knowledge-Based Retrieval: Upgrading Scholarly Article Searches for Systematic Reviews

This paper addresses the time-intensive nature of systematic reviews (SRs) and proposes a solution leveraging advancements in Generative AI (e.g., ChatGPT) and external knowledge augmentation (e.g., Retrieval-Augmented Generation). The proposed system, GEAR-Up, automates query development and translation in SRs, enhancing efficiency by enriching user queries with context from language models and knowledge graphs. Collaborating with librarians, qualitative evaluations demonstrate improved reproducibility and search strategy quality. Access the demo at https://youtu.be/zMdP56GJ9mU.

AI-Based Medical Chatbot for Disease Prediction By IJISRT

This research paper presents the development and implementation of an AI-based medical chatbot for disease prediction. Leveraging machine learning and artificial intelligence technologies, the chatbot utilizes natural language processing (NLP) to understand user queries and provide accurate information, guidance, and assistance for various infectious diseases. Motivated by the global spread of infectious diseases and the need for accessible healthcare support, the paper outlines the objectives, algorithm design, dataset description, and application of the chatbot. The algorithm involves receiving user input, extracting symptoms, classifying diseases, and suggesting prevention measures. The dataset, structured in JSON format, facilitates training and pattern recognition. The chatbot interface, accessible across platforms, offers information on symptoms, prevention measures, hospital bed availability, and medication options. In conclusion, the research highlights the potential of AI-based chatbots in revolutionizing healthcare accessibility and personalized diagnosis, thereby bridging the gap between users and healthcare systems.

Large Language Model for Chatbot

The integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) has led to the development of sophisticated Chatbots capable of mimicking human conversation and providing automated responses. In the context of the mining industry, which operates under a complex framework of Acts, Rules, and Regulations, there is a growing need for a comprehensive and easily accessible information system. This research proposes the implementation of a 24/7 available Chatbot, equipped with the ability to address stakeholder and customer queries regarding various legal aspects, including the Coal Mines Act, 1952, Indian Explosives Act, 1884, Colliery Control Order, 2000, Colliery Control Rules, 2004, The Coal Mines Regulations, 2017, and The Payment of Wages (Mines) Rules, 1956. Furthermore, the Chatbot's scope will encompass land-related laws, such as Community Benefits Agreement (CBA), Land Acquisition (LA), and Resettlement and Rehabilitation (RandR), thereby establishing a robust Management Information System tailored to the specific needs of the mining industry

Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets.

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

A Study to Know the Impact of AI on Sustainability of Products in the Carbonated Beverages

Carbonated beverage businesses are using artificial intelligence (AI) technology to sustainability in a variety of ways to streamline their processes, boost output, and improve the customer experience. The businesses in the carbonated beverage sector use AI in the following ways, Coca-Cola is enhancing its supply chain administration by utilizing AI. The business has created a platform powered by AI that applies machine learning algorithms to improve its distribution and manufacturing procedures. The platform aids the business in improved demand forecasting, inventory level optimization, cost-effective transportation, voice activated vending machines. PepsiCo: To create novel product formulations, PepsiCo is utilizing AI. The business has developed an AI-powered platform that examines customer preferences and spots market trends. The business can develop new products thanks to this platform. Dr. Pepper Snapple Group: Dr. Pepper Snapple Group is enhancing its marketing initiatives with artificial intelligence. The business has created an AI-driven platform that analyses customer data and enables it to develop more specialized and individualized marketing campaigns. The platform enables the business to deliver more pertinent and efficient marketing messages by assisting in the better understanding of customer preferences and behaviour. Keurig Dr Pepper: Keurig Dr Pepper is using AI to enhance its customer service. The business has created an AI-powered chatbot that can instantly respond to customer inquiries and help. The chatbot uses machine learning algorithms and natural language processing to comprehend customer queries and deliver pertinent and useful responses. Overall, the carbonated beverage industry is using AI technology in a variety of methods. Keywords- carbonated beverage, artificial intelligence (AI), supply chain administration, demand forecasting, inventory optimization, transportation optimization, voice activated vending machines, product formulation, customer preferences, market trends, marketing campaigns, customer service, chatbot, machine learning algorithms, natural language processing.

Empowering personalized pharmacogenomics with generative AI solutions.

