Search Function Research Articles

A Comprehensive Exploration of Text, Web, Social Media, and Geospatial Analytics for Informed Decision Making

Text Analytics is the process of turning unstructured text data into useful information for analysis, to gauge consumer opinions, feedback, and product evaluations. It also provides search functionality, entity modelling, and emotional analysis to enable fact-based decision making. Analysis of website visitor behaviour is done using Web Analytics. The number of websites and users on the internet is growing daily. It is the process of tracking website information that can help to improve the web application features and evaluate the behaviour of users. By performing social media analytics on social media for example twitter, Instagram, Facebook. Data such as likes, comments, shares and saves can be obtained and analyse to know how the society think about the product. Geospatial analysis is used to make visualizations that include maps, graphs, statistics that show data according to geographic location. This is important to be analysed as it tells which area or country has the highest product sold or lowest subscription of the service. To make the data much easier for human brain to analyses or to make a conclusion, data visualization design is the process of putting all data information collected into a visual context. For instance, graph or map. The main objectives of data visualization are to make it simpler or easier to spot the outliers and patterns trends in big data sets. There are a variety of clustering processes or techniques available to arrange the data efficiently to its related data. The clustering process that is used in data mining is presented in this work.

A Series of Suicides and Homicides by Cyanide in Türkiye: Exploring the Role of Media Reports and the Copycat Effect

Objectives: This paper focuses on the intentional deaths by cyanide that occurred in Turkey and aims to discuss the effect of internet and the media on these cases. Methods: Five highly circulated daily newspapers were selected and the term “siyanür”, which means “cyanide” in Turkish, were searched within their online search function between 2017-2021. A Google Trends analysis was performed with “siyanür+siyanur” as the keyword, “Turkey” as the region, and “1/1/2017-12/31/2021” as the custom time range. Results: Twenty different incidents and news reports of cyanide related suicides and/or homicides with 29 total deaths were found. In Google Trends analysis, seven out of total 8 periods of increased search interest coincided with the news reports related to cyanide deaths. Conclusions: The authors observed a surge in cyanide-related incidents following a highly publicized case in May 2019, with a peak in November 2019. The study highlights the influence of media coverage on subsequent incidents, with newspapers often using sensational headlines and providing detailed information on suicide methods. Reporting a news article on suicide requires great care and caution. Thus, the guidelines on reporting suicide related news should be implemented nationwide with a collaboration between media professionals and health-care experts.

“What I Wish I Knew”: Clinically Relevant Concerns Women Express on Social Media About Oocyte Cryopreservation [ID 2683486

INTRODUCTION: This is the first study to assess the clinically relevant understandings, beliefs, and concerns expressed about oocyte cryopreservation in spontaneous and unfiltered social media platforms as diverse as Reddit and TikTok. METHODS: We selected Reddit with its daily 56 million users to represent those aged 18–49 years and TikTok with its daily 1.6 billion users to represent those under 30 years. Search terms “Egg Freezing" and “Egg Freezing Support” as well as the subreddit thread “r/EggFreezing” were used on Reddit; search functions of “egg freezing” and “egg freezing journey” were used on TikTok. Exclusion criteria included having less than 10 relevant comments, non-U.S. account, embryo freezing, sponsored posts, and medical advice from non-patient medical professionals. Thematic saturation was achieved when 10 consecutive comments were reached with no new themes. Institutional review board approval was obtained, deemed as exempt. RESULTS: A total of 4,021 entries were evaluated, 355 were excluded, and 189 posts and 3,477 comments were included. Focusing on the 1,330 entries with clinically relevant themes, we found that the most common were “Recommended Age” (26%), “Out of Pocket Costs” (25%), “Side Effects” (18%), “What Can/Can’t I Do Before/During Egg Freezing” (18%), and “Number of Eggs Retrieved” (13%). Good examples addressing issues include “the hormonal roller coaster, the nightmare no one warns you about” and “I wish more doctors were more upfront about this.” CONCLUSION: Social media can be used to demonstrate educational gaps. In particular, physicians should be aware of unanswered questions women have about age and behaviors needed to maximize success in cryopreservation.

AdmetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370000 high-quality experimental ADMET data for 104652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

Search Engine for Open Geospatial Consortium Web Services Improving Discoverability through Natural Language Processing-Based Processing and Ranking

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques.

ANNAPURNA CONNECT

In today's increasingly digital landscape, the agricultural sector is primed for transformation through the development of a Full-Stack Website dedicated to a Farmers' Marketplace. This initiative aims to create an integrated online platform connecting farmers, producers, and consumers, streamlining the exchange of agricultural products. With a focus on both front-end and back-end development, the website will offer a user-friendly interface with features such as product listings, search functionality, user profiles, reviews, and a simple checkout process. Meanwhile, the robust back-end infrastructure ensures data management, security, and scalability. By leveraging technology to bridge the gap between farmers and consumers, the Farmers' Marketplace Full-Stack Website has the potential to revolutionize agricultural trade, fostering sustainability and efficiency in the process. Keyword: Digital Marketplace, Mern stack, Farmers and buyers.

Implementation of Levenshtein Distance Algorithm for Product Search Query Suggestions on Koro Pedang Edutourism E-Commerce

Users sometimes write queries that are inaccurate or typos in the product search contained in the Koro Pedang Educational Tourism e-commerce, so the system is not find product search results because the query entered in the system is incorrect. This can frustrate users because they cannot find the product they are looking for, so the users leave the website. According to these problems, it is necessary to suggest a query on the product search function. This is expected to assist users in finding the product they are looking for if there is an error in typing the query. This research purposes were to implement the Levenshtein Distance Algorithm for product search query suggestions on Koro Pedang Educational Tourism e-commerce. The stages of this research, namely the development of the search module, implementation of the Levenshtein Distance Algorithm and testing. The implementation of the Levenshtein Distance Algorithm in the search function for Koro Pedang Educational Tourism e-commerce products, a Suggestion Query is generated for Query typos in the search function with an accuracy value of 90%, Precision 95% and Recall 90.9%. This shows that the performance of the algorithm that has been applied to the search function for query suggestion is very good. The application of the Levenshtein Distance Algorithm gives a positive value to the usability of searching for e-commerce products for Koro Pedang Educational Tourism.

Spatial data infrastructure components to provide regional climate information services

The assessment of expected climate changes on a regional scale requires appropriate data and information products that must be easily accessible and usable for decision makers. The regional climate information platform ReKIS is a long-established project of three German federal states to address this purpose. However, with increasing content, this web-based data exchange platform lacked in suitable metadata descriptions, search functionalities and interoperability, thus hampering the discovery and access of suitable information. This paper describes the extending and enhancing of ReKIS using state-of-the-art components of Spatial Data Infrastructures (SDI). The components support metadata acquisition, maintenance and publishing using a metadata catalogue. The implementation of a WebGIS server increases interoperability by offering OGC-compliant services and a web framework for knowledge-transfer allows for non-expert access to climate services. The approaches were developed and implemented in a real-world scenario and are suitable for a transfer to other comparable platforms and use cases.

RIPM Jazz Periodicals

RIPM Jazz Periodicals, a collaborative effort between the Institute of Jazz Studies (IJS) at Rutgers University and RIPM Partner and Participating Libraries, offers a unique collection of historical primary source jazz periodicals. With a focus on rare and hard-to-find content, the database has grown from 105 to 138 titles, spanning 350,000 citation records and 204,000 full-text pages. The product’s strength lies in exclusive content and the readability and quality of its scans. However, user experience challenges arise from the complexity of search functions. Despite minor drawbacks, continuous updates and plans for additional content highlight the product’s commitment to improvement. While there are no direct competitors, related products in the RIPM series cover different historical periods and geographic regions.

