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International Journal of Interactive Mobile Technologies 2024年第7期18卷 34-52页

作者： Sahraie, Faranak Rezvanfar, Ahmad Movahedmohammadi, Seyed Hamid Ebner, Martin Alambeigi, Amir Farrokhnia, Mohammadreza Department of agricaltural extension and education Agricultural colledge University of Tehran Karaj Iran Graz University of Technology Graz Austria Department of Learning Data Analytics and Technology Instructional Technology Section University of Twente Enschede Netherlands

The present study aimed to examine students’ emotions in e-learning classes through facial expressions and investigate the influence of different instructional methods on students’ emotional responses. In this study, we examined the facial expressions of 17 undergraduate students using three different methods of presenting educational content (PowerPoint, video, and Kahoot) in online classes and analyzed the data with face reader software. The findings demonstrated that students experienced various positive and negative emotions with different methods of content delivery. Furthermore, comparing the three methods revealed that the Kahoot method elicited the highest average of positive emotions among students compared to the other two methods. This difference can be attributed to the visual attractiveness and interactive nature of the Kahoot environment. Additionally, this study highlights that simply incorporating multimedia materials, such as PowerPoint presentations and videos, is not sufficient to enhance effectiveness and cultivate positive emotions in e-learning. While multimedia materials serve as supportive tools and enhance visualization, interaction at various levels (content, teacher, peers, etc.) is necessary. Nevertheless, the significance of this research lies in the innovative application of a tool for analyzing emotions in online learning classrooms, thereby enhancing the measurement of genuine and objective emotional responses in e-learning environments. © 2024 by the authors of this article.

关键词： Students

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Reducing noise using neighbourhood pixel analysis and interpretable custom kernel in CNN model for CP handwritten digit recognition

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International Journal of Information technology (Singapore) 2024年 1-11页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India Information Processing Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India

Individuals with Cerebral Palsy (CP) are impacted lifetime barriers in their everyday activities, especially in writing phrase, which results from innate neural motor in co-ordination. Numerous studies have focused on recognizing normal people’s handwritten digits using benchmark datasets. However, there has been limited exploration into recognizing handwritten digits by individuals with CP. The handwriting of CP individuals is significantly more difficult to recognize than that of normal people because the CP digits are inaccurate, uneven, and complex to recognize. In this study, the authors improve the digit recognition accuracy of existing Convolutional Neural Network (CNN) models on collected CP handwritten digit’s dataset using two notable methods, first one is to reduce the noise using neighbourhood pixel analysis through Fast Non-Local Means (FNLM) algorithm and second one is to extract the feature using customized kernel technique. The experimental results show that the accuracy of existing state of art models are improved by 3% to 7.14% with the integration of aforementioned techniques. Moreover, a custom CNN model hybridised with the Prewit, Laplacian, Sobel filters, and custom kernel has following CP digit recognition accuracy values of 97.96%, 98.08%, 99.17%, and 99.60% respectively. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cerebral Palsy CNN Customized kernel initialization Edge detection Handwritten digit recognition

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Toward a Race-Conscious Entrepreneurship Education

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Entrepreneurship Education and Pedagogy 2024年第2期7卷 161-189页

作者： Monroe-White, Thema McGee, Ebony Department of Technology Entrepreneurship and Data Analytics Campbell School of Business Berry College Mount Berry GA United States Department of Teaching and Learning Peabody College of Vanderbilt University Nashville TN United States

This article advocates for race-conscious science, technology, engineering/computer science, and mathematics (STEM) innovation and entrepreneurship (I&E) education. Results from a national survey of the academic plans and career trajectories of underrepresented and minoritized (URM) STEM students and alumni suggest that URM STEM students need robust opportunities to apply their scientific and engineering insights to societal problems, while empowering them to be entrepreneurs. Specifically, we find evidence of a relationship between racialized stress and racial activism for nascent URM STEM entrepreneurs among master’s and doctoral STEM students the majority of whom identify as African American/Black and/or Latinx/Hispanic/Chicano. Furthermore, entrepreneurial intent among Black STEMers is greatest among those who have experienced social suffering coupled with a desire for racial justice, and this relationship is amplified for students and alumni of minority serving institutions (MSIs). This study makes the case for advancing a critical and socio-historical scholarship of STEM I&E education to promote URM STEM entrepreneurship. © The Author(s) 2023.

