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A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

Applied Soft Computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

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An Efficient Implementation of Canonical Genetic Algorithm for Combinatorial Optimization Scheme Issues

An Efficient Implementation of Canonical Genetic Algorithm f...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： N. V. Balaji Nirmala. D Yogesh D. Deshpande Deepak Mehta Rajesh Kumar Samala Dipannita Mondal Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon

Canonical Genetic algorithm (CGA) is a blended seek and exploitation technique to combinatorial optimization troubles, which combines the conventional search of evolutionary algorithms with heuristics to correctly identify solutions to the underlying trouble. The CGA achieves this by searching the answer space using a widespread evolutionary set of rules method after which systematically exploiting promising solutions with a number heuristics tailor-made to the problem. This paper offers an efficient implementation of the CGA for different combinatorial optimization troubles. The technique uses modern computing engineering and a statistics-pushed approach to generate and hold an exoskeleton-like swarm of candidate answers. via a singular approach to examining candidate answers, the end result of the set of rules remains strong and without oscillations of solutions. The approach is evaluated in one of a kind combinatorial optimization troubles and it's miles proven to attain anticipated effects.

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Language Person Search with Pair-Based Weighting Loss 1

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zhang, Peng Ouyang, Deqiang Jiang, Chunlin Shao, Jie Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China

ISBN: (数字)9783030678326

ISBN: (纸本)9783030678319

Language person search, which means retrieving specific person images with natural language description, is becoming a research hotspot in the area of person re-identification. Compared with person re-identification which belongs to image retrieval task, language person search poses challenges due to heterogeneous semantic gap between different modal data of image and text. To solve this problem, most existing methods employ softmax-based classification loss in order to embed the visual and textual features into a common latent space. However, pair-based loss, as a successful approach of metric learning, is hardly mentioned in this task. Recently, pair-based weighting loss for deep metric learning has shown great potential in improving the performance of many retrieval-related tasks. In this paper, to better correlate person image with given language description, we introduce pair-based weighting loss which encourages model to assign appropriate weights to different image-text pairs. We have conducted extensive experiments on the dataset CUHK-PEDES and the experimental results validated the effectiveness of our proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiayi Lei E Zhang Yingying She Xin Wang Yuhan Liao Bin Hu Hang Wu Minqiang Yang Jiajia Tian Yong Wang School of Informatics Xiamen University China Department of Occupational Therapy Education University of Kansas Medical Center USA School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Pediatric Department Fujian Medical University Union Hospital China

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance.

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Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation

arXiv

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arXiv 2023年

作者： Peng, Bo Burns, Ben Chen, Ziqi Parthasarathy, Srinivasan Ning, Xia Department of Computer Science and Engineering The Ohio State University Columbus United States Department of Biomedical Informatics The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhanced sequential recommendation (SR) methods. Considering that most existing LLMs are not specifically optimized for recommendation tasks, adapting them for SR becomes a critical step in LLM-enhanced SR methods. Though numerous adaptation methods have been developed, it still remains a significant challenge to adapt LLMs for SR both efficiently and effectively. To address this challenge, in this paper, we introduce a novel side sequential network adaptation method, denoted as SSNA, for LLM-enhanced SR. SSNA features three key designs to allow both efficient and effective LLM adaptation. First, SSNA learns adapters separate from LLMs, while fixing all the pre-trained parameters within LLMs to allow efficient adaptation. In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i.e., recommendation performance). We compare SSNA against five state-of-the-art baseline methods on five benchmark datasets using three LLMs. The experimental results demonstrate that SSNA significantly outperforms all the baseline methods in terms of recommendation performance, and achieves substantial improvement over the best-performing baseline methods at both run-time and memory efficiency during training. Our analysis shows the effectiveness of integrating adapters in a sequential manner. Our parameter study demonstrates the effectiveness of jointly adapting the top-a layers of LLMs. © 2023, CC BY.

关键词： Computational linguistics

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Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Multi-Object Tracking Based on Anchor-Free Detection and Mes...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhou Fang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.

关键词： Industrial electronics Target tracking Statistical analysis Message passing Switches Feature extraction Real-time systems

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Intelligent Outlier Detection for Smart Farming Application using Deep Neural Network

Intelligent Outlier Detection for Smart Farming Application ...

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

作者： N. Murali A. Sasi Kumar A. Karunamurthy R. Suseendra S. Manikandan Department of Computer Science and E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Cloud Technology and Data Science Institute of Engineering and Technology Srinivas University Mukka Surathkal Mangalore India Department of Computer Science and Engineering Chirst College of Engineering and Technology Mulakulam Pudhucherry India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (纸本)9781665491129

Agriculture is backbone of India. In the emerge of human civilization, agriculture has been an essential part of every human society due to the basic fact that the sustenance of any civilization directly depends on agriculture. Agriculture has the great impact on the economy of the country. This paper mainly concentrates on increasing crop production in large scale agricultural area. Due to changing climatic conditions, insufficient water level, insufficient or excessive use of fertilizers etc. the crop production may be affected. Astonishingly, agriculture has not been blessed with the latest advancements in the high-tech space unlike other areas like transport, education, finance, etc. Advancement in agriculture is necessary to balance the demands of people needs as the population grows day by day. The detection of outliers in large-scale area is most challenging tasks. Hence, we explore machine learning methodologies namely Deep Neural Network and Tucker Decomposition is used to detect those anomalies in large scale agriculture area to increase crop production. In this smart farming, the system automatically learns the soil features from IoT sensor data then provides anomaly detection accuracy which helps the farmer to understand the parameters affect the crop growth. This will enhance the crop productivity and economy of farmer. Our system automatically learns the data, analyzes the data and predicts the future.

