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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Raman Kumar Laith H. Fezaa Ahmed Alkhayyat Bhagwati Lal Pareek Jatinder Kumar Abhilasha Jadhav School of Mechanical Engineering Rayat Bahra University Mohali India Department of Optical Techniques Al-Zahrawi University College Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq Sangam University Bhilwara Rajasthan India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This work aims to examine meta-learning on few-shot learning schemes, specifically on the assessment of how well current approaches manage imbalance in the examined datasets. Meta-learning to improve model performance with a small amount of data by learning from related tasks is tested against benchmarks such as MAML, ProtoNet and RelationNet. An important insight of our research is that these methods are still facing important issues, such as scalability or the problem of domain generalization, as well as the major issue of the extreme imbalance of the data distribution. To overcome these problems, we suggest a new solution, which employs elaborate methods and is characterized by increased correspond and F1 coefficient, as well as the degree of balanced accuracy at different degrees of unbalanced data. However, the proposed study also points out the main challenges for future research including new and efficient algorithms for high-dimensional and real-time scenario, better methods for handling massive shift, as well as the possibility to increase the interpretability of models. The research also highlights the possibilities of applying meta-learning in the few-shot classification, but at the same time also highlight the imperfections and the research needs to be continued in order to further improve the existing gaps and to meet the practical requirements. This study adds to the overall knowledge base concerning meta learning approaches and provides an initial frameworkfor work to be conducted in the future in an effort to produce methods that are considerably more portable, generalizable and also more comprehensible in nature across the broader field of study.

关键词： Metalearning Training Accuracy Scalability MIMICs Knowledge based systems Data models Real-time systems Classification algorithms Overfitting

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Large Language Model-Driven Curriculum Design for Mobile Networks

arXiv

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arXiv 2024年

作者： Erak, Omar Alhussein, Omar Naser, Shimaa Alabbasi, Nouf Mi, De Muhaidat, Sami KU 6G Research Centre Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates KU 6G Research Centre Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates Future Information Networks Research Cluster College of Computing Birmingham City University United Kingdom Department of Systems and Computer Engineering Carleton University Ottawa Canada

This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobile networks evolve towards the 6G era, managing their increasing complexity and dynamic nature poses significant challenges. Conventional RL approaches often suffer from slow convergence and poor generalization due to conflicting objectives and the large state and action spaces associated with mobile networks. To address these shortcomings, we introduce curriculum learning, a method that systematically exposes the RL agent to progressively challenging tasks, improving convergence and generalization. However, curriculum design typically requires extensive domain knowledge and manual human effort. Our framework mitigates this by utilizing the generative capabilities of LLMs to automate the curriculum design process, significantly reducing human effort while improving the RL agent's convergence and performance. We deploy our approach within a simulated mobile network environment and demonstrate improved RL convergence rates, generalization to unseen scenarios, and overall performance enhancements. As a case study, we consider autonomous coordination and user association in mobile networks. Our obtained results highlight the potential of combining LLM-based curriculum generation with RL for managing next-generation wireless networks, marking a significant step towards fully autonomous network operations. Copyright © 2024, The Authors. All rights reserved.

关键词： Wireless networks

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CNN-Bidirectional LSTM based Approach for Financial Fraud Detection and Prevention System

CNN-Bidirectional LSTM based Approach for Financial Fraud De...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： N. Madhusudhana Reddy K A Sharada Daniel Pilli R.Nithya Paranthaman K Subba Reddy Amit Chauhan Department of CSE Rajeev Gandhi Memorial College of Engineering and Technology Nandyal Andhra Pradesh India Department of CSE HKBK College of Engineering Bangalore Karnataka India MBA Department Koneru Lakshmaiah Educational Foundation Andhra Pradesh India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu India Department of Computer Science and Engineering Prakasam Engineering College Kandukur Andhra Pradesh India Department of Life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India

Detecting fraudulent activity has become a pressing issue in the ever-expanding realm of financial services, which is vital to ensuring a positive ecosystem for everyone involved. Traditional approaches to fraud detection typically rely on rule-based algorithms or manually pick a subset of attributes to perform prediction. Yet, users have complex interactions and always display a wealth of information when using financial services. These data provide a sizable Multiview network that is underutilized by standard approaches. The proposed method solves this problem by first cleaning and normalizing the data, then using Kernel principal component analysis to extract features, and finally using these features to train a model with CNN-BiLS TM, a neural network architecture that combines the best parts of the Bidirectional Long Short-Term Memory (BiLS TM) network and the Convolution Neural Network (CNN). BiLSTM makes better use of how text fits into time by looking at both the historical context and the context of what came after.

