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检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

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Importance of Search Engine Optimization in Digitalization - A Comprehensive Analysis

Importance of Search Engine Optimization in Digitalization -...

引用

2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Tatikonda, Reshmi Ponnala, Jainath Tatikonda, Ramya Yendluri, Dileep Kumar Kempanna, M. Bhuvanesh, A. University of the Central Missouri Department of Computer Information Systems Warrensburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India University of the Cumberlands Department of Information Technology Williamsburg United States University of the Cumberlands Department of Computer Science Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This article examines the complex realm of search engine optimization and its importance in the era of digitalization. With an emphasis on different facets of search engine optimization, including optimizing on-page elements, off-page optimization, technology optimization, and optimizing content, this document thoroughly comprehends how enterprises can boost their online appearance and generate natural website visitors. Moreover, it explores the complexities of search engine optimization algorithms and advancements and reviews the available resources and methods for enhancement. Additionally, it covers recommended approaches and plans for the successful implementation of SEO. This article further explores upcoming patterns within search engine optimization, including enhancing voice search and incorporating AI and ML. Furthermore, it discusses potential hurdles and potential openings in SEO's future. Lastly, the study provides suggestions to explore further and improve optimizing search engines. It includes regional SEO, UX optimization, sound search, and socially conscious SEO practices. © 2024 IEEE.

关键词： Search engines

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Enhancing Healthcare Data Security in Cloud Computing Systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

引用

2024 IEEE international conference on Recent advances in science and engineering Technology, ICRASET 2024

作者： Sudha, I. Ramesh, P.S. Ponmalar, A. Jagannathan, J. Pandiarajan, Rajkumar Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient's data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems. © 2024 IEEE.

关键词： Electronic health record

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Cloud-Driven Collaborative Filtering and Waiter Robots Transforming Customer Experiences in Restaurants

Cloud-Driven Collaborative Filtering and Waiter Robots Trans...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Srinivasan, V. Prasanna Mohankumar, N. Prabakaran, T. Sairam, A. Elangovan, K. Velmurugan, S. R.M.D. Engineering College Department Of Information Technology Tamil Nadu Chennai India Maharashtra Nagpur India Joginpally B.R. Engineering College Department Of Computer Science And Engineering Telangana Hyderabad India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Thandalam Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department Of Mechanical Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

To improve customer relations, this research investigates the possibility of using cloud-enabled collaborative filtering with robot waiters in restaurant environments. With the use of innovation, to revolutionize the way people eat out. It suggests using smart robots that can interact with customers and help the wait staff out. Customer preferences are analyzed, order suggestions are streamlined, and service is personalized using cloud-based collaborative filtering algorithms. Test this novel approach in a real-world restaurant setting to view how it changes things for the better in terms of customer happiness, service efficiency, and employee output. It seems that the combination of autonomous waiters and collaborative filtering on the cloud might completely change the way people dine out by making everything more streamlined and personalized to each individual's preferences. To the existing conversation about how robots, cloud computing, and customer service intersect in the restaurant industry, which is important since technology is still playing a major influence on how businesses engage with their customers. © 2024 IEEE.

关键词： Collaborative filtering

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Road Extraction From Remote Sensing Images Using U-Net

Road Extraction From Remote Sensing Images Using U-Net

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sai Kumar, D. Lokesh Jyothi, V. Esther Vardhani, Chennu Meghana, Bayarla Priya, Bobbili Hema Sharika, Alladi Computer Science And Engineering Pvp Siddhartha Engineering College Vijayawada India Vr Siddhartha Engineering College Department Of Computer Applications Vijayawada India

ISBN: (纸本)9798350371567

The enhancement of the transportation database through updated road networks is a critical aspect with a wide range of applications. Traditional methods rely on onsite measurements or human interpretation of remote sensing images for mapping purposes. However, these methods have significant drawbacks, including high expenses, lengthy processing times, and a substantial workforce requirement. To overcome this challenge, we propose a method leveraging the U-Net architecture for road extraction from multispectral satellite images. The satellite imagery used is sampled from the DigitalGlobe Images dataset, covering images captured over Thailand, Indonesia, and India, with a ground resolution of 50 cm/pixel. The U-Net model is trained on both original and augmented data, employing convolutional layers, max-pooling, and up-sampling to extract and reconstruct road masks. Performance evaluation is conducted using custom validation sets, measuring metrics such as IOU and loss. Results demonstrate the effectiveness of the proposed system, achieving high accuracy and validation scores. The extracted road networks contribute to enhancing transportation databases, offering valuable insights for urban planning and management. © 2024 IEEE.

