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Decision Making: Applications in Management and Engineering 2024年第2期7卷 15-34页

作者： Yang, Youpeng Lee, Sanghyuk Kim, Kyeong Soo Zhang, Haolan Huang, Xiaowei Pedrycz, Witold School of Advanced Technology Xi’an Jiaotong-Liverpool University Jiangsu Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science New Uzbekistan University Tashkent Uzbekistan SCDM Centre Zhejiang University Ningbo China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

A novel score function based on the Poincaré metric is proposed and applied to a decision-making problem. Decision-making on Fuzzy Sets (FSs) has been considered due to the flexibility of the data, and it is applied to the decision-making. However, decisions with FSs are sometimes nondecisive even for different membership degrees. Hence, Intuitionistic Fuzzy Sets (IFSs) data is applied to design a score function for the decision-making with the Poincaré metric. This function is supported by the profound information of IFSs;IFSs include hesitation degree together with membership and non-membership degree. Hence, IFS membership and non-membership degree are expressed as two-dimensional vectors satisfying the Poincaré metric for simplification. At the same time, the proposed approach addresses the hesitation information in the IFS data. Next, a score function is proposed, constructed and provided. The proposed score function has a strict monotonic property and addresses the preference without resorting to the accuracy function. The strict monotonic property guarantees the preference of all attributes. Additionally, the existing problem of score function design in IFSs is addressed: they return zero scores even with different meanings for the same membership and non-membership degree. The advantages of the proposed score function over existing ones are demonstrated through illustrative examples. From the calculation results, the proposed decision score function discriminates between all candidates. Hence, the proposed research provides a solid foundation for the hesitation analysis on the decision-making problem. © 2024 The Authors.

关键词： Decision making

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Enhancing TVM VTA Simulator Performance Through SIMD Vectorization 10

Enhancing TVM VTA Simulator Performance Through SIMD Vectori...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Huang, Kuo-Ting Lin, Tzer-Ying Cheng, Po-Wen Chen, Peng-Sheng Advanced Institute of Manufacturing for High-tech Innovations National Chung Cheng University Department of Computer Science and Information Engineering Chia-Yi Taiwan Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (纸本)9798350394924

In this paper, we enhance TVM VTA (Versatile Tensor Accelerator) simulator performance via SIMD vectorization. The VTA simulator is analyzed and identified the performance bottleneck of the whole execution. Subsequently, the hotspot of the VTA simulator was optimized using two SIMD ISAs: ARM Neon and ARM SVE. The approaches of using SIMD intrinsic functions and inline assembly were adopted in the modifications. We evaluate the execution performance of the proposed modifications running on a general-purpose CPU with SIMD instructions. For the tested benchmark programs, the experiment shows that the SIMD technique can significantly improve the execution performance of the VTA simulator, with a maximum speedup of 2.27 times. © 2024 IEEE.

关键词： Benchmarking

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An Effectual Strategy to Augment the Hello Interval of Each Node Using Enhanced Humpback Whales Optimization

An Effectual Strategy to Augment the Hello Interval of Each ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Purohit, Richa Saravanan, T. Dharwadkar, Rashmi Vahidhabanu, Y. Sasirekha, S.P. Krishnaveni, N. School of Computer Studies Sri Balaji University Pune India Dept. of Cse Bengaluru India Sri Balaji University Dept. of Computer Science Pune India Kongunadu college of Engineering and Technology Department of Computer Science and Engineering Trichy India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350379716

Wireless networks are highly utilized, especially Mobile Ad-hoc Networks (MANETs) for real-time data transmission. However, current routing protocols for MANETs face challenges that limit their effectiveness. Design issues include minimizing energy usage and ensuring node security. A commonly used routing protocol, AODV, involves periodic broadcasting of hello messages to monitor link connectivity and maintain routing tables. When link failure occurs due to node mobility, hello messages are broadcasted more frequently, which consumes more node energy and increases network overhead. To address this issue, this research proposes an Enhanced Humpback Whales Optimized Routing Approach (EHWORA) that calculates an optimal hello interval based on a node's energy and mobility using an adaptive fuzzy inference system. This approach involves optimizing the hello interval of AODV routing through the use of an adaptive fuzzy inference system. Each node can dynamically adjust the transmission of hello messages based on the fuzzy-based hello interval. Simulation results demonstrate that ORA reduces network overhead, end-to-end delay, hello message fraction, average energy consumption, packet delivery ratio, and improves throughput compared to a recent method. The proposed protocol is evaluated using MATLAB software. To assess the validity of the research, we utilized MATLAB software to compare the proposed method with a recent approach. During the simulation, the proposed protocol depleted the energy of 3 nodes after 10 seconds, 5 nodes after 20 seconds, 9 nodes after 30 seconds, 11 nodes after 40 seconds, and 13 nodes after 50 seconds, respectively. © 2024 IEEE.