Validity of Major Osteoporotic Fracture Diagnoses in the Danish National Patient Registry.

To evaluate the validity of diagnosis codes for Major Osteoporotic Fracture (MOF) in the Danish National Patient Registry (NPR) and secondly to evaluate whether the fracture was incident/acute using register-based definitions including date criteria and procedural codes. We identified a random sample of 2400 records with a diagnosis code for a MOF in the NPR with dates in the year of 2018. Diagnoses were coded with the 10th revision of the International Classification of Diseases (ICD-10). The sample included 2375 unique fracture patients from the Region of Southern Denmark. Medical records were retrieved for the study population and reviewed by an algorithmic search function and medical doctors to verify the MOF diagnoses. Register-based definitions of incident/acute MOF was evaluated in NPR data by applying date criteria and procedural codes. The PPV for MOF diagnoses overall was 0.99 (95% CI: 0.98;0.99) and PPV=0.99 for the four individual fracture sites, respectively. Further, analyses of incident/acute fractures applying date criteria, procedural codes and using patients' first contact in the NPR resulted in PPV=0.88 (95% CI: 0.84;0.91) for hip fractures, PPV=0.78 (95% CI: 0.74;0.83) for humerus fractures, PPV=0.78 (95% CI: 0.73;0.83) for clinical vertebral fractures and PPV=0.87 (95% CI: 0.83;0.90) for wrist fractures. ICD-10 coded MOF diagnoses are valid in the NPR. Furthermore, a set of register-based criteria can be applied to qualify if the MOF fracture was incident/acute. Thus, the NPR is a valuable and reliable data source for epidemiological research on osteoporotic fractures.

Open-source large language models in action: A bioinformatics chatbot for PRIDE database.

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo-ranking system-based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM-based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open-source (https://github.com/PRIDE-Archive/pride-chatbot).

HealthCare education Platform

Our health education platform is designed to provide reliable health information and resources in a user-friendly manner. It features a responsive web design using modern technologies for seamless browsing. The backend utilizes Django or Express.js for robust logic and database handling, with PostgreSQL or MongoDB for efficient data storage. Elasticsearch or Solr powers fast and accurate search functionality. Secure authentication and authorization are ensured through JWT or OAuth. Comments and feedback are managed with moderation tools and notifications. Performance is optimized with monitoring tools, caching, and CDN integration. Scalability is achieved through cloud infrastructure and load balancing. Comprehensive security measures include HTTPS encryption and regular audits. The platform allows users to explore a wide range of health-related content, including articles, blogs, and expert opinions, with personalized content suggestions. By offering a comprehensive and user-friendly platform, we aim to empower individuals to make informed decisions about their health and well-being.

AI Study Partner : Development of an LLM and Gen AI-Enhanced Study Assistant Tool

In recent years, Generative AI has started to play a pivotal role in transforming the educational landscape, making learning more personalized, engaging, and accessible. Unlike traditional educational tools, Gen AI can adapt to each student's unique learning style and pace, offering customized support that can significantly enhance understanding and retention of information. It enables the creation of intelligent tutoring systems, interactive study aids, and personalized learning experiences that can assess and respond to individual needs in real time This paper introduces the AI Study Partner, an innovative tool designed to revolutionize the educational landscape by integrating Generative Artificial Intelligence (Gen AI) and Large Language Models (LLMs). The AI Study Partner is engineered to cater to the diverse needs of learners by offering a suite of six key features: the ability to upload and interact with any type of content, summarization of extensive lessons, generation of flashcards for effective study, automated question creation with auto-evaluation capabilities, a conversational chatbot assistant, and an advanced smart search function. These features collectively aim to create a more personalized, engaging, and efficient learning experience. The development of the AI Study Partner responds to the pressing need for educational tools that accommodate the varying paces and styles of learning, making education more accessible and effective. By leveraging the latest advancements in AI, the tool not only facilitates a deeper understanding of complex subjects but also encourages independent study habits and critical thinking skills. This research outlines the conceptual framework, design methodology, and technical implementation of the AI Study Partner, highlighting its potential to positively impact education by providing a versatile and interactive learning platform.The AI Study Partner represents a significant step forward in the pursuit of creating adaptive, responsive, and personalized educational experiences for learners worldwide. Key Words : Artificial Intelligence in Education, Personalized Learning , Large Language Models (LLMs), Generative AI (Gen AI), User Engagement in Learning Data-Driven Education, Vector database, AI based Feedback Conversational Chatbots, Smart Search.

