Artificial Intelligence Approach Research Articles

Predicting Airbnb pricing: a comparative analysis of artificial intelligence and traditional approaches

Airbnb's distinctive model accommodates a broad spectrum of hosts, ranging from non-professionals to traditional establishments, resulting in a nuanced pricing system that poses challenges for prediction. This paper delves into Airbnb pricing and, to address this exercise, introduces computational approaches that combine traditional linear methods and advanced artificial intelligence techniques. Utilizing real data from the Netherlands, the study specifically focuses on all active Airbnb listings until September 2019. Our contribution stands out by incorporating the financial history of diverse rental offerings, a novel aspect compared to existing literature. Unlike other studies, our investigation spans various cities in the Netherlands, providing a comprehensive view beyond individual regions. Our results highlight the robust predictive capabilities of Artificial Intelligence techniques, that lead to lower susceptibility to overfitting and to superior overall performances. However, traditional methods also prove to be valuable, aiding in determining the significance of predictors in the predicting exercise. Collectively, these approaches offer valuable insights to enhance pricing strategies and overall performances within the Airbnb market.

Developing predictive models for HIV Drug resistance: A genomic and AI approach

 This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With the increasing prevalence of drug-resistant strains of HIV, there is a critical need for innovative strategies to predict and manage resistance mutations, thereby optimizing treatment outcomes and prolonging the efficacy of antiretroviral therapy (ART). Drawing on advances in genomics and AI, this study outlines a conceptual framework for the development of predictive models that can identify potential drug-resistance mutations in HIV genomes and inform clinical decision-making. The proposed framework integrates genomic data from HIV-infected individuals with AI algorithms capable of learning complex patterns within the data. By analyzing genomic sequences obtained from HIV-positive patients, the models aim to identify genetic variations associated with drug resistance, predict the likelihood of resistance development, and guide the selection of appropriate treatment regimens. This approach holds promise for personalized medicine in HIV care, enabling clinicians to tailor therapy based on an individual's genetic profile and risk of resistance. Key components of the conceptual framework include data preprocessing to extract relevant genomic features, model training using machine learning techniques such as deep learning and ensemble methods, and validation of predictive performance through cross-validation and independent testing. Furthermore, the integration of clinical data, such as treatment history and viral load measurements, enhances the predictive accuracy of the models and provides valuable insights into treatment response dynamics.The development of predictive models for HIV drug resistance represents a paradigm shift in HIV care, offering a proactive approach to treatment management and surveillance. By leveraging genomic and AI technologies, healthcare providers can anticipate and address emerging resistance mutations before they compromise treatment efficacy. Ultimately, the implementation of predictive models holds the potential to improve patient outcomes, reduce the transmission of drug-resistant HIV strains, and advance the global fight against HIV/AIDS. Keywords:  Developing, Predictive Models, HIV Drug Resistance, Genomic, AI Approach.

Employment of Generative Artificial Intelligence in Classroom Environments to Improve Financial Education in Secondary School Students

Financial education is considered an essential skill that enables students to effectively manage their economic resources. However, it is still at an embryonic stage in several countries, and the ability of young people to apply financial education in life contexts has not substantially improved. To address these challenges, this study proposes and evaluates a novel approach to improve financial education among high school students by using generative artificial intelligence tools. Following a quasi-experimental design, we randomly assigned a total of 110 high school students to two conditions: an experimental group that participated in learning experiences under the financial education approach using artificial intelligence tools such as ChatGPT and a control group that engaged in the same learning activities following the traditional teaching approach. The results of the Mann-Whitney U test indicate that there are significant differences between the scores of the experimental group and the control group (p=1.64E-19<0.05,  group experimental=82.92>  group control=28.08), demonstrating the effectiveness of the generative artificial intelligence approach in enhancing financial education compared to the traditional approach. Furthermore, the Kruskal-Wallis test revealed a significance p-value of less than 0.05 (p=0.000935<0.05), indicating that the use of AI significantly improves the five indicators of financial education according to the post-test evaluation phase of the experimental group. On the other hand, Dunn's test for multiple comparisons reveals a significantly greater influence of the innovative approach using artificial intelligence in the following dimensions: Financial Planning Actions, Financial Analysis Actions, Financial Behavior, and Strategic Expense Management; however, the Investment Initiative dimension shows a significantly lesser influence ( =103.46). The implementation of artificial intelligence in the classroom to promote student learning is favored by innovative approaches to pedagogical action. In this way, from the classroom, we can address the lack of skills and financial education in our students.     Received: 11 December 2023 / Accepted: 19 March 2024 / Published: 5 May 2024

