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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Resume Recommendation using RNN Classification and Cosine Similarity 7th

Resume Recommendation using RNN Classification and Cosine Si...

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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Huseyinov, Ilham Diallo, Issa Raed, Mhd Wasim Software Engineering Department Istanbul Aydin University Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey Computer Engineering Department Istanbul Aydin University Istanbul Turkey

ISBN: (纸本)9783031437885

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification;2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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Comments on a Blockchain-Enabled Privacy-Preserving Scheme for Demand-Response Management in Smart Grid Environments

Comments on a Blockchain-Enabled Privacy-Preserving Scheme f...

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International Conference on Applied System Innovation (ICASI)

作者： Ya-Fen Chang Jia-Yen Wu Wei-Liang Tai Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

To protect privacy, Park et al. proposed a blockchain-enabled privacy-preserving scheme (BPPS) to achieve demand response in the smart grid environment. Park et al. claimed that their scheme could resist various attacks and ensure both of privacy and data integrity. However, with thorough analysis of their scheme, we find that it suffers from three flaws.

关键词： Privacy Technological innovation Resists Load management Denial-of-service attack Demand response Smart meters

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Improved IP lookup technology for trie-based data structures

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Journal of computer and System Sciences 2023年 133卷 41-55页

作者： Lin, Yen-Heng Hsieh, Sun-Yuan Master Degree Program on Artificial Intelligence National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Manufacturing Information Systems Institute of Medical Informatics National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Puli Township54561 Taiwan

Many Internet protocol (IP) lookup algorithms have been formulated to improve network performance. This study reviewed and experimentally evaluated technologies for trie-based methods that reduce memory access, memory consumption and IP lookup time. Experiments involving binary tries (for simplicity) were conducted on four real-world data sets, two of which were on IPv4 router tables (855,997 and 876,489 active prefixes) and two of which were on IPv6 router tables (155,310 and 157,579 active prefixes). Technologies designed to reduce time taken (at the expense of memory consumption) worked well for IPv4, and those designed to reduce memory consumption worked well for IPv6. Various combinations of these technologies were applied together in the experiments. © 2022 Elsevier Inc.

关键词： Table lookup

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An Improvement of A Lightweight NFC Authentication Algorithm Based on Modified Hash Function 14

An Improvement of A Lightweight NFC Authentication Algorithm...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Hwang, Min-Shiang Li, Hou-Wen Yang, Cheng-Ying Asia University Department of Computer Science and Information Engineering Taichung354 Taiwan Asia University The Ph.D. Program in Artificial Intelligence Taichung354 Taiwan University of Taipei Department of Computer Science Taipei Taiwan

ISBN: (纸本)9798350313277

In NFC applications, user privacy information must be protected first. Cao and Liu recently proposed a lightweight NFC authentication scheme based on an improved hash function to ensure that the user's private information will not be leaked. Although their method is highly efficient and has Mutual Authentication, Forward Security, Backward Security, and security against attacks such as Replay, Location, and Fake, once out of synchronization occurs, the method must reestablish synchronization data. Therefore, this article will propose a lightweight synchronization method. © 2023 IEEE.

关键词： authentication algorithm hash function near field communication privacy

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PLM-based World Models for Text-based Games

PLM-based World Models for Text-based Games

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Kim, Minsoo Jung, YeonJoon Lee, Dohyeon Hwang, Seung-Won Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Department of Artificial Intelligence Yonsei University Korea Republic of

World models have improved the ability of reinforcement learning agents to operate in a sample efficient manner, by being trained to predict plausible changes in the underlying environment. As the core tasks of world models are future prediction and commonsense understanding, our claim is that pre-trained language models (PLMs) already provide a strong base upon which to build world models. Worldformer is a recently proposed world model for text-based game environments, based only partially on PLM and transformers. Our distinction is to fully leverage PLMs as actionable world models in text-based game environments, by reformulating generation as constrained decoding which decomposes actions into verb templates and objects. We show that our model improves future valid action prediction and graph change prediction. Additionally, we show that our model better reflects commonsense than standard PLM. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry 8

Improving Supplier Evaluation Model using Ensemble Method-Ma...

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8th International Conference on computer Science and Computational intelligence, ICCSCI 2023

作者： Asrol, Muhammad Wahyudi, Sofyan Suharjito Harito, Christian Utama, Ditdit N. Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS 12

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

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AI-Powered Personalized Deep Learning Framework for Early Detection of Glioblastoma Using Multi-Modal MRI Data

AI-Powered Personalized Deep Learning Framework for Early De...

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Data Science, Agents & artificial intelligence (ICDSAAI), International Conference on

作者： Sunitha T Veeramalai Sankaradass V.K Manindra Manish Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The prognosis of glioblastoma, which stands as the most malignant variant of a brain tumour, is clearly unfavourable due to the late diagnosis and the severe heterogeneity it is known to possess. Standard diagnostic techniques nowadays that employ MRI data quite often have a low-level performance in terms of diagnosing glioblastoma during its early stages owing to the diverse manner in which the tumours may grow in various patients. In light of this background, we present a new deep learning framework suitable for multi-modal MRI data and aimed at improving glioblastoma prognosis at an early stage. The framework utilizes convolutional neural networks (CNNs) to obtain features from T1, T2, and FLAIR MRI images, and combines this with an AI-guided individualized diagnostic system. This model utilizes relevant clinical data such as age and genetic data in a more individualized manner to meet patient’s needs during diagnosis. One more novel feature of our approach is the ensemble learning that combines CNNs with RNNs to detect minor tumour deposits more effectively over time. The proposed framework delivered an AUC of 0.94 on large glioblastoma MRI databases, thus progressing the early-stage diagnosis of the tumour type which was the aim. This advanced personalized method not only allows for superior diagnosis but also improves on the treatment plans that are available for such patients. Further development will concern clinical verification as well as increasing the capacity of the framework.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Genetics Feature extraction History Prognostics and health management Standards Tumors

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on artificial intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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