This study evaluates an AI assistant developed using OpenAI's GPT-4 for interpreting pharmacogenomic (PGx) testing results, aiming to improve decision-making and knowledge sharing in clinical genetics and to enhance patient care with equitable access. The AI assistant employs retrieval-augmented generation (RAG), which combines retrieval and generative techniques, by harnessing a knowledge base (KB) that comprises data from the Clinical Pharmacogenetics Implementation Consortium (CPIC). It uses context-aware GPT-4 to generate tailored responses to user queries from this KB, further refined through prompt engineering and guardrails. Evaluated against a specialized PGx question catalog, the AI assistant showed high efficacy in addressing user queries. Compared with OpenAI's ChatGPT 3.5, it demonstrated better performance, especially in provider-specific queries requiring specialized data and citations. Key areas for improvement include enhancing accuracy, relevancy, and representative language in responses. The integration of context-aware GPT-4 with RAG significantly enhanced the AI assistant's utility. RAG's ability to incorporate domain-specific CPIC data, including recent literature, proved beneficial. Challenges persist, such as the need for specialized genetic/PGx models to improve accuracy and relevancy and addressing ethical, regulatory, and safety concerns. This study underscores generative AI's potential for transforming healthcare provider support and patient accessibility to complex pharmacogenomic information. While careful implementation of large language models like GPT-4 is necessary, it is clear that they can substantially improve understanding of pharmacogenomic data. With further development, these tools could augment healthcare expertise, provider productivity, and the delivery of equitable, patient-centered healthcare services.

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

AbstractThe W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain

Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting verifiable outsourced K-ANNS on the blockchain. ANNProof utilizes two innovative authenticated data structures (ADS), the Merkle HNSW node tree, and the Merkle vector identifier tree, for efficient K-ANNS query verification. Additionally, we employ the Merkle sharding tree as an ADS optimization technique, reducing the overhead of delivering verifiable queries. We implement the ADS construction protocol based on blockchain smart contracts to ensure tamper-evident datasets and enhance execution efficiency via a contract state consistency checking scheme. Extensive evaluations show that ANNProof reduces VO generation time, result verification time, and VO size by 160, 120, and 28×, respectively, compared to the state-of-the-art systems. Moreover, ADS construction using ANNProof takes at most 2% of the index construction time, resulting in a negligible overhead for implementing verifiable queries. Meanwhile, the sharding optimization accelerates ADS updates by 53×.

Transformer-Based Classification of User Queries for Medical Consultancy

Transformer-Based Classification of User Queries for Medical Consultancy

Transformer-Based Classification of User Queries for Medical Consultancy

Представлен новый подход, использующий модель RuBERT для классификации пользовательских запросов в области медицинских консультаций с учетом специализации эксперта. Входе исследования был собран обширный набор данных, который использовался для дообучения модели RuBERT. Метрика качества полученной модели F1-score составила более 91,8% как при использовании блоковой кросс-валидации, так и при разделении набора данных на обучающую и тестовую выборки. Подход демонстрирует высокую обобщающую способность для различных медицинских подобластей, таких как кардиология, неврология и дерматология. Предложенный подход позволяет сократить время на определение наиболее подходящего специалиста и тем самым повышает качество консультации и медицинской помощи.

Transformer-Based Classification of User Queries for Medical Consultancy

The need for skilled medical support is growing in the era of digital healthcare. This research presents an innovative strategy, utilizing the RuBERT model, for categorizing user inquiries in the field of medical consultation with a focus on expert specialization. By harnessing the capabilities of transformers, we fine-tuned the pre-trained RuBERT model on a varied dataset, which facilitates precise correspondence between queries and particular medical specialisms. Using a comprehensive dataset, we have demonstrated our approach’s superior performance with an F1-score of over 91.8%, calculated through both cross-validation and the traditional split of test and train datasets. Our approach has shown excellent generalization across medical domains such as cardiology, neurology and dermatology. This methodology provides practical benefits by directing users to appropriate specialists for prompt and targeted medical advice. It also enhances healthcare system efficiency, reduces practitioner burden, and improves patient care quality. In summary, our suggested strategy facilitates the attainment of specific medical knowledge, offering prompt and precise advice within the digital healthcare field.

PDF-Driven Q&A: A Review

Abstract: Traditional methods of PDF retrieval often suffer from inefficiencies and inaccuracies due to their reliance on keyword-based search algorithms. These methods usually don't understand what words really mean and have trouble with things like different words meaning the same thing, one word meaning many things, and understanding the context. This makes the search results not very good. This project proposes a novel approach to address these shortcomings by developing a comprehensive system for efficient PDF document management and context-aware question answering. The system integrates various components, including a user-friendly interface for PDF upload, Longchain techniques for breaking down lengthy PDFs into manageable chunks, and vectorization using OpenAI's language models. These vectorized chunks are stored in the Faiss Vector Database for rapid retrieval. User queries are converted into vectors using the same language model, and a semantic search algorithm matches them against the stored PDF chunks to retrieve contextually relevant answers. By presenting these answers in a user-friendly format, the system aims to enhance accessibility and usability, enabling seamless access to pertinent information within PDF documents.

