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Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

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AITA: A new framework for Trading Forward Testing with an Artificial Intelligence Engine

AITA: A new framework for Trading Forward Testing with an Ar...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Letteri, Ivan University of L'Aquila Department of Information Engineering Computer Science and Maths via Vetoio snc AQ Coppito67100 Italy

In general, traders test their trading strategies by applying them to historical market data (backtesting), and then reapply those that have made the most profit on that past data. In this article, we propose AITA (Artificial Intelligence Trading Assistant), our framework for generating trading systems by applying the new trading strategy called DNN-forwardtesting [1] by determining the best strategy based on the prediction issued by a deep neural network. In this work, we show the experiment with AITA involves the use of 10 stocks that are first filtered according to their volatility using the Kmean++ model. Having determined the assets with average volatility, we use this historical data to train a deep feed-forward neural network to predict price trends over the next 30 days of the open stock market. Finally, the trading system, created by AITA, calculates the most effective technical indicator by applying it to the DNN forecasts to generate the trading strategy. The results confirm that neural networks outperform classical statistical techniques by increasing Sharpe, Sortino and Calmar ratios compared to even strategies choosen through traditional backtesting. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Financial markets

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Performance Analysis of Data Processing in Distributed File Systems with Near Data Processing

Performance Analysis of Data Processing in Distributed File ...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Hou, Shiyue Tallent, Nathan R. Wang, Li Mi, Ningfang Northeastern University Department of Electrical and Computer Engineering MA United States Pacific Northwest National Laboratory Advanced Computations and Maths Division RichlandWA United States

ISBN: (纸本)9798350364910

In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark [1] and Hadoop [2] manage the increasing amount and velocity of data by improving data placement and processing speeds. However, they face inherent limitations due to the essential data movement required for processing. In this paper, we explore the Skyhook framework, a novel extension of the Ceph distributed system, which significantly reduces the need for data movement. We present an extensive case study using the Skyhook framework, applying it with the TPC-H and K-means clustering algorithms. More specifically, we leverage the TPC-H benchmark to distinguish between CPU-intensive and I/O-intensive tasks. We explore the integration of K-means clustering into SQL, coupled with a near-data processing system to offload the computational burden of the K-means clustering algorithm to storage nodes. We conduct a comprehensive performance evaluation of distributed data processing applications across three processing approaches: traditional layout (baseline), optimized layout, and near-data processing. Additionally, we introduce the use of the FIO tool to simulate real-world system workloads, enabling the measurement of performance metrics such as average latency and CPU utilization. Our research is a significant advance in understanding how to optimize data processing systems to meet the demands of the modern data landscape. © 2024 IEEE.

关键词： Data assimilation

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Online Banking Security: An Experimental Study of Transaction Authorization Usability

Online Banking Security: An Experimental Study of Transactio...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Prasad, J Siva Ram Kumar, K. Parish Venkata Reddy, S Srihari Chaitanya, Pagadala Ravi Kumar, E.S.R. Reddy, G Venkata Ramana V. R. Siddhartha Engineering College Department of Maths Vijayawada India Brindavan Institute of Technology & Science Department of Computer Science and Engineering Kurnool India St.Martin's Engineering College Dhulapally Department of Computer Science and Engineering Secunderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317060

The security measures used for online banking have undergone significant transformations throughout the comparatively short duration of its existence. The first implementations of authenticating and identity management systems were, and in some cases continue to be, susceptible to various assaults, including phishing. One of the current state-of-the-art options is using out-of-band channels for user re-authentication before each transaction. This article details the findings of a security analysis of such a system. According to the study results, it is secure against specific threats while being open to others. As attackers continue improving their methods, the remaining vulnerabilities will likely be taken advantage of in the future. Measures that might be taken toward against such assaults in the future are detailed. © 2023 IEEE.

关键词： Authentication

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Classification of EEG Signals on SEED Dataset Using Improved CNN 2

Classification of EEG Signals on SEED Dataset Using Improved...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Ramar, B. Ramalakshmi, R. Gandhi, Vaibhav Pandiselvam, P. Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Middlesex University Department of Design Engineering and Maths Education London United Kingdom

ISBN: (纸本)9798350347579

The proposed research introduces an Improved Convolutional Neural Network (ICNN) to construct EEG-based emotion detection models. This study has utilized an EEG dataset of 15 subjects available from a BCMI laboratory. In our work, differential entropy characteristics obtained from multichannel EEG data are used to train the Improved CNN. The best classification accuracy is 95.67% which is significantly higher than that of the original 62 channels. The most important channels and frequency bands are identified by Improved CNN. The outcomes of our study also demonstrate the existence of neuronal signatures linked to various emotions, which are consistent between sessions and people. Finally, the effectiveness of deep and shallow models are compared and also the performance of improved CNN is compared with benchmark algorithms. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Stress Prediction of Working Employees in Different Environments using Machine Learning Techniques 6