关键词： MATLAB

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An Enhanced Super Resolution Technique Using a Modified Temporally Coherent General Adversarial Network Model

An Enhanced Super Resolution Technique Using a Modified Temp...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Karl Josef M. Acot Patrick S. Collera William Almie T. Valdez Great Allan M. Ong Hazel San L. Patillano College of Computer Studies Technological Institute of the Philippines Computer Science Department National University Philippines

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In this paper, the researchers explored a hybrid video super-resolution technique with two generative models that aim to generate a denoised output and a high-resolution video from a low-resolution video. The proposed approach leverages variational autoencoder (VAE) for denoising and TecoGAN for video super-resolution, aiming to address the challenges of noise reduction and detail enhancement in low-resolution surveillance videos. The system architecture involves the sequential processing of video frames, beginning with denoising using VAE. Its latent space generates an output to preserve important image details while effectively reducing noise artifacts. Subsequently, TecoGAN which is an already existing video-super resolution model is employed to generate a high-resolution and or high-quality video and also makes the frames temporally coherent making the process of frames flow smoothly for the denoised frames, resulting in enhanced visual fidelity and finer details. This study is a two-stage method, and the developed method was able to denoise and enhance video movements using both the VAE and TecoGAN.

关键词： Training Visualization Computational modeling Surveillance Superresolution Noise reduction Autoencoders Systems architecture Generative adversarial networks Workstations

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Exploring Nanoparticle Dynamics: Simulating Brownian Motion in Blood and Seawater Using Monte Carlo Simulation 15

Exploring Nanoparticle Dynamics: Simulating Brownian Motion ...

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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Clarin, G.C. Neal Christian Llantos, Orven E. Limbaco, Maynard E. Department of Computer Science College of Computer Studies MSU-Iligan Institute of Technology Andres Bonifacio Lanao del Norte Philippines Iligan City9200 Philippines Department of Physics College of Science and Mathematics MSU-Iligan Institute of Technology Andres Bonifacio Lanao del Norte Philippines Iligan City9200 Philippines

To observe and study complex biological interactions, establishing a simple simulated foundation is essential for identifying optimal solutions for the therapeutic behavior of different mediums. Using the Einstein-Langevin equation of Brownian motion, which accounts for both systematic and random forces acting on particles, this study explores the dynamic behavior of nanoparticles in two mediums: seawater and blood. With the addition of a smoothing algorithm, the results show that the diffusion coefficient in seawater is approximately 415% higher than in blood. Visual representations further confirm that seawater exhibits a greater scale of nanoparticle movement compared to blood. This study provides critical benchmarks for recalibrating models used in drug delivery and diagnostics, offering new pathways for therapeutic optimization. Additionally, it successfully quantifies the range gap in particle diffusivity between these two mediums. © 2024 The Authors.

关键词： Blood

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics 2

A Three-Step Procedural Paradigm for Domain-Specific Social ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sundaram, Aishwarya Subramaniam, Hema Ab Hamid, Siti Hafizah Nor, Azmawaty Mohamad Universiti Malaya Institute for Advanced Studies Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia Universiti Malaya Faculty of Education Department of Educational Psychology and Counselling Kuala Lumpur Malaysia

ISBN: (纸本)9798350384277

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics. © 2024 IEEE.