Come back when you're infected: pharmacy access to sterile syringes in an Arizona Secret Shopper Study, 2023.

Pharmacies are critical healthcare partners in community efforts to eliminate bloodborne illnesses. Pharmacy sale of sterile syringes is central to this effort. A mixed methods "secret shopper" syringe purchase study was conducted in the fall of 2022 with 38 community pharmacies in Maricopa and Pima Counties, Arizona. Pharmacies were geomapped to within 2 miles of areas identified as having a potentially high volume of illicit drug commerce. Daytime venue sampling was used whereby separate investigators with lived/living drug use experience attempted to purchase syringes without a prescription. Investigator response when prompted for purchase rationale was "to protect myself from HIV and hepatitis C." A 24-item instrument measured sales outcome, pharmacy staff interaction (hostile/neutral/friendly), and the buyer's subjective experience. Only 24.6% (n = 28) of 114 purchase attempts across the 38 pharmacies resulted in syringe sale. Less than one quarter (21.1%) of pharmacies always sold, while 44.7% never sold. Independent and food store pharmacies tended not to sell syringes. There emerged distinct pharmacy staff interactions characterized by body language, customer query, normalization or othering response, response to purchase request and closure. Pharmacy discretion and pharmacy policy not to sell syringes without a prescription limited sterile syringe access. Investigators reported frequent and adverse emotional impact due to pharmacy staff negative and stigmatizing interactions. Pharmacies miss opportunities to advance efforts to eliminate bloodborne infections by stringent no-sale policy and discretion about syringe sale. State regulatory policy facilitating pharmacy syringe sales,limiting pharmacist discretion for syringe sales, and targetingpharmacy-staff level education may help advance the achievement of public health goals to eliminate bloodborne infections in Arizona.

Гейміфікація програм лояльності споживачів

Increased competition in the digital enviro­nment leads to the search for effective tools to attract targeted traffic to websites. Search engine marketing (SEM), which includes search engine (contextual) advertising (SEA) and search engine optimization (SEO), allows toachieve this aim by means of technical settings and consideration of marketing aspects in creating user experiences. The effectiveness of search engine marketing is tested in practice with the help of web analytics, which allows real-time tracking of traffic, its sources, and user actions, which helps identify the most effective tools and advertising campaigns. In the context of the emerging impression economy, improving the user experience of potential and existing customers at every touchpoint depends on the speed and convenience of achieving user goals in the digital environment. Search engine marketing is based on users’ search queries in the face of actualized demand. Therefore, websites that are quickly accessible in search and contain a value proposition in accordance with the search queries of potential customers will be convenient for users, have a competitive advantage by providing positive impressions based on an improved customer experience. This approach ensures that the interests of both users and businesses are satisfied, thus achieving the goals of digital marketing and business efficiency in general. The aim of the recearh is to substantiate the relationship between search contextual advertising and search engine optimi­zation as search engine marketing tools and to identify their synergistic impact on creating positive user experiences, increasing targeted traffic and increasing website conver­sion. In the reseach, the general scientific methods of analysis and synthesis, system-structural analysis, comparison, systematic, logical generali­zation were used. Based on the semantic mechanism of working with search queries, the article substantiates the relationship between search engine advertising and search engine optimi­zation as search engine marketing tools and reveals their synergistic impact on creating positive user experiences, increasing targeted traffic and increasing website conver­sion. The proposed algorithms for search engine adver­tising and internal search engine optimi­zation, taking into account the technical and marketing aspects of the implementation of these digital marketing tools, help in this regard.

Пошуковий маркетинг у створенні вражень користувачів вебсайтів

Increased competition in the digital environment leads to the search for effective tools to attract targeted traffic to websites. Search engine marketing (SEM), which includes search engine (contextual) advertising (SEA) and search engine optimization (SEO), allows toachieve this aim by means of technical settings and consideration of marketing aspects in creating user impressions. The effectiveness of search engine marketing is tested in practice with the help of web analytics, which allows real-time tracking of traffic, its sources, and user actions, which helps identify the most effective tools and advertising campaigns. In the context of the emerging impression economy, improving the user experience of potential and existing customers at every touchpoint depends on the speed and convenience of achieving user goals in the digital environment. Search engine marketing is based on users’ search queries in the face of actualized demand. Therefore, websites that are quickly accessible in search and contain a value proposition in accordance with the search queries of potential customers will be convenient for users, have a competitive advantage by providing positive impressions based on an improved customer experience. This approach ensures that the interests of both users and businesses are satisfied, thus achieving the goals of digital marketing and business efficiency in general. The aim of the recearh is to substantiate the relationship between search contextual advertising and search engine optimization as search engine marketing tools and to identify their synergistic impact on creating positive user impressions, increasing targeted traffic and increasing website conversion. In the reseach, the general scientific methods of analysis and synthesis, system-structural analysis, comparison, systematic, logical generalization were used. Based on the semantic mechanism of working with search queries, the article substantiates the relationship between search engine advertising and search engine optimization as search engine marketing tools and reveals their synergistic impact on creating positive user impressions, increasing targeted traffic and increasing website conver­sion. The proposed algorithms for search engine advertising and internal search engine optimi­zation, taking into account the technical and marketing aspects of the implementation of these digital marketing tools, help in this regard.