关键词： Roads and streets

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Personalized Healthcare Monitoring Through AR Wearables and Predictive Analytics Algorithms 1

Personalized Healthcare Monitoring Through AR Wearables and ...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Jagbir Ragothaman, Hariharan Jaggi, Arjun Solaev, Ogabek Dilbar, Urazbayeva Chauhan, Akanksha HCLTech India Urgench state university Department of Pedagogy and psychology Urgench Uzbekistan Mamun university Khive Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India India

ISBN: (纸本)9798331518578

Wearable Augmented Reality integrated with predictive analytics is revolutionizing the healthcare monitoring system innovative and individualized. This paper outlines a new paradigm integrating AR devices for capturing and, especially, showing user health statistics and using algorithms to predict health issues. The AR visualization integration with data analytics ensures that users can observe important signs such as pulse, blood oxygen, and activity in real-time. Some of them are device calibration, synchronized data, and the prophecy of the model that forms the framework's design to provide a sound professional health monitoring system. The users' data is submitted to the comprehensive predictive analytics algorithms to define certain patterns to create specific recommendations and ensure early health management. The performance analysis shows a high percentage of system accuracy, and the provided findings show its effectiveness during clinical application or everyday use. This approach reflects a major advancement in improving healthcare delivery by employing innovative wearable technology and data analysis. © 2025 IEEE.

关键词： Electronic health record

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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Optimized Constrained Application Protocol for IoT Smart Home Data Security: A Novel Lightweight Approach

Optimized Constrained Application Protocol for IoT Smart Hom...

引用

2024 IEEE international conference on Recent advances in science and engineering Technology, ICRASET 2024

作者： Subhashini, R. Jyothi, D.G. Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka India Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Department of Artificial Intelligence and Machine Learning Karnataka India

ISBN: (纸本)9798350388602

By increasing simplicity and efficiency, a smart environment aims to enhance human existence. The IoT paradigm has evolved into a crucial technology for creating smart buildings, and the majority of computer networks now have IoT technologies. Nevertheless, during this shift, IoT devices are susceptible to cyberattacks. Thus, in practical smart home applications, security and privacy are key issues. In order to improve data security in smart home settings while lowering computing costs, this publication proposes the Broken-Stick Regressive Lightweight Speck Ephemeral Cryptography-based Constrained Application Protocol (BRLSC-CoAP). Real-time data is gathered by IoT devices and sent to authorized users. BRLSC-CoAP includes processes for authentication, encryption, decryption, and registration to safeguard this process. Forbes indexive Broken-stick regression is used to verify user authentication, and a lightweight Speck ephemeral cryptography technique is utilized for encryption. The experimental findings demonstrate that BRLSC-CoAP performs better than traditional techniques by increasing confidentiality, accuracy, and precision in authentication while lowering computing overhead, delay, and cost.. © 2024 IEEE.

关键词： Smart homes

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

引用

2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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computer-Aided engineering in the Design of an Experimental Blade Cascade for Fluid-Structure Interaction Studies 14th

Computer-Aided Engineering in the Design of an Experimental ...

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14th international conference on the Theory of Machines and Mechanisms (TMM)

作者： Snabl, Pavel Pesek, Ludek Prochazka, Pavel Prasad, Chandra Shekhar Inst Thermomech CAS Dolejskova 1402-5 Prague 18200 8 Czech Republic Czech Tech Univ Fac Mech Engn Tech 4 Prague 16000 6 Czech Republic

ISBN: (纸本)9783031702532;9783031702518;9783031702501

This paper summarises the importance of experimental studies of fluid-structure interaction in blade cascades with 3D flow. Such an experiment is currently being designed at the Institute of Thermomechanics of the CAS and must be carefully designed to meet the desired specifications. The use of computer Aided engineering (CAE) is now desirable in the design phase. The experiment is modelled in computer Aided Design (CAD) software Autodesk Inventor and Finite Element Method (FEM) calculations in Ansys Workbench are used to test critical parts of the design. The use of these tools to design a composite blade with a high stiffness to weight ratio is demonstrated here in detail.

关键词： computer-aided engineering finite element method composite design modal analysis

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An Efficient Algorithm for Generating Interactive Visualization of Large Datasets 1

An Efficient Algorithm for Generating Interactive Visualizat...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Chandani Babu Chilluri, Venkata Suresh Kumar, Ankit Goyal, Shweta CSE Department Mullana Haryana Ambala India Intuit Inc. Mountain ViewCA94043 United States Chandigarh University Department of Computer Science and Engineering Mohali140413 India Graphic Era Deemed to be University Department of Electrical Engineering Dehradun248002 India

ISBN: (纸本)9798331518578

Easy-to-use visualisation tools are essential for identifying patterns in unstructured data as it grows larger and more complicated. The majority of large data visualisation techniques either knowledge of a programming language or access to commercial tools. These days, data is growing in size, and statistical visualisation of the data needs numerous displays to represent all of the data's viewpoints. Model tuning, optimisation, and quick decision-making are greatly aided by the intriguing patterns produced by the models that are visualised. However, traditional techniques like histograms, pie charts, box plots, and bar graphs are typically insufficient to adequately communicate the intriguing pattern that needs to be extracted from massive datasets. As a result, this paper introduces a tree-plot method that uses the h2o package's in-memory node technique to store the enormous dataset in memory. Data. Tree's charting capabilities were employed to display the modelled trees, which were built using an Ada Boosted Model as the underlying learning methods. Visualising the modelled tree and evaluating the effectiveness of the data training process are based on the AUC, RMSE, execution procedure time, and MSE results. © 2025 IEEE.

关键词： Large datasets

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