Learning Methodology Research Articles

Using active learning methodologies to teach sequence analysis and molecular phylogeny.

The great development of high-throughput molecular biology techniques and the consequent generation of massive data have made Bioinformatics essential for undergraduate Bioscience students. The importance of this scientific discipline is evidenced by the huge number of specialized publications, tools, and databases available. Training in Bioinformatics equips undergraduates with transferable skills that can be applied in all fields of Biology, such as programming abilities, data analysis, database management, biological knowledge, statistics, problem solving, and interdisciplinary collaboration. Over the past decade, there has been a notable increase in the number of higher education institutions worldwide that have adopted a competency-based curricula. This approach places a significant emphasis on the actions and skills that students are expected to develop, rather than merely focusing on the information, they are required to memorize. In this educational context, the use of active learning strategies has been demonstrated to enhance student comprehension and competency development. This paper describes the implementation of an active learning approach in a hands-on lesson performed by undergraduate students of Biology at the University of Malaga (Spain). Its main objective is to introduce students to molecular databases and information search systems on genes, proteins, and phylogeny. This is achieved within the framework of a smart campus, which integrates technological and sustainable resources to promote a positive and productive learning environment for the university community. This work presents the content and procedure of this practical activity, as well as the evaluation method and the results of a student survey to assess their opinions.

Automated lepidopteran pest developmental stages classification via transfer learning framework.

The maize crop is highly susceptible to damage caused by its primary pests, which poses considerable challenges in manually identifying and controlling them at various larval developmental stages. To mitigate this issue, we propose an automated classification system aimed at identifying the different larval developmental stages of 23 instars of 4 major lepidopteran pests: the Asian corn borer, Ostrinia furnacalis (Guenée; Lepidoptera: Crambidae), the fall armyworm, Spodoptera frugiperda (J.E. Smith; Lepidoptera: Noctuidae), the oriental armyworm, Mythimna separata (Walker; Lepidoptera: Noctuidae), and the tobacco cutworm, Spodoptera litura (Fabricius; Lepidoptera: Noctuidae). Employing 5 distinct Convolutional Neural Network architectures-Convnext, Densenet121, Efficientnetv2, Mobilenet, and Resnet-we aimed to automate the process of identifying these larval developmental stages. Each model underwent fine-tuning using 2 different optimizers: stochastic gradient descent with momentum and adaptive moment estimation (Adam). Among the array of models tested, Densenet121, coupled with the Adam optimizer, exhibited the highest classification accuracy, achieving an impressive 96.65%. The configuration performed well in identifying the larval development stages of all 4 pests, with precision, recall, and F1 score evaluation indicators reaching 98.71%, 98.66%, and 98.66%, respectively. Notably, the model was ultimately tested in a natural field environment, demonstrating that Adam_Densenet121 model achieved an accuracy of 90% in identifying the 23 instars of the 4 pests. The application of transfer learning methodology showcased its effectiveness in automating the identification of larval developmental stages, underscoring promising implications for precision-integrated pest management strategies in agriculture.

Deep Analogical Generative Design and Evaluation: Integration of Stable Diffusion and LoRA

Abstract The rapid evolution of generative design through artificial intelligence has opened new avenues for innovative product styling. Integrating this efficient generative technology with established professional theories presents a novel challenge in contemporary international design research. In response to this challenge, this paper introduces a pioneering and collaborative approach for the swift generation of automobile styling designs. The primary objective is to investigate an intelligent generation method that incorporates analogical reasoning and Stable Diffusion to support industrial designers in innovating product styling. This study scrutinizes traditional analogical reasoning design alongside the intelligent analogical reasoning design proposed herein, elucidating the distinctions through multidimensional comparisons using illustrative examples. The proposed methodological framework encompasses several key steps. Initially, a dataset comprising branded automobile images is meticulously constructed. Subsequently, an exclusive style model is trained leveraging Stable Diffusion techniques, coupled with advanced computer graphics and machine learning methodologies. Following this, design requirements are inputted, facilitating intelligent analogical reasoning design across multiple spatial dimensions to yield diverse and innovative automobile styling solutions. Finally, eye-tracking experiments are conducted to quantitatively compare the traditional analogical reasoning design approach with the Stable Diffusion-based analogical reasoning design method. The results substantiate that the latter effectively generates innovative and diversified automobile design solutions. This research contributes to enhancing the quality of automobile styling design, optimizing the design efficiency of enterprises, and catalyzing innovation in the automobile styling design process.

Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

Prediction of electron-solid interaction parameters using machine learning.

Electron backscattering coefficient and electron-stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measurements, and simulations of electron interactions with matter, which contribute to advancing science, technology, and safety in a variety of applications. The availability of these data is fundamental to scientific research to validate hypotheses, conduct experiments, and explore new theories. A relatively novel machine learning approach has demonstrated notable success in enhancing data quality and completeness, significantly contributing to the facilitation of data discovery. Using fundamental material property data, the stacking ensemble machine learning (EML) technique was established in this study to generate electron-solid interaction parameters for any target material over a wide range of energies. The final stacking EML was built using the base and meta learners bagging regressor (BR), K-nearest neighbors (k-NN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGB). In this study, two publicly available databases with a total of 4030 data points were used. Training datasets have 785 and 525 data points for electron backscattering coefficient and stopping power, respectively, whereas testing datasets contain 262 and 175 data points. Fivefeatures were used as input variables to train different individual algorithms and their combinations. On both the training and test datasets, the model was evaluated using different error metrics, including R-squared (R2), mean-absolute-error (MAE), root-mean-squared-error (RMSE), and mean-absolute-percentage-error (MAPE). Our model evaluation tests revealed that combining RF and XGB with a k-NN meta-learner outperformed other algorithms. The analysis of error metrics demonstrated a very close fit to all samples in each training dataset. Furthermore, predictions made by the model on unseen test data indicated accurate estimations of new backscattering and stopping power data. The developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges.

Classroom-Based Experiential Learning: Experiences from Kosovo Primary Schools

Background: Studies show that key significant variables related to effectiveness of teaching and learning are students' attitudes towards learning, teacher competencies and teaching methodologies applied (Riera et. al, 2023; Glazier & Bean, 2019; Mulala, 2015; Amirtha & Jebaseelan, 2015). Purpose: To explore changes in primary student attitudes towards learning pre- and post-implementation of a curriculum based on experiential learning methodology. Furthermore, the study also measured specific pre-service teacher variables (perceived self-efficacy, motivation, and power) pre and post implementation of experiential learning methodology. Methodology/ Approach: The study followed a carefully designed intervention, using comparison groups pre/post study design to compare outcomes of the groups before and after the study. The study was conducted with a sample of 856 fifth-grade students, and 30 pre-service teachers. Findings/ Conlusions: Results indicated that there were significant increases in positive attitudes of students in intervention group classes. Results also indicate higher motivation for engagement in the teaching process of intervention group pre-service teachers compared to control group pre-service teachers and a significant increase in perceived power during classroom hours for intervention group pre-service teachers from the pre-test to the post-test phase. Implications: The implications of this research extend to other educational systems, as incorporating hands-on practical experiences in teacher education programs results in capable teachers committed to lifelong learning and creation of positive learning environments, and incorporating experiential learning methodologies can enhance student engagement, positive attitudes and motivation to learn and excel academically.

Effectiveness of different strategies to teach empathy among medical students: A randomized controlled study

ObjectivesTo explore the effectiveness of two educational strategies for teaching empathy to medical students, focusing on their skills and on the theoretical knowledge and self-perception of empathy by patients, professors, and observers. MethodsThis is a randomized controlled study. Clerkship students were divided into two groups: an active group - Role Play and Reflection Group(RRG), and a more traditional group - Lecture Presentation Group(LPG). They were assessed pre- and post-intervention using the Jefferson Empathy Scale(JSPE), ESWIM Empathy Scale, CARE, empathic skills through simulated patient encounters, and a theoretical knowledge test. ResultsA total of 169 students were included. The RRG had higher scores (d=0.81) and greater gains (d=0.82) in empathic skills than the LPG. However, there were no significant differences in knowledge or other scales. Upon assessing skill acquisition (Post–Pre gains), there was a significant increase in both groups for all outcomes, except for JSPE in the LPG group and ESWIM for both groups. ConclusionMedical empathy can be taught, and active learning methodologies can lead to better outcomes, especially on the ability to provide empathic care. Practice implicationsThese findings reinforce existing literature and helps medical schools make informed decisions regarding their teaching methodologies. Trial registryhttps://ensaiosclinicos.gov.br/rg/RBR-8rwx89c.

Integrating digital and interactive approaches in adolescent health literacy: a comprehensive review.