BERT-Based Medical Chatbot: Enhancing Healthcare Communication through Natural Language Understanding

The advent of modern technologies like Artificial Intelligence(AI), Internet of Things(IoT) and Deep Learning(DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscore its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.

The impact of search engine data sharing on competition and consumer welfare

ABSTRACT Search engines match user queries with webpages. Search efficiency, increases when data-driven network effects attract more users and generate economies of scale and scope in data aggregation. Network effects enhance user welfare but also trigger antitrust concerns because they may “tip” the market towards a single dominant search engine – Google Search. The EU Digital Markets Act imposes an obligation on very large gatekeepers search engines to share their user data with smaller search engines to facilitate market entry and competition. The available empirical literature on search engine efficiency shows that asymmetric data sharing, from larger to smaller search engines, may increase competition but reduce user welfare because it fragments the user data pool that is crucial for efficiency, in particular for rare queries. Tension between competition and user welfare can be overcome by symmetric and mutual data sharing between all search engines, irrespective of market share.

Pareto Optimization of Analog Circuits Using Reinforcement Learning

Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require the designer to optimize for multiple competing objectives, which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to (i) extrapolate current techniques in Multi-Objective Reinforcement Learning to continuous state and action spaces and (ii) provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.

Assisted design of data science pipelines

When designing data science (DS) pipelines, end-users can get overwhelmed by the large and growing set of available data preprocessing and modeling techniques. Intelligent discovery assistants (IDAs) and automated machine learning (AutoML) solutions aim to facilitate end-users by (semi-)automating the process. However, they are expensive to compute and yield limited applicability for a wide range of real-world use cases and application domains. This is due to (a) their need to execute thousands of pipelines to get the optimal one, (b) their limited support of DS tasks, e.g., supervised classification or regression only, and a small, static set of available data preprocessing and ML algorithms; and (c) their restriction to quantifiable evaluation processes and metrics, e.g., tenfold cross-validation using the ROC AUC score for classification. To overcome these limitations, we propose a human-in-the-loop approach for the assisteddesignofdatasciencepipelines using previously executed pipelines. Based on a user query, i.e., data and a DS task, our framework outputs a ranked list of pipeline candidates from which the user can choose to execute or modify in real time. To recommend pipelines, it first identifies relevant datasets and pipelines utilizing efficient similarity search. It then ranks the candidate pipelines using multi-objective sorting and takes user interactions into account to improve suggestions over time. In our experimental evaluation, the proposed framework significantly outperforms the state-of-the-art IDA tool and achieves similar predictive performance with state-of-the-art long-running AutoML solutions while being real-time, generic to any evaluation processes and DS tasks, and extensible to new operators.

Customer Service Automation Using Machine learning

The abstract for a customer service automation using machine learning might focus on the application of machine learning techniques to enhance and streamline customer service processes. It could highlight how these technologies aim to improve response accuracy, speed, and personalization by leveraging algorithms for sentiment analysis, natural language processing, and automated decision-making. The abstract could also discuss the potential benefits of such automation, including increased efficiency, cost-effectiveness, and improved customer satisfaction. This research paper explores the integration of machine learning techniques in automating customer service processes. As businesses strive to enhance efficiency and customer satisfaction, the application of machine learning algorithms in handling customer queries, sentiment analysis, and personalized interactions has gained significant traction. This study investigates the utilization of natural language processing (NLP) models, sentiment analysis, recommendation systems, and chatbots to streamline and personalize customer service experiences. Various machine learning approaches are examined for their effectiveness in handling diverse customer inquiries across different industries. Moreover, the challenges, ethical considerations, and future prospects of employing machine learning for customer service automation are also discussed. The findings demonstrate the potential of machine learning-driven automation in revolutionizing customer service, paving the way for more efficient, responsive, and tailored customer interactions.