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共 12570 条记录，以下是111-120 订阅

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Comprehensive Analysis on Optimal Routing in wireless Mesh Network Using Optimization and Machine/Deep Learning algorithms

Comprehensive Analysis on Optimal Routing in Wireless Mesh N...

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Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Kaata Ranjith Kumar K. Bala Kalyanapu Srinivas Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Vaagdevi Engineering College Warangal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

wireless Mesh Networks (WMNs) is an important component of wireless communications by enhancing the network coverage. The mesh network topology is interconnecting all nodes in the cluster. However, the existing approaches utilized for optimal routing in WMN face challenges such as increased delays, packet loss, network congestion that lead to inefficient data transmission. WMN has an ability to deliver efficient performance even with a large number of devices. Additionally, WMNs support a wide range of applications, by using its effectiveness in various scenarios. The fundamental architecture of a mesh network integrates multiple wireless nodes interconnected through base stations for efficient routing communication. Mesh networks able to adopt different client-based structures, including infrastructure and hybrid configurations. Optimization, learning techniques and Software defined network methods are utilized for efficient optimal routing to enhance the efficiency of data transmission, while reducing delay. In this study, routing-based optimization and Machine (ML), Deep Learning (DL) algorithms are reviewed along with its advantages and limitations. The performance ML or DL model is utilized for optimal routing in WMN is evaluated based on performance metrics such as Delay, Throughput, Packet Delivery Ratio (PDR) and Connected Client Ration (CCR).

关键词： wireless communication Mesh networks Network topology wireless mesh networks Packet loss Routing Delays Topology Data communication Optimization

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Smart Intelligence-Driven Deep Learning Framework for Reliable Detection and Accurate Classification of Skin Cancer Diseases 3

Smart Intelligence-Driven Deep Learning Framework for Reliab...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Rathish Babu, T.K.S. Chandramohan, S. Be, Hajira Niroshini Infantia, H. Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya Department of Computer Science Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Computer Applications Maduranthakam Taluk India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331509675

The interest in skin cancer detection is growing remarkably because of the prevalence of this health condition worldwide. It improves the chances of survival and enhances general treatment outcomes when diagnosis has been done early. Most the traditional methods for skin lesion detection involve visual inspection performed by dermatologists;hence, it is time-consuming and, to some extent, subjective. Therefore, with the rise of deep learning-Artificial Intelligence, auto-detection of skin cancer has gained a foothold in improving diagnostic accuracy, hence treatment outcomes. The presented paper proposes a GAN-based approach for skin cancer detection by deep learning analysis of dermoscopic images, classifying them as malignant and benign. The proposed approach uses the generator and discriminator parts of GAN to obtain an effective discrimination between cancerous and non-cancerous lesions with high precision. The proposed method incorporates the native abilities of GANs for boosting diagnostic precision. GANs consist of two major components: a generator and a discriminator. These two adversaries collaborate with each other in a way to encourage the classification performance of the framework. The generator synthesizes the realistic data representations by augmenting the dataset with various variations of dermoscopic images. This helps not only to compensate for the insufficiency of annotated data but also to make the model generalize more robustly to diverse skin lesion patterns. At the same time, the discriminator learns to distinguish malignant from benign lesions with high accuracy by using the adversarial nature in GAN training. These results clearly indicate the effectiveness of the proposed GAN-based approach over state-of-the-art traditional deep learning models. GAN makes not only the detection system more robust but also reduces its dependency on large annotated datasets. © 2025 IEEE.

