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共 34580 条记录，以下是711-720 订阅

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Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse 29

Using Machine Learning Algorithms in Edge Intelligence for U...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Mohammadvand, Reza Mozayani, Naser Dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency. © 2025 IEEE.

关键词： Reinforcement learning

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Enhanced Brain Tumor Segmentation: A Study on the Efficacy of Multi-Model Ensembling Utilizing Diverse Backbones 26

Enhanced Brain Tumor Segmentation: A Study on the Efficacy o...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md. Hedayatul Hossain, Md. Rakib Akter, Farzana Srizon, Azmain Yakin Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Islam, Md. Minhazul Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Gliomas account for approximately 80% of all malignant brain tumors and are associated with the most unfavourable survival rates amongst all brain tumor types. Segmenting these tumors is integral to evaluating their dimensions and morphology, formulating tailored treatment plans, and estimating patients' life expectancy. Various approaches for brain tumor segmentation have been proposed, yet the model described herein stands out, offering promising results in this vital task. A comprehensive examination of three eminent convolutional neural network (CNN) models, namely Resnext50, Resnet50, and InceptionV3, was conducted, each characterised by its own unique structural framework. The enhancement of the segmentation approach's effectiveness was a focal point, leading to the exploration of ensemble modeling. This approach leverages the collective strengths of the aforementioned models, facilitated by softmax averaging. This collaborative initiative culminated in the creation of the Ensemble model, which outshoned its individual counterparts in performance metrics. Furthermore, the Ensemble model is poised to instigate significant advancements in brain tumor diagnostic procedures. The ensemble methodology is instrumental in refining the precision and automation of brain tumor diagnoses. Deep learning, as evidenced by the research, is a transformative element in medical image analysis, elevating the accuracy and efficiency of brain tumor segmentation. Consequently, this refined approach is set to become an invaluable asset for medical professionals in enhancing the accuracy of diagnoses and the efficacy of treatments for patients afflicted with brain tumors. © 2023 IEEE.

关键词： Backbone Architectures Brain Tumor Segmentation Deep Learning Ensemble Learning Evaluation Metrics Medical Image Analysis MRI Images U-Net

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Leveraging AI-Driven Strategies to Mitigate Employee Turnover in Commercial Industries 26

Leveraging AI-Driven Strategies to Mitigate Employee Turnove...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hossain, Md. Shakib Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Islam, Hedayatul Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Employee attrition remains a significant obstacle in the corporate sector, adversely influencing organizational coherence and productivity. This article introduces a well-rounded system, meticulously crafted to curtail employee turnover. An extensive analysis employing a diverse set of machine learning models, namely, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Random Forest, CatBoost, and XGBoost, has been performed to construct an efficacious predictive architecture. Despite the widespread adoption of machine learning in forecasting attrition, the interpretational challenges persist, largely attributable to the dependency on feature sets. This research underscores that the Min-Max Scaler empowers the Random Forest classifier to attain an exceptional 88.24% accuracy utilizing a condensed three-feature subset, underscoring the efficacy of feature reduction amidst expansive datasets. The study employs LIME and SHAP frameworks to delve into the interpretability of models, examining the influence of risk variables meticulously. SHAP illuminates both overarching and specific insights, while LIME focuses on dissecting local black-box outputs. This scholarly contribution extends beyond offering a potent AI resolution to the pressing challenge of employee attrition. It accentuates the paramountcy of model interpretability in deriving insightful HR decisions. Furthermore, the investigation accentuates the capability of AIinfused systems to bolster workforce consistency and efficiency in the corporate sphere. It unfolds actionable intelligence regarding the determinants of attrition, marking a significant stride towards a more informed and strategic approach to employee retention and organizational productivity. © 2023 IEEE.

