An automated machine learning approach for predicting chemical laboratory material consumption

This paper address a relevant business analytics need of a chemical company, which is adopting an Industry 4.0 transformation. In this company, quality tests are executed at the Analytical Laboratories (AL), which receive production samples and execute several instrumen- tal analyses. In order to improve the AL stock warehouse management, a Machine Learning (ML) project was developed, aiming to estimate the AL materials consumption based on week plans of sample analy- ses. Following the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, several iterations were executed, in which three input variable selection strategies and two sets of AL materials (top 10 and all consumed materials) were tested. To reduce the mod- eling effort, an Automated Machine Learning (AutoML) was adopted, allowing to automatically set the best ML model among six distinct re- gression algorithms. Using real data from the chemical company and a realistic rolling window evaluation, several ML train and test iterations were executed. The AutoML results were compared with two time series forecasting methods, the ARIMA methodology and a deep learning Long Short-Term Memory (LSTM) model. Overall, competitive results were achieved by the best AutoML models, particularly for the top 10 set of materials.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Arabic Educational Neural Network Chatbot

Chatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers all questions about educational regulations in the United Arab Emirates

Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients

Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India

Automated Reading Passage Generation with OpenAI's Large Language Model

The widespread usage of computer-based assessments and individualized learning platforms has resulted in an increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts at each step of the process. AIG has been used in test development for some time. Still, the use of machine learning algorithms has introduced the potential to improve the efficiency and effectiveness of the process greatly. The approach presented in this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. For each prompt, we generated multiple passages, the final passage was selected according to the Lexile score agreement with the original passage. In the final round, the selected passage went through a simple revision by a human editor to ensure the text was free of any grammatical and factual errors. All AI-generated passages, along with original passages were evaluated by human judges according to their coherence, appropriateness to fourth graders, and readability

Authorship attribution of late 19th century novels using GAN-BERT

Authorship attribution aims to identify the author of an anonymous text. The task becomes even more worthwhile when it comes to literary works. For example, pen names were commonly used by female authors in the 19th century resulting in some literary works being incorrectly attributed or claimed. With this motivation, we collated a dataset of late 19th century novels in English. Due to the imbalance in the dataset and the unavailability of enough data per author, we employed the GANBERT model along with data sampling strategies to fine-tune a transformer-based model for authorship attribution. Differently from the earlier studies on the GAN-BERT model, we conducted transfer learning on comparatively smaller author subsets to train more focused author-specific models yielding performance over 0.88 accuracy and F1 scores. Furthermore, we observed that increasing the sample size has a negative impact on the model’s performance. Our research mainly contributes to the ongoing authorship attribution research using GAN-BERT architecture, especially in attributing disputed novelists in the late 19th century

SOHBET BOTLARI VE YAPAY ZEKÂ ETİĞİ

Tezsiz Yüksek Lisans Bitirme ProjesiSon yıllarda meydana gelen teknolojik gelişmeler sonucu bizlere sunulan dijital dönüşümler, etkili strateji ve planlama ile rakip markalardan sıyrılabilmek için kritik bir öneme sahiptir. Sohbet robotları dijital çağla birlikte ürün, hizmet ve markaların müşteri ile etkin iletişiminde popüler uygulamalardan biri olarak karşımıza çıkmaktadır. Sohbet botları yapay zeka çalışmalarının bir ürünü olarak ortaya çıkmıştır. Yapay zeka, günlük dijital teknolojik sistemlere gömülü olduğu için bugün zaten yaygın bir etkiye sahip ve vaatleri ve çekicilikleri yakın gelecekte bu etkiyi artıracak gibi görünüyor. Yapay zekanın birçok faydası olması muhtemel olsa da bazıları iyi bilinen çeşitli etik konular da gündeme gelmiştir. Bu konuların çoğu yalnızca bireysel düzeyde ortaya çıkmamakta, aynı zamanda toplumlardaki ve ekonomilerdeki dönüşümleri de ilgilendirmektedir. Bu, özellikle makinelerin görevleri insanlardan devralmasını sağlayan yapay zeka destekli otomasyonda geçerlidir. Bu çalışmada sohbet botları ve yapay zeka etiği kavramı incelenmiştir

Bias and Fairness in Chatbots: An Overview

Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed

Gamificación de un chatbot educacional

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: Inmaculada Rodríguez Santiago[en] Gamification, a strategy that involves applying game elements and techniques in nongame contexts, has gained popularity in various fields, including education. Implementing gamification in a chatbot designed to provide learning support offers numerous benefits and aims to increase students’ motivation and engagement in their learning process. In this final degree project, a gamified system is designed and implemented for the chatbot LovelaceUB [11]. This chatbot is a support tool for programming students, to whom they can ask questions about different subjects and request programming exercises that, once they have solved them, they can ask the chatbot to correct. To implement the gamified system, the types of players that exist have been studied, and the gamification has been designed using the Gamification Model Canvas template [24], where, among other things, the game mechanics by which the chatbot will be governed are defined, such as receiving points when the chatbot corrects an exercise. Finally, this system has been implemented in Python for a Telegram chatbot

Efficient Network Domination for Life Science Applications

With the ever-increasing size of data available to researchers, traditional methods of analysis often cannot scale to match problems being studied. Often only a subset of variables may be utilized or studied further, motivating the need of techniques that can prioritize variable selection. This dissertation describes the development and application of graph theoretic techniques, particularly the notion of domination, for this purpose. In the first part of this dissertation, algorithms for vertex prioritization in the field of network controllability are studied. Here, the number of solutions to which a vertex belongs is used to classify said vertex and determine its suitability in controlling a network. Novel efficient scalable algorithms are developed and analyzed. Empirical tests demonstrate the improvement of these algorithms over those already established in the literature. The second part of this dissertation concerns the prioritization of genes for loss-of-function allele studies in mice. The International Mouse Phenotyping Consortium leads the initiative to develop a loss-of-function allele for each protein coding gene in the mouse genome. Only a small proportion of untested genes can be selected for further study. To address the need to prioritize genes, a generalizable data science strategy is developed. This strategy models genes as a gene-similarity graph, and from it selects subset that will be further characterized. Empirical tests demonstrate the method’s utility over that of pseudorandom selection and less computationally demanding methods. Finally, part three addresses the important task of preprocessing in the context of noisy public health data. Many public health databases have been developed to collect, curate, and store a variety of environmental measurements. Idiosyncrasies in these measurements, however, introduce noise to data found in these databases in several ways including missing, incorrect, outlying, and incompatible data. Beyond noisy data, multiple measurements of similar variables can introduce problems of multicollinearity. Domination is again employed in a novel graph method to handle autocorrelation. Empirical results using the Public Health Exposome dataset are reported. Together these three parts demonstrate the utility of subset selection via domination when applied to a multitude of data sources from a variety of disciplines in the life sciences