Enhancing Control Over Nitric Oxide Photorelease via a Molecular Keypad Lock.

Based on Boolean logic, molecular keypad locks secure molecular information, typically with an optical output. Here we investigate a rare example of a molecular keypad lock with a chemical output. To this end, the light-activated release of biologically important nitric oxide from a ruthenium complex is studied, using proton concentration and photon flux as inputs. In a pH-dependent equilibrium, a nitritoruthenium(II) complex is turned into a nitrosylruthenium(II) complex, which releases nitric oxide under irradiation with visible light. The precise prediction of the output nitric oxide concentration as function of the pH and photon flux is achieved with an artificial intelligence approach, namely the adaptive neuro-fuzzy inference system. In this manner an exceptionally high level of control over the output concentration is obtained. Moreover, the provided concept to lock a chemical output as well as the output prediction may be applied to other (photo)release schemes.

Co‐designing enduring learning analytics prediction and support tools in undergraduate biology courses

AbstractEven highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co‐redesign methods in partnership with disciplinary experts to create high‐structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory‐ and context‐relevant labels to reflect active and self‐regulated learning processes involving LMS‐hosted course materials, formative assessments, and help‐seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory‐relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.Practitioner notesWhat is already known about this topic Learning analytics includes an evolving collection of methods for tracing and understanding student learning through their engagements with learning technologies. Prediction models based on demographic data can perpetuate systemic biases. Prediction models based on behavioural event data can produce accurate predictions of academic success, and validation efforts can enrich those data to reflect students' self‐regulated learning processes within learning tasks. What this paper adds Learning analytics can be successfully applied to predict performance in an authentic postsecondary STEM context, and the use of context and theory as guides for feature engineering can ensure sustained predictive accuracy upon reapplication. The consistent types of learning resources and cyclical nature of their provisioning from lesson to lesson are hallmarks of high‐structure active learning designs that are known to benefit learners. These designs also provide opportunities for observing and modelling contextually grounded, theory‐aligned and temporally positioned learning events that informed prediction models that accurately classified students upon initial and later reapplications in subsequent semesters. Co‐design relationships where researchers and instructors work together toward pedagogical implementation and course instrumentation are essential to developing unique insights for feature engineering and producing explainable artificial intelligence approaches to predictive modelling. Implications for practice and/or policy High‐structure course designs can scaffold student engagement with course materials to make learning more effective and products of feature engineering more explainable. Learning analytics initiatives can avoid perpetuation of systemic biases when methods prioritize theory‐informed behavioural data that reflect learning processes, sensitivity to instructional context and development of explainable predictors of success rather than relying on students' demographic characteristics as predictors. Prioritizing behaviours as predictors improves explainability in ways that can inform the redesign of courses and design of learning supports, which further informs the refinement of learning theories and their applications.

Abstract GS03-04: Novel Mechanisms of CDH1 Inactivation in Breast Invasive Lobular Carcinoma Unveiled by the Integration of Artificial Intelligence and Genomics

