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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sudhamsu, Gadug Agarwal, Ankita Dhivrani, Rekha Bhanu, K.S. Jeyalaxmi, M. Nawadkar, Ashwini R. Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation. © 2023 IEEE.

关键词： information organizational predictive processing security

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One data ASN Framework (ODAF) for Indonesian State Civil Apparatus 3

One Data ASN Framework (ODAF) for Indonesian State Civil App...

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Cahyaningsih, Elin Silalahi, Natascha Lestari Eunike Noprisson, Handrie Ayumi, Vina Directorate of Data Management and Dissemination of Personnel Information Jakarta Indonesia University of Mercu Buana Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665427333

data management and governance framework purpose to support data life cycle and ensure the consistency within an organizational function in terms of framework implementation to achieve organizational objectives. data accuracy, integrity, interoperability, and quality become a challenge that occurs in One data State Civil Apparatus (ASN) implementation. Therefore, it is needed to define a suitable framework for ASN data management and governance. This research conducted identify strategic issues and current problems, literature review and previous study to determine the theoretical framework, evaluation the theoretical framework, hybrid, and synthesis framework to define the suitable component, and validation the proposed framework by expert judgment. The One data ASN Framework (ODAF) consists of eight components there are regulation/policy, vision, mission, goals, principles, committee, type, standard, metadata, data organizing, data dissemination, coaching, monitoring, and evaluation. © 2021 IEEE.

关键词： Information management

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SHADE: Deep Density-based Clustering

SHADE: Deep Density-based Clustering

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IEEE International Conference on data Mining (ICDM)

作者： Anna Beer Pascal Weber Lukas Miklautz Collin Leiber Walid Durani Christian Böhm Claudia Plant Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Database Systems and Data Mining LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany ds: UniVie University of Vienna Vienna Austria

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE, the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss function that captures density-connectivity. It thereby learns a representation that enhances the separation of density-connected clusters. SHADE detects a stable clustering and noise points fully automatically without any user input. It outperforms existing methods in clustering quality, especially on data that contain non-Gaussian clusters, such as video data. Moreover, the embedded space of SHADE is suitable for visualization and interpretation of the clustering results as the individual shapes of the clusters are preserved.

关键词： data analysis Shape Clustering methods Noise Autoencoders data visualization Clustering algorithms Noise measurement data mining

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Visual-Semantic Graph Matching Net for Zero-Shot Learning

arXiv

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arXiv 2024年

作者： Duan, Bowen Chen, Shiming Guo, Yufei Xie, Guo-Sen Ding, Weiping Wang, Yisong The School of Computer Science and Technology Guizhou University Guizhou550025 China The Institute for Artificial Intelligence Guizhou University Guiyang China The Mohamed bin Zayed University of AI Abu Dhabi19282 United Arab Emirates The Intelligent Science & Technology Academy of CASIC Beijing China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau 999078 China

Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process. To address the aforementioned challenges, this paper proposes a Visual-Semantic Graph Matching Net, termed as VSGMN, which leverages semantic relationships among classes to aid in visual-semantic embedding. VSGMN employs a Graph Build Network (GBN) and a Graph Matching Network (GMN) to achieve two-stage visual-semantic alignment. Specifically, GBN first utilizes an embedding-based approach to build visual and semantic graphs in the semantic space and align the embedding with its prototype for first-stage alignment. Additionally, to supplement unseen class relations in these graphs, GBN also build the unseen class nodes based on semantic relationships. In the second stage, GMN continuously integrates neighbor and cross-graph information into the constructed graph nodes, and aligns the node relationships between the two graphs under the class relationship constraint. Extensive experiments on three benchmark datasets demonstrate that VSGMN achieves superior performance in both conventional and generalized ZSL scenarios. The implementation of our VSGMN and experimental results are available at github: https://***/dbwfd/VSGMN. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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Retraction Note: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in Engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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Multi-features learning via Attention-BLSTM for Speech Emotion Recognition

Multi-features learning via Attention-BLSTM for Speech Emoti...

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Adil Chakhtouna Sara Sekkate Abdellah Adib Team Data Science & Artificial Intelligence Laboratory of Mathematics Computer Science and Applications (LMCSA) Faculty of Sciences and Technologies Hassan II University Mohammedia Morocco Higher National School of Arts and Crafts Hassan II University Casablanca Morocco

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

Within the domain of human-computer interaction, Speech Emotion Recognition (SER) has assumed critical importance, where the careful selection of feature representation stands as a key determinant. This study is driven by the objective of conducting a comprehensive comparative analysis through the implementation of a parallel multi-feature design, strategically aimed to effectively characterize the emotional information within speech signals. The innovation of our proposed approach lies in the integration of frameworks, including ImageBind, WavLM, HuBERT, Wav2vec 2-xlsr, along with the commonly used MFCC features. Leveraging their knowledge and adaptability, we tailor these frameworks to meet the specific demands of SER task. Furthermore, to encompass a rich set of multi-features, we introduced an Attention-based Bidirectional Long ShortTerm Memory (attention-BLSTM) network to capture and learn the distinct emotional speech representations extracted through various methods. The experimental evaluations were executed using the IEMOCAP database, and the most remarkable results were attained by employing ImageBind embeddings with our proposed attention-BLSTM, achieving an accuracy of $81.03 \%$. This outcome outshines the current state-of-the-art approaches in SER.