关键词： Adversarial machine learning

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Ensemble-based Deep Learning Framework for Tuberculosis Detection in Radiographs 3

Ensemble-based Deep Learning Framework for Tuberculosis Dete...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Shameem Sulthana, S.H. Rathiya, R. Sarathy, J. Partha Kishore, Gudipudi Srimathi, S. Jegajothi, B. Srm Institute of Science and Technology Department of Computer Science and Applications Vadapalani Chennai India Dr. N. G. P Institute of Technology Department of Information Technology Coimbatore India Jayalakshmi Institute Of Technology Thoppur Dharmapuri India University College of Arts and Science Krishna University Department: Biosciences and Biotechnology Machilipatnam521004 India Saveetha School of Engineering Smats Department of Bio Technology Chennai India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331509675

Tuberculosis (TB) continues to be a serious worldwide health concern, and in order to effectively treat it, it is necessary to diagnose it accurately and in a timely manner. The purpose of this research is to provide a robust framework for tuberculosis diagnosis that makes use of a deep learning-based model called DenseNet201 in conjunction with a stacking ensemble method in order to improve diagnostic accuracy from chest X-ray pictures. Deep features are extracted in an effective manner by the DenseNet201 architecture, which captures intricate patterns that are suggestive of tuberculosis. Support Vector Machine (SVM), Random Forest (RF), and XGBoost are the three base classifiers that receive these features as input. The outputs of these classifiers are then aggregated with a meta-classifier to get the final prediction. This helps to further improve the performance of the prediction. This stacking strategy helps to minimize the flaws of individual classifiers, hence minimizing the number of false positives and negatives seen in medical diagnostics, which is an extremely important skill. A high accuracy of 95.3% and an area under the curve (AUC) of 0.973 are demonstrated by the model after it has been trained and assessed on the TBX11K dataset. This greatly outperforms the performance of older approaches. In order to increase the quality of the data and better the performance of the model, preprocessing processes were used. These stages included normalization, data augmentation, and contrast enhancement. Due to the fact that the suggested ensemble model generalizes well to data that has not been encountered before, it is a trustworthy and effective instrument for early and accurate tuberculosis screening. This method has the potential to be implemented in clinical settings that are representative of the real world in order to provide assistance to medical professionals in the diagnosis of tuberculosis. © 2025 IEEE.

关键词： Support vector machines

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Application of wireless ECG in Saving People in Disasters and Accidents 3rd

Application of Wireless ECG in Saving People in Disasters an...

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3rd international conference on Environmental Protection and Disaster Risks, EnviroRISKs 2024, and 12th Annual CMDR COE conference on Crisis management and Disaster Response, CMDR 2024

作者： Ivanova, Veronika Boneva, Ani Institute of Robotics Bulgarian Academy of Sciences Bl. 2 Acad. G. Bonchev Str. Sofia1113 Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Bl. 2 Acad. G. Bonchev Str. Sofia1113 Bulgaria

ISBN: (纸本)9783031747069

Examining the casualty’s cardiac activity is an important component of first aid procedures before transporting the casualty to a specialised medical facility. During disasters and accidents, there are many victims and emergency teams must be equipped with specialised portable medical devices for measuring ECG, pulse, blood sugar, temperature, etc. This paper describes an innovative solution for ECG monitoring of victims during disasters and accidents. The proposed device provides the possibility of a quick warning of a deviation from the heart rate norms. A novel concept for communication is suggested based on uMAC. The results obtained from the device can be monitored remotely using a management program of the Local Operator Station (laptop, medical tablet or mobile device). AR/VR-based simulation software is also planned to be developed to train emergency medical personnel to operate the device. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Remote patient monitoring

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

3rd International Conference on Language Processing and Know...

