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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Conv-attention ViT for classification of multi-label class imbalanced data of lung thoracic diseases

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Multimedia Tools and Applications 2024年 1-30页

作者： Oommen, Lintu Nikhila Nagajyothi, Chiluka Chebrolu, Srilatha Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem India

Chest X-ray image classification is a key study topic, and in order to increase performance and accuracy, the efficiency of vision transformers for this task has been examined. However, imbalanced datasets pose a significant challenge during model training, leading to biased results. The main focus is the classification of multi-labeled lung thoracic diseases using imbalanced data. To tackle class imbalance, synthetic data was generated using GANs and combined with images from diverse data sources, which enhanced generalization. To classify multi-labeled chest X-rays, this work proposes a Conv-Attention Vision Transformer (CA-ViT) model that uses local and global attention mechanisms to improve the average ROC-AUC score and F1-score. Proposed model, CA-ViT’s performance was evaluated on an imbalanced dataset and compared with ResNet-50, VGG-19, and ViT 32/384 models. The studies demonstrated that CA-ViT surpassed the performance of the other models, scoring an average ROC-AUC of 0.76 as opposed to 0.72, 0.75, and 0.68 for ResNet-50, VGG-19, and ViT 32/384 models, respectively. All models displayed improved assessment metrics when the dataset was balanced via synthetic data creation and the combination of several data distributions. When compared to the other models, CA-ViT performed best, earning an average ROC-AUC of 0.81. In contrast, the average ROC-AUC values for the ResNet-50, VGG-19, and ViT 32/384 models were 0.7, 0.73, and 0.79 respectively. CA-ViT achieved micro-average F1-score of 0.70. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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The IoT resource allocation and scheduling using Elephant Herding Optimization (EHO-RAS) in IoT environment

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International Journal of Information technology (Singapore) 2024年第5期16卷 3283-3293页

作者： Mageswari, Umaa Deepak, Gerard Santhanavijayan, A. Mala, C. Department of Computer Science and Engineering National Institute of Technology Tamilnadu Trichy 620015 India Department of Computer Science and Engineering Manipal Institute of Technology Karnataka Bengaluru 560064 India

IoT is one of the most significant technological breakthroughs and promises a higher level of connection and control in the future. The IoT network continues to expand rapidly, and the IoT ecosystem comprises millions of interconnected ad hoc devices across the network. Effective resource utilization guarantees the improvement of service quality. Everything is connected to the Internet through the distribution system known as the Internet of Things (IoT). Plenty of gateways and resources are in IoT infrastructure. Resource allocation (RA) is challenging due to network heterogeneity and the diversity of IoT devices;numerous practical approaches, strategies, and implementations are being presented and employed to resolve the RA problem (RAP). IoT resource allocation and scheduling (RAS) performance is essential in such a system since RAS allocates resources to open gateways and handles mapping resources and gateways. A gateway is needed to connect to hundreds of resources in the IoT environment. The proposed work is based on the RAS problem and aims to achieve optimal RA in the IoT by using the Elephant Herding Optimization (EHO) algorithm to lower the total Communication Cost between gateways and resources. The proposed EHO algorithm has been contrasted with others already in use, and the results show that the suggested algorithm performs as expected. The proposed solution is superior to others regarding TCC and Convergence rate than Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Gray Wolf Optimization (GWO). © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Communication cost EHO Fitness IoT Gateways IoT Resources Resource Allocation ToT

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A Computational Deep Fuzzy Network-Based Neuroimaging Analysis for Brain Hemorrhage Classification

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-8页

作者： Malik, Payal Vidyarthi, Ankit Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

The boundaries and regions between individual classes in biomedical image classification are hazy and overlapping. These overlapping features make predicting the correct classification result for biomedical imaging data a difficult diagnostic task. Thus, in precise classification, it is frequently necessary to obtain all necessary information before making a decision. This paper presents a novel deep-layered design architecture based on Neuro-Fuzzy-Rough intuition to predict hemorrhages using fractured bone images and head CT scans. To deal with data uncertainty, the proposed architecture design employs a parallel pipeline with rough-fuzzy layers. In this case, the rough-fuzzy function functions as a membership function, incorporating the ability to process rough-fuzzy uncertainty information. It not only improves the deep model's overall learning process, but it also reduces feature dimensions. The proposed architecture design improves the model's learning and self-adaptation capabilities. In experiments, the proposed model performed well, with training and testing accuracies of 96.77% and 94.52%, respectively, in detecting hemorrhages using fractured head images. The comparative analysis shows that the model outperforms existing models by an average of 2.6$\pm$0.90% on various performance metrics. IEEE

关键词： Classification (of information)

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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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OptiFog: A Framework for Acquiring State Information and Predicting Resource Availability for Task Offloading in Cooperative Fog-Networks

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IEEE Transactions on Services Computing 2024年 1-13页

作者： Alam, Mehbub Ahmed, Nurzaman Ghosh, Shyamal Matam, Rakesh Barbhuiya, Ferdous Ahmed Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Dartmouth College Hanover USA School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper, OptiFog: a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show that OptiFog lowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art. IEEE

关键词： computer architecture

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A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4489-4501页

作者： Ghosh, Anay Dhara, Bibhas Chandra Pero, Chiara Umer, Saiyed Department of Computer Science and Engineering University of Engineering and Management Newtown Kolkata India Department of Information Technology Jadavpur University Kolkata India Department of Computer Science University of Salerno Fisciano Italy Department of Computer Science and Engineering Aliah University Kolkata India

This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-based sentiment analysis system to perform classification tasks such as predicting the classes into non-aggressive, covertly aggressive, and overtly aggressive classes. The remaining two components are related to an image-based sentiment analysis system. A deep learning-based approach has been employed to do feature learning and predict the three types of pain classes. An aggression dataset for the text-based system and the UNBC-McMaster database for an image-based system has been employed, respectively. Experimental results have been compared with the state-of-the-art methods, showing the superiority of the proposed approach. Finally, the scores due to text-based and image-based sentiment analysis systems are fused to obtain the performance for the proposed multimodal sentiment analysis system. © 2023, The Author(s).

关键词： Sentiment analysis

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Autonomous embodied navigation task generation from natural language dialogues

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science China(Information sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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