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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data

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Petroleum Research 2022年第2期7卷 204-219页

作者： Rakesh Kumar Pandey Anil Kumar Ajay Mandal Department of Petroleum and Energy Studies School of Engineering and TechnologyDIT UniversityDehradun248009India Data Science Research Group School of ComputingDIT UniversityDehradun248009India Department of Petroleum Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India

A robust deep learning model consisting of long short-term memory and fully connected neural net-works has been proposed to automatically interpret homogeneous petroleum reservoirs having infinite,no flow,and constant pressure outer boundary *** pressure change data recorded during the well test operation along with its derivative is input into the model to perform the classification for identifying the reservoir model and,further,regression to estimate output *** noise was added to analytical models while generating the synthetic training *** hyperparameters were regulated to perform model optimization,resulting in a batch size of 64,Adam optimization algorithm,learning rate of 0.01,and 80:10:10 data split ratio as the best choices of *** perfor-mance accuracy also increased with an increase in the number of samples during *** classification and regression metrics have been used to evaluate the performance of the *** paper also demonstrates the prediction performance of the optimized model using simulated and actual oil well pressure drawdown test *** proposed model achieved minimum and maximum relative errors of 0.0019 and 0.0308,respectively,in estimating output for the simulated test cases and relative error of 0.0319 for the real test case.

关键词： Well test Reservoir characterization Automatic interpretation Prediction model Hyperparameter tuning Performance indicator

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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IoT-based trusted wireless communication framework by machine learning approach

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Measurement: Sensors 2024年 34卷

作者： Chakaravarthi, S. Saravanan, S. Jagadeesh, M. Nandhini, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system. © 2024 The Authors

关键词： Authentication

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UNVEILING MOLECULAR SECRETS: AN LLM-AUGMENTED LINEAR MODEL FOR EXPLAINABLE AND CALIBRATABLE MOLECULAR PROPERTY PREDICTION

arXiv

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arXiv 2024年

作者： Li, Zhuoran Sun, Xu Lin, Wanyu Cao, Jiannong Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong

Explainable molecular property prediction is essential for various scientific fields, such as drug discovery and material science. Despite delivering intrinsic explainability, linear models struggle with capturing complex, non-linear patterns. Large language models (LLMs), on the other hand, yield accurate predictions through powerful inference capabilities yet fail to provide chemically meaningful explanations for their predictions. This work proposes a novel framework, called MoleX, which leverages LLM knowledge to build a simple yet powerful linear model for accurate molecular property prediction with faithful explanations. The core of MoleX is to model complicated molecular structure-property relationships using a simple linear model, augmented by LLM knowledge and a crafted calibration strategy. Specifically, to extract the maximum amount of task-relevant knowledge from LLM embeddings, we employ information bottleneck-inspired fine-tuning and sparsity-inducing dimensionality reduction. These informative embeddings are then used to fit a linear model for explainable inference. Moreover, we introduce residual calibration to address prediction errors stemming from linear models’ insufficient expressiveness of complex LLM embeddings, thus recovering the LLM’s predictive power and boosting overall accuracy. Theoretically, we provide a mathematical foundation to justify MoleX’s explainability. Extensive experiments demonstrate that MoleX outperforms existing methods in molecular property prediction, establishing a new milestone in predictive performance, explainability, and efficiency. In particular, MoleX enables CPU inference and accelerates large-scale dataset processing, achieving comparable performance 300× faster with 100,000 fewer parameters than LLMs. Additionally, the calibration improves model performance by up to 12.7% without compromising explainability. The source code is available at https://***/MoleX2024/MoleX. © 2024, CC BY.

关键词： Embeddings

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Development of an Enhanced Quality Score Calculation Method for Accurate Assessment of Hotel Quality

Development of an Enhanced Quality Score Calculation Method ...

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Advancements in computing (ICAC), International Conference on

作者： A. A. A. Gaweshini Athurugiriya Prasanna Sumathipala K.M. Tharindu Anupa Hemachandra Department of Data Science Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

In this research paper, an enhanced hotel quality scoring method (EHQSM), is presented as a revolutionary way to analyze hotel quality. The EHQSM combines sentiment analysis, HOLSERV Plus dimensions classification, and amenity scores to deliver a more thorough review. The study generates an overall sentiment score by doing sentiment analysis on reviews categorized into HOLSERV Plus dimensions then thoughtfully averaging it using dimensionspecific weights collected from a survey. The availability of necessary amenities is considered when calculating the amenity score. The novel feature of this approach is how the amenity score, and sentiment score are carefully combined and efficiently weighted using values from the survey. The result is an improved quality score that accurately and thoroughly describes the quality of hotel services. With the goal of facilitating informed decisions for both hotel owners and attentive tourists, this technique establishes an updated standard for the evaluation of hospitality quality.

