Applied AI letters最新文献

{"title":"Benford's Law in Basic RNN and Long Short-Term Memory and Their Associations","authors":"Farshad Ghassemi Toosi","doi":"10.1002/ail2.70002","DOIUrl":"https://doi.org/10.1002/ail2.70002","url":null,"abstract":"<p>Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term memory (LSTM). Additionally, this study investigates whether models with higher performance exhibit a stronger presence of DBLE. Two neural network language models, namely: (1) simple RNN and (2) LSTM, were selected as the subject models for the experiment. Each model is tested with five different optimizers and four different datasets (textual corpora selected from Wikipedia). This results in a total of 20 different configurations for each model. The neuron weights for each configuration were extracted at each epoch, and the following metrics were measured at each epoch: (1) DBLE, (2) training set accuracy, (3) training set error, (4) test set accuracy, and (5) test set error. The results show that the weights in both models, across all optimizers, follow Benford's Law. Additionally, the findings indicate a strong correlation between DBLE and the performance on the training set in both language models. This means that models with higher performance on the training set exhibit a stronger correlation of DBLE.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing AI in Business and Entrepreneurship: Implications for Complex Decision-Making in Engineering and Product Development Settings","authors":"Nnamdi Gabriel Okafor,&nbsp;Patrick J. Murphy","doi":"10.1002/ail2.70001","DOIUrl":"https://doi.org/10.1002/ail2.70001","url":null,"abstract":"<p>Artificial intelligence (AI) is rapidly transforming decision-making in business and entrepreneurship, with particularly significant implications for engineering and product development. This paper reviews existing literature and theoretical models to elucidate AI's role in strategic decision-making, while also identifying critical gaps in current research. To gain a comprehensive perspective, we employed a mixed-methods approach comprising surveys of 105 industry professionals and semi-structured interviews with key stakeholders. Our findings indicate that, although AI integration improves operational efficiency and enhances strategic insights, challenges related to data privacy, ethical concerns, and workforce training persist. These results underscore the need for balanced human–AI collaboration and robust governance frameworks to fully realize AI's potential in complex decision-making environments.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time Variant Node Ranking Technique for Chatbot Neural Graph","authors":"Ahmed Imtiaz,&nbsp;A. F. M. Zainul Abadin,&nbsp;Md. Harun Or Rashid","doi":"10.1002/ail2.70003","DOIUrl":"https://doi.org/10.1002/ail2.70003","url":null,"abstract":"<p>This study seeks to put repetitiveness characteristics into AI. Closer ties between AI and human psychology can enhance the implementation of chatbots. Repetitiveness is a common characteristic of human behavior. Repetitiveness indicates which node is updated frequently and its importance. A chatbot needs to solve a situation regarding how quickly it will access its neural memory to retrieve information. Thus, the ranking of nodes in a neural network is necessary to allocate them to the chatbot's memory. The proposed ranking methodology takes affinity, number of edges, adjacency, average weight, and update time interval parameters into account to calculate the ranked value of each node. After that, a ranking tree is generated. This tree is finally considered the memory navigation path in that neural graph. If a node updates regularly with each clock pulse, which resembles a repetitive task, then its ranked value increases. This node should get preference over other low-ranked nodes. This study provides an approach to convert a neural graph into a ranking tree and a path to navigate through it. Thus, the chatbot can identify which node is more promising and has a shorter path than other nodes for information retrieval.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision Transformer-Enhanced Multi-Descriptor Approach for Robust Age-Invariant Face Recognition","authors":"Justice Kwame Appati,&nbsp;Emmanuel Tsifokor,&nbsp;Daniel Kwame Amissah,&nbsp;David Ebo Adjepon-Yamoah","doi":"10.1002/ail2.70000","DOIUrl":"https://doi.org/10.1002/ail2.70000","url":null,"abstract":"<p>This study presents a robust age-invariant face recognition framework, addressing challenges posed by age-related facial variations. Evaluated on the FGNet and Morph II datasets, the system integrates Viola-Jones for face detection, SIFT and LBP for feature extraction, and Vision Transformers (ViTs) for global feature representation. Feature fusion and dimensionality reduction (KPCA, IPCA, UMAP) enhance efficiency while retaining key discriminative information. Using Random Forest, KNN, and XGBoost classifiers, the model achieves 96% accuracy, demonstrating the effectiveness of combining traditional and deep learning techniques in advancing age-invariant face recognition.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Reinforcement Learning Agents for Autonomous Cyber Defence","authors":"Abby Morris,&nbsp;Rachael Procter,&nbsp;Caroline Wallbank","doi":"10.1002/ail2.125","DOIUrl":"https://doi.org/10.1002/ail2.125","url":null,"abstract":"<p>Artificial Intelligence (AI) is set to become an essential tool for defending against machine-speed attacks on increasingly connected cyber networks and systems. It will allow self-defending and self-recovering cyber-defence agents to be developed, which can respond to attacks in a timely manner. But how can these agents be trusted to perform as expected, and how can they be evaluated responsibly and thoroughly? To answer these questions, a Test and Evaluation (T&amp;E) process has been developed to assess cyber-defence agents. The process evaluates the performance, effectiveness, resilience, and generalizability of agents in both low- and high-fidelity cyber environments. This paper demonstrates the low-fidelity part of the process by performing an example evaluation in the Cyber Operations Research Gym (CybORG) environment on Reinforcement Learning (RL) agents trained as part of Cyber Autonomy Gym for Experimentation (CAGE) Challenge 2. The process makes use of novel Measures of Effectiveness (MoE) metrics, which can be used in combination with performance metrics such as the RL reward. MoE are tailored for cyber defence, allowing a greater understanding of agents' defensive abilities within a cyber environment. Agents are evaluated against multiple conditions that perturb the environment to investigate their robustness to scenarios not seen during training. The results from this evaluation process will help inform decisions around the benefits and risks of integrating autonomous agents into existing or future cyber systems.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model-Based Deep-Learning Approach to Reconstructing the Highly Articulated Flight Kinematics of Bats","authors":"Yihao Hu,&nbsp;Chi Nnoka,&nbsp;Rolf Müller","doi":"10.1002/ail2.126","DOIUrl":"https://doi.org/10.1002/ail2.126","url":null,"abstract":"<p>Bats are capable of highly dexterous flight maneuvers that rely heavily on highly articulated hand skeletons and malleable wing membranes. To understand the underlying mechanisms, large amounts of detailed data on bat flight kinematics are required. Conventional methods to obtain these data have been based on tracing landmarks and require substantial manual effort. To generate 3D reconstructions of the entire geometry of a flying bat in a fully automated fashion, the current work has developed an approach where the pose of a trainable articulated mesh template that is based on the bat's anatomy is optimized to fit a set of binary silhouettes representing views from different directions of the flying bat. This is followed by post-processing to smooth the reconstructed kinematics and simulate the non-rigid motion of the wing membranes. To evaluate the method, 10 flight sequences that represent several flight maneuvers (e.g., straight flight, takeoff, u-turn) and were recorded in a flight tunnel instrumented with 50 synchronized cameras have been reconstructed. A total of 4975 reconstructions are generated in this fashion and subject to qualitative and quantitative evaluations with promising results. The reconstructions are to be used for quantitative analyses of the maneuvering kinematics and the associated aerodynamics.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChemQuery: A Natural Language Query-Driven Service for Comprehensive Exploration of Chemistry Patent Literature","authors":"Shubham Gupta,&nbsp;Rafael Teixeira de Lima,&nbsp;Lokesh Mishra,&nbsp;Cesar Berrospi,&nbsp;Panagiotis Vagenas,&nbsp;Nikolaos Livathinos,&nbsp;Christoph Auer,&nbsp;Michele Dolfi,&nbsp;Peter Staar","doi":"10.1002/ail2.124","DOIUrl":"https://doi.org/10.1002/ail2.124","url":null,"abstract":"<p>Patents are integral to our shared scientific knowledge, requiring companies and inventors to stay informed about them to conduct research, find licensing opportunities, and manage legal risks. However, the rising rate of filings has made this task increasingly challenging over the years. To address this issue, we introduce <span>ChemQuery</span>, a tool for easily exploring chemistry-related patents using natural language questions. Traditional systems rely on simplistic keyword-based searches to find patents that <i>might be</i> relevant to a user's request. In contrast, <span>ChemQuery</span> uses up-to-date information to return specific answers, along with their sources. It also offers a more comprehensive search experience to the users, thanks to capabilities like extracting molecules from diagrams, integrating information from PubChem, and allowing complex queries about molecular structures. We conduct a thorough empirical evaluation of <span>ChemQuery</span> and compare it with several baseline approaches. The results highlight the practical utility and limitations of our tool.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an Intelligent Resume Screening Tool With AI-Driven Analysis and Recommendation Features","authors":"K. L. Abhishek,&nbsp;M. Niranjanamurthy,&nbsp;Shonit Aric,&nbsp;Syed Immamul Ansarullah,&nbsp;Anurag Sinha,&nbsp;G. Tejani,&nbsp;Mohd Asif Shah","doi":"10.1002/ail2.116","DOIUrl":"https://doi.org/10.1002/ail2.116","url":null,"abstract":"<p>Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face challenges in identifying qualified candidates due to oversight and time constraints. Inconsistent evaluation criteria hinder decision-making. These issues result in prolonged hiring processes, missed opportunities, and potential bias in candidate selection. The goal of this project is to develop an AI-powered Resume Analysis and Recommendation Tool, catering to the trend of recruiters spending less than 2 min on each CV. The tool will rapidly analyze all resume components while providing personalized predictions and recommendations to applicants for improving their CVs. It will present user-friendly data for recruiters, facilitating export to CSV for integration into their recruitment processes. Additionally, the tool will offer insights and analytics on popular roles and skills within the job market. Its user section will enable applicants to continually test and track their resumes, encouraging repeat usage and driving traffic. Colleges can benefit from gaining insights into students' resumes before placements. Overall, this AI-powered tool aims to enhance the resume evaluation process, benefiting both job seekers and employers. The primary aim of this project is to develop a Resume Analyzer using Python, incorporating advanced libraries such as Pyresparser, NLTK (Natural Language Toolkit), and MySQL. This automated system offers an efficient solution for parsing, analyzing, and extracting essential information from resumes. The user-friendly interface, developed using Streamlit, allows for seamless resume uploading, insightful data visualization, and analytics. The Resume Analyzer significantly streamlines the resume screening process, providing recruiters with valuable insights and enhancing their decision-making capabilities.