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Towards Securing Machine Learning Models Against Membership Inference Attacks

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computers, Materials & Continua 2022年第3期70卷 4897-4919页

作者： Sana Ben Hamida Hichem Mrabet Sana Belguith Adeeb Alhomoud Abderrazak Jemai Departement of STIC Higher Institute of Technological Studies of GabesGeneral Directorate of Technological StudiesRades2098Tunisia Research Team on Intelligent Machines National Engineering School of GabesGabes UniversityGabes6072Tunisia SERCOM-Lab. Tunisia Polytechnic SchoolCarthage UniversityTunis1054Tunisia Department of IT College of Computing and InformaticsSaudi Electronic UniversityMedina42376Saudi Arabia School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Science College of Science and Theoretical StudiesSaudi Electronic UniversityRiyadh11673Saudi Arabia INSAT SERCOM-Lab.Tunisia Polytechnic SchoolCarthage UniversityTunis1080Tunisia

From fraud detection to speech recognition,including price prediction,Machine Learning(ML)applications are manifold and can significantly improve different ***,machine learning models are vulnerable and are exposed to different security and privacy ***,these issues should be addressed while using ML models to preserve the security and privacy of the data *** is a need to secure ML models,especially in the training phase to preserve the privacy of the training datasets and to minimise the information *** this paper,we present an overview of ML threats and vulnerabilities,and we highlight current progress in the research works proposing defence techniques againstML security and privacy *** relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks(MIA)and the related *** this paper,we introduce a countermeasure against membership inference attacks(MIA)on Conventional Neural Networks(CNN)based on dropout and L2 *** experimental analysis,we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the ***,using CNN model training on two datasets CIFAR-10 and CIFAR-100,we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different ***,we present a solution to achieve a trade-off between the performance of themodel and the mitigation of MIA attack.

关键词： Machine learning security and privacy defence techniques membership inference attacks dropout L2 regularization

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Jarvis in Motion: A Research Artifact for Circulating Lifestyle Values in Public

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Proceedings of the ACM on Human-computer Interaction 2021年第CSCW1期5卷 1-27页

作者： Cheon, Eun Jeong Han, Shenshen Su, Norman Makoto Aalborg University Department Of Computer Science Selma Lagerlofs Vej 300 Aalborg9220 Denmark University Of California 101 The City Drive South Irvine United States Indiana University Bloomington Luddy School Of Informatics Engineering And Computing 919 E 10th St BloomingtonIN47408 United States

We report on a research through design study of how our everyday objects, augmented with technologies, could contribute to disseminating people's lifestyle values in public. We developed Jarvis, a hybrid object combining a glass jar with a location-tracking system. The Jarvis endeavors to make lifestyle values visible, while sometimes hiding its values across different places. As a design research artifact, the Jarvis seeks to spark meaningful interactions as zero-waste practitioners carry it around in their daily routines in public. The data visualized through the Jarvis demonstrate how users' values are spread out locally. 18 zero-waste followers participated in a week-long deployment with a design probe followed by post-deployment interviews. Our findings highlight the role of different agencies in circulating values represented by the Jarvis. We discuss our methodological insights on hybrid objects, and how the circulation of values may inform us about the alternative roles of technologies in speaking for our lifestyle values. © 2021 ACM.

关键词： Tracking (position)

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Developing Hybrid Privacy Channels in Cloud Architecture using Several Agents

Developing Hybrid Privacy Channels in Cloud Architecture usi...

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Aman Sharma Mukesh Joshi Sh. J Mamatkulov Shivani Thakur Saif O. Husain Shweta Goyal Department of Mechanical Engineering GLA University Mathura India School of Computing Graphic Era Hill University Bhimtal Campus Uttarakhand India Docent of the Department of Marketing Samarkand Institute of Economics and Service Uzbekistan Samarkand Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

