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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Mandala, Vishwanadham Surabhi, Srinivas Naveen R Dolu Selvan, R. Senthamil Indiana University Department of Analytics Data Engineering Lead IN United States General Motors R & D Department of Control System IN United States Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

In shared spectrum networks, proper signal type identification is essential for equitable coexistence and effective resource allocation. To increase system throughput and prevent accidents, particularly in situations when the environment is changing quickly, it may be utilised for scheduling gearbox chances. To train deep neural networks, or DNNs, to distinguish coexisting signal kinds without encoding them, researchers use the In-phase/Quadrature (I/Q) sample as input. Two neural networks, one convolutional and one recurrent, are trained using input segments taken from the received signal samples utilising categorical cross-entropy (CE) optimisation. Results for the 5-6 GHz unlicensed band, where LTE LAA, Wi-Fi, and 5G NR-U signals coexist, demonstrate that the suggested architecture is quite accurate. After that, use spectral analysis on the I/Q sequence to boost classification precision even further. More data in the frequency domain may be shown as a spectrogram by using a Short-time Fourier Transform (STFT). Increasing the size of the DNN's input accordingly. Researchers use USRP radios to carry out over-the-air (OTA) tests that validate the efficacy of the suggested detection system. In both software and hardware trials, the suggested method achieves precise categorization. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass 1

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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***: a Context-Aware Chatbot for Emotional Detection and Support 14

***: a Context-Aware Chatbot for Emotional Detection and Sup...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kallivalappil, Neave D'Souza, Kyle Deshmukh, Afif Kadam, Chinmay Sharma, Neha Fr. Conceicao Rodrigues College of Engineering Computer Engineering Department Mumbai India Tata Consultancy Services Data and Analytics Practice Pune India

ISBN: (纸本)9798350335095

The increasing prevalence of mental health disorders highlights the urgent need for innovative technologies that can provide support and improve individuals' emotional well-being. Intelligent Virtual Friends (IVFs) have emerged as a promising solution to offer personalized and effective therapeutic experiences. This study introduces ***, an emotionally intelligent multimodal chatbot therapist powered by machine learning. *** aims to recognize and respond to users' emotions, providing a unique and tailored experience by combining cognitive-behavioural therapy techniques with empathetic communication. This integration of facial, text, and voice emotion recognition empowers the chatbot to deliver a holistic and tailored approach to emotional support, ensuring that users receive the assistance they need in a comprehensive and empathetic manner and serves as a valuable resource for those seeking accessible alternatives to traditional therapy methods. © 2023 IEEE.

关键词： Emotion Recognition

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Effect of N doping on Electronic Band Structure and Magnetic Moments in Graphene Sheets: Density Functional Theory Study 3

Effect of N doping on Electronic Band Structure and Magnetic...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Nenohai, A.J.W.T. Santana, F.M. Asih, R. Arifin, R. Darminto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Mechanical Engineering Faculty of Engineering Universitas Muhammadiyah Ponorogo Ponorogo63471 Indonesia

Graphene has become a material that is often discussed recently because of its unique properties. One of the obstacles in using graphene as a nanoelectronic device is its zero band gap energy. One effort that can be made to open this energy gap is by substituting N atoms. In this research, spin-polarization density functional theory is studied. We simulated 4 x 4 single-layer graphene supercells with varying numbers of N atom substitutions (N = 0, 1, 2 atoms). The GGA - PBE function is used in this modeling to complement the exchange - correlation potential. The result obtained is an open energy band gap when the Nitrogen atom is substituted. Nitrogen substituted in the form of graphite - N also causes the emergence of a magnetic moment in graphene, with a Fermi energy value for pristine graphene of -0.4607 eV, for G1N of -1.6140 eV, and for G2N of -1.3346 eV. These results pave the way for research and development of graphene in its application as electronic nanodevices. © Published under licence by IOP Publishing Ltd.

关键词： Graphene

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Harnessing Medical Big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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Design and Evaluation of Abstract Aggregated Avatars in VR Workspaces

Infocommunications Journal

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Infocommunications Journal 2024年 92-101页

作者： Persa, György Csapó, Ádám B. Doctoral School of Multidisciplinary Engineering Sciences Széchenyi István University Győr Hungary Corvinus Institute for Advanced Studies Institute of Data Analytics and Information Systems Corvinus University of Budapest Hungary Hungarian Research Network Budapest Hungary

Avatars are commonly used in digital platforms to provide a visual representation of individual users to each other. Generally, avatar design in the past has focused on achieving visual fidelity and realism of social interactions. In this paper, we broaden the concept of avatars to incorporate displays using an abstract visual language and conveying information on aggregated, interpersonal information from the perspective of the digital platform as a whole. We propose a general design methodology for such aggregated avatars, and also introduce and experimentally evaluate an aggregated avatar which we have developed on the MaxWhere VR platform. Results are promising in that users were able to discern several key states of the avatar and correctly associate them with the correct virtual reality scenarios in a statistically meaningful way. © 2024 Scientific Association for Infocommunications. All rights reserved.

关键词： Virtual reality

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Accelerated Malaria Diagnosis with Deep Convolutional Neural Network 1

Accelerated Malaria Diagnosis with Deep Convolutional Neural...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Krishna Kumar, V. Geetha Devasana, M.S. Saravana Balaji, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India De Montfort University Kazakhstan Department of Business Data Analytics Almaty Kazakhstan

ISBN: (纸本)9798350359756

The disease known as malaria is a communicable illness that is spread by the bites of mosquitoes. There are already diagnostic approaches that include manually counting the amount of red blood cells (RBC) that are contaminated. This is accomplished by doing a microscopic examination of the stained blood cells of the individual who is suffering from the disease. However, this is a hard job that requires careful attention both visually and intellectually. In order to accomplish this, a comprehensive manual examination must be undertaken by an expert who is informed about the subject matter. It is possible that the implementation of deep learning algorithms will simplify the process of disease diagnosis and make this analysis more straightforward. Furthermore, because they are diagnosing malaria, they are expected to manage a vast quantity of data, which includes photographs of microscopic blood smears. This is because they are responsible for finding the disease. In order to get a better level of accuracy, it is necessary to train models that are more in-depth. This requires fast computing since it requires a substantial amount of computer resources. An accelerated convolutional neural network technique that is based on a graphics processing unit (GPU) is utilized by the Accelerated Malaria Diagnosis System (AMDS) that has been created in order to diagnose malaria. © 2023 IEEE.

关键词： Diseases

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A Hybrid Approach to Classify the Multiclass Imbalanced datasets 5

A Hybrid Approach to Classify the Multiclass Imbalanced Data...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Sridhar, S. Anusuya Srm Institute of Science and Technology Department of ComputerScience and Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Department of Data Analytics TN Chennai India

ISBN: (纸本)9798350321425

The imbalanced nature of a dataset often leads classifiers to show more favor towards recognizing the class with a higher number of data instances. Handling this imbalance in a multiclass environment presents an additional challenge compared to binary class environments. Previous research primarily focused on addressing the imbalance in binary class scenarios, but there is a growing shift towards addressing the imbalance in multiclass environments. In imbalanced environments, data balancing methods are used to address the imbalance issue by equalizing the number of data instances in both the major and minor class groups. This approach aims to avoid bias in learning by modifying the class distribution. Algorithmic-level methods enable the learning model to adjust its learning process towards the class with a lower number of data instances, aiming to improve the prediction rate. The existing multiclass imbalance handling methods either attempted to solve the imbalance problem either by transforming the problem into binary class problems or by approaching the problem as a whole and finds the solution. In both the cases the classification model suffers from the issues like overlapping problem, least recognition of minority class samples, ignoring the minority class samples as outlier and over fitting. This research study proposes a hybrid approach that combines both data level balancing and algorithm level tuning methods. The proposed hybrid approach combines the effort of algorithm level tuning to create a cost associated dataset which avoids the models' biasness towards majority class samples and the data level balancing method to improve the count of the least class samples count and thereby avoiding the ignorance problem and makes the learning model to have pay more attention towards the minority class samples. © 2023 IEEE.

关键词： Decision trees

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Effect of material variability on the seismic response of reinforced concrete box-girder bridges for different pier heights

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Materials Today: Proceedings 2022年 65卷 564-571页

作者： Srivastava, Charu Pandikkadavath, Muhamed Safeer Mangalathu, Sujith National Institute of Technology Calicut Department of Civil Engineering Kozhikode India Data Analytics Division Equifax Inc. Atlanta GA United States

RC bridges situated in regions prone to seismic activity are susceptible to undesirable pier column movements and deck displacements. The component-level nonlinear response evaluation can give a better picture of the seismic safety of the existing bridge structures. Many times, the material strength variability also affects the inelastic response of the structures significantly. The present study looks into the effects of column heights and material strength variability on the seismic performance of reinforced concrete bridge models adopted from past investigations, subjected to bi-directional loading. Chosen suite of 40 far-field ground excitations are employed to simulate the seismic response of chosen models in OpenSees and their non-linear response is evaluated. The seismic response of the structure is evaluated as curvature ductility of piers (primary component), and deck displacements (secondary component). Fragility curves so obtained across the damage states considered depict the difference in seismic performance of the model considering only record variability and considering record and material strength uncertainties. The results showed that for the primary component, damage probability for different states is found to be increased under material variability irrespective of the column pier height, while secondary components showed an increase in the damage state due to material strength variability for the shorter pier. © 2022

关键词： Seismic response

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Hands on PROTÉGÉ for Ontology Construction and Query Retrieval in Context of Automobile Domain 3

Hands on PROTÉGÉ for Ontology Construction and Query Retri...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Chowhan, Santhosh Gupta, Arun University Department of Data Analytics and Mathematical Sciences Bangalore India Teerthanker Mahaveer University Department of Mechanical Engineering Uttar Pradesh India

ISBN: (纸本)9798350330724

This paper provides a detailed exposition of the concept of ontology, focusing on the processes and methodologies involved in its construction. The utilization of ontologies has played a significant role in the advancement of the Semantic Web. It is an expansion of the existing World Wide Web, characterized by the provision of well-defined meanings to information. This facilitates the translation of unstructured data into meaningful representations, thereby enabling effective collaboration between humans and computers. Ontology can be described as a compilation of a group of concepts, along with their corresponding definitions and interconnections, which are structured hierarchically and referred to as Taxonomy. Several ontology development tools are accessible, such as Hozo, DOML, and AltovaSemantic Works. Protégé, a commonly utilized ontology development editor, has been employed to define ontology concepts. In addition, it provides support for multiple ontology representation languages, such as OWL. The present study demonstrates the process of ontology development through the utilization of Protégé 3.4.1, with a specific focus on the domain of automobiles. A case study is presented to exemplify the application of this methodology. © 2023 IEEE.

关键词： Ontology

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