Intelligent Expert Research Articles

DigFrag as a digital fragmentation method used for artificial intelligence-based drug design.

Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures. The rapid development of Artificial Intelligence (AI) technology offers a transformative opportunity to rethink traditional methods. Here, we present DigFrag, a digital fragmentation method that highlights important substructures by focusing locally within the molecular graph. In addition, we feed the fragments segmented by machine intelligence and human expertise into the deep generative model to compare the preference for data from different sources. Experimental results show that the structural diversity of fragments segmented by DigFrag is higher, and more desirable compounds are generated based on these fragments. These results also demonstrate that data generated based on AI methods may be more suitable for AI models. Moreover, a user-friendly platform called MolFrag ( https://dpai.ccnu.edu.cn/MolFrag/ ) is developed based on various fragmentation techniques to support molecular segmentation.

Experts' Cognition-Driven Ensemble Deep Learning for External Validation of Predicting Pathological Complete Response to Neoadjuvant Chemotherapy from Histological Images in Breast Cancer

In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard treatment option for patients who have locally advanced cancer and some large operable tumors. A patient will have better prognosis when he has achieved a pathological complete response (pCR) with the treatment of NAC. There has been a trend to directly predict pCR to NAC from histological images based on deep learning (DL). However, the DL-based predictive models numerically have better performances in internal validation than in external validation. In this paper, we aim to alleviate this situation with an intrinsic approach. We propose an experts’ cognition-driven ensemble deep learning (ECDEDL) approach. Taking the cognition of both pathology and artificial intelligence experts into consideration to improve the generalization of the predictive model to the external validation, ECDEDL can intrinsically approximate the working paradigm of a human being which will refer to his various working experiences to make decisions. ECDEDL was validated with 695 WSIs collected from the same center as the primary dataset to develop the predictive model and perform the internal validation, and was also validated with 340 WSIs collected from other three centers as the external dataset to perform the external validation. In external validation, ECDEDL improves the AUCs of pCR prediction from 61.52(59.80-63.26) to 67.75(66.74-68.80) and the Accuracies of pCR prediction from 56.09(49.39-62.79) to 71.01(69.44-72.58). ECDEDL was quite effective for external validation of predicting pCR to NAC from histological images in breast cancer, numerically approximating the internal validation.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

The Impact of Applying Artificial Intelligence on Human Resources Management in Jordanian Banks

The study aims to investigate artificial intelligence (expert systems, knowledge representation and reasoning, automatic learning and effectiveness) on human resources management in Jordanian banks. The descriptive analytical method was used by developing a digital questionnaire that was distributed to a purposive sample of the study population, which includes employees of directors of human resources departments and workers in the human resources department. To answer the study’s questions and hypotheses, the data was analyzed using the SPSS program. It was found that artificial intelligence variables (expert systems, knowledge representation and reasoning, automatic learning, and effectiveness) are associated with a positive, statistically significant relationship with training and development. ? It was also found that artificial intelligence variables (expert systems, knowledge representation and reasoning, automatic learning, and effectiveness) are associated with a positive, statistically significant relationship with selection and appointment. Finally, the results concluded that artificial intelligence explains a large portion of the variance in incentives, which indicates a positive and statistically significant relationship. The study recommended that the expanding the use of artificial intelligence techniques in ?human ?resources ?management?, and conduct more future studies about the relationship between the use of artificial intelligence technologies and other human resources practices such as: (performance evaluation, wages and compensation, talent management). Received: 11 July 2024 / Accepted: 31 October 2024 / Published: 05 November 2024

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.

Dystocia, Delivery, and Artificial Intelligence in Labor Management: Perspectives and Future Directions.

Labor management remains a critical issue in obstetrics, with dystocic labor presenting significant challenges in both management and outcomes. Recent advancements in intrapartum ultrasound have facilitated substantial progress in monitoring labor progression. This paper explores the integration of artificial intelligence (AI) into obstetric care, focusing on the Artificial Intelligence Dystocia Algorithm (AIDA) for assessing spatial dystocia during labor. The AIDA utilizes intrapartum ultrasonography to measure four geometric parameters: the angle of progression, the degree of asynclitism, the head-symphysis distance, and the midline angle. These measurements are analyzed using machine learning techniques to predict delivery outcomes and stratify risk. The AIDA classification system categorizes labor events into five classes, providing a nuanced assessment of labor progression. This approach offers several potential advantages, including objective assessment of fetal position, earlier detection of malpositions, and improved risk stratification, placing labor events within a broader context of labor dystocia and obstetric care and discussing their potential impact on clinical practice. This paper serves as a more comprehensive overview and discussion of the AIDA approach, its implications, perspectives, and future directions. However, challenges such as the technological requirements, training needs, and integration with clinical workflows are also addressed. This study emphasizes the necessity for additional validation across diverse populations and careful consideration of its ethical implications. The AIDA represents a significant advancement in applying AI to intrapartum care, potentially enhancing clinical decision-making and improving outcomes in cases of suspected dystocia. This paper explicates the key methodological approaches underpinning the AIDA, illustrating the integration of artificial intelligence and clinical expertise. The innovative framework presented offers a paradigm for similar endeavors in other medical specialties, potentially catalyzing advancements in AI-assisted healthcare beyond obstetrics.

Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network

With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems.

Artificial Intelligence: A Twenty First Century International Regulatory Challenge

Artificial Intelligence (AI) is a twenty first century evolution. Certain aspects of AI have been integrated into daily living. AI applications have also been incorporated into the aviation, banking, cyber security, educational, employment, health, and military sectors respectively. However, the unpredictable nature of AI is a cause for concern because ‘In many instances, AI remains under the control of users and designers, but in increasing numbers of applications, the behaviour of a system cannot be predicted by those involved in design and application […]. Newly developed machines are able to teach themselves and even collect data’. Consequently, ‘The potential benefits and harms of AI have led to calls for governments to adapt quickly to the changes AI is already delivering and the potentially transformative changes to come. These include calls to pause AI development and for countries […] to deliver a step-change in regulation’. ‘In March 2023, more than 1,000 artificial intelligence experts, researchers and backers signed an open letter calling for an immediate pause on the creation of “giant” AIs for at least six months, so the capabilities and dangers of such systems can be properly studied and mitigated’. What are the benefits of AI? What are the risks of AI? Which crimes can be committed via AI? What are the regulatory challenges? What has been the international response? In this article, we will explore whether there is a justification for regulating AI from ethical, legal and law enforcement perspectives. Keywords: Artificial intelligence; Ethics; Regulation; Law enforcement

Highlights of the 15th Japan Bioanalysis Forum Symposium.

The 15th Japan Bioanalysis Forum (JBF) Symposium was held in Kyoto, Japan, between 5 and 7 February 2024. The conference theme, 'Toward the new world - Science as a universal endeavor' indicated that universal discussion based on science is making a basis of regulatory and analytical sciences, now internationally harmonized. The symposium discussed a wide range of topics, including ICH M10, quantitative PCR, immunogenicity, peptide LC-MS, e-notebooks, artificial intelligence, reliability standards, carrier development, inviting domestic and overseas experts. Approximately 360 attendees from various fields, including pharmaceutical companies, contract research organizations, academia and regulators, gathered in person or online.

AI2 – Where Appreciative Inquiry and Artificial Intelligence Meet

The year 2022 marked a turning point in the world of Artificial Intelligence. Several powerful tools based on generative language models were introduced, making Artificial Intelligence technology a lot easier to use. This issue includes articles written by a diverse group of contributors, some experienced Appreciative Inquiry practitioners, others experts in Artificial Intelligence. They explore the topic from different angles, covering what is already possible, what might be possible in the future and what we should keep in mind when bringing in this powerful technology into our practice.

Assessing ChatGPT's Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study.

ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. The aim of this study was to evaluate ChatGPT's competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. We developed a model termed PanelGPT to assess ChatGPT's competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.

Accelerating photoacoustic microscopy by reconstructing undersampled images using diffusion models

Photoacoustic Microscopy (PAM) integrates optical and acoustic imaging, offering enhanced penetration depth for detecting optical-absorbing components in tissues. Nonetheless, challenges arise in scanning large areas with high spatial resolution. With speed limitations imposed by laser pulse repetition rates, the potential role of computational methods is highlighted in accelerating PAM imaging. We propose a novel and highly adaptable algorithm named DiffPam that utilizes diffusion models to speed up the photoacoustic imaging process. We leveraged a diffusion model trained exclusively on natural images, comparing its performance with an in-domain trained U-Net model using a dataset focused on PAM images of mice brain microvasculature. Our findings indicate that DiffPam performs similarly to a dedicated U-Net model without needing a large dataset. We demonstrate that scanning can be accelerated fivefold with limited information loss. We achieved a 24.70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$24.70\\%$$\\end{document} increase in peak signal-to-noise ratio and a 27.54%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$27.54\\%$$\\end{document} increase in structural similarity index compared to the baseline bilinear interpolation method. The study also introduces the efficacy of shortened diffusion processes for reducing computing time without compromising accuracy. DiffPam stands out from existing methods as it does not require supervised training or detailed parameter optimization typically needed for other unsupervised methods. This study underscores the significance of DiffPam as a practical algorithm for reconstructing undersampled PAM images, particularly for researchers with limited artificial intelligence expertise and computational resources.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.

Breast tumor classification using adam and optuna model optimization based on CNN architecture

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.

Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data

Credit scoring plays an important role in determining the accessibility of credit in the financial sector. This in turn has a significant impact on how economic opportunities are distributed. Our study examines the use of AI and alternative data in fintech lending through the lens of Information Asymmetry Theory. By employing a qualitative research design using the Gioia method, we extract, analyze, and synthesize insights from a diverse group of 26 experts in fintech lending, artificial intelligence, machine learning, data science, and academia. Our results reveal several important findings: the enhancement of predictive proficiency and risk management, the decrease in default rates, the extension of credit access by including previously ‘unbanked populations’, the introduction of real-time creditworthiness assessment and new business models for entrepreneurs, the enhancement of credit market efficiencies and positive effects on the stability of financial markets. In addition, our study highlights the necessity for rigorous and critical ethical considerations of important challenges such as the question of consent, algorithmic transparency, data quality, data misuse, representativeness, traceability, responsibility, bias and discrimination. The reasonable goal of a more fair, resilient, sustainable and accessible credit system will require a joint effort to balance leveraging technological innovations with respecting peoples' right to privacy.

Research Frontiers in the Field of Agricultural Resources and the Environment

From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the “interaction mechanism of plant–rhizosphere–microbial diversity” was a hot research frontier in the years 2016–2021. The themes of “wastewater treatment technology and efficient utilization of water resources”, the “value-added utilization of agricultural wastes and sustainable development”, the “soil ecological response mechanism under agronomic management measures”, and the “mechanism of soil landslide, erosion, degradation and prediction evaluation” were judged as potential research frontiers. The theme of “ecosystems management and pollution control of agricultural and animal husbandry” was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.

Research, analytics and intelligence in issues and crises

AbstractToday, corporate crises inherently pose a threat to brand reputation. Multiple stakeholders, online influence and the interconnectedness of reputation with major social issues raise the stakes for crisis teams. The level of complexity has perhaps never been so great, and with it, the need for data‐driven decision‐making. In practice, research, analytics, and intelligence experts are full members of corporate risk and crisis teams from planning, to response, to evaluation. These professionals develop the insights to fuel smart, timely decision‐making by surfacing insights to ameliorate crises. Additionally, they are responsible for measuring and evaluating progress and performance improving the brand's readiness for future issues. Insights, intelligence and performance measures drive conclusions necessary to determine what, if any, brand damage has occurred, how to fix it, and how to prepare better for the next time.

Diagnostic accuracy of artificial intelligence versus dental experts in predicting endodontic outcomes: A systematic review

Abstract Introduction: In the rapidly evolving landscape of health care, artificial intelligence (AI) has emerged as a promising tool to enhance diagnostic accuracy across various medical disciplines. Within the realm of dentistry, one critical area of focus is endodontics, which involves the diagnosis and treatment of dental pulp diseases. This systematic review investigates the diagnostic precision of AI in contrast to dental experts when predicting endodontic outcomes. Materials and Methods: The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review was registered on the Open Science Framework database. A systematic search was performed of the literature on the application of AI versus dental experts in endodontics. The search was conducted on PubMed, Scopus, Web of Science, and OVID until December 2022. The QUADAS-2 tool was used to evaluate the risk of bias. Results: The initial search retrieved 972 records, of which eight articles were included in the study. The studies reported application of AI for working length determination, periapical lesions, pulp and tooth segmentation, apical periodontitis, vertical root fracture, and C-shaped canals. QUADAS-2 tool revealed a low risk of bias in five out of the eight included studies. Conclusion: AI models demonstrated a notable increase in accuracy and effectiveness in endodontic diagnosis and treatment planning. These results suggest that the integration of AI technology in endodontic diagnosis and treatment planning has immense potential to serve as a promising aid.

Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

Social4Fashion: An intelligent expert system for forecasting fashion trends from social media contents

The fashion field is continually expanding and evolving, and social media play a significant role in shaping current fashion trends through the influence of online personalities, such as influencers. As a result, fashion designers often turn to social media to gain insights into the latest trends and draw inspiration, while in the past they used to physically visit fashion districts. To automate and speed up this process, an expert system is much needed; thus, Social4Fashion has been created, an end-to-end framework that leverages deep learning-based techniques in order to support creatives in their research and decision-making process, with the final goal of analyzing and predicting trends. This system employs several steps, starting with the automatic data collection from Instagram, using hashtags provided by domain experts. Next, retrieved images are filtered to remove non-fashion related pictures, leaving only those pertaining to the fashion area for further processing. Then, to obtain more specific information about the images, the handbags present (if any) are detected and classified, based on their type; finally, dominant colors of the handbags are retrieved through clustering on the images. All the data collected with this system are then stored and analyzed via user-friendly dashboards, created with the objective of highlighting relevant information, in order to perform analysis on current and future fashion trends. Results show the effectiveness of the proposed system, with an accuracy of 97% (95% confidence interval 0.95–1) for the fashion image classification and a mAP of 0.77 (95% confidence interval 0.73–0.82) for the handbag detection, which makes it suitable for fashion domain analysis. Also, as a result of this work, a novel fashion-related dataset has been made available to the research community. This system can greatly improve the way fashion trends are analyzed, and allow for more efficient and effective design processes in the future.