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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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Security Risks of ML Models: Adverserial Machine learning 4

Security Risks of ML Models: Adverserial Machine Learning

引用

4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Askhatuly, Aidos Berdysheva, Dinara Yedilkhan, Didar Berdyshev, A. Kazakh National University after alFarabi Almaty Kazakhstan Astana IT University Astana Kazakhstan International Information Technology University Almaty Kazakhstan

ISBN: (纸本)9798350374865

Machine learning has emerged as a transformative technology with diverse applications across various sectors. Despite its widespread adoption, the susceptibility of machine learning algorithms to security threats presents a significant challenge. While conventional security risks at hardware and network levels are well-documented, the unique vulnerabilities of machine learning systems require specialized attention. In this paper, we delve into the critical issue of security risks in machine learning, emphasizing the growing concerns surrounding adversarial attacks. To illustrate the practical implications of these security risks, we present a detailed case study on adversarial attacks. By implementing and analyzing an adversarial attack on image classification models, we demonstrate their detrimental effects on model performance and robustness. Our findings underscore the urgent need for proactive measures to safeguard machine learning systems against security threats. By raising awareness and fostering collaboration between researchers and practitioners, we can pave the way for more secure and resilient machine learning frameworks. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time optimization of Banking Processes using Data Processing and Machine learning 4

Real-Time Optimization of Banking Processes using Data Proce...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kondam, Anusha Logeshwaran, J. JPMorgan Chase Co United States Christ University Department of Computer Science Karnataka Bengaluru560029 India

ISBN: (纸本)9798331529635

Optimizing processes in the banking industry is largely responsible for growth, which allows it to become more efficient. As the trend of data generation is increasing in financial institutions, there is a dire requirement for real-time data processing along with machine learning approaches to cater to banking processes most efficiently. It analyses these massive data sets on a rolling basis to produce insights and assist in making informed decisions. the bank can then use this to find inefficiencies quickly, determine where they would be at risk, and if anything should be changed or modified. On the other hand, Machine learning is a way to learn algorithms from data and predictive modules for optimal results based on input parameters/without being directly programmed. Using real-time data processing by machine learning allows banks to constantly diagnose and move changes toward achieving optimal performance. algorithms can, for instance, detect fraudulent activities in real time, which minimizes losses banks sustain due to fraud. In addition, with machine learning, banks can create tailor-made services for each customer (e.g. personalized investment products or levels of credit risk individually), anticipate the behavior and needs of their customers, and give them just-at-time contentment =). this can improve customer satisfaction and loyalty. © 2024 IEEE.

关键词： Adversarial machine learning

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A Study of Multi-Constraint Planning Problem for Time Series Data with Correlation 4

A Study of Multi-Constraint Planning Problem for Time Series...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Yu, Fangda Yu, Xiaotong Northeast Forestry University Mechanical and Electrical Engineering Heilongjiang Harbin China

ISBN: (纸本)9798350352931

this paper studies the multi-constraint optimization problem under the background of time series data correlation, which is particularly important in the fields of finance, energy and meteorology. Aiming at the multiple constraints that need to be considered at the same time in practical applications, such as time, resources and risks, this paper proposes an optimization algorithm that combines time series correlation analysis. through theoretical analysis and model design, the algorithm has been verified to be effective in multiple application scenarios, especially in improving financial forecasting and decision-making efficiency. then, we use the financial market time series data into the algorithm of this paper for optimization experiments. the experimental results show that our new algorithm can not only improve the accuracy of similar financial market prediction models, but also significantly improve the decision-making efficiency. Finally, this paper looks forward to the improvement of the method in complex constraints and the direction of more efficient algorithm, which provides ideas for further research. © 2024 IEEE.

关键词： dynamic dependency analysis intelligent optimization algorithms multi-constraint optimization multi-objective optimization time series correlation

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Grapes to Grades: Machine learning Insights into Grape Nectar Quality 4

Grapes to Grades: Machine Learning Insights into Grape Necta...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Charan, Vankadaru Jashwanth, Ramala Krishna, Kotla Sai Reddy, S Ranjith Makka, Shanthi Vardhaman College of Engineering Information Technology Hyderabad India Vardhaman College of Engineering Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350364729

In the present days Wine is a popular and luxurious beverage worldwide, particularly in Europe and Russia. the wine industry heavily relies on wine quality certification, which can have a significant impact on the human body and is expensive. Wine quality is predicted based on various traits that change over time, refining over time. Various ML algorithms including SVM, Regression algorithms, Random Forest Algorithm, KNN, Linear Regression & Decision Tree Algorithm are utilized to assess wine quality. Random Forest Algorithm has the optimal accuracy score at 97.44%. this study contributes to the broader discourse on machine learning applications in viticulture and offers practical insights for stakeholders within the wine industry. It accentuates the criticality of model selection in achieving accurate and reliable wine quality prediction and providing accurate data related to wine quality. © 2024 IEEE.

关键词： Wine

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Pulmonary Nodule Detection on Chest X-rays: Exploring Deep learning algorithms and their Clinical Impact 4

Pulmonary Nodule Detection on Chest X-rays: Exploring Deep L...