An information security plan often assesses the risk, the likelihood that it would materialize as a result of an unusual incident, and the related ramifications for cloud enterprises. Different computer safety and risk mitigation techniques are implemented by different cloud organizations and disciplines. When comparing the risk management techniques that cloud firms now use, it becomes clear that they have shortcomings since they only take into account traditional criteria and ignore an agent-based approach that takes into account every aspect of the risk involved. The failure to include well-known risk mitigation frameworks, appropriate comparisons, and agent procedures is what spurred this investigation. This paper presents the Agent-Based Security of Knowledge Model for Blended Cloud computing, a complete methodology that combines cloud-specific methodologies for analyzing and comparing well-known systems now in use, to solve challenges in cloud computing risk. New elements are also included from the assessed techniques. The goal of introducing intelligent agents and software agents is to provide a decision-making framework that will direct organizational actions against danger agents by using data from security agents. These agents are in charge of gathering trustworthy data and organizing it for frameworks. The research focuses on risk assessment methods that compute results by examining resources, possible threats, weaknesses, and related actions. ISO/IEC 27005:2011 is the best technique inside the Agent-Based security professionals Framework (ABISF) after a comprehensive comparison with popular Individual Security Assessment of Risk (ISRA) methodologies. The suggested framework is developed using a fuzzy inference method based on fuzzy set theory, and its effectiveness is verified using fuzzy logic rules in MATLAB®. The inconclusive results confirm that the proposed technique has potential to protect privacy of information in cloud settings.

关键词： Fuzzy logic Cloud computing Privacy ISO Standards Information security computer architecture Security Intelligent agents IEC Standards Risk mitigation

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Role of activity-based learning and ChatGPT on students' performance in education

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computers and Education: Artificial Intelligence 2024年 6卷

作者： Al Shloul, Tamara Mazhar, Tehseen Abbas, Qamar Iqbal, Muhammad Ghadi, Yazeed Yasin Shahzad, Tariq Mallek, Fatma Hamam, Habib General Education Department Liwa College Abu Dhabi15222 United Arab Emirates Department of Computer Science Virtual University of Pakistan Lahore51000 Pakistan Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad 44000 Pakistan Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Faculty of Engineering University de Moncton MonctonNBE1A3E9 Canada Hodmas University College Taleh Area Mogadishu Somalia Bridges for Academic Excellence Tunis Tunisia School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa

Purpose: This study investigates the impact of activity-based learning and the utilization of ChatGPT on students' academic performance within the educational framework. Objectives: The study aims to assess the effectiveness of activity-based learning in comparison to traditional methods, while also evaluating the potential benefits and drawbacks of integrating ChatGPT as an educational tool. Methods: The study employs a comparative approach, analyzing the outcomes of students exposed to activity-based learning versus those using conventional methods. Additionally, the study examines the usage of ChatGPT in education through surveys and trials to determine its contribution to personalized feedback, interactive learning, and innovative teaching methods. Results: The findings reveal that activity-based learning enhances students' engagement, motivation, and critical thinking skills. Students participating in activity-based learning demonstrate improved academic achievement, which is attributed to their active involvement and practical application of knowledge. Similarly, the integration of ChatGPT offers novel avenues for interactive learning and individualized assistance, fostering students' understanding and exploration of complex concepts. Conclusion: In conclusion, activity-based learning proves to be a student-centered approach that enhances learning outcomes by fostering active participation and practical engagement. The utilization of ChatGPT in education showcases its potential to enhance educational experiences through interactive conversations and innovative teaching methodologies, despite considerations regarding potential limitations and ethical implications. © 2024 The Authors

关键词： Students

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QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

arXiv

引用

arXiv 2025年

作者： Meghanath, Ashtakala Das, Subham Behera, Bikash K. Khan, Muhammad Attique Al-Kuwari, Saif Farouk, Ahmed Department of Physics Indian Institute of Science Education and Research Thiruvananthapuram India Pvt. Ltd. WB Mohanpur741246 India Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU† method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate QDCNN’s —key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework. © 2025, CC BY.

关键词： Convolutional neural networks

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Kamalakkannan A. Yasmin Arunkumar. R P. Kavitha Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Science JBAS College for women Chennai India Faculty of Computing Engineering and Science University of South Wales Treforest/Newport Campus United Kingdom Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price.

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Improvement of Task Scheduling with Energy Efficiency including Fault Tolerant with Resilient computing System in Parallel and Distributed Communication Network

Improvement of Task Scheduling with Energy Efficiency includ...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Vijay Kumar P. Madhuri R. Yamini Palla Sravani Muthukumar K Department of Physics Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Gowdavelly(V) Medchal(M) Hyderabad Telangana India Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability.

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