Abstract Background: Invasive lobular carcinoma (ILC) of the breast is the second most common histologic subtype of breast cancer (BC), following invasive ductal carcinoma of no special type (IDC-NST). The hallmark histologic feature of ILC is cellular discohesiveness, the result of bi-allelic inactivation of CDH1, and represents an important genotypic-phenotypic correlation in BC. Although most ILCs harbor CDH1 loss-of function mutations associated to loss-of-heterozygosity (LOH) of the wild type-allele, a subset of ILCs lack these alterations despite displaying a typical lobular phenotype. Here, we sought to identify alternative molecular mechanisms converging on CDH1 inactivation by employing an integrative artificial intelligence (AI) and genomics approach. Materials and Methods: A genomics-driven AI-based algorithm using hematoxylin and eosin (H&E) whole slide images (WSIs) as input, previously developed to detect bi-allelic CDH1 mutations (inactivating mutation associated to LOH) in BC was employed. WSIs of 1,057 BCs including ILCs (n=187) and non-lobular BCs (n=870) previously subjected to FDA-cleared tumor/normal targeted sequencing were subjected to analysis with the AI-based algorithm. Cases predicted to harbor CDH1 bi-allelic mutations by the AI-model but lacking CDH1 bi-allelic mutations by targeted sequencing were assessed through targeted sequencing data re-analysis, CDH1 gene promoter methylation evaluation and/or whole genome sequencing analysis. Results: AI-based analysis WSIs corresponding to 1,057 BCs resulted in the identification of 34 cases found to lack CDH1 bi-allelic mutations by targeted sequencing but predicted to harbor these genetic alterations by the AI-based model. CDH1 gene promoter methylation assessment revealed CDH1 promoter methylation in 18 cases. Targeted sequencing data reanalysis revealed other genetic mechanisms of CDH1 inactivation including CDH1 homozygous deletions (n=3), intragenic deletion with LOH (n=1), and likely pathogenic non-coding CDH1 alterations associated with LOH (n=2). WGS analysis of an ILC revealed a novel deleterious CDH1 fusion stemming from translocation t(13;16), resulting in loss of the 5’UTR, transcription start site and exons 1 and 2 of CDH1, associated with complete loss of E-cadherin protein expression. Taken together, we identified alternative/novel mechanisms of bi-allelic CDH1inactivation in 74% (25/34) cases analyzed. Conclusions: By applying an AI-based algorithm trained to detect a genetic alteration (i.e., CDH1 bi-allelic mutations), we were able to identify alternative epigenetic and genetic molecular mechanisms of CDH1 inactivation in ILCs, including novel non-coding CDH1 genetic alterations and a new inactivating CDH1 fusion gene. These findings indicate that molecular mechanisms affecting a single gene or process converging on the same phenotype can be unveiled by the integration of AI and genomics, highlighting the robustness of this approach for the discovery of novel biology. Citation Format: Fresia Pareja, Higinio Dopeso, Yikan Wang, Andrea Gazzo, David Brown, Pier Selenica, Jan Bernhard, Fatemeh Derakhshan, Edaise M. da Silva, Lorraine Colon-Cartagena, Thais Basili, Antonio Marra, Jillian Sue, Qiqi Ye, Arnaud Da Cruz Paula, Selma Yeni, Xin Pei, Hunter Green, Kaitlyn Gill, Yingjie Zhu, Matthew Lee, Ran Godrich, Adam Casson, Britta Weigelt, Nadeem Riaz, Hanna Y Wen, Edi Brogi, Matthew Hanna, Diana Mandelker, Jeremy Kunz, Brandon Rothrock, Sarat Chandarlapaty, Christopher Kanan, Gerard Oakley III, David Klimstra, Thomas Fuchs, Jorge Reis-Filho. Novel Mechanisms of CDH1 Inactivation in Breast Invasive Lobular Carcinoma Unveiled by the Integration of Artificial Intelligence and Genomics [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr GS03-04.

ANALYSIS OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON BIG DATA ANALYSIS

The combination of Big Data and Artificial Intelligence (AI) has become a revolutionary force in the era of unparalleled data creation. The volume, pace, and variety of data have grown exponentially, pushing the boundaries of standard analytic techniques. This paper investigates the ways in which artificial intelligence (AI) is transforming big data analysis and enabling its application in a variety of sectors. We examine the particular contributions made by AI approaches and explore the advantages, difficulties, and potential future directions of this revolutionary partnership. In-depth tables complement this analysis to give a more thorough grasp of AI's effects.

Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study

Efficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study. The study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression. Among the 2810 participants (mean Age=68.5±9.3 years, average Follow-up=5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index=0.72, 95% CI: 0.70-0.74), outperforming that of the known risk factors (c-index=0.59, 95% CI 0.57-0.61). This proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer. The Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).

Verified localization and pharmacognosy of herbal medicinal plants in a combined network framework

Pharmacognosy from medicinal plants involves the scientific domain of medicinal compounding based on their medicinal properties. Accurate identification of medicinal plants is crucial, especially by examining their leaves. Choosing the wrong plant species for medicinal preparations can have adverse side effects. This study presents a Human-Centered Artificial Intelligence approach for medicinal plant identification, combining a YOLOv7-based Leaf Localizer with a leaf Class Verifier based on DenseNet through a Confidence Score Analyser algorithm. The Confidence Score Analyser ensures reliability by evaluating predicted categories against predefined thresholds, and the ensemble technique through majority voting enhances robustness. An average performance gain of 25.66% sensitivity is observed when comparing the YOLO object detection model with 77.45% precision to the YOLO integrated with the class verifier model with 97.33% precision. Consistent sensitivities are achieved through the ensemble technique, showcasing robustness across diverse scenarios. The final step incorporates automated textual and audio pharmacognosy information about the identified medicinal plant properties and their utility. Real-time applicability as a smart phone application makes this approach invaluable for medicinal plant collectors and experts.

Healthcare Virtual Assistant using Generative AI

Abstract: Documentation is a clumsy task for the front- desk staff. They've to fill in patient data, schedule appointments, and attend to patient queries. Even healthcare providers must enter EHR data, which takes a lot of time, and they end up spending lesser time with their patients. In the evolving landscape of healthcare administration, the burden of administrative tasks continues to challenge healthcare professionals and institutions. However, with generative AI, doctors can produce clones of patient data and automate form- filling tasks. It can also be integrated with EHR for documentation work. This project introduces a result — a generative artificial intelligence (AI) system designed to ease administrative burdens and enhance operating efficiency. By employing the power of AI, this web- grounded platform aims to streamline administrative processes, automate routine tasks, and facilitate absolute communication between patients, medical staff, and administrative personnel. The proposed website leverages advanced generative AI approaches to tackle crucial administrative challenges. Through natural language processing (NLP) and image analysis, the system interprets unstructured data such as medical notes, images, and documents. It enables automated documentation generation, medical coding and billing, appointment scheduling, and patient inquiries through a responsive and user-friendly interface

Recent developments in the diagnosis, treatment, and management of cardiovascular diseases through artificial intelligence and other innovative approaches

Recent developments in the diagnosis, treatment, and management of cardiovascular diseases through artificial intelligence and other innovative approaches

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.

Neuroimage analysis using artificial intelligence approaches: a systematic review.

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.

Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning

The movement of organic anionic drugs across cell membranes is partly governed by interactions with SLC and ABC transporters in the intestine, liver, kidney, blood–brain barrier, placenta, breast, and other tissues. Major transporters involved include organic anion transporters (OATs, SLC22 family), organic anion transporting polypeptides (OATPs, SLCO family), and multidrug resistance proteins (MRPs, ABCC family). However, the sets of molecular properties of drugs that are necessary for interactions with OATs (OAT1, OAT3) vs. OATPs (OATP1B1, OATP1B3) vs. MRPs (MRP2, MRP4) are not well-understood. Defining these molecular properties is necessary for a better understanding of drug and metabolite handling across the gut–liver–kidney axis, gut–brain axis, and other multi-organ axes. It is also useful for tissue targeting of small molecule drugs and predicting drug–drug interactions and drug–metabolite interactions. Here, we curated a database of drugs shown to interact with these transporters in vitro and used chemoinformatic approaches to describe their molecular properties. We then sought to define sets of molecular properties that distinguish drugs interacting with OATs, OATPs, and MRPs in binary classifications using machine learning and artificial intelligence approaches. We identified sets of key molecular properties (e.g., rotatable bond count, lipophilicity, number of ringed structures) for classifying OATs vs. MRPs and OATs vs. OATPs. However, sets of molecular properties differentiating OATP vs. MRP substrates were less evident, as drugs interacting with MRP2 and MRP4 do not form a tight group owing to differing hydrophobicity and molecular complexity for interactions with the two transporters. If the results also hold for endogenous metabolites, they may deepen our knowledge of organ crosstalk, as described in the Remote Sensing and Signaling Theory. The results also provide a molecular basis for understanding how small organic molecules differentially interact with OATs, OATPs, and MRPs.

