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检索条件"机构=dept. of Computer Computer Systems Engineering"

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Investigation of OWC System Incorporating Different Filters 4

Investigation of OWC System Incorporating Different Filters

引用

4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kumari, Meet Sharma, Neha Chauhan, Rahul Joshi, Kireet Madaan, Vijay Sunil, G. Uie Chandigarh University Dept. of Ece Gharuan Mohali140413 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350307757

Optical wireless communication (OWC) system is growing quite fast these days due to its potential applications in 5G wireless networks. A 4×10Gbps OWC system is designed and investigated incorporating Rectangular, Butterworth and Bessel filters over long reach at 4×10Gbps data rate. Simulation results indicate that the system utilizing Rectangular filter shows better performance than Butterworth and Bessel filters. It offers 90km range at 40Gbps traffic rate. Also, the system can tolerate maximum 5 and 3μrad pointing errors at transmitter and receiver respectively. Moreover, optimum performance can be obtained for the proposed architecture over existing works. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Comprehensive Review: Home Automation Technologies

A Comprehensive Review: Home Automation Technologies

引用

Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： P Boopathy A Muthumari M Vijayraj Dept. of Computer Science and Engineering Anna University Regional Campus Madurai Dept. of Computer Science and Engineering Mohamed Sathak Engineering College Madurai Ramanathapuram

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Automation is the process of managing a system’s functions with the least amount of human involvement. The development of technology has improved our quality of life. A system for home automation is one example of this kind of technology. Because of its greater openness and nominality, the home automation market is expanding quickly, and smart home automation has become more widely available very quickly. The salvation arrangement heavily relies on the automation of the smart *** bulk of home automation systems that have been created to date have prioritized functionality above overcoming more obstacles. Intelligent decision making, secure device identification and authentication, constant connectivity, data security, and privacy concerns are the main obstacles in a smart home. The solutions only address one or two of these problems; however, it is time for a smart home automation system that is not only safe but also capable of making wise decisions and doing analysis. This paper aims to investigate the technologies utilized in home automation. This paper aims to provide a detailed analysis of the technical components of home automation technology.

关键词： Automation Reviews Microcontrollers Entertainment industry Smart homes Market research Sensor systems

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model

Automated Identification of Pneumonia Employing Convolution ...

引用

Computing, Sciences and Communications (ICCSC), International Conference on

作者： Anamika Srivastava Anuj Gupta Deepti Kaushik Neha Gupta Shikha Chadha Rosey Chauhan Dept. of Computer Science & Engineering Noida Institute Of Engineering Technology Gerater Noida India Dept. of Computer Science & Engineering Sharda Unviersity Gerater Noida India Dept. of Computer Science and Engineering School of Engineering and Technology IIMT University Meerut U.P India Dept. of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned.

关键词： Pneumonia Pain Computational modeling Mortality Production Organizations Predictive models Feature extraction Convolutional neural networks Diagnostic radiography

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Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc Explainability Framework*

Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc E...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Cagla Acun Ali Ashary Dan O. Popa Olfa Nasraoui Dept. of Computer Science & Engineering Knowledge Discovery & Web Mining Lab Next Generation Systems Group

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Enhancing the explainability of machine learning (ML) models is crucial for bridging their use gap for practical applications of robotic autonomy. The common standard for adding explainability has been to use a post-hoc explanation approach. However, post-hoc approaches have recently attracted criticism for their lack of transparency, specifically because the post-hoc methods learn a surrogate model after the predictive model has already been learned, which means that the explanations are not authentically explaining the model’s behavior. This study aims to add explainability to the task of robot grasping failure prediction using Factorization Machines as the predictive model and using a novel pre-hoc explainability framework to learn an explainable Factorization Machine model. Unlike post-hoc methods, pre-hoc explainability starts with learning the explainable model before training the black-box model and then provides guidance while learning the latter to make predictions that are faithful to the explanations through a regularization mechanism. Through a detailed case study, we explore the trade-off between prediction accuracy and explanation fidelity and show that our framework is able to make predictions that are more accurate than an explainable white-box model while simultaneously learning a model whose pre-hoc explanations achieve a high level of fidelity relative to the predictions. Results show that our framework can predict the robustness of the grasp with 83% accuracy while explaining that increased effort exerted in Joint 2 of Finger 3 contributes tremendously to producing grasp failure, which is in contrast to increased efforts exerted at Joint 2 of Fingers 1 and 2 and at joint 1 of Finger 3, that all lead to reducing grasp failure.

关键词： Training Knowledge engineering Accuracy Fingers Grasping Predictive models Robustness Robots Standards Optimization

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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT)

Trust Based Cooperative Secure Routing Protocols for Interne...

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Recent Trends in Microelectronics, Automation, Computing and Communications systems (ICMACC), International Conference on

作者： Sagar Mekala Bhukya Ramji Dept. of Computer Science and Engineering CVR College of Engineering Hyderabad India Dept. of Computer Science and Engineering Kakatiya Institute of Technology and Science Hanmakonda India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV.