关键词： Human computer interaction Emotion recognition Technological innovation Image recognition Accuracy databases Speech recognition

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PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging

arXiv

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arXiv 2024年

作者： He, Jinlong Li, Pengfei Liu, Gang He, Genrong Chen, Zhaolin Zhong, Shenjun College of Computer Science and Technology Heilongjiang Harbin China Monash Biomedical Imaging MelbourneVIC Australia Department of Data Science and AI Faculty of Information Technology MelbourneVIC Australia National Imaging Facility BrisbaneQLD Australia

Multimodal large language models (MLLMs) represent an evolutionary expansion in the capabilities of traditional large language models, enabling them to tackle challenges that surpass the scope of purely text-based applications. It leverages the knowledge previously encoded within these language models, thereby enhancing their applicability and functionality in the reign of multimodal contexts. Recent works investigate the adaptation of MLLMs as a universal solution to address medical multi-modal problems as a generative task. In this paper, we propose a parameter efficient framework for fine-tuning MLLMs, specifically validated on medical visual question answering (Med-VQA) and medical report generation (MRG) tasks, using public benchmark datasets. We also introduce an evaluation metric using the 5-point Likert scale and its weighted average value to measure the quality of the generated reports for MRG tasks, where the scale ratings are labelled by both humans manually and the GPT-4 model. We further assess the consistency of performance metrics across traditional measures, GPT-4, and human ratings for both VQA and MRG tasks. The results indicate that semantic similarity assessments using GPT-4 align closely with human annotators and provide greater stability, yet they reveal a discrepancy when compared to conventional lexical similarity measurements. This questions the reliability of lexical similarity metrics for evaluating the performance of generative models in Med-VQA and report generation tasks. Besides, our fine-tuned model significantly outperforms GPT-4v. This indicates that without additional fine-tuning, multi-modal models like GPT-4v do not perform effectively on medical imaging tasks. The code will be available here: https://***/jinlHe/PeFoMed. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Hybrid Genetic Algorithm and Ant Colony Algorithm for Solving Travelling Salesman Problem

Hybrid Genetic Algorithm and Ant Colony Algorithm for Solvin...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Jirawat Thongpiem Narong Punkong Chiabwoot Ratanavilisagul Somkiat Kosolsombat Department of Computer and Information Sciences Faculty of Applied Science King Mongkut's University of Technology North Bangkok (KMUTNB) Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Rajabhat University(KRU) Kanchanaburi Thailand Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

The traveling salesman problem (TSP) is a combinatorial optimization problem and a NP-complete problem. It is a well-known problem for comparison of algorithm performance. Many researchers try to solve the TSP by the meta-heuristic algorithms. Ant Colony Optimization (ACO) algorithm is a popular method to solve the TSP. To enhance performance of ACO for solving TSP, the recently proposed improving the ACO algorithm by the results from searching for 2-OPT algorithms that are applied with pheromones of ants. Moreover, when the population of ACO occurs trapping in local optimum, the pheromone of ants is re-initialized to solve trapping in local optimum problem. This algorithm is called Modified Ant Colony Optimization with updating Pheromone by Leader and Re-initialization Pheromone (MACO-LR). However, performance searching of MACO-LR can be improved. In this research, we propose to solve TSP using Hybrid Genetic Algorithm and Ant Colony Algorithm (HGAACO) to optimize the total travelled distance. The proposed technique is tested on twenty-three maps from the Traveling Salesman Problem Library (TSPLIB) and it is composed with MACO-LR. The results from the experiment show the proposed technique can enhance performance searching of MACO-LR.

关键词： Ant colony optimization Metaheuristics Traveling salesman problems Search problems Libraries Reliability NP-complete problem Genetic algorithms

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Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models

arXiv

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arXiv 2025年

作者： Hagemann, Niklas Kneib, Thomas Möllenhoff, Kathrin Institute of Medical Statistics and Computational Biology Faculty of Medicine University of Cologne Germany Division of Mathematics Department of Mathematics and Computer Science University of Cologne Germany Chair of Statistics and Campus Institute Data Science Georg August University Göttingen Germany

A central focus in survival analysis is examining how covariates influence survival time. These covariate effects are often found to be either time-varying, heterogeneous - such as being specific to patients, treatments, or subgroups - or exhibit both characteristics simultaneously. While the standard model, the Cox proportional hazards model, allows neither time-varying nor heterogeneous effects, several extensions to the Cox model as well as alternative modeling frameworks have been introduced. However, no unified framework for incorporating heterogeneously time-varying effects of covariates has been proposed. Such effects occur when a covariate influences survival not only in a heterogeneous and time-varying manner, but when the time-variation is also heterogeneous. We propose to model such effects by introducing heterogeneously time-varying coefficients to piecewise exponential additive mixed models. We deploy functional random effects, also known as factor smooths, to model such coefficients as the interaction effect of heterogeneity and timevariation. Our approach allows for non-linear time-effects due to being based on penalized splines and uses an efficient random effects basis to model the heterogeneity. Using a penalized basis prevents overfitting in case of absence of such effects. In addition, the penalization mostly solves the problem of choosing the number of intervals which is usually present in unregularized piecewise exponential approaches. We demonstrate the superiority of our approach in comparison to competitors by means of a simulation study. Finally, the practical application and relevance are outlined by presenting a brain tumor case study. © 2025, CC BY.

关键词： Regression analysis

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