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CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1883-1908页

作者： Kumari, J. Jesy Janet Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Vehicular ad hoc networks (VANETs) are an essential element and building block of the autonomous vehicle system. VANETs, a subcategory of mobile ad hoc networks (MANETs), stand out due to certain predetermined attributes. In VANETs, optimal path routing offers solutions to address issues like traffic density, node mobility, link failure, and bandwidth. Finding the optimal VANET road path between source and destination conditions is difficult. Extensive paths increase network overhead, communication costs, and failure probability, reducing routing efficiency. We present the new traffic density-based stigmergic ant colony optimization routing (CSG_IACO) to improve routing performance. Improved ant colonies stimulate the proposed CSG_IACO, which measures inter-vehicular density. Ant behavior serves as the inspiration for the improved ACO technique, a meta-heuristic methodology. Phase 1 of the CSG_IACO involves using the traffic density to form a cluster among the vehicles. Phase 2 of the suggested approach is Stigmergy-based ant colony optimization, which guides ants to indirectly communicate with each other to act upon the change in the environment to find a best path. This improves protocol efficiency in all aspects. The simulations using NS 2.35 and SUMO indicate that the proposed system considerably reduces obstacles to finding the optimal path compared to existing systems. The simulations show that our suggested scheme performs better than AODV-DATRLD, ACO-DATRLD, and CaCOIoV in terms of performance including the count of delivered packets, throughput, end-to-end delay, the average count of cluster formations per cluster head, and the number of dropped packets. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Ant colony optimization Cluster Optimal path QoS Stigmergy Traffic density VANETs

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Generating diverse clothed 3D human animations via a generative model

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Computational Visual Media 2024年第2期10卷 261-277页

作者： Min Shi Wenke Feng Lin Gao Dengming Zhu School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Data-driven garment animation is a current topic of interest in the computer graphics *** approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human *** address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion *** the heart of our method is a twostage ***,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder *** the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion *** means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial *** verify our ideas *** is the first generative model that directly dresses human animation.

关键词： Transformer garment animation conditional variational autoencoder(CVAE) computer graphics

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Electricity Cost Minimization for Multi-Workflow Allocation in Geo-Distributed Data Centers

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IEEE Transactions on Services computing 2025年第3期18卷 1397-1411页

作者： Wang, Shuang Zhang, He Wu, Tianxing Zhang, Yueyou Zhang, Wei Emma Sheng, Quan Z. Southeast University School of Computer Science and Engineering Nanjing211189 China The University of Adelaide School of Computer and Mathematical Sciences Australian Institute for Machine Learning Australia Macquarie University School of Computing SydneyNSW2109 Australia

Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to reduce electricity costs while satisfying the deadline constraints of workflow applications is important in GDCs, which is determined by the execution time of servers, power, and electricity price. Determining the completion time of workflows with different server frequencies can be challenging, especially in scenarios with heterogeneous computing resources in GDCs. Moreover, the electricity price is also different in geographical locations and may change dynamically. To address these challenges, we develop a geo-distributed system architecture and propose an Electricity Cost aware Multiple Workflows Scheduling algorithm (ECMWS) for servers of GDCs with fixed frequency and power. ECMWS comprises four stages, namely workflow sequencing, deadline partitioning, task sequencing, and resource allocation where two graph embedding models and a policy network are constructed to solve the Markov Decision Process (MDP). After statistically calibrating parameters and algorithm components over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art methods over two types of workflow instances. The experimental results demonstrate that our proposed algorithm significantly outperforms other algorithms, achieving an improvement of over 15% while maintaining an acceptable computational time. The source codes are available at https://***/public-artifacts/ecmws-experiments. © 2025 IEEE.

关键词： Graph embeddings

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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On Neutrosophy logic for Multiple Correlation Analysis in Fuzzy Uncertain Database

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Neutrosophic Sets and Systems 2025年 76卷 1-17页

作者： Wazir, Samar Beg, M.M. Sufyan Ahmad, Tanvir Sohail, Shahab Saquib Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Computer Engineering Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India

The primary goal of this paper is to introduce a novel method for mining frequent and interesting items by incorporating correlation analysis between two items in an uncertain transactional database using the OWA operator. The work is further expanded by proposing two additional methods for mining frequent and interesting items, utilizing fuzzy means and the OWA operator along with multiple correlation analysis for more than two items in uncertain transactional databases. The effectiveness of the proposed methods is evaluated by running the algorithms on both standard and example datasets, with results compared to the traditional probabilistic approach for identifying frequent and interesting items through multiple correlation. While multiple correlation analysis highlights the relationships of interestingness and uninterestingness between items, the OWA operator enhances the results when combined with fuzzy means and probabilistic methods. Additionally, the paper suggests a future research direction using Neutrosophy logic, which is anticipated to open new avenues for further exploration in this field. © 2025, University of New Mexico. All rights reserved.