Stress Prediction of Working Employees in Different Environm...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Shanmugapriya, P. Balasubramanie, P. Dhivyaa, C.R. Senthil Raja, D. Dhivya, P. Department of CSE Kongu Engineering College Tamilnadu Erode India School of Computer Science & Engineering VIT Tamilnadu Vellore India Dept of Maths K S Rangasamy College of Technology Tamilnadu Tiruchengode India Department of CT-PG Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798331522667

Mental stress is a growing concern, especially among employees across sectors, affecting both well-being and job performance. Stressors like excessive workload, tight deadlines, and high employer expectations often lead to health issues such as anxiety, depression, cognitive decline, cardiovascular diseases, and suicidal thoughts. This study aims to predict employee stress using physiological and behavioral indicators. Real-time data was collected from employees in medical, teaching, and IT sectors through an online stress survey. Three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were utilized for prediction. Among the models, the Random Forest (RF) algorithm showed the best performance with a 92% accuracy rate. The study’s findings highlight the effectiveness of real-time data and machine learning in stress prediction. A web-based application was also developed to help organizations monitor and manage employee well-being. This research demonstrates the potential of predictive analytics and machine learning to support stress management, encouraging proactive measures for a healthier work environment. © 2025 IEEE.

关键词： Adversarial machine learning

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Point-based Gesture Recognition Techniques 15

Point-based Gesture Recognition Techniques

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15th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Sharma, Varun Kolivand, Hoshang Asadianfam, Shiva Al-Jumeily, Dhiya Jayabalan, Manoj Liverpool John Moores University Department Computer Science Liverpool United Kingdom School of Computer Science and Maths Liverpool John Moores University Liverpool United Kingdom Qom University of Technology Faculty of Electrical and Computer Engineering Qom Iran Islamic Azad University Qom Branch Department of Computer Engineering Qom Iran

Gesture recognition is a computing process that attempts to recognize and interpret human gestures through the use of mathematical algorithms. In this paper, we describe Point Based Gesture Recognition and Point Cloud... 详细信息

ISBN: (纸本)9798350335149

关键词： Gesture recognition

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Gesture Recognition Techniques 15

Gesture Recognition Techniques

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15th International Conference on Developments in eSystems engineering, DeSE 2023

ISBN: (纸本)9798350335149

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. In this paper, we describe some of Gesture recognition techniques such as Vision based gesture recognition and Graph based gesture recognition. Also, we explore these techniques with previous studies. © 2023 IEEE.

关键词： computer vision

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Exploiting Intrinsic Multi-Agent Heterogeneity for Spatial Interference Reduction in an Idealised Foraging Task

Exploiting Intrinsic Multi-Agent Heterogeneity for Spatial I...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Bennett, Chris Lawry, Jonathan Bullock, Seth Department of Engineering Maths University of Bristol Bristol United Kingdom Department of Computer Science University of Bristol Bristol United Kingdom

Typically, collective behaviour research has tended to focus on behaviour arising in populations of homogeneous agents. However, humans, animals, robots and software agents typically exhibit various forms of heterogeneity. In natural systems, this heterogeneity has often been associated with improved performance. In this work, we ask whether spatial interference within a population of co-operating mobile agents can be managed effectively via conflict resolution mechanisms that exploit the population’s intrinsic heterogeneity. An idealised model of foraging is presented in which a population of simulated ant-like agents is tasked with making as many journeys as possible back and forth along a route that includes tunnels that are wide enough for only one agent. Four conflict resolution schemes are used for determining which agent has priority when two or more meet within a tunnel. These schemes are tested in the context of heterogeneous populations of varying size. The findings demonstrate that a conflict resolution mechanism that exploits agent heterogeneity can achieve a significant reduction in the impact of spatial interference. However, whether or not a particular scheme is successful depends on how the heterogeneity that it exploits is implicated in the population-wide dynamics that underpin system-level performance. © Artificial Life Conference Proceedings 2022.

关键词： Mobile agents

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Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs 37

Scalable Edge Blocking Algorithms for Defending Active Direc...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Guo, Mingyu Ward, Max Neumann, Aneta Neumann, Frank Nguyen, Hung School of Computer and Mathematical Sciences University of Adelaide Australia School of Physics Maths and Computing Computer Science and Software Engineering University of Western Australia Australia Department of Molecular and Cellular Biology Harvard University CambridgeMA United States

ISBN: (纸本)9781577358800

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender's aim is to minimize the attacker's success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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