Adolescent health literacy is critical for navigating the complex landscape of modern healthcare and making informed decisions that influence long-term health outcomes. This comprehensive review synthesizes current research on integrating digital tools and interactive learning approaches to enhance health literacy among adolescents. We explore the use of digital technologies, such as mobile apps and virtual reality, which cater to the preferences of this tech-savvy generation, offering personalized and accessible health information. The effectiveness of interactive learning methodologies, including simulations and role-playing, is also examined, highlighting their potential to increase engagement and retention of health-related knowledge. We address the importance of mental health literacy and the role of social media and peer education in disseminating health information effectively. Our review identifies gaps in the current literature, particularly the need for studies that consider long-term outcomes and the impact of socioeconomic and cultural factors on health literacy initiatives. We propose a multidimensional approach to health literacy education that incorporates innovative technologies and interactive methods to meet the diverse needs of adolescents in various contexts. The findings suggest that an integrated approach, including digital and critical health literacy, is essential for developing comprehensive health education programs that are both informative and engaging for adolescents.

Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology

Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.

DETECTION OF ORAL SQUAMOUS CELL CARCINOMA USING PRE-TRAINED DEEP LEARNING MODELS.

Oral squamous cell carcinoma (OSCC), the 13th most common type of cancer, claimed 364,339 lives in 2020. Researchers have established a strong correlation between early detection and better prognosis for this type of cancer. Tissue biopsy, the most common diagnostic method used by doctors, is both expensive and time-consuming. The recent growth in using transfer learning methodologies to aid in medical diagnosis, along with the improved 5-year survival rate from early diagnosis serve as motivation for this study. The aim of the study was to evaluate an innovative approach using transfer learning of pre-trained classification models and convolutional neural networks (CNN) for the binary classification of OSCC from histopathological images. The dataset used for the experiments consisted of 5192 histopathological images in total. The following pre-trained deep learning models were used for feature extraction: ResNet-50, VGG16, and InceptionV3 along with a tuned CNN for classification. The proposed methodologies were evaluated against the current state of the art. A high sensitivity and its importance in the medical field were highlighted. All three models were used in experiments with different hyperparameters and tested on a set of 126 histopathological images. The highest-performance developed model achieved an accuracy of 0.90, a sensitivity of 0.97, and an AUC of 0.94. The visualization of the results was done using ROC curves and confusion matrices. The study further interprets the results obtained and concludes with suggestions for future research. The study successfully demonstrated the potential of using transfer learning-based methodologies in the medical field. The interpretation of the results suggests their practical viability and offers directions for future research aimed at improving diagnostic precision and serving as a reliable tool to physicians in the early diagnosis of cancer.

Parameter optimization for stable clustering using FlowSOM: a case study from CyTOF.

High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of CyTOF, we need advanced clustering and machine learning methodologies to enable automated gating of the complex data. Here we show that critical modifications to a machine learning based FlowSOM package and precise parameter optimization can enable us to reliably analyze the complex CyTOF data. We show the impact of key parameters on clustering outcomes while addressing bugs within the publicly available package. We modified the FlowSOM pipeline to fix the bugs, enable scalability to handle large datasets and perform parameter optimization. We further validated this modified pipeline on a substantial external immunological dataset demonstrating the need of data-specific tailored parameter optimization to ensure reliable definition and interrogation of immune cell populations associated with immune disorders.

Machine Learning Algorithm for Enhanced Cybersecurity: Identification and Mitigation of Emerging Threats

Machine learning (ML) methodologies have significantly transformed cybersecurity by offering sophisticated instruments that not only identify but also avert and alleviate cyber threats. This research study seeks to examine the convergence of machine learning and cybersecurity, focusing on diverse methodologies and their use in enhancing cybersecurity measures. The study examines several machines learning methods, including Graph Neural Networks, Adversarial Learning, Federated Learning, Explainable AI, and Reinforcement Learning. Every algorithm is essential for enhancing the identification and mitigation of cyber assaults. Graph Neural Networks facilitate the modelling of intricate linkages within cybersecurity data. It aids not just in forecasting future events but also in identifying anomalies and analyzing network traffic. Adversarial Learning assists in training machine learning models to address the difficulty of producing misleading input data that can deceive any model, hence enhancing their efficacy. Federated Learning is examined as a method for training machine learning models across decentralized networks while preserving data privacy and enhancing model accuracy. Explainable AI methodologies primarily offer transparency and interpretability in machine learning-driven cybersecurity decisions, which are crucial for comprehension and confidence in automated security systems. Reinforcement Learning is focused on a trial-and-error methodology, wherein the model acquires new tasks through a system of rewards and penalties. These sophisticated algorithms jointly improve the effectiveness, precision, and clarity of cybersecurity protocols, offering strong protection against emerging cyber threats.