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

ISBN: (纸本)9783031850660

The proceedings contain 29 papers. The special focus in this conference is on Language Processing and Knowledge Management. The topics include: Machine Learning Serving the School Orientation Process;improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on Natural Language Processing;towards a Maude-Based Approach for Formal Modeling Deep Neural Networks;Anti-pattern Based IoRT-Aware Business Process Structure Verification Approach;opinion Analysis Based on a Sentiment Lexical Ontology and Deep Learning Models: Tunisian Dialect Case;disfluent-to-Fluent Tunisian Dialect Speech Translation with Fine-Tuning Pre-trained Language Models;BERT-Based Model for Sarcasm Detection in Arabic Texts;typology of Event Data Imperfections;towards Sentiment Analysis for Libyan Dialect;text Categorization Can Enhance Domain-Agnostic Stopword Extraction;normalized Orthography for Tunisian Arabic;deep Learning Approach for Early Prediction of Depression on Social Network;adapting Large Language Models to Biomedical Domain: A Survey of Techniques and Approaches;tunisian Arabic Understanding: Resources Analysis and Evaluation;from Data to Decisions: An Ontology-Driven Method for Opinion Mining;LLMs for Cyberbullying Detection in Political Social Media;Assessing BERT Models for Arabic Named Entity Recognition in a Multi-dialectal Context;tunisian Normalized Pronunciation;traffScOnto: Ontology for Traffic Management in the Context of Smart City Domain;SERTUS Dataset Collection from Spontaneous Environments;an Agricultural Sentiment Dataset for Pest Control and Crop Diseases;deep Learning Approach to Identify and Classify Arabic Verbal Multi-word Expressions;a Design Pattern-Based Approach for Analyzing MapReduce applications;analyzing the Impact of Big Data in Mental Health;The Impact of AI on Knowledge Management;a Rule-Based System for Translating Libyan Dialect Dual Forms to Modern Standard Arabic.

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Deep Reinforcement Learning-Based Energy-Efficient Aggregation Model for wireless Sensor Network 3rd

Deep Reinforcement Learning-Based Energy-Efficient Aggregati...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Alsalmi, Nada Navaie, Keivan Rahmani, Hossein Department of Computing and Communications Lancaster University LA1 4WA Lancaster United Kingdom

ISBN: (纸本)9789819726707

The proposed work discusses aggregation methods for deep reinforcement learning (DRL)-based wireless sensor networks. The aggregation algorithm plays a vital role in reducing overall energy consumption. The aggregation method aggregates the data samples, removes the redundant data, and reduces the number of overall packets, reducing overall energy consumption. We begin with the introduction, followed by the discussion on routing protocols with aggregation for MWSNs, which presents an overview of different routing protocols with aggregation. Optimized Link State Routing (OSLR) performance without aggregation is compared with the OLSR protocol with aggregation. Subsequently, routing protocols based on SOM concerning WSNs are elaborated. The SoM-OLSR with aggregation is reviewed and compared with the SoM-based routing protocol without aggregation for MWSNs. Similarly, the DRL with aggregation is reviewed and compared with the DRL-based routing protocol without aggregation for MWSNs. The result proves that DRL-OLSR ‘with aggregation’ saves 50% of energy with respect to ‘without aggregation.’ The results prove that the average energy saving is more than 30% for a high-density network. Thus, we can conclude that the proposed DRL-based aggregation-based method performs much better with respect to other existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aggregation Deep reinforcement learning Optimized link state routing wireless sensor network

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Selection of Optimized Antenna Design for Improved Performance in Gain, Return Loss, and Efficiency for Access Point in WLAN

Selection of Optimized Antenna Design for Improved Performan...

引用

Disruptive Technologies (ICDT), international conference on

作者： Simarjit Singh Malhi Raj Kumar Computer Science and Engineering RIMT University Mandi Gobindgarh India Lovely Professional University Phagwara India Computer Applications RIMT University Mandi Gobindgarh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Optimizing antenna design is critical for improving performance metrics such as gain, return loss, and efficiency in modern communication systems. This paper evaluates two antenna configurations, including patch, and array designs, to achieve improved performance. The primary goal is to improve gain, minimize return loss, and maximize efficiency by optimizing key design parameters such as antenna frequency, feed technique, and substrate materials. Using simulation tool such as IE3D, antenna designs are modeled and analyzed to assess the impact of designing parameters. The results demonstrate that optimized designs lead to more efficient, high-gain antennas suitable for applications like next-generation wireless networks. The study concludes that efficient design and designing parameters significantly enhance antenna performance, contributing to the development of robust and reliable communication systems.

