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检索条件"任意字段=SAI Computing Conference"

共 6834 条记录，以下是1-10 订阅

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Proceedings of 2016 sai computing conference, sai 2016

Proceedings of 2016 SAI Computing Conference, SAI 2016

引用

2016 sai computing conference, sai 2016

ISBN: (纸本)9781467384605

The proceedings contain 203 papers. The topics discussed include: a deep learning based approach to classification of CT brain images;numerical solution of fuzzy heat equation with two different fuzzifications;optic disc segmentation by weighting the vessels density within the strongest candidates;study on the computational cost of EEG dynamic modeling methods;a MapReduce fuzzy techniques of big data classification;teeth periapical lesion prediction using machine learning techniques;a proposed model for predicting the drilling path based on hybrid PSO-BP neural network;real time text speller based on eye movement classification using wearable EEG sensors;resilience and survivability of ATM node-node network failures using ant colony swarm intelligent modelling;posterior self-information based uncertainty measurement for data classification and learning;algorithm selection for classification problems;recent advances for handling imbalancement and uncertainty in labelling in medicinal chemistry data analysis;a neural decision forest scheme with application to EMG gesture classification;an image complexity measurement algorithm with visual memory capacity and an EEG study;and hybrid active contours in multiphase level set framework for images segmentation.

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Osmotic computing-Based Task Offloading: A Fuzzy Logic-Based Approach 5th

Osmotic Computing-Based Task Offloading: A Fuzzy Logic-Based...

引用

5th International conference on computing Science, Communication and Security

作者： Neha, Benazir Panda, Sanjaya Kumar Sahu, Pradip Kumar Veer Surendra Sai Univ Technol Burla Odisha India Natl Inst Technol Warangal Telangana India

ISBN: (纸本)9783031751691;9783031751707

The swift expansion of the information technology (IT) industry has led to a surge in compute-intensive and latency-sensitive applications. While cloud computing can satiate the demands of such applications, its centralized architecture may cause delays in the execution of tasks. To address such issues, edge computing brings computation closer to data sources. However, limited resources on Internet of things (IoT) devices make local execution quite challenging. Therefore, a pliable approach is to consider task offloading for moving heavy tasks to resource-extensive systems like edge/cloud. Osmotic computing, leveraging edge and cloud resources, aims to enhance IoT services. However, the dynamic nature of IoT, edge, and cloud introduces challenges for task offloading. This paper proposes an offloading algorithm using fuzzy logic to manage uncertainty. Furthermore, we introduce an osmotic decision manager (ODM) that employs fuzzy logic for optimized offloading decisions, considering IoT/edge for latency-sensitive tasks and cloud for latency-tolerant tasks. This algorithm aims to improve overall system performance by efficiently offloading tasks based on their specific requirements and constraints. The proposed algorithm undergoes simulation and assessment with diverse synthetic test cases to demonstrate its efficacy.

关键词： Cloud computing Internet of Things Edge computing Osmotic computing Task Offloading

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MFC based Smart Irrigation System using IoT and Machine Learning 6

MFC based Smart Irrigation System using IoT and Machine Lear...

引用

6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Chaitanya, Manne sai Uday Kiran Reddy, B. Sreekumar, K. Amaran, Sibi Department of Computing Technologies Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing SRM Institue of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798331522667

This research study proposes a novel Smart Irrigation System that integrates Microbial Fuel Cells (MFCs) with IoT and Machine Learning for sustainable agricultural practices. The system uses MFCs to generate electricity from soil microorganisms, providing a renewable energy source for irrigation. IoT sensors monitor real-time environmental parameters such as temperature, humidity, and soil moisture, and transmit data to cloud platforms for analysis. Machine learning algorithms are used to process the historical data, weather forecast, and sensor information in real-time to predict the irrigation requirement and optimize water usage. Renewable energy generation, IoT-based monitoring, and machine learning-driven decision support strategy are integrated in this system to improve water efficiency, reduce energy consumption, and support sustainable agricultural practice. © 2025 IEEE.

关键词： Smart agriculture

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Counting the Number of Potholes in a Video Sequence 3

Counting the Number of Potholes in a Video Sequence

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3rd International conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kumar, Anish Reddy, R. Mohit sai Sharath, B. Laxmi, V. Mona Bnmit Department of Ise India Bnmit Department of Cse India

ISBN: (纸本)9798331515911

Pothole detection holds significant importance for road maintenance and safety. However, accurately counting potholes within dynamic video sequences poses substantial challenges due to varying lighting conditions, camera perspectives, object occlusions and limited availability of training and testing of dataset. To address these challenges, a methodology based on Cosine similarity, leveraging visual pattern recognition to quantify the number of potholes. Our approach achieves a promising standard error count. This study contributes to enhancing the accuracy and efficiency of pothole detection and counting systems, crucial for ensuring road infrastructure integrity and reducing hazards like pothole deaths, traffic congestion, gross wastage of fuel. © 2025 IEEE.

关键词： Traffic congestion

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Evolving GAN-BERT Architecture for Efficient Text Categorization with Minimal Labeled Data 8th

Evolving GAN-BERT Architecture for Efficient Text Categoriza...

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8th IFIP TC 12 International conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Bagavathi, C. Raakesh, M. Bhanu, Chanamala Vipasyana Reddy, Katheram Venkata sai Ram Sathwik, Kancharla Rupesh sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031736162

Categorical text generation is an upcoming field within natural language processing focusing on producing text tailored to specific categories or domains. The goal of categorical text production is to produce text that complies with the requirements and characteristics of the chosen category while also adhering to the grammatical and structural conventions of the language. Text classification works across multiple data categories, and the synthetic data generated usually adheres to a preset pattern and ignores the text’s nuanced emotional content. The proposed approach begins with using BERT to get the text’s deep contextual embeddings. Using these text embeddings, the underlying semantics of the target category can be better understood by adding a task-specific layer that places the sentences in the closest corresponding class. After that, the architecture and settings of the GAN are optimized through evolutionary computing techniques to guarantee that the generated text conforms to the constraints and characteristics specific to each category. © IFIP International Federation for Information Processing 2025.

