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检索条件"机构=Dept. Computer Technology and Computer Graphics"

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Reducing Client-Side Cost in Secure Inner Product Evaluations

Reducing Client-Side Cost in Secure Inner Product Evaluation...

引用

International Conference on Information Networking

作者： Changhee Hahn Dongyoung Koo Junbeom Hur dept. Electrical and Information Engineering Seoul National University of Science and Technology South Korea dept. Convergence Security Hansung University South Korea dept. Computer Science and Engineering Korea University South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption (IPFE), which enables the computation of inner products on encrypted vectors without exposing the vectors themselves. However, the computational intensity of IPFE decryption poses challenges, particularly for resource-limited client devices and high-dimensional vectors. To facilitate partial decryption, prior IPFE schemes require the client to derive a partial decryption key from an assigned secret key, incurring computation costs linear to the vector dimension. In this paper, we provide a framework for the challenges of IPFE, enabling efficient decryption outsourcing, maintaining data privacy, and facilitating key generation outsourcing in IPFE schemes. In the proposed framework, outsourced decryption using a partial decryption key generates a masked plaintext that only the client can unmask. Furthermore, the process of deriving the partial decryption key can be fully outsourced, thereby ensuring that the client’s computational burden remains constant.

关键词： Data privacy Costs Data collection Vectors Outsourcing Encryption Computational efficiency

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A Hybrid CNN-SEU-Net Approach for Improved Melanoma Detection in Dermoscopy Images : Combining CNN and SEU-Net for Robust Melanoma Detection

A Hybrid CNN-SEU-Net Approach for Improved Melanoma Detectio...

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Electronic Information Engineering and computer Communication (EIECC), International Conference on

作者： Asaad Ahmed Guangmin Sun Yu Li Mohamed Saadeldin School of Information Science and Technology Beijing University of Technology Beijing China Dept. School of Computer Science University College Dublin Dublin Ireland

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Melanoma is a highly aggressive form of skin cancer with high mortality rates if not diagnosed early. Accurate diagnosis poses significant challenges, particularly for nonspecialist healthcare providers, due to the complexity and variability of skin lesions. Current diagnostic methods often lack the precision needed for reliable early detection, highlighting the need for innovative solutions. This paper proposes a novel hybrid deep learning framework for melanoma detection that integrates SEU-Net and Convolutional Neural Networks (CNNs) to address these challenges. The framework begins with SEU-Net, an encoder-decoder segmentation model enhanced with skip connections and SE attention mechanisms, to generate a segmentation mask that identifies the region of interest (ROI) in dermoscopic images. The segmentation mask is multiplied with the original image to isolate the lesion area, creating a segmented image that minimizes irrelevant background information. This segmented image is then passed to a CNN, classifying the lesion as malignant or benign. The proposed approach was evaluated on the ISBI 2016 dataset, achieving an accuracy of 93.60\%, outperforming existing state-of-the-art methods. These results demonstrate the potential of the proposed framework to significantly improve the precision and reliability of melanoma diagnosis, enabling earlier detection and ultimately reducing mortality rates.

关键词： Deep learning Image segmentation Accuracy Computational modeling Mortality Melanoma Skin Lesions Convolutional neural networks Reliability

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Analysis of API Architecture: A Detailed Report 12

Analysis of API Architecture: A Detailed Report

引用

12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Kumar, Kamal Jain, Anuj Kumar Tiwari, Raj Gaurang Jain, Nitin Gautam, Vinay Trivedi, Naresh Kumar National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Uttarakhand Srinagar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Manav Rachna International Institute of Research and Studies Career Development Centre Haryana Faridabad India

ISBN: (纸本)9781665462617

Web services provided by various companies in the form of various APIs (Application Programming Interfaces) have increased to a very large extent in recent years. Service Oriented Architectures (SOA) and a message exchange protocol SOAP (Simple Object Access Protocol) have become prominent methods to serve the data exchange process for web services. But Recent reports state that a large number of companies are migrating from SOAP-based architecture to Representational State Transfer or REST. REST architecture is a resourceoriented architecture where resources are the entities of the system. The APIs are implemented around these resources. Also, REST architecture is stateless architecture as the client and server do not store. RESTful services return resources in some representation format. REST is a way by which a company provides a method to use its services that is provided by them exclusively. Various websites that were SOAP oriented went to REST implementation to simplify and modernize their operations. It was found that APIs were using simple HTTP verbs with no headers. REST web APIs are developed using HTTP protocol. Therefore, it makes use of all the HTTP properties like HTTP verbs, HTTP responses, header information, etc. The work presented here elaborates on the analysis of APIs using HTTP protocols and how the APIs are validated. © 2023 IEEE.

关键词： HTTP

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

引用

computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Neeraj V Pattanashetti R Mayur Adarsh Prakash Tryambaka B Adiga Ashwini Kodipalli Trupthi Rao Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Information science & Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results $(92.02 \%)$, demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future.

关键词： Machine learning algorithms Shape Biological system modeling Pancreatic cancer Predictive models Biomarkers Boosting Prediction algorithms Classification algorithms Tuning

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems 18

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Arshad, Usama Ellahie, Hamza Hasan Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-dept. exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions. © 2023 IEEE.

