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A Compact Tri-Port MIMO Antenna With High Isolation for Broadside and Conical Radiation

IEEE Internet of Things Journal 2025年第9期12卷 12219-12225页

作者： Gao, Guo-Ping Bai, Jin-Ming Jin, Li-Jun Guo, Wen-Di Ma, Ge Hu, Bin Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou730000 China

Wearable Internet of Things (IoT) devices are widely used in many fields. Such as smart bracelets for health, smart watches for sports, smart safety helmets for industry, and so on. These devices make life easier and more efficient. Multiple-input–multiple-output (MIMO) antenna technology improves data transmission rates and communication reliability by using multiple antennas in a device, so as to meet the demand for real-time data transmission and multidevice connectivity in the IoT. A compact tri-port MIMO antenna with high isolation suitable for wearable applications is proposed. The antenna is designed to work in the frequency band of 2.45 GHz industrial, scientific, and medical (ISM). The antenna has two layers and three ports with low profile suitable for wearable applications. The upper layer is a circular patch with four shorting pins, which excites the TM01 mode with a conical radiation pattern. The lower layer is a circular patch inlaid in an annular patch, which respectively excite the TM11 mode and the TM31 mode with a broadside radiation pattern. To achieve high isolation, characteristic mode analysis (CMA) was conducted on the circular patch, the circular patch with four shorting pins, and the annular patch. The positions of the three ports are reasonably arranged based on mode diversity. The measured bandwidth of the port for conical radiation is 35 MHz (2.43–2.465 GHz), while the measured bandwidths of the two ports for broadside radiation are basically the same which is 50 MHz (2.417–2.467 GHz). The measured overall isolation exceeds 50 dB. © 2014 IEEE.

关键词： Wearable antennas

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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms

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Green Chemical engineering 2025年第2期6卷 193-199页

作者： Ao Yang Shirui Sun Lu Qi Zong Yang Kong Jaka Sunarso Weifeng Shen College of Safety Science and Engineering Chongqing University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center School of Chemistry and Chemical Engineering Chongqing University School of Chemistry and Chemical Engineering Yangtze Normal University Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology School of Petrochemical EngineeringChangzhou University School of Engineering and Technology Sunway University Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of Technology

This study aims to significantly improve existing quantitative structure-property relationship（QSPR） models for predicting the octanol-water partition coefficient(KOW). This is because accurate predictions of KOWare crucial for assessing the environmental behavior and bioaccumulation potential of chemicals. Previous models have reported determination coefficient（R2） values between 0.9451 and 0.9681, and this research seeks to exceed these benchmarks. Three machine learning（ML） models are explored, r.e., feed-forward neural networks（FNN）,extreme gradient boosting（XGBoost）, and random forest（RF）. Using a dataset of 14,610 solvents（14,580 after data cleaning） and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error（MAE）, root mean squared error（RMSE）, and mean relative error（MRE）.Notably, the best model developed, the XGBoost-based QSPR, demonstrated exceptional performance, exhibiting an impressive R2value of 0.9772, surpassing benchmarks set by prior research models. Additionally, shapley additive explanation（SHAP） analysis is also employed for model interpretation, and it is revealed that the top five influential input features include SMR＿VSA8, SMR＿VSA3, Kappa2, Heavy Atom Count, and fr＿furan. This study not only sets a new benchmark for KOWprediction accuracy but also enhances the interpretability of QSPR models.

关键词： Octanol-water partition coefficient Environmental property QSPR Interpretable model Machine learning

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HDeFC: Hierarchical Secure Fuzzy Deduplication Based on Fog computing

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IEEE Internet of Things Journal 2025年第9期12卷 12658-12668页

作者： Tang, Zehui Zeng, Shengke Han, Song Liu, Jun He, Mingxing Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China

Secure deduplication not only optimizes cloud storage but also prevents data leakage. However, traditional schemes are with high computation and communication costs to deal with large-scale multimedia data. To address this problem, we propose a fog computing-based fuzzy deduplication (HDeFC), a two-stage privacy-preserving deduplication protocol. Our approach prevents guessing attacks and single point of failure, besides that also implements noninteractive Proof of Ownership and label consistency verification. In addition, we also achieve the access permission for only legitimate users. Our performance evaluation shows that the hierarchical setting is efficient in deduplication. © 2014 IEEE.