关键词： Support vector machines Smart agriculture Productivity Neural networks Crops Soil Feature extraction

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Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing

Binary Iterative Hard Thresholding Converges with Optimal Nu...

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Annual IEEE Symposium on Foundations of computer science

作者： Namiko Matsumoto Arya Mazumdar Department of Computer Science and Engineering University of California San Diego La Jolla California USA Halɩcɩoğlu Data Science Institute University of California San Diego La Jolla California USA

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signals have to be quantized before storing or processing them. 1(One)-bit compressed sensing is a heavily quantized version of compressed sensing, where each linear measurement of a signal is reduced to just one bit: the sign of the measurement. Once enough of such measurements are collected, the recovery problem in 1-bit compressed sensing aims to find the original signal with as much accuracy as possible. The recovery problem is related to the traditional “halfspace-learning” problem in learning theory. For recovery of sparse vectors, a popular reconstruction method from one-bit measurements is the binary iterative hard thresholding (BIHT) algorithm. The algorithm is a simple projected subgradient descent method, and is known to converge well empirically, despite the nonconvexity of the problem. The convergence property of BIHT was not theoretically justified, except with an exorbitantly large number of measurements (i.e., a number of measurement greater than $\max\{k^{10},24^{48}, k^{3.5}/\epsilon\}$, where k is the sparsity and $\epsilon$ denotes the approximation error, and even this expression hides other factors). In this paper we show that the BIHT estimates converge to the original signal with only ${\tilde{O}}\left(\frac{k}{\epsilon}\right)$ measurements. Note that, this dependence on k and $\epsilon$ is optimal for any recovery method in 1-bit compressed sensing. With this result, to the best of our knowledge, BIHT is the only practical and efficient (polynomial time) algorithm that requires the optimal number of measurements in all parameters (both k and $\epsilon$). This is also an example of a gradient descent algorithm converging to the correct solution for a nonconvex problem, under suitable structural conditions.

关键词： computer science Measurement uncertainty Reconstruction algorithms Approximation error Time measurement Iterative algorithms Sensors

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Advancing Speaker Embedding Learning: Wespeaker Toolkit for Research and Production

SSRN

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SSRN 2024年

作者： Wang, Shuai Chen, Zhengyang Han, Bing Wang, Hongji Liang, Chengdong Zhang, Binbin Xiang, Xu Ding, Wen Rohdin, Johan Silnova, Anna Qian, Yanmin Li, Haizhou Shenzhen Institute of Big Data Guangdong Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Tencent Ethereal Audio Lab Tencent Corporation Shenzhen China Horizon Robotics Beijing China NVIDIA Shanghai China Brno University of Technology Faculty of Information Technology IT4I Centre of Excellence Czech Republic School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen China

Speaker modeling plays a crucial role in various tasks, and fixed-dimensional vector representations, known as speaker embeddings, are the predominant modeling approach. These embeddings are typically evaluated within the framework of speaker verification, yet their utility extends to a broad scope of related tasks including speaker diarization, speech synthesis, voice conversion, and target speaker extraction. This paper presents Wespeaker, a user-friendly toolkit designed for both research and production purposes, dedicated to the learning of speaker embeddings. Wespeaker offers scalable data management, state-of-the-art speaker embedding models, and self-supervised learning training schemes with the potential to leverage large-scale unlabeled real-world data. The toolkit incorporates structured recipes that have been successfully adopted in winning systems across various speaker verification challenges, ensuring highly competitive results. For production-oriented development, Wespeaker integrates CPU- and GPUcompatible deployment and runtime codes, supporting mainstream platforms such as Windows, Linux, Mac and on-device chips such as horizon X3’PI. Wespeaker also provides off-the-shelf high-quality speaker embeddings by providing various pretrained models, which can be effortlessly applied to different tasks that require speaker modeling. The toolkit is publicly available at https://***/wenet-e2e/wespeaker. © 2024, The Authors. All rights reserved.

关键词： Speech recognition

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Accurate Loss Prediction of Realistic Hollow-core Anti-resonant Fibers Using Machine Learning

arXiv

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arXiv 2024年

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Amezcua-Correa, Rodrigo Habib, Md Selim The Department of Data Science and Business Analytics Florida Polytechnic University 4700 Research Way LakelandFL33805 United States The Faculty of Electrical Engineering and Information Technology University of Applied Sciences KarlsruheBW-76133 Germany The CREOL University of Central Florida OrlandoFL32816 United States The Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States

Hollow-core anti-resonant fibers (HC-ARFs) have proven to be an indispensable platform for various emerging applications due to their unique and extraordinary optical properties. However, accurately estimating the propagation loss of nested HC-ARFs remains a challenging task due to their complex structure and the lack of precise analytical and theoretical models. To address this challenge, we propose a supervised machine-learning framework that presents an effective solution to accurately predict the propagation loss of a 5-tube nested HC-ARF. Multiple supervised learning models, including random forest, logistic regression, quadratic discriminant analysis, tree-based methods, extreme gradient boosting, and K-nearest neighbors are implemented and compared using a simulated dataset. Among these methods, the random forest algorithm is identified as the most effective, delivering accurate predictions. Notably, this study considers the impact of random structural perturbations on fiber geometry, encompassing random variations in tube wall thicknesses and tube gap separations. In particular, these perturbations involve randomly varying outer and nested tube wall thicknesses, tube angle offsets, and randomly distributed non-circular, anisotropic shapes within the cladding structure. It is worth noting that these specific perturbations have not been previously investigated. Each tube exhibits its unique set of random values, leading to longer simulation times for combinations of these values compared to regular random variables in HC-ARFs with similar tube characteristics. The comprehensive consideration of these factors allows for precise predictions, significantly contributing to the advancement of HC-ARFs for many emerging applications. © 2024, CC BY.

关键词： Fibers

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