关键词： Employee Turnover LIME Machine Learning Random Forest SHAP

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Microstrip Patch Antenna for Single Resonant Frequency Using Various Types of Slots

Microstrip Patch Antenna for Single Resonant Frequency Using...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Islam, Md Amirul Stea, Giovanni Uddin, Md Ashraf Mahmud, Sultan Rahma, Kh. Mustafizur University of Pisa Dept. of Information Engineering Pisa Italy Jagannath University Dept. of Computer Science & Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350322972

Microstrip patch antennas are widely utilized in WLAN applications, and body-centric wireless communication relies on multiple network links to get messages through. But the existing antenna size is big with less bandwidth, which increases the cost. This paper has taken a rigorous approach to design an antenna that will provide a single resonant frequency at the targeted bandwidth (2.45GHz). The patch size in the proposed antenna is 28mm by 27mm and energized by a 50 ohm microstrip feed line with dimensions 3.137mm by 18.35mm. The dielectric substrate of permittivity Er = 4.3 is used in the fabrication process. The optimal size and other antenna characteristics are found by finite difference time domain method based CST Studio Suite. Other parameters like return loss, input impedance, and bandwidth of the microstrip patch antennas are calculated using VNA and WATS software to verify the proposed work. Finally, the proposed slot-loaded antenna attains an improved bandwidth and gain while providing a single resonant frequency. © 2023 IEEE.

关键词： Bandwidth

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Futuristics Technologies for Reduction of Plastic Waste 13

Futuristics Technologies for Reduction of Plastic Waste

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Dhiman, Riya Sharma, Ashish Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Electrical Engineering Mohali India

ISBN: (纸本)9798350380583

Plastic plays a vital role in daily life most of the food items, Baggies, vegetables are packed in plastic material. As plastic is non-biodegradable there are harming effect on human beings and animals. Due to these global warming also increases. Although 80% Fishes, 88% Vertebrates, 56% Mammals and 52% Reptiles are affected due to plastic waste. In this work with the various techniques proposed like Eco-bricks, Furniture, Replace Plastic By Bamboo and also Doormats are made through the Plastic Waste. It is seen that by using above techniques significantly reduce the harmful effect of plastic and there is improvement in life of human beings and animals also the alternative techniques to reuse the plastic and to replace plastic are proposed with which there is a increase in the earning of poor peoples. Above techniques are helpful to decrease the employment and also by using these techniques more than 60% of plastic reuse, with this the temperature is also decreased. © 2024 IEEE.

关键词： Invertebrates

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EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series

EBV: Electronic Bee-Veterinarian for Principled Mining and F...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Hossain, Shamima Faloutsos, Christos Baer, Boris Kim, Hyoseung Tsotras, Vassilis J. Dept. of Computer Science and Engineering University of California Riverside United States School of Computer Science Carnegie Mellon University United States Dept. of Entomology University of California Riverside United States Dept. of Electrical and Computer Engineering University of California Riverside United States

ISBN: (纸本)9781611978032

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else? We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers;(ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly coincide with domain expert's opinions, showcasing our approach's potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data. Copyright © 2024 by SIAM.

关键词： Forecasting

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep National Institute of Technology Dept. of Computer Science -and Engineering Uttarakhand Pauri India IGDTUW Department of Information Technology New Delhi India Doon University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical;therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Rainfall Prediction: Leveraging Ensembling Models for Maximum Forecasting Performance 1

Enhanced Rainfall Prediction: Leveraging Ensembling Models f...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dasari, Krishna Kumar Nuthanakanti, Bhaskar Raju, K.Srujan Madhusudhan, Mudimela Nazare, Sanjana S Raji Reddy, Avala Cmr Technical Campus Dept. Computer Science and Engineering Telangana Hyderabad India Cmr Dept. of Mechanical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350367720

Many important fields rely on accurate rainfall predictions, including agriculture, water resource management, and emergency preparation. The many nonlinear interactions included in weather data are notoriously difficult for traditional approaches to grasp. Agriculture, water resource management, and disaster preparation are just a few of the many sectors that might benefit greatly from accurate rainfall predictions. Because meteorological data is both complicated and dynamic, traditional methods often get it wrong. Recent years have seen encouraging results in the use of machine learning (ML) approaches, especially ensemble models, to enhance the precision and consistency of precipitation forecasts. This research provides a new method for predicting precipitation using an ensemble of ML models. The ensemble model EERP algorithm combines the predictions of numerous base learners to create a more reliable and accurate forecast. Decision trees, random forests, gradient boosting machines, neural networks, and other ML approaches serve as the ensemble's base learners. Assess the effectiveness of the proposed approach and then compare it with some existing classifiers in which the proposed classifier gave the best accuracy when compared with the other i.e., 96 %. © 2024 IEEE.

关键词： Prediction models

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An ensemble learning-based machine learning with voting mechanism for chronic disease prediction

An ensemble learning-based machine learning with voting mech...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

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