Application of artificial intelligence for feature engineering in education sector and learning science

This study investigates the utilization of artificial intelligence (AI) for feature engineering in the education sector, highlighting its potential to enhance individualized learning and improve academic outcomes. The correlation analysis, performed using a correlation matrix of the feature set, indicated that specific pairings of characteristics exhibit a strong association, resulting in the ineffectiveness of conventional models. In order to tackle this issue, we utilized three sophisticated machine learning methodologies: Adaptive Lasso (ALasso), Artificial Neural Networks (ANN), and Support Vector Regression (SVR). The ALasso model discovered several influential characteristics, namely Gender (X5), Education (X1), Hours of Work (X4), and Marital Status (X6), that significantly affect salaries. Subsequently, a comparative evaluation of these methods was conducted using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results demonstrated that SVR outperformed the other techniques, with the most optimal RMSE of 0.595 and MAE of 0.423. These findings emphasize the significance of using data-driven strategies in policymaking and propose further investigation into the use of AI methods in various educational contexts to improve the identification of features and the performance of models.

Advanced Edge AI Navigation and Assistance System for Blind User

- Despite substantial advancements in visual assistance technology, many existing systems are limited by sensor capabilities, computational resources, and power consumption. Traditional computer vision algorithms struggle to perform complex tasks required for real-time navigation. This paper presents the design and implementation of an advanced computer vision-based navigation system specifically tailored for Blender users, aimed at facilitating independent navigation in diverse environments .By leveraging edge Artificial Intelligence (AI) and deep learning methodologies, the proposed system achieves real-time object detection, person recognition, and environmental awareness, addressing the critical challenges posed by conventional systems. The use of cost-effective, low-power mobile computing platforms, such as smart depth sensors like the OpenCV AI Kit-Depth (OAK-D), enables efficient processing while ensuring portability. This system not only enhances the ability to identify and navigate obstacles but also incorporates additional functionalities, including reading written notices aloud and responding to traffic signals. Key design considerations, such as training data collection, computational efficiency, and portability, have been meticulously addressed to ensure reliable performance in real-world scenarios. The incorporation of an AI-driven voice interface facilitates user-friendly interaction, making this system an innovative and unobtrusive visual assistance device.

Improving learning experience through implementing standardized team-based learning process in undergraduate medical education

IntroductionTeam-Based Learning (TBL) is a highly structured active learning methodology shown to have positive learning outcomes in undergraduate medical education (UME). While many medical schools have increasingly adopted TBL, there is considerable variation reported in the literature in the implementation of the standard sequence and key components of TBL. The extent to which this variation affects the benefits of TBL is not well understood. This study aims to assess the impact of implementing the standardized framework of the TBL process on student perception of the benefits of TBL.MethodsFaculty at our institute were surveyed to determine the frequency of implementation of TBL standards, as described in the literature. Second-year osteopathic medical students (OMS II) were surveyed for their perception of the experience of TBL at the beginning of the academic year. Curricular interventions targeting the enforcement of the standard steps and key components of TBL were implemented in a particular 6-week system course. The students were re-surveyed at the end of the course. Descriptive and thematic analyses were performed on quantitative and qualitative data.ResultsTwenty four of 31 (77.4%) faculty participated in the faculty survey, 53 of 105 OMS II students (50.4%) participated in the pre-intervention survey, and 72 of 104 OMS II students (69.2%) participated in the post-intervention survey. Faculty survey results indicated inconsistent implementation of key steps and components of the TBL process. Comparisons of student pre- and post-survey means of the perceived value of various aspects of TBL showed significant improvement in multiple aspects of the TBL process, including readiness assurance tests, immediate feedback, and application activities. Both before and after the intervention, students highly rated the value of teamwork and opportunities for practical application of concepts within TBL.ConclusionWhile medical students value teamwork in UME and professional development, their perception of TBL can be less than positive if not conducted deliberatively. Assessment of the implementation of the standard TBL steps and components and development of corresponding targeted interventions may improve the TBL experience of medical students. Our process could be generalized to help educators interested in improving TBL in their home institutes.