引用

4th international conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Wang, Yifan School of Computer Science and Engineering University of New South Wales SydneyNSW Australia

ISBN: (纸本)9798331531409

Lung cancer is one of the leading causes of death worldwide, and early detection of lung nodules is crucial for the prevention and treatment of lung cancer. Early detection of lung nodules is a key step in lung cancer screening and prevention, because compared with the clinical characteristics of lung cancer, nodules appear at an earlier stage, enabling patients to have more medical intervention and prevention. In traditional methods, the detection of pulmonary nodules often relies on manual labeling, which usually takes a lot of time and is prone to errors and omissions. So that patients can get better treatment outcomes. However, there are many technical challenges in lung nodule detection in X-ray, such as the diversity of nodule size and low contrast. In recent years, with the increasing research of deep learning in this field, automated detection methods have made remarkable progress. this paper analyzes several outstanding nodule detection algorithms in the NODE21 Challenge, and summarizes the advantages, disadvantages, and potential optimization space of each algorithm. © 2024 IEEE.

关键词： Lung cancer

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the Analysis of Student learning Behavior Based on Teachermate Data 4

The Analysis of Student Learning Behavior Based on Teacherma...

引用

4th international conference on Educational Technology, ICET 2024

作者： Deng, Tianping Chen, Shiying Zhou, Tong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China

ISBN: (纸本)9798350376944

In the era of big data, thanks to the development of digital information technology (e.g., data mining), the concept of the smart campus has emerged and changed the traditional education model. However, the smart education products, mainly the teachermate platform, cannot provide algorithm-based personalized education services for teachers and students by targeting and analyzing student data. Based on this background, this paper takes some students as the sample, and uses the data of various learning behavior characteristics on the teachermate platform and the final exam paper results of the student grade management system on the teacher's side as the data source, and analyzes the data through data mining algorithms. the final results of the analysis were obtained, and suggestions for optimization of the platform based on academic alerts were obtained for faculty, teachers, and the teachermate platform. © 2024 IEEE.

关键词： Contrastive learning

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Implementing Resource-Aware Scheduling Algorithm for Improving Cost optimization in Cloud Computing 4

Implementing Resource-Aware Scheduling Algorithm for Improvi...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Reddy, Kiran Jagannadha Udayaraju, Pamula Pamaiahgari, Venkata Pradeep Reddy Kumar, V. Dilip Walmart BentonvilleAR United States Srm University Department of Cse AP India Wendys International Llc OH United States SRKR Engineering College Department of CSE India

ISBN: (纸本)9798331523923

An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud Computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free;otherwise, it must schedule the same or relevant resource. this paper demonstrates a Resource-Aware Scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. the proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. the simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. the evaluation found that the RAS model is more suitable for cloud resource allocation. © 2025 IEEE.

关键词： Cloud Computing Cost optimization Resource Allocation Resource-Aware Scheduling Scheduling

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Proceedings of 2024 4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

Proceedings of 2024 4th International Conference on Artifici...

引用

4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

ISBN: (纸本)9798400710247

the proceedings contain 194 papers. the topics discussed include: analysis and verification of fluid-structure coupling vibration characteristics of flood gate based on virtual mass method;spatiotemporal prediction of land subsidence by integrating multi-source data;real time monitoring and statistical analysis of water intake based on computer information technology;landslide displacement prediction based on the random forest and optimized support vector regression;research on rockburst intensity prediction based on grey wolf optimization algorithm and random forest;research on the design of blasting protection hose thickness based on machine learning;prediction of surface and tunnel deformation using a random field theory-based bp neural network method;and research and application of BIM-based digital seepage control technology for large hydropower project.

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Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing 4

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Joon, Rakesh Kumar Anitha, R. Selvan, Saravana Latha Mageshwari, P.S. Gayathri Priya, S. Malathi, K. Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India S.A. Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600077 India School of Professional Engineering Manukau Institute of Technology Auckland2104 New Zealand Tamil Nadu Kavaraipettai601206 India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai601206 India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. the objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications. © 2025 IEEE.

关键词： Sensor nodes

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Advancements in AI for Depression Detection: A Survey of Machine learning and Deep learning applications 4

Advancements in AI for Depression Detection: A Survey of Mac...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Subuhi, Afreen Vadlamudi, Muniraju Naidu Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Bowrampet Telangana Hyderabad500090 India

ISBN: (纸本)9798331518981

this survey paper delves into the sophisticated applications of machine learning (ML) and deep learning (DL) techniques in the realm of depression detection. By leveraging a variety of analytical methods, including text analysis, speech analysis, social media monitoring, and image and video analysis, the research highlights the role of advanced algorithms such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Ensemble Methods, Random Forests, Gradient Boosting Machines (GBM), and Graph Neural Networks (GNN) in identifying depressive symptoms. the study conducts a thorough review of existing literature, showcasing the efficacy of these techniques while pinpointing critical gaps and challenges, such as data quality and integration with clinical practice. this comprehensive examination underscores the necessity for robust, generalizable models capable of real-world application. Ultimately, the survey aims to demonstrate how ML and DL technologies can significantly enhance early detection and intervention strategies in mental health management, paving the way for improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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