关键词： Wireless communication Wireless sensor networks Target tracking Routing Throughput Routing protocols Software Product delivery Internet of Things Security

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Impact of Machine Learning-Driven Predictive Models on Patient Outcomes in Modern Healthcare systems 1

Impact of Machine Learning-Driven Predictive Models on Patie...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Madhuri Gautam, Rishikesh Kumar Shukla, Medhavi Kumar, Jitendra Singh, Ajay Pal Yaminipriya, S. CHRI Campus Dept. of Life Sciences Gwalior India Madhav University Dept. of Humanities & S.Science Pindwara India J.S. University Dept. of Computer Science Shikohabad India Lingayas Vidyapeeth Dept. of Pharmacy Faridabad India Dhanalakshmi Srinivasan College Dept. Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350356816

Traditional healthcare systems often struggle to meet the diverse needs of patients, leading to inefficiencies and less-than-optimal outcomes. The integration of machine learning (ML) is transforming healthcare by focusing on value-based care and enabling personalized, effective treatments. Modern healthcare devices are equipped with applications that collect and store comprehensive patient data, forming the basis for ML-driven predictive *** research investigates the transformative impact of ML in healthcare, highlighting its ability to improve patient care and optimize resource allocation. A proposed predictive model utilizes patient data and key parameters to forecast diseases, leveraging extensive datasets from diverse populations. These tools empower healthcare providers to identify and manage high-risk cases proactively, improving outcomes while streamlining care delivery *** study also explores the broader implications of ML adoption in value-based healthcare systems. By emphasizing personalized and proactive interventions, it demonstrates how ML-powered models can revolutionize traditional practices. The integration of ML in healthcare offers efficient, patient-centric solutions, enhancing the responsiveness and capacity of healthcare systems to meet growing patient demands and address complex medical challenges effectively. © 2024 IEEE.

关键词： ML Modern healthcare Patient data Predictive models Value- based treatment

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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A Comparative Analysis of Two Dielectric Nanostructures to Enhance Efficiency of Perovskite Solar Cells

A Comparative Analysis of Two Dielectric Nanostructures to E...

引用

2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Hasen, Nayem Rahman, Aminur Singha, Showmik Chowdhury, Tahmid Aziz Shahjalal University of Science and Technology Dept. of Electrical and Electronic Engineering Sylhet3114 Bangladesh Rensselaer Polytechnic Institute Dept. of Electrical Computer and Systems Engineering TroyNY12180 United States

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Perovskite (CH3NH3PbI3) solar cells (PSCs) have recently been invented due to their desirable characteristics such as high absorption, low cost, ease of fabrication, and rapidly improving efficiencies. In recent years, the effectiveness of organometallic halide perovskite as an absorber layer has already surpassed 20%. Dielectric nanostructures are being considered as a possible option for third generation photovoltaics, such as PSCs, due to lower losses and simpler fabrication. Dielectric nanostructured antireflection coatings, on the other hand, have recently emerged as a method for reducing reflection and improving the power conversion efficiency (PCE) of a thin-film absorber that was previously inefficient. In this research, two different types of nanostructures are used on the perovskite absorber layer that reduced light reflection and enhanced the power absorption, short-circuit current, and efficiency of the solar cells. Furthermore, as demonstrated how changing the nanostructure size has an effect on the absorption enhancement. Utilizing the finite difference time domain (FDTD) and coupled optical-electrical models, the proposed optimum nanostructures exhibit greater than 90% power absorption, with the maximum enhanced photocurrent. Total photocurrent increased by 17-20% compared to conventional planar PSCs, with a maximum PCE of 16.78%, representing 37% enhancement throughout the entire wavelength range. © 2022 IEEE.

关键词： Perovskite

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Exceptional Points of Degeneracy Induced by Time-Periodic Modulation 17

Exceptional Points of Degeneracy Induced by Time-Periodic Mo...

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17th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2023

作者： Rouhi, K. Nikzamir, A. Figotin, A. Capolino, F. University of California Dept. of Electrical Engineering and Computer Science IrvineCA92697 United States University of California Dept. of Mathematics IrvineCA92697 United States

ISBN: (纸本)9798350332445

An exceptional point of degeneracy (EPD) is a point in parameter space where at least two eigenvalues and eigenvectors of a system coalesce. We explore the occurrence of EPD in electronic, electromagnetic and mechanical time-varying systems. Remarkably, when using time variation of a system component, EPDs exist in a single resonator and they can be found by just tuning the modulation frequency. We demonstrate EPDs of the second order in linear-time-periodic (LTP) systems and offer examples of their applications, such as sensing and power harvesting. © 2023 IEEE.

关键词： Time varying systems

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Efficacy of Customer Churn Prediction System

Efficacy of Customer Churn Prediction System

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Ritik Raj Siddhant Gupta Ritik Mishra Medha Malik Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Customer churn, defined as customer defection is a big problem for businesses in today’s competition. This paper takes an in-dept. look at the application of machine learning (ML) in customer churn prediction to identify the best models for various industries. quantitative performance metrics of seven commonly used machine learning algorithms: Logistic Regression, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and XGBoost. The theoretical background, application context, and performance evaluation of each model are examined, focusing on precision, accuracy, recall, and F1 score. Evaluation of the performance of the model on different business data is also done to ensure that the application is real in the world. This comparison shows the advantages and disadvantages of each model and guides providers in their selection based on the specific needs of the business and profile features. Additionally, this paper explores future changes in predictions and highlights the changing role of machine learning technologies, especially deep learning, in improving the accuracy of predictions and personalization. This review is very useful for professionals and experts; It provides a better understanding of the current machine learning model for customer churn prediction and provides insight into future research and practical applications.

关键词： Industries Logistic regression Accuracy Recurrent neural networks Machine learning algorithms Predictive models Nearest neighbor methods

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