关键词： wireless LAN Communication systems Design methodology wireless networks Antenna feeds Reliability engineering Antennas Substrates Next generation networking Antenna arrays

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Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security applications 3rd

Performance of Machine Learning Classifiers for Anomaly De...

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3rd international conference on Advances in Computing Research, ACR 2025

作者： Haug, Markus Velarde, Gissel IU International University of Applied Sciences Erfurt99084 Germany

ISBN: (纸本)9783031876462

This work empirically evaluates machine learning models on two imbalanced public datasets (KDDCUP99 and Credit Card Fraud 2013). The method includes data preparation, model training, and evaluation, using an 80/20 (train/test) split. Models tested include eXtreme Gradient Boosting (XGB), Multi Layer Perceptron (MLP), Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Multiple-Objective Generative Adversarial Active Learning (MO-GAAL), with XGB and MLP further combined with Random-Over-Sampling (ROS) and Self-Paced-Ensemble (SPE). Evaluation involves 5-fold cross-validation and imputation techniques (mean, median, and IterativeImputer) with 10, 20, 30, and 50 % missing data. Findings show XGB and MLP outperform generative models. IterativeImputer results are comparable to mean and median, but not recommended for large datasets due to increased complexity and execution time. The code used is publicly available on GitHub (***/markushaug/acr-25). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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An Analytical Study of H-2 Visa applications Acceptance Rates in the U.S.A. 3rd

An Analytical Study of H-2 Visa Applications Acceptance Rate...

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3rd international conference on Advances in Computing Research, ACR 2025

作者： Pannangi, Sri Vidya Gayathri Nagella, Srija Senbel, Samah Sacred Heart University FairfieldCT United States

ISBN: (纸本)9783031876462

This paper represents an analytical study of visa applications for H-2A and H-2B categories in the United States from 2000 to 2022. The study gives detailed information regarding the certification outcomes, geographic location, and work type of the applicants. Using Python and other data analytics tools, the study exposes trends reflecting the effects on visa application numbers and approval rates of changing immigration policies, labor market demand, and economic cycles. Research results show a consistent increase in H-2A applications driven by agricultural sector labor shortages and changes in H-2B applications resulting from the visa restriction and developments in non-agricultural sectors, including hotel and construction. The findings reveal regional differences, with states like California, Texas, and Florida leading in visa requests and dominating the visa field. Particularly in seasonal and low-paying jobs, this paper highlights the vital role foreign workers play in fulfilling U.S. labor market needs. The study offers a data-driven basis for future research and policy decisions addressing temporary labor migration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cultivation

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Digital Twin applications in Aircraft Design Process 3rd

Digital Twin Applications in Aircraft Design Process

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3rd international conference on Cognitive Mobility, CogMob 2024

作者： Dişlitaş, Ayşe Nur Yıldız, Melih Kale-Halasz, Gyorgyi Department of Aeornautical Engineering Erciyes University Kayseri Turkey Robert Bosch Kft. Budapest Hungary

ISBN: (纸本)9783031817984

A digital twin is a digital model of the physical asset, a method that enables the simulation of the system using real-time data. It enables complex systems to become traceable by combining the physical and digital environments. Due to the cost and time problems caused by physical tests, it has started to be used in many sectors and has become important for the aviation industry, which includes very comprehensive steps. In this study, the integration of the digital twin into the aircraft design process is investigated. The contributions of the digital twin are detailed by examining its applications in the field of aviation. As a result, it has been observed that it optimizes the system in the aircraft design process, increases efficiency in the production line, reduces costs in maintenance and repair phases, and increases flight safety with early fault detection. In addition, it prevents loss of time and operational costs by enabling rapid testing of innovations to be made or conditions to be changed in the design. It is concluded that it is important to adopt the use of digital twin to achieve competition in the field of aviation and adaptation to constantly developing technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Operating costs

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