关键词： Generative adversarial networks

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Cloud Load Balancing Through the Cloud Armor in HTTP Load Balancer 5th

Cloud Load Balancing Through the Cloud Armor in HTTP Load Ba...

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5th International conference on Data Science, Machine Learning and Applications

作者： Prashanth, Mallellu sai Karnati, Ramesh Velpuru, Muni Sekhar Reddy, H. Venkateshwara Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad India Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The combination of an HTTP Load Balancer and Cloud Armor in cloud computing environments provides a robust solution for ensuring high availability and security for web applications. An HTTP Load Balancer distributes incoming traffic across multiple backend instances, optimizing performance and scalability. Cloud Armor, a security service in the cloud platform, adds an additional layer of protection by implementing access control rules and mitigating DDoS attacks. This abstract explores the detailed procedure of setting up anHTTP Load Balancer with Cloud Armor in Google Cloud Platform (GCP), including the configuration of instance groups, firewall rules, backend services, and load balancer settings. The integration of Cloud Armor allows the application to benefit from advanced security features, such as IP-based access controls and traffic filtering, thereby safeguarding against malicious activities. By implementing an HTTP Load Balancer with Cloud Armor, organizations can ensure reliable and secure delivery of web applications to their users in the cloud environment.

关键词： Cloud computing Load Balancer Firewall GCP Armor

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kola, sai Sindhu Kenchi, Harsha Rama, P. Angayarkanni, V. Kalaivani, J. Sivakumar, S. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India Srm Institute of Science and Technology Computing TechnologiesCollege of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Faculty of Engineering and Technology Srmist Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331529833

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based. Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting. © 2025 IEEE.

关键词： Face recognition

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A Review on Synergizing Convolutional and Swin Transformer Approaches for Superior Remote Sensing Detection 6

A Review on Synergizing Convolutional and Swin Transformer A...

引用

6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Reddy, Allipurapu Meghanath Gangadhara, sai Likhith Reddy, R. Ravinder Padmalatha, E. Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

Remote sensing object detection plays a crucial role in environmental monitoring, yet traditional methods often face difficulties with varying object scales, intricate backgrounds, and densely populated small objects. This research study elaborates on various methodologies carried out in the remote sensing detection area, with the support of the latest advances in traditional and deep learning techniques. We make a critical presentation of CNNs, Swin Transformers, and hybrid models, their adaptations, benefits, and limitations. Performance metrics along with adaptability across different datasets and environments are presented through comparative analysis of various models. The paper provides in-depth analysis into the ever-changing landscape of remote sensing technologies and the latest tangible applications regarding such technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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Machine Learning for Detecting the Phishing Threats 6

Machine Learning for Detecting the Phishing Threats

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6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Shaik, Mohammed Ali Rakshitha, Gangula saipriya, Katakam Thrisha, Thakalapally Varshini, Morthala sai, Jasti Geethika SR University Telangana Warangal506371 India

ISBN: (纸本)9798331522667

In this research, a system is built that detects whether or not a website is getting phished by using machine learning techniques to help enhance cybersecurity and safeguard user data. First, a dataset of legitimate and phishing websites is collected for training and evaluation of different detection models. Random Forests, Decision Tree and K nearest neighbors (KNN) are used with a discussion on the performance of Random Forests and a suggested hybrid model. The models of the approaches proved more accurate on the task of separating legitimate and fraudulent websites and underscore the importance of the feature extraction and selection strategies for boosting classification results. This thesis highlights the requirement to adapt phishing detection model continuously to mitigate techniques employed by cybercriminals which changes continually. This research is to contribute to the field of cybersecurity by implementing such adaptive systems to infuse internet environments with better security for users. © 2025 IEEE.

关键词： Phishing

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A Secured Data Sharing Using Proxy Re-encryption and Blockchain in Cloud Storage 5th

A Secured Data Sharing Using Proxy Re-encryption and Blockch...

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5th EAI International conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

作者： Dunesh Madhav, B. Teja sai Vijaya babu, A. Naga sai Anil Chowdary, A. Victo Sudha George, G. Jayaprakash, J. Department of Computer Science and Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Tamil Nadu Chennai95 India

ISBN: (纸本)9783031770777

The development of data sharing in cloud computing has proven valuable. The Paper explores the complexities of ensuring data security within cloud environments and proposes a solution centered around proxy re-encryption. The core objective is to strengthen the security aspects related to sharing data within the scope of cloud computing. The proposed system entails data owners securely archive their encrypted data into cloud storage utilizing identity-based encryption. Authorized users can subsequently access the data through proxy re-encryption. To mitigate resource limitations, an edge device acts as a proxy server to manage computationally intensive tasks. The work leverage information-centric networking to optimize service quality and network bandwidth by delivering cached content through the proxy. The foundational system model relies on blockchain technology, facilitating decentralized data sharing and offering precise access control. A comprehensive security analysis and evaluation of the proposed scheme, illustrate its potential to guarantee data confidentiality, integrity, and overall security Overall, paper presents a holistic strategy to confront data security challenges using proxy re-encryption, edge computing, information-centric networking, and blockchain technology, offering a promising approach for mitigating data security risks in cloud environments. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Cloud security

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