关键词： Data Analytics

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Sparse Autoencoder (SAE) based Image Classification with Enhanced Security for College Attendance System

Sparse Autoencoder (SAE) based Image Classification with Enh...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Sangeetha Karunakaran Akila Department of Computer Science School of Computing Sciences VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

An innovative attendance system that uses image fusion techniques to combine facial and iris images to improve recognition accuracy. Using a sparse autoencoder (SAE), we efficiently combine these two distinct biometric methods to create a comprehensive fused image that preserves the important features of both. This fusion solves the disadvantages of traditional recognition methods, such as sensitivity to illumination changes and face expressions, while leveraging the consistent uniqueness of iris patterns. The merged images are then processed by a Convolutional Neural Network (CNN), which excels at extracting hierarchical features for accurate classification. By combining facial and iris images, the system benefits from the complementary strengths of the two modalities. The SAE and CNN are produced with an accuracy of 92.74 % of fusion images. The proposed system not only improves overall authentication accuracy but also improves security by reducing the risk of unauthorized access through dual biometric verification

关键词： Accuracy Image recognition Face recognition Autoencoders Lighting Feature extraction Convolutional neural networks Security Image fusion Iris recognition

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Detection of Brain Tumor Using Transfer Learning Using Conventional Autoencoder with Long Short Term Memory Method

Detection of Brain Tumor Using Transfer Learning Using Conve...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： D Agalya S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in health care industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications.

关键词： Training Recurrent neural networks Computational modeling Autoencoders Transfer learning Predictive models Convolutional neural networks Long short term memory Image classification Testing

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A Comprehensive Development of Human Emotion Detection based on Facial Features using Digital Image Processing Methodology 5

A Comprehensive Development of Human Emotion Detection based...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Karthikayani, K. Rawat, Rishikesh Soundararajan, S. Kalai Lakshmi, T.R. Seekoli, Avinash Moharekar, Tejashree Tejpal Srm Institute of Science and Technology Chennai India Bansal College of Engineering Department of Computer Science & Engineering India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India School of Management Studies Sathyabama Institute of Science and Technology Chennai India St.Martins Engineering College Department of Cse Secunderabad India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India

ISBN: (纸本)9798350385649

This research introduces the Human Emotion Detection utilizing Facial Features (HEDFF) framework, presenting a comprehensive and advanced approach to emotion analysis. Focused on the integration of the AffectNet dataset, renowned for its diversity in annotated facial expressions, our methodology employs digital image processing techniques. Gabor filtering enhances relevant features crucial for subsequent analysis, followed by Convolutional Neural Network (CNN) utilization for robust feature extraction. The EfficientNet architecture refines emotion classification, optimizing efficiency. The AffectNet dataset serves as a cornerstone for model training and evaluation, contributing to the framework's efficacy. Gabor filtering during data preprocessing emphasizes critical textures. The CNN architecture captures intricate facial features, and EfficientNet optimizes classification efficiency. Particle Swarm Optimization (PSO) fine-tunes hyperparameters, enhancing the overall model performance. Rigorous evaluations against existing benchmarks showcase promising results in accuracy, precision, recall, and F1-score, with an outstanding overall accuracy of 98.87%. This research makes a significant contribution to the evolving landscape of emotion detection, providing a robust and efficient framework applicable across diverse domains. From human-computer interaction to mental health assessments, the proposed methodology demonstrates its versatility. © 2024 IEEE.

关键词： Feature extraction

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Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning

Early Prediction of Chronic Heart Disease Based on Electroni...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al/Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Chronic Heart Disease (CHD) is the main reason for mortality around the world, constituting about 15% of all natural deaths, which confirms the importance of early detection to mitigate these rates. CHD can be detected by measuring multiple health parameters such as blood pressure, SpO2, ECG, and other parameters. Therefore, there is a persistent need for effective and precise prediction methods. Traditional techniques for prediction and analysis are limited due to the complexity of the data and correlations. First approach for developing these techniques is by using machine learning (ML) algorithms. ML can examine vast amounts of patient information, including medical records, test outcomes, and imaging scans to identify patterns and predict the possibility of developing heart disease or having a cardiac event. This study aims to improve the accuracy of prediction by utilizing ML techniques to predict CHD using medical data, reduce the incidence of misdiagnosis, and ultimately save lives. Supervised learning techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), AdaBoost, Logistic Regression (LR), K-Nearest Neighbor (KNN) and others are employed to identify correlations in CHD data and enhance prediction rates. The results indicate that SVM-derived probabilistic models outperform other algorithms in predicting CHD and the accuracy of the model was 96.2%, as revealed through various performance evaluation metrics. © 2023 IEEE.

关键词： Support vector machines

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