关键词： Genetic algorithms

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Explainable AI for Enhanced Intrusion Detection Systems

Explainable AI for Enhanced Intrusion Detection Systems

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Advancement in technology (ICONAT), International Conference for

作者： G Ananya N Siri Ashwini Kodipalli Trupthi Rao Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Electronic and Communication Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection Dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of $\mathbf{9 9. 9 9 \%}$, a precision of $\mathbf{9 9. 9 9 \%}$, and an F1-score of $\mathbf{9 9. 9 9 \%}$. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems

关键词： Support vector machines Machine learning algorithms Accuracy Explainable AI Computational modeling Intrusion detection Nearest neighbor methods Turning Reliability Random forests

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Machine Learning-Based Detection of Cardiovascular Disease using Classification and Feature Selection 12

Machine Learning-Based Detection of Cardiovascular Disease u...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Jain, Anuj Kumar Kumar, Kamal Tiwari, Raj Gaurang Jain, Nitin Gautam, Vinay Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Uttarakhand Srinagar India Manav Rachna International Institute of Research and Studies Career Development Centre Haryana Faridabad India

ISBN: (纸本)9781665462617

The application of ameliorative datasets to foretell chronic diseases has grasped considerable attention amongst data scientists all around the globe. The study is based on the Cardiovascular disease dataset. Cardiovascular disease is a broad term concerning disorders of the heart and blood vessels and is ranked to be the topmost cause of demise worldwide. Various feature selection techniques have been applied to each of the given classification algorithms and the features which give the best accuracy are then utilized for that particular algorithm. © 2023 IEEE.

关键词： Neural networks

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations 18

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Majeed, Omar Kashif Ul Abideen, Zain Arshad, Usama Ali, Raja Hashim Habib, Abdullah Mustafa, Rafay BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty. © 2023 IEEE.

关键词： Reinforcement learning

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An Effectual Strategy to Augment the Hello Interval of Each Node Using Enhanced Humpback Whales Optimization

An Effectual Strategy to Augment the Hello Interval of Each ...

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Richa Purohit T. Saravanan Rashmi Dharwadkar Y Vahidhabanu S.P. Sasirekha N. Krishnaveni School of Computer Studies Sri Balaji University Pune India Dept. of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Dept. of Computer Science Sri Balaji University Pune India Department of Computer Science and Engineering Kongunadu college of Engineering and Technology Trichy India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Wireless networks are highly utilized, especially Mobile Ad-hoc Networks (MANETs) for real-time data transmission. However, current routing protocols for MANETs face challenges that limit their effectiveness. Design issues include minimizing energy usage and ensuring node security. A commonly used routing protocol, AODV, involves periodic broadcasting of hello messages to monitor link connectivity and maintain routing tables. When link failure occurs due to node mobility, hello messages are broadcasted more frequently, which consumes more node energy and increases network overhead. To address this issue, this research proposes an Enhanced Humpback Whales Optimized Routing Approach (EHWORA) that calculates an optimal hello interval based on a node's energy and mobility using an adaptive fuzzy inference system. This approach involves optimizing the hello interval of AODV routing through the use of an adaptive fuzzy inference system. Each node can dynamically adjust the transmission of hello messages based on the fuzzy-based hello interval. Simulation results demonstrate that ORA reduces network overhead, end-to-end delay, hello message fraction, average energy consumption, packet delivery ratio, and improves throughput compared to a recent method. The proposed protocol is evaluated using MATLAB software. To assess the validity of the research, we utilized MATLAB software to compare the proposed method with a recent approach. During the simulation, the proposed protocol depleted the energy of 3 nodes after 10 seconds, 5 nodes after 20 seconds, 9 nodes after 30 seconds, 11 nodes after 40 seconds, and 13 nodes after 50 seconds, respectively.

关键词： Fuzzy logic Adaptive systems Routing Whale optimization algorithms Ad hoc networks Software Routing protocols MATLAB Artificial intelligence Mobile computing

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A No-Reference Image Quality Assessment Method Based on Random Forest for Power Scenario 3

A No-Reference Image Quality Assessment Method Based on Rand...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Li, Junwei Zeng, Jijun Kong, Tianwen Su, Huaquan Wu, Yang Huo, Yejing Wu, Shiting Shi, Hanyu Information Center Guangdong Power Grid Co. Ltd. Guangzhou China Guangdong University of Technology Dept. School of Computer Science Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Guangdong University of Finance and Economics Dept. School of Statistics and Mathematics Guangzhou China

ISBN: (纸本)9798350365443

No-reference image quality assessment (NR-IQA) is a crucial task in computer vision, aiming to evaluate the visual quality of images solely through distorted images without the need for original (reference) images. This evaluation method is particularly useful in practical applications where obtaining the original image is not feasible, such as in the task scenario discussed in this article: assessing whether a camera is blurred, overexposed, etc., in power grid scenarios. Traditional no-reference image quality assessment methods usually face issues such as over-reliance on manually designed features, which may not comprehensively capture all aspects of image quality, and good performance on specific datasets but significant performance degradation across different datasets or types of image distortion, resulting in poor consistency with actual subjective scores. Although with the continuous development of deep learning, many models are able to extract the features of an image with relatively high efficiency for effective evaluation, many of the models require high computational resources, which is not conducive to the needs of real-world scenarios where low-computing power is deployed to achieve timely image quality assessment. In order to solve these problems, we propose a random forest-based approach for reference-free image quality assessment in power scenarios. First, we collect original power scene images and perform image expansion, including abnormal and normal images, to increase the diversity of the dataset. The expanded images were merged with the original images to form a balanced dataset. Next, we take the merged dataset as input and perform data processing, including random cropping and simulated image damage, to enhance the model's adaptability to different observation viewpoints and damage scenarios. The processed images are used for feature extraction, and these features are subsequently used to train the random forest model. The experimental result

关键词： Image quality

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