Board Games and Entrepreneurial Skills Acquisition: A Mixed-Methods Study on the Efficacy of the EntreComp Framework in Higher Education

The landscape of higher education has witnessed a paradigmatic shift towards methodologies that are more interactive and engaging. The integration of gamification and board games as active learning methodologies in higher education, as well as the instruction in entrepreneurial competencies, has garnered significant interest for its potential to engage and motivate students, thereby enhancing learning outcomes. Gamification, characterized by the incorporation of game design elements into non-game contexts, offers a captivating and novel learning experience that fosters intrinsic motivation and active participation among students. One of the main ways of applying gamification is through board games, providing hands-on opportunities for students to apply theoretical knowledge in practical scenarios. A critical aspect of this educational innovation is its contribution to the instruction of entrepreneurial competencies, a domain gaining traction within higher educational settings. The European Entrepreneurship Competence Framework (EntreComp) stands at the forefront of this educational endeavor, offering a comprehensive blueprint for cultivating entrepreneurial skills and mindsets. This study is anchored in the context described above, with a focused aim to delineate which entrepreneurial competencies, as outlined by the EntreComp framework, are most effectively developed through these novel educational interventions. To facilitate this investigation, the "Game It Away!" project was initiated, encompassing three pilot studies. These pilots innovatively employed board games as the medium through which participants could acquire and hone their entrepreneurial skills. A key component of the learning process within these pilots was the maintenance of a learning journal by the students. This journal, filled with various reflective questions, provided a structured mechanism for participants to document and reflect upon their learning journey throughout the project. In this way, by using qualitative and quantitative methodologies, it is possible to verify the evolution of certain competences. The integration of gamification and board games within educational settings, particularly through the lens of the "Game It Away!" project, represents a pioneering approach to fostering entrepreneurial competencies among students. This study, leveraging both qualitative and quantitative methodologies, has illuminated the substantial potential of these innovative pedagogical strategies to revolutionize the teaching and acquisition of entrepreneurial skills as outlined by the EntreComp framework.

Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

Comparative Study on Stock Movement Prediction Using Hybrid Deep Learning Model

Applying machine learning techniques in stock market prediction has evolved significantly, with deep learning methodologies gaining prominence. Conventional algorithms such as Linear Regression and Neural Networks initially dominated but struggled to capture complex temporal dependencies in financial data. Recent research has explored deep learning architectures like LSTM and CNN and hybrids such as CNN-LSTM and LSTM-CNN, showcasing promising results. However, there's a gap in research comparing these models across different datasets, particularly in predicting stock movements. This study addresses this gap by conducting a comparative analysis of deep learning and hybrid models for stock movement prediction in the Indonesian banking sector. The evaluation based on RMSE and MAE reveals that the LSTM-CNN hybrid consistently outperforms other models, showcasing its versatility and accuracy across different data characteristics. Then, exploration through hyperparameter tuning demonstrates the criticality of parameter selection in optimizing model performance. These findings contribute to advancing predictive modeling in financial markets, offering valuable insights for investors, analysts, and policymakers. Further research in hyperparameter tuning and model optimization holds promise for enhancing accuracy and reliability in stock price prediction.

Identifying optimal feature sets for acoustic signal classification in environmental noise measurements

Whatever the sound source to be evaluated, spurious events or unwanted sounds will always be present in environmental noise measurements. Spurious events are not characteristic of standard residual noise and must be removed prior to subsequent analyses. Currently, the removal step is deferred solely to the objective evaluation of the sound pattern and/or spectrogram by an operator. This results in the loss of many man-hours. Machine learning can be used to develop a tool capable of recognizing and removing spurious events in noise measurements. The tool must be able to account for various sounds, whether human-made or animal, and must be applicable to any environmental scenario. This is not a straightforward task, in fact if humans can easily distinguish between two sounds, such as a birds' chirps and a car passing by, based on prior experience, a machine may not be able to do so without apprenticeship. Therefore, a learning methodology must be constructed for the machine by establishing recognizable patterns. The aim of this paper is to identify the feature sets which allow the algorithm to differentiate spurious sounds in the best way. These features will represent the semantic value of the signal.

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington’s disease using single-cell RNA sequencing data

BackgroundHuntington’s disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data.ResultsWe used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology.ConclusionWe developed the model capable of analyzing HD at single-cell transcriptomic level.