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Advancing Indian Stock Market Prediction with TRNet: A Graph Neural Network Model

Advancing Indian Stock Market Prediction with TRNet: A Graph...

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Modeling, Simulation & Intelligent computing (MoSICom), International Conference on

作者： Manali Patel Krupa Jariwala Chiranjoy Chattopadhyay Department of CSE SVNIT Surat India School of Computing and Data Science FLAME University Pune India

Indian stock market is unique with high volatility and complexity, so special approaches are needed for its computational understanding. This study proposes Temporal Relational Network (TRNet), a new Graph Neural Network model designed for Indian stock market prediction. TRNet considers pre-defined industry-sector links and time to predict future stock prices in NIFTY-50 index, a significant measure for Indian stock market. Using a financial knowledge graph and Graph Convolution Network, TRNet effectively captures complexities and relationships in non-Euclidean financial data. TRNet outperforms existing models in different timeframes, showing its importance for Indian stock market. We also explore how connections between stocks affect predictions and provided an explainable framework to understand the working mechanisms of the proposed approach. Combining finance and intelligent computing, TRNet paves the way for possibilities in the areas of computational finance using deep learning.

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Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning

arXiv

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arXiv 2024年

作者： He, Tao Wu, Tongtong Zhang, Dongyang Duan, Guiduo Qin, Ke Li, Yuan-Fang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Department of Data Science and AI Faculty of Information Technology Monash University Australia

Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm may necessitate repetitive training on all previously observed samples whenever new relationships emerge, mitigating the risk of forgetting previously acquired knowledge. This work seeks to address this pitfall inherent in a suite of prior relationship predictions. Motivated by the achievements of in-context learning in pretrained language models, our approach imbues the model with the capability to predict relationships and continuously acquire novel knowledge without succumbing to catastrophic forgetting. To achieve this goal, we introduce a novel and pragmatic framework for scene graph generation, namely Lifelong Scene Graph Generation (LSGG), where tasks, such as predicates, unfold in a streaming fashion. In this framework, the model is constrained to exclusive training on the present task, devoid of access to previously encountered training data, except for a limited number of exemplars, but the model is tasked with inferring all predicates it has encountered thus far. Towards LSGG, we propose to present visual content as textual representations as the input tokens for a pretrained language model, e.g., GPT-2, and develop a rehearsal strategy via an in-context prompt based on a novel knowledge-aware prompt retrieval mechanism. Rigorous experiments demonstrate the superiority of our proposed method over state-of-the-art SGG models in the context of LSGG across a diverse array of metrics. Besides, extensive experiments on the two mainstream benchmark datasets, VG and Open-Image(v6), show the superiority of our proposed model to a number of competitive SGG models in terms of continuous learning and conventional settings. Moreover, comprehensive ablation experiments demonstrate the effectiveness of each component in our model. © 2024, CC BY.

关键词： Forecasting

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Enhancing Hotel Customer Satisfaction Assessment with Semantic Search-based Scoring

Enhancing Hotel Customer Satisfaction Assessment with Semant...

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Advancements in computing (ICAC), International Conference on

作者： K.M Tharindu Anupa Hemachandra Prasanna Sumathipala A.A.A Gaweshini Athurugiriya Department of Data Science Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Within the hospitality sector, ensuring customer satisfaction takes precedence, particularly in economies such as Sri Lanka, where tourism serves as a vital growth catalyst. Interestingly, disparities often arise between customer ratings and their written evaluations. This study presents an innovative strategy that utilizes semantic search techniques to extract comprehensive insights from customer feedback, leading to the development of a robust hotel scoring system. By exploring intricate emotions via advanced linguistic analysis, this method reveals underlying cues that conventional metrics often overlook. The method's effectiveness is validated through a comparative study with established hotel ratings. This advancement not only refines precise customer satisfaction assessment for hotels but also bears strategic significance for countries aiming to fortify their tourism sectors. In this era centered around data, this research provides a pivotal standpoint for optimizing hospitality evaluation, thereby enriching guest experiences, and guiding well-informed operational choices.

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