关键词： Black and Latinx graduate students entrepreneurial intent racial activism racial justice equity ethic STEM entrepreneurship

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Membership Functions, Set-Theoretic Operations, Distance Measurement Methods Based on Ambiguous Set Theory: A Solution to a Decision-Making Problem in Selecting the Appropriate Colleges

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International Journal of Fuzzy Systems 2023年第4期25卷 1311-1326页

作者： Singh, Pritpal Huang, Yo-Ping Deep Learning Lab Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

To deal with uncertainty in a very precise way, the ambiguous set theory has been proposed. This theory supports the representation of ambiguities in terms of four membership degrees, namely "true," "false," "true-ambiguous," and "false-ambiguous." These membership degrees are linearly dependent on each other, because they are defined over the same event. However, these four membership degrees have the difficulty of unclear margins that prevent them to be separated from each other in the ambiguities representation of an event. To address this problem of unclear margins in the ambiguous set, the membership degrees are defined using four different functions, and called ambiguous membership functions (AMFs). These membership degrees are defined in such a way that their sum should be less than or equal to 2, and the individual membership degree should be in the range [0, 1]. To visually represent the uncertain margins for the membership degrees, the concept of an ambiguous region (AR) is introduced. In this study, various set-theoretic operations, their properties, and methods for measuring distances in support of the ambiguous sets are discussed. Finally, we discuss a real-time application of the ambiguous set theory with respect to solving the decision-making problem of choosing the appropriate colleges for students. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Membership functions

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Automated coding of student chats, a trans-topic and language approach

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Computers and Education: Artificial Intelligence 2023年 4卷

作者： de Araujo, Adelson Papadopoulos, Pantelis M. McKenney, Susan de Jong, Ton Department of Learning Data Analytics and Technology University of Twente Postbox 217 Enschede7500 AE Netherlands

Computer-Supported Collaborative learning (CSCL) is known to be productive if well structured. In CSCL, students construct knowledge by performing learning tasks while communicating about their work. This communication is most often done through online written chats. Understanding what is happening in chats is important from both research and practical perspectives. From a research perspective, insight into chat content offers a window into student interaction and learning. From a more practical standpoint, insight into chat content can (potentially) be used to trigger supportive elements in CSCL environments (e.g., context-sensitive tips or conversational agents). The latter requires real-time, and therefore automated, analysis of the chats. Such an automated analysis is also helpful from the research perspective, since hand-coding of chats is a very time and labour-consuming activity. In this article, we propose a new machine learning-based system for automated coding of student chats, which we labelled ConSent. The core of ConSent is an algorithm that uses contextual information and sentence encoding to produce a reliable estimation of chat message content (i.e. code). To optimize usability, ConSent was designed in such a way that it can cover various topics and various languages. To evaluate our approach, we used two sets of chats coming from different topics (within the domain of physics) and different languages (Dutch and Portuguese). We tested different algorithm configurations, including two multilingual sentence encoders, to find the model that yields the best reliability. As a result, analysis revealed that ConSent models can perform with substantial reliability levels and are able to transfer reliable coding of chats in a similar topic and different language. Finally, we discuss how ConSent can form the basis for a conversational agent, we explain the limitations of our approach, and we indicate possible paths for future work to contribute towards reliabl

关键词： Machine learning

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

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Unsupervised Anomaly Detection on Node Attributed Networks: A Deep learning Approach 21

Unsupervised Anomaly Detection on Node Attributed Networks: ...

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4th International Conference on Information Science and Systems, ICISS 2021

作者： Kavehzadeh, Parsa Samadi, Mohammadreza Amir Haeri, Maryam Computer Engineering Department Amirkabir University of Technology Iran Learning Data-Analytics and Technology Department University of Twente Netherlands

ISBN: (纸本)9781450389136

Anomaly detection has been one of the important issues in social network analysis in recent years due to the crucial role it plays in different applications such as fraud and spammer detection. Using both graph and node characteristics leads to more accurate results in detecting anomalous nodes of node attributed networks. Most of the research works in this field are concentrated on supervised methods for anomaly detection. However, in real-world problems, there is not enough labeled data to use supervised methods for anomaly detection. This paper proposes an unsupervised method for detecting anomalous nodes in node attributed networks. The methods used a two-step deep learning approach. In the first step, structural features of the network are extracted using node2vec;in the next step, Variational AutoEncoder (VAE) is used to detect the anomalies considering both structural and node attributes. The anomalous nodes are recognized by their higher reconstruction loss. Our experimental results on two datasets, BlogCatalog and Flickr, show that the suggested method can compete with the state-of-the-art approaches of anomaly detection in attributed networks. Our method (Deep2NAD) outperforms the state-of-the-art result on the Flickr dataset based on AUC. Moreover, it receives an acceptable AUC over the BlogCatalog dataset in comparison to the state-of-the-art methods. © 2021 ACM.

关键词： Anomaly detection

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