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Few-Shot Learning Approach for a Multilingual Agro-Information Question Answering System","authors":"Fiskani Ella Banda,&nbsp;Vukosi Marivate,&nbsp;Joyce Nakatumba-Nabende","doi":"10.1002/ail2.122","DOIUrl":"https://doi.org/10.1002/ail2.122","url":null,"abstract":"<p>Across numerous households in Sub-Saharan Africa, agriculture plays a crucial role. One solution that can effectively bridge the support gap for farmers in the local community is a question–answer system based on agricultural expertise and agro-information. The more recent advancements in question answering research involve the use of large language models that are trained on an extensive amount of data. Due to this, conventional fine-tuning approaches have demonstrated a significant decline in performance when using a significantly smaller amount of data. One proposed alternative to address this decline is to use prompt-based fine-tuning, which allows the model to be fine-tuned with only a few examples, thus addressing the disparities between the objectives of pretraining and fine-tuning. Extensive research has been done on these methods, specifically on text classification and not question answering. In this research, our objective was to study the feasibility of recent few-shot learning approaches such as FewshotQA and Null-prompting for domain-specific agricultural data in four South African languages. We first explored creating a cross-lingual domain-specific extractive question answering dataset through an automated approach using the GPT model. Through exploratory data analysis, the GPT model was able to create a dataset, which requires minor improvements. We then evaluated the overall performance of the different approaches and investigated the effects of adapting these approaches to suit the new dataset. Results show these methods effectively capture semantic relationships and domain-specific terminology but exhibit limitations, including potential biases in automated annotation and plateauing F1 scores. This highlights the need for hybrid approaches that combine artificial intelligence and human supervision. Beyond academic insights, this study has practical significance for industry, demonstrating how prompt-based methods can help tailor AI models to specific use cases in low-resource settings.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical Recommendations for Artificial Intelligence and Machine Learning in Antimicrobial Stewardship for Africa","authors":"Tafadzwa Dzinamarira,&nbsp;Elliot Mbunge,&nbsp;Claire Steiner,&nbsp;Enos Moyo,&nbsp;Adewale Akinjeji,&nbsp;Kaunda Yamba,&nbsp;Loveday Mwila,&nbsp;Claude Mambo Muvunyi","doi":"10.1002/ail2.123","DOIUrl":"https://doi.org/10.1002/ail2.123","url":null,"abstract":"<p>The challenge of antimicrobial resistance (AMR) represents one of the most pressing global health crises, particularly, in resource-constrained settings like Africa. In this paper, we explore artificial intelligence (AI) and machine learning (ML) potential in transforming the potential for antimicrobial stewardship (AMS) to improve precision, efficiency, and effectiveness of antibiotic use. The deployment of AI-driven solutions presents unprecedented opportunities for optimizing treatment regimens, predicting resistance patterns, and improving clinical workflows. However, successfully integrating these technologies into Africa's health systems faces considerable obstacles, including limited human capacity and expertise, widespread public distrust, insufficient funding, inadequate infrastructure, fragmented data sources, and weak regulatory and policy enforcement. To harness the full potential of AI and ML in AMS, there is a need to first address these foundational barriers. Capacity-building initiatives are essential to equip healthcare professionals with the skills needed to leverage AI technologies effectively. Public trust must be cultivated through community engagement and transparent communication about the benefits and limitations of AI. Furthermore, technological solutions should be tailored to the unique constraints of resource-limited settings, with a focus on developing low-computational, explainable models that can operate with minimal infrastructure. Financial investment is critical to scaling successful pilot projects and integrating them into national health systems. Effective policy development is equally essential to establishing regulatory frameworks that ensure data security, algorithmic fairness, and ethical AI use. This comprehensive approach will not only improve the deployment of AI systems but also address the underlying issues that exacerbate AMR, such as unauthorized antibiotic sales and inadequate enforcement of guidelines. To effectively and sustainably combat AMR, a concerted effort involving governments, health organizations, communities, and technology developers is essential. Through collaborations and sharing a common goal, we can build resilient and effective AMS programs in Africa.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}