Grocery Shop Application

The grocery shop app is a user-friendly mobile application designed to streamline the grocery shopping experience. With a clean and intuitive interface, users can easily browse a wide selection of products, add items to their virtual cart, and conveniently place orders for home delivery or in-store pickup. The app features personalized recommendations based on past purchases, real-time inventory updates, and a robust search function for efficient product discovery. Users can create and manage shopping lists, take advantage of exclusive deals and discounts, and enjoy a seamless checkout process with various payment options. The app aims to enhance the overall convenience and efficiency of grocery shopping, providing users with a modern and hassle-free way to meet their daily food and household needs. KEYWORDS - Fresh,Convenient, Variety, Quality, Affordable, Fresh produce, Organic options,Dairy products, Household essentials

Analysis of user experience during interaction with automotive repair workshop websites

The study presented here focuses on analyzing and comparing the user experience of two automotive repair workshop website search engines. The research tools used were usability tests, user interviews and eye tracking. These were aimed at assessing the functionality, intuitiveness and overall user experience when using the analyzed sites. The results show that users encountered a variety of problems with navigation and search functions, and made suggestions for improving and expanding the website functions. Nevertheless, despite some differences in the evaluations of specific aspects of both services, there was little difference in the effectiveness of the set scenarios.

An Investigation into Antecedents of Customer Satisfaction and Loyalty with Reference to Online Purchasing Behavior

Customer satisfaction and loyalty in the context of online purchasing behavior are influenced by various antecedents, which encompass a wide array of factors ranging from website usability to post-purchase services. Understanding these antecedents is crucial for businesses aiming to thrive in the competitive landscape of e-commerce. The primary antecedents of customer satisfaction in online shopping are website usability. Factors such as intuitive interface design, clear navigation menus, and efficient search functionalities contribute to a positive user experience, leading to higher levels of satisfaction. The study concluded that action and loyalty in online purchasing behavior are influenced by a myriad of antecedents, including website usability, product information, security, and customer service quality, delivery services, perceived value, trust, personalized experiences, and loyalty programs. By understanding and addressing these antecedents effectively, businesses can enhance the overall shopping experience, cultivate loyalty, and drive sustainable growth in the dynamic landscape of e-commerce.

Object Detection in E-Commerce Using YOLO in Real Time

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.

A Novel Deep Learning-Based Visual Search Engine in Digital Marketing for Tourism E-Commerce Platforms

Visual search technology, because of its convenience and high efficiency, is widely used by major tourism e-commerce platforms in product search functions. This study introduces an innovative visual search engine model, namely CLIP-ItP, aiming to thoroughly explore the application potential of visual search in tourism e-commerce. The model is an extension of the CLIP (contrastive language-image pre-training) framework and is developed through three pivotal stages. Firstly, by training an image feature extractor and a linear model, the visual search engine labels images, establishing an experimental visual search engine. Secondly, CLIP-ItP jointly trains multiple text and image encoders, facilitating the integration of multimodal data, including product image labels, categories, names, and attributes. Finally, leveraging user-uploaded images and jointly selected product attributes, CLIP-ItP provides personalized top-k product recommendations.

Advanced Techniques for Geospatial Referencing in Online Media Repositories

In the digital transformation era, video media libraries’ untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Mediathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study’s findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications.