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Geo-EnvironMeet 2025: Innovative Developments toward Sustainability

作者： Murase, Sota Ruan, Kunlin Ito, Daichi Wang, Hailong Komine, Hideo Dept. of Civil and Environmental Engineering Faculty of Science and Engineering Waseda Univ. Okubo Shinjuku-ku Tokyo Japan Faculty of Science and Engineering Waseda Univ. Okubo Shinjuku-ku Tokyo Japan Dept. of Civil Engineering Univ. of Tokyo Japan

ISBN: (数字)9780784485682

ISBN: (纸本)9780784486016

Bentonite was selected as candidate buffer material in deep geological disposal project for handling high-level radioactive waste. In the context of the buffer construction, efficient and accurate assessment of parameters such as water content and dry density is essential. This study explores the possibility of using near-infrared spectroscopy to non-destructively measure these properties of Kunigel-V1 bentonite. Infrared reflectance spectra of 60 specimens with different water contents and compaction pressures were obtained using a near-infrared spectral sensor. The data were processed, and calibration curves were generated through partial least squares (PLS) regression. The results demonstrated high coefficients of determination between measured and estimated water contents, as well as for wet density, degree of saturation, and volumetric water content. For parameters indirectly related to water content, such as dry density and void ratio, exhibited promising coefficients of determination. These findings indicate the potential application of near-infrared spectroscopy and PLS regression for estimating the state quantities of compacted Kunigel-V1 bentonite. © ASCE.

关键词： Bentonite

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'Liver Disease Risk Prediction Using Regularized Deep Learning: A Novel Approach' 2

'Liver Disease Risk Prediction Using Regularized Deep Learni...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350390179

Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy-Liver, Low-Risk, Moderate-Risk, High-Risk, and Severe-Liver-Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model's scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings. © 2024 IEEE.

关键词： Prediction models

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Harmonizing the Musical Metaverse: unveiling needs, tools, and challenges from experts’ point of view

Harmonizing the Musical Metaverse: unveiling needs, tools, a...

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Boem, Alberto Tomasetti, Matteo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy

The Musical Metaverse (MM) represents an innovative frontier for the field of New Interfaces for Musical Expression (NIME). The MM holds the potential to redefine areas such as musical composition and performance via immersive environments based on technologically mediated social interactions. Despite substantial research on single-user immersive systems, the intersection of NIME and the MM remains largely unexplored. In this paper, we systematically explore this domain by examining previous and current approaches, alongside conducting interviews with eleven experts who have created multi-user immersive musical environments and authored publications on this topic. The goal is to map such an uncharted territory by collecting valuable insights and leveraging the perspective of experts to provide an understanding of the potentials and challenges inherent in creating immersive social environments for musical activities. Our results reveal that existing multi-user immersive environments make use of diverse implementation approaches but face challenges due to the absence of standardized technology stacks, particularly in networking and data synchronization. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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Enhancing Pneumonia Detection: CNN Interpretability with LIME and SHAP 6

Enhancing Pneumonia Detection: CNN Interpretability with LIM...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Hosen, Md. Hamid Saha, Arnob Uddin, Altaf Ashraf, Kahakashan Nawar, Sadia East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

A bacterial or viral infection of the lungs can cause pneumonia, one of the dangerous and potentially fatal illnesses that can have dire repercussions in a short amount of time. Therefore, a key component of a successful treatment plan is an early diagnosis. Therefore, a sophisticated and automated system that can diagnose chest X-rays and make the process of diagnosing pneumonia easier for both specialists and novices is required. This research aims to create a CNN model that will help with the accurate classification of pneumonia. In this work, we have presented our Deep Learning method for the classification challenge, which is taught using modified images through several pre-processing stages. With an overall accuracy of 93.30%, we were able to classify X-ray images of pneumonia using a custom CNN model. Our proposed model is able to precisely detect pneumonia from X-ray images with amazing accuracy and loss. Furthermore, we employed the LIME and SHAP tools of the XAI technique to generate a noteworthy conclusion to persuade medical practitioners. © 2024 IEEE.

关键词： Lime

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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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Hybrid RNN-CNN model for predicting stock market trends 1

Hybrid RNN-CNN model for predicting stock market trends

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Bhanujyothi, H.C. Jacob, I Jeena GITAM Deemed to be University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356816

Stock market prediction has become a challenging task in today's world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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'RNN-Driven Prognosis of Heart Disease Based on Health Parameters' 2

'RNN-Driven Prognosis of Heart Disease Based on Health Param...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Singh, Inderjeet Singh, Ajay Pal Kumar, Alok Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification. © 2024 IEEE.

关键词： Recurrent neural networks

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Optimal Trajectory Planning of an Omni-directional Mobile Manipulator for Obstacle Avoidance

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Transactions of the Korean Institute of Electrical Engineers 2023年第8期72卷 952-958页

作者： Kim, Minsu Sung, Young Whee School of Electrronic Engineering Kumoh National Institute of Technolgy Korea Republic of Dept. of Electrical Engineering and Computer Science DGIST Korea Republic of

The ultimate purpose of a mobile robot is to do some tasks in addition to moving to a desired point, so it is very natural for a mobile robot to be equipped with a manipulator. Among many types of mobile robots, omni-directional mobile robot has been very popular because it has advantages in trajectory planning and control. In this paper, we deal with an omni-directional mobile manipulator that consists of a four Mecanum wheeled mobile robot platform and a manipulator mounted on it. In general, it is very difficult to solve inverse kinematics for a mobile manipulator because it has infinite solutions. Omni-directional mobile robot platform can move in any direction and can rotate on a pivot in a plane, so we propose it to be modeled as a manipulator with two prismatic joints and one revolute joint. Then the overall omni-directional mobile manipulator can be considered as a manipulator with an (n+3) DOF manipulator if the mounted manipulator has n DOF. We propose a weighted pseudo-inverse based method to solve the inverse kinematics problem at velocity level for the omni-directional mobile manipulator. In our method, in addition to a primary task of following end-effector trajectory, a secondary task of obstacle avoidance is accomplished by maximizing a performance index that represents a distance between a link of the manipulator and an obstacle. Simulation results showed the feasibility of the proposed method. © 2023 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Manipulators

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ProctorXam - Online Exam Proctoring Tool 1

ProctorXam - Online Exam Proctoring Tool

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Ashwini, S. Bhuvan Kumar, M.S. Chandra Sai, D. Sathya, V. Atria Institute of Technology Dept. of Computer Science & Engineering Bangalore India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371802

Robust exam monitoring solutions are now essential in light of the most notable Covid-19 outbreak and the increasing inclination towards virtual learning. The automated method for supervising online exams that is suggested in this paper is intended to lessen the difficulties associated with exam cheating. With the assistance of our system's clever face detection algorithms, instructors may efficiently identify suspicious conduct by monitoring students' web activity and camera feed in real-time. Unlike earlier systems, ours provides improved accuracy and functionality, guaranteeing the authenticity of online tests. In light of the difficulties presented by the epidemic, this research attempts to offer a workable option for academic institutions and people wishing to administer fair and secure online exams. © 2024 IEEE.

关键词： Face recognition

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