关键词： Database systems

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The impact of backbone selection in YOLOv8 models on brain tumor localization

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Iran Journal of computer Science 2025年 1-23页

作者： Ranjbarzadeh, Ramin Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

This study investigates the utilization of the You Only Look Once (YOLOv8) deep learning framework for accurately identifying the location of brain tumors in medical imaging. We investigate the effects of model size and pretraining on the accuracy and computational efficiency of tumor detection by utilizing different setups of the YOLOv8 model. These setups include various configurations, ranging from very small to large, and can be pretrained on the COCO dataset or not. The experimental results, carried out on Google Colaboratory using NVIDIA Tesla T4 GPUs, show that pretrained models often achieve better performance by utilizing the extensive feature representations learned from the COCO dataset, resulting in increased precision in tumor location. For instance, the YOLOv8-XS model pretrained on COCO achieves an IoU of 0.278 and a Dice coefficient of 0.435, whereas its non-pretrained counterpart attains only 0.241 IoU and 0.388 Dice, indicating a 15% improvement in tumor localization accuracy. Similarly, pretrained YOLOv8-L achieves 0.269 IoU, outperforming standard object detection models such as Mask R-CNN (IoU: 0.212) and Faster R-CNN (IoU: 0.228). These results highlight the impact of pretraining on model performance, particularly for lightweight architectures, while also revealing diminishing returns for larger models. The research uncovers a subtle connection between the complexity of the model, pretraining, and the time required for training. It emphasizes the possible advantages and constraints of pretraining for various sizes of models. © The Author(s) 2025.

关键词： Brain tumor localization COCO dataset Deep learning Object detection Transfer learning

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of computer Science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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Development of a diacritic-aware large vocabulary automatic speech recognition for Hausa language

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International Journal of Speech Technology 2024年第3期27卷 687-700页

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Vekkot, Susmitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics & amp Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Research on voice recognition for African languages is limited due to the scarcity of digital resources for training and adaptation, despite its broad usefulness. The Hausa language, spoken by almost fifty million inhabitants in West and Central Africa, is an example of a linguistic domain that has not been thoroughly studied. The Hausa language employs diacritics, which are symbols located above alphabetical characters to convey further information. By removing diacritics, the number of homographs increases, making it difficult to distinguish between similar words. This paper presents a study on speech recognition in the Hausa Language, specifically focusing on diacritized words. The study utilises the state-of-the-art wave2vec2.0 and Whisper deep learning architecture models, for transcribing audio signals into corresponding Hausa text. According to the results obtained in the study, the Whisper-large deep model emerged as the best, achieving a word error rate of 4.23% representing a considerable improvement of 43.9% when compared to the existing state-of-the-art model for Hausa language speech recognition. Additionally, the Whsiper-large model demonstrated a diacritic coverage of 92%, precision of 98.87%, with a diacritic error rate of 2.1%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Robust Hybrid Machine Learning Model for Financial Fraud Detection in Credit Card Transactions 2

Robust Hybrid Machine Learning Model for Financial Fraud Det...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Jahnavi, Dingari Mona, A. Pulata, Sandeep Sami, Sasank Vakamullu, Bharadwaj Mohan Bharathi, G. Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

Financial fraud is a rising problem that affects both organizations and people, necessitating cutting-edge solutions to lessen its effects. The majority of machine learning models used now in the field of fraud detection include Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). While these traditional models frequently lack sensitivity, have trouble figuring out complex fraud patterns, and are rigid in their ability to adjust to new threats. To overcome them, this research work provides a significant advancement by combining Decision Trees and Logistic Regression into a hybrid model, boasting an impressive accuracy of 98.1%. The proposed methodology not only improves the system's ability to detect fraud accurately but also makes it more flexible to the changing fraud strategies. Additionally, a thorough comparison analysis highlights the approach's undeniable benefits. In a nutshell, this work provides a method for detecting financial fraud using a hybrid model that expertly strikes a compromise between sensitivity and accuracy. In addition to improving fraud detection, this innovation also strengthens the security of financial systems. © 2024 IEEE.

关键词： Comparison analysis Contemporary world Financial fraud Flexibility Fraudulent transactions Hybrid model

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Advancements in Multi-Objective Personalized Drug Design: A Comprehensive Review of Innovative Approaches and Technologies 2

Advancements in Multi-Objective Personalized Drug Design: A ...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mallavarapu, Jashritha Reddy Navanithi, V.K. Bejjanki, Mohit Manomay Mithun Menon, M. Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

This comprehensive review paper presents an in-depth exploration of diverse approaches employed in multi-objective personalized drug design, encompassing cutting-edge methodologies that leverage advanced technologies. The multifaceted landscape of drug design is scrutinized through the lenses of neural network approaches, stochastic modeling, decentralization, blockchain-based solutions, topological optimization strategies, genetic algorithm optimization, energy consumption optimization, and blockchain-based pharmaceutical supply chain management. Each approach's unique strengths and applications are thoroughly examined, shedding light on their respective contributions to the burgeoning field of personalized medicine. This review not only synthesizes the current state of research but also identifies opportunities for synergistic integration of these approaches to further enhance the precision and efficacy of personalized drug design, ultimately paving the way for a transformative era in healthcare. © 2024 IEEE.

关键词： Blockchain

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