关键词： Information leakage

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A Green Cloud-Based Framework for Energy-Efficient Task Scheduling Using Carbon Intensity Data for Heterogeneous Cloud Servers

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IEEE Access 2025年 13卷 73916-73938页

作者： Beena, B.M. Prashanth, C.S.R. Manideep, Thotapalli Sri Surya Saragadam, Sneha Karthik, Garikipati Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India University of Windsor WindsorON Canada

Managing modern data centre operations is increasingly complex due to rising workloads and numerous interdependent components. Organizations that still rely on outdated, manual data management methods face a heightened risk of human error and struggle to adapt quickly to shifting demands. This inefficiency leads to excessive energy consumption and higher CO2 emissions in cloud data centres. To address these challenges, integrating advanced automation within Infrastructure as a Service (IaaS) has become essential for IT industries, representing a significant step in the ongoing transformation of cloud computing. For data centres aiming to enhance efficiency and reduce their carbon footprint, intelligent automation provides tangible benefits, including optimized resource allocation, dynamic workload balancing, and lower operational costs. As computing resources remain energy-intensive, the growing demand for AI and ML workloads is expected to surge by 160% by 2030 (Goldman Sachs). This heightened focus on energy efficiency has driven the need for advanced scheduling systems that reduce both carbon emissions and operational expenses. This study introduces a deployable cloud-based framework that incorporates real-time carbon intensity data into energy-intensive task scheduling. By utilizing AWS services, the proposed algorithm dynamically adjusts high-energy workloads based on regional carbon intensity fluctuations, using both historical and real-time analytics. This approach enables cloud service providers and enterprises to minimize environmental impact without sacrificing performance. Designed for seamless integration with existing cloud infrastructures—including AWS, Google Cloud, and Azure—this scalable solution utilizes Kubernetes-based scheduling and containerized workloads for intelligent resource management. By combining automation, real-time analytics, and cloud-native technologies, the framework significantly enhances energy efficiency compared to traditional

关键词： Infrastructure as a service (IaaS)

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Network Coding for Efficient Data Transmission in COVID-19 Genomic Research

Network Coding for Efficient Data Transmission in COVID-19 G...

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2025 International Conference on Intelligent Control, computing and Communications, IC3 2025

作者： Alam, Sultan Mathur, Aryan Srivastava, Akshat Bisht, Siddharth Singh Solanki, Sandeep School of Computing Science and Engineering Vit Bhopal University Sehore466116 India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore466116 India

ISBN: (纸本)9798331527495

We looked at the potential impact of network coding in terms of efficiency during the transmission of data related to protein folding simulations. Classical methods for data transmission encounter significant inefficiencies arising from the loss of packets. Our method utilizes algebraic combination for improving transmission efficiency while using network coding for efficient transmission of packet data. Results from tests showed that the network coded achieved high improvements: while packet loss rates were set at 10%, 20%, and 30% respectively, the network was transmitting 120 Mbps, 90 Mbps, and 60 Mbps. While this was in contrast to 80 Mbps, 60 Mbps, and 40 Mbps for the traditional methods used. This means that every one of the scenarios had been improved by a consistent rate of 50%. This research explores the possibility of network coding improving data transmission in high packet loss environments, thus speeding up simulations and improving resource management. Network coding will lead to faster and more accurate results in protein folding simulations, significantly benefiting computational biology and related fields. © 2025 IEEE.

关键词： Resource allocation

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Enhancing Connectivity of AODV in Urban Scenarios for VANETs: A QoS-Aware Approach 4

Enhancing Connectivity of AODV in Urban Scenarios for VANETs...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Janet Kumari, J Jesy Thangam, S. Ghosh, Archishman Kolhe, Dhruv Vijay Ghosh, Soumish Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

As cities expand, vehicles and congestion become more complex. Efficient vehicle-to-vehicle contact networks are needed for road safety and efficient traffic flow. Thus, Vehicular Ad Hoc Networks are needed to overcome all the issues. The Adhoc On-Demand Distance Vector (AODV) routing protocol is used in VANETs. However, this protocol has many challenges in urban settings due to high vehicle density, rapid topology changes, and non-line-of-sight conditions. This work improves AODV by integrating Quality of Service (QoS) metrics. The prioritizing factors such as overhead and end-to-end delay are used to create a more efficient protocol for urban traffic. The SUMO simulator is used for realistic traffic patterns and NS-2 for network analysis. An evaluation of the protocol against the classical AODV is done and important metrics for real-time communication are focused. The enhanced AODV showed marked improvements. It achieved lower overhead in route finding, an increase in the number of packets sent, and lower end-to-end delay. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

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Agri-Ontology: A Novel Ontology Framework for Agricultural Domain 3

Agri-Ontology: A Novel Ontology Framework for Agricultural D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Deepa, R. Jayaraman, Venkatesh Srinivasulu, Senduru Umamageswari, B. Surendran, R. Srm Institute of Science & Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Srm Institute of Science & Technology School of Computing Department of Computing Technologies Chennai600119 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The agricultural information system deals with massive amounts of data from heterogeneous sources. It helps the farmers gain accurate information by providing better insights. A significant issue in agricultural data is its unstructured form, which can be resolved through ontology - a novel framework known as AgriOntology is proposed to address this issue for the Agrovoc dataset. The data in the unstructured form is initially pre-processed through the Natural Language Processing approach to generate features for ontology construction. The BERT and Jaccard similarity are applied to establish a relationship between the features used to construct the Agri-Ontology. Post ontology construction, the BiGAN framework is used to analyze the performance. The performance of the constructed ontology is observed to have an accuracy of 94.64%, which is higher than the existing models. © 2025 IEEE.

关键词： Data assimilation

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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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