Validating predictions of burial mounds with field data: the promise and reality of machine learning

PurposeThis paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.Design/methodology/approachAutomated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.FindingsValidation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.Research limitations/implicationsOur attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.Practical implicationsImproving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.Social implicationsOur literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.Originality/valueUnlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

An Artificial Intelligence Approach to Monitor and Predict Student Academic Performance

Recent progress in technology has altered the learning behaviours of students. It can easily be said that the improvements in technologies especially from the area of artificial intelligence and data mining can empower students to learn more efficiently, effectively, and contentedly. The purpose of this paper is aimed at developing a prediction model that gives a guide to the stakeholders likes university, student’s sponsor and parents that can monitor and interpret the on-going student’s performance. The proposed system will help the user to identify students who have a high risk or low risk to ending the semester with unsatisfactory results through discovering the essential features that can influence student’s academic performance. The methodology used were mixed method approach which is qualitative and quantitative. For the part of quantitative technique, the data set for this research will be collected from the academic records section. Meanwhile, the part of qualitative will go through a few main stages which are focused on developmental phase. The prediction model used in this research are RepTree, k-NN and Naïve Bayes. The finding states that RepTree had the highest accuracy compared to k-NN and Naïve Bayes techniques. It also suggests that the RepTree algorithm would be the best prediction model which has focused the research in term of developing the system of monitoring students’ academic performance at UPTM.

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{82}$$\\end{document}Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model (p<0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<0.001$$\\end{document}), where the best AUC of 73.9% (CI 72.5–75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3–74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.

The role of biomarkers in the early detection of pancreatic cancer.

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Modeling the depressive mind: An artificial intelligence approach to deciphering Beck’s cognitive triad

Mental and cognitive well-being is of paramount significance for human beings. Consequently, the early detection of issues that may culminate in conditions such as depression holds great importance in averting adverse outcomes for individuals. Depression, a prevalent mental health disorder, can severely impact an individual’s quality of life. Timely identification and intervention are critical to prevent its progression. Our research delves into the application of Machine Learning (ML) and Deep Learning (DL) techniques to potentially facilitate the early recognition of depressive tendencies. By leveraging the cognitive triad theory, which encapsulates negative self-perception, a pessimistic outlook on the world, and a bleak vision of the future, we aim to develop predictive models that can assist in identifying individuals at risk. In this regard, we selected The Cognitive Triad Dataset, which takes into account six different categories that encapsulate negative and positive postures about three different contexts: self context, future context and world context. Our proposal achieved great performance, by relying on a strict preprocessing analysis, which led to the models obtaining an accuracy value of 0.97 when classifying aspect contexts; 0.95 when classifying sentiment-aspects; and a value of 0.93 in accuracy was achieved under the aspect-sentiment paradigm. Our models outperformed those reported in the literature.

СУПУТНИКОВИЙ МОНІТОРИНГУ ОЦІНКИ СТАНУ ЗАБРУДНЕННЯ ВОДНИХ ОБ’ЄКТІВ НА ОСНОВІ МАШИННОГО НАВЧАННЯ

Satellite monitoring is a reliable tool for obtaining operational information about the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availability of archival data becomes a significant advantage, opening up the possibility of comparing the obtained results and analyzing factors affecting the state of water bodies and the environment. The paper describes the main methods and datasets that can be used to apply remote monitoring methods based on machine learning. The use of medium spatial resolution data is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of water pollution in large areas are analyzed. Possible machine learning methods for solving an applied problem are presented, the new artificial intelligence (AI) approach of convolutional long-short-term memory (ConvLSTM) model significantly outperforms classical methods.