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Cybersecurity in the Age of Blockchain

2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Singh, Pancham Pant, Mamta Kansal, Mrignainy Singh, Janmejay Singh, Gaurav Jauhari, Sheradha Ajay Kumar Garg Engineering College Information Technology Uttar Pradesh Ghaziabad India Ajay Kumar Garg Engineering College Computer Science and Information Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350314946

The frequency and complexity of cyber assaults have risen considerably in recent years, resulting in major financial losses and reputational harm for both corporations and people. Traditional cybersecurity measures, such as firewalls and encryption, have been insufficient in protecting against these threats. Due to a rise in cyber assaults and data breaches, there is an increased need for effective cybersecurity solutions. With its decentralized nature blockchain technology has come out as a promising option for enhancing cybersecurity. This article investigates how blockchain may be adapted to enhance cybersecurity in areas such as decentralized identity management, safe data storage and sharing, and smart contract security. The article also investigates the potential benefits and barriers of using blockchain in cybersecurity including some cases of successful blockchain implementations in the field of cybersecurity. The report contends that blockchain technology has been able to enhance cybersecurity by offering a secure and transparent platform for digital transactions and data sharing. © 2023 IEEE.

关键词： Blockchain

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The Role of ERNIE Model in Analyzing Hotel Reviews using Chinese Sentiment Analysis

The Role of ERNIE Model in Analyzing Hotel Reviews using Chi...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Jie, Lee Hao Ayyasamy, Ramesh Kumar Sangodiah, Anbuselvan Jalil, Norazira Binti A Krishnan, Kesavan Chinnasamy, P. University Tunku Abdul RahmanKampar Faculty of Information and Communication Technology Malaysia Faculty of Computing and Engineering Quest International University Ipoh Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350348217

This research aims to find the best deep learning model to do Chinese sentimental analysis. BERT's model may work well in the English language but not work in the Chinese language. English is easier to encode in embedding space, and words are separated by space characters, unlike Chinese words, which do not exist separate by the area. BERT Chinese model work on character level encoding is not enough to make the BERT model adapt to Chinese NLP tasks. The BERT Model and ERNIE models are the current state of art pre-training language models. Word level embeddings and input is needed to improve the BERT model to perform in Chinese Sentiment Analysis. With the motivation to improve the Chinese Sentiment Analysis, this study will explore the role of different models to propose a better version of the ERNIE model. This study will limit the scope to improve Chinese sentiment analysis among different NLP tasks. © 2023 IEEE.

关键词： Sentiment analysis

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Supplier Qualification Using FMEA in a Meat Company

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Periodica Polytechnica Transportation engineering 2023年第4期51卷 323-328页

作者： Boldizsár, Adrienn Török, Erika Pásztor, Attila Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10. KecskemétH-6000 Hungary

The analysis of the processes between supplier and customer and the detection and handling of defects is based on objective, quantified criteria so that customer complaints can be handled as efficiently as possible, with the least possible inconvenience. One possible risk-management method is FMEA, or Failure Mode(s) and Effect(s) Analysis, which allows a team to anticipate and prevent failures from occurring anywhere. It is an analytical methodology to identify and check for potential problems when designing a product, service or process. It can be used to reduce the number of defective supplies and thus indirectly increase customer satisfaction (by more efficient purchasing, the price can be kept lower and the quality higher, and fewer customer complaints from end users can be expected). It was used in this study to assess the supplier quality of a Hungarian industrial company in the FMCG sector and to make tangible improvements in a short time to eliminate supplier defects. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Defects

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Visualization of Covid 19 pattern using machine learning

Visualization of Covid 19 pattern using machine learning

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Thanjaivadivel, M. Pious, Ignatious K. Jagannathang, J. School of Computing and Information Technology Reva University Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore Dt Tamil Nadu Katpadi India

ISBN: (纸本)9781032669663

The Wuhan market in China is the source of a terrible and undetectable threat to the entire planet. The time between the outbreak and the epidemic was barely a few months. Visual data analysis makes it easier to get information from numerous data sources in a better method because it helps to understand how the world combats this disease. In this essay, we’ll demonstrate how one might visualise a map of their own nation, along with the regions where this dreadful sickness has spread. This essay discusses the COVID-19 epidemic’s data identification and analysis. In a smart, smart, and global nation, data recognition aids in the integration of all data sources, including the number of instances discovered, deaths, and current cases. Data is disseminated by putting it into visual aids like graphs and maps. This offers current and trustworthy information that enables us to monitor the number of persons admitted to hospitals in every region of India as well as in other nations around the world. This clarifies how you can handle the threat that the Coronavirus poses. In this essay, we’ll show how our theory can be used to analyse and forecast the spread of COVID-19. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Boosting Offline Optimizers with Surrogate Sensitivity 41

Boosting Offline Optimizers with Surrogate Sensitivity

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41st International Conference on Machine Learning, ICML 2024

作者： Dao, Manh Cuong Le Nguyen, Phi Truong, Thao Nguyen Hoang, Trong Nghia School of Information and Communications Technology Hanoi University of Science and Technology Hanoi Viet Nam National Institute of Advanced Industrial Science and Technology Tokyo Japan School of Electrical Engineering and Computer Science Washington State University Washington United States

Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in silico maximization of a surrogate of the black-box function. Although such a surrogate can be learned from offline data, its prediction might not be reliable outside the offline data regime, which happens when the surrogate has narrow prediction margin and is (therefore) sensitive to small perturbations of its parameterization. This raises the following questions: (1) how to regulate the sensitivity of a surrogate model;and (2) whether conditioning an offline optimizer with such less sensitive surrogate will lead to better optimization performance. To address these questions, we develop an optimizable sensitivity measurement for the surrogate model, which then inspires a sensitivity-informed regularizer that is applicable to a wide range of offline optimizers. This development is both orthogonal and synergistic to prior research on offline optimization, which is demonstrated in our extensive experiment benchmark. Copyright 2024 by the author(s)

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Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke 2

Utilizing Gradient Boosting Models to Identify Risk Factors ...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Tamilselvi, P. Harini, C. Rao, Guduri Padma Narasimha Raju, P.V. Subbulakshmi, R. Channabasava, Ugranada Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Maisammaguda Secunderabad India SRKR Engineering College Andhra Pradesh Bhimavaram India Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore India Department of Information Science and Engineering Don Bosco Institute of Technology VTU Karnataka Belagavi India

ISBN: (纸本)9798350383867

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals. © 2024 IEEE.

关键词： Risk assessment

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning 2

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

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A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

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computers, Materials & Continua 2023年第4期75卷 959-976页

作者： MZeeshan Sarwar Mohammed Hamad Alatiyyah Ahmad Jalal Mohammad Shorfuzzaman Nawal Alsufyani Jeongmin Park Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

关键词： Artificial intelligence convolutional neural network depth images interactive-object detection machine learning

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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