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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 811 条记录，以下是321-330 订阅

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Leveraging Data-Driven Decision Making for E-Commerce Growth: A machine learning Framework 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Mahmud, Tanjim Chakma, Rishita Akther, Roksana Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Rangamati Bangladesh University of Chittagong Chittagong Bangladesh Luleå University of Technology Luleå Sweden

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

this paper illustrates the pivotal role of K-means clustering in shaping effective e-commerce strategies by identifying customer segments with similar buying behaviors. this leads to targeted marketing, personalized product recommendations, and optimized pricing. the elbow method is employed to enhance cluster count determination. through K-Means clustering analysis, color-coded points highlight distinct customer groups, aiding strategic decision-making for price and location predictions. Logistic regression, a vital supervised learning algorithm, accurately predicts categorical outcomes like customer purchasing behavior, achieving a 97% accuracy on test data. the Support Vector machine (SVM) and Random Forest models attain remarkable accuracies of 99 and 100%, respectively. Ural’s integration of machine learning and data analysis has resulted in improved customer understanding, personalized offerings, and optimized inventory management. this strategy significantly boosted sales, emphasizing the value of continuous monitoring and adjustments. In summary, this paper underscores how data-driven techniques enhance e-commerce strategies, fostering better customer experiences, increased revenue, and a competitive advantage. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Transforming from Mathematical Model to ML Model for Meteorology in Macao's Smart City Planning 23

Transforming from Mathematical Model to ML Model for Meteoro...

引用

7th international conference on E-Commerce, E-Business and E-Government, ICEEG 2023

作者： Ian, Vai-Kei Tse, Rita Tang, Su-Kit Pau, Giovanni Macao Meteorological and Geophysical Bureau Macao China Faculty of Applied Sciences Macao Polytechnic University Macao China Engineering Research Centre of Applied Technology on Machine Translation Artificial Intelligence of Ministry of Education Macao Polytechnic University Macao China Department of Computer Science and Engineering - Disi University of Bologna Bologna Italy Computer Science Department Ucla Los AngelesCA United States

ISBN: (纸本)9798400708398

Recently, with the rapid development and use of technology in network infrastructures, Macao is being transformed into a smart city progressively, even though it is recognized as a World heritage city for its architectural wonders and rich cultural traditions. the benefits of machine learning technology open up new avenues for environmental monitoring, such as weather forecasting, early extreme or severe weather alerts and safeguards, disaster mitigation enhancement, and technological revolution as part of overall smart city planning in Macao. this study focuses on the evolution of artificial intelligence, the important use of machine learning technology in the field of meteorology, and suggestions on the major future development trends in Macao to the migration of a smart *** discussed solutions tackling the problems in weather forecasting in Macao while creating advances in its transformation to be a smarter city, starting with the essence of machine learning and turning challenges into opportunities. © 2023 ACM.

关键词： machine learning

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GPU Job Scheduling based on Deep Reinforcement learning 23

GPU Job Scheduling based on Deep Reinforcement Learning

引用

7th international conference on High Performance Compilation, computing and Communications, HP3C 2023

作者： Chen, Kexuan Chen, Jiayi Faculty of Science and Technology Beijing Normal University China Hong Kong Baptist Union International College Hong Kong

ISBN: (纸本)9781450399883

the development of GPU and machine learning workloads has been profound;however, resource management and allocation have become difficult problems. the methods used nowadays are not suitable for handling numerous data. Inspired by recent advancements in reinforcement learning, which have shown success in solving real AI problems, this paper explores the use of Policy Gradient, Deep Q-Network, and Double Deep Q-Network approaches to optimize GPU job scheduling. the paper introduces a new GPU job scheduling model called DeepGJS (deep GPU job scheduling), which enables efficient resource management by allocating jobs to servers in an optimized manner. DeepGJS aims to minimize slowdown and completion time during job allocation and scheduling. Initial results and analysis indicate that DeepGJS can adapt to various conditions, converge quickly, and outperform baseline methods such as short job first and packer, as well as the DeepRM [11] model. the observed differences between these methods help determine the most suitable approach for different scenarios. © 2023 ACM.

关键词： Reinforcement learning

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Detection of Spam in SMS Using machine learning Algorithms 7th

Detection of Spam in SMS Using Machine Learning Algorithms

引用

7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Terli, Niharika Chintakayala, Pavan Angaluri, Venu Madhavi Sodagudi, Suhasini Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9789819908370

the Growth of electronic gadget usage has been rapidly increasing day by day. Around 80% of people are dependent on mobile devices for accessing, storing, and sharing information. this requires security which is critical to managing. In this regard, security is missing due to the presence of spam messages in mobility devices. It is important to detect spam messages because it contains unnecessary information and data leakage could be possible. It is used to develop two different solutions to this problem by using machine learning algorithms, for finding the Greek word we use Snowball Stemmer Algorithm, Porter Stemmer, and use some machine learning techniques like the TF_IDF algorithm to increase accuracy. To combine these two, we use a multinational Naive Bayes algorithm, a Support Vector machine. A vectorized model is obtained then we can classify the messages. Upon completion of the project, the society can be secure from hackers, protect society against viruses in their devices, and also unwanted malicious behavior can be identified which will be affecting the social network users. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Adaptive Quantum learning Frameworks for Real-Time IIoT Attack Identification 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Joshi, Poonam Goyal, S.B. Rajawat, Anand Singh Solanki, Ram Kumar Chen, Chuqiao Department of Computer Engineering Thakur College of Engineering and Technology Mumbai India Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Science & Engineering Sandip University Nashik422213 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Securing its networks from cyber-attacks is of utmost importance as the Industrial Internet of things (IIoT) becomes a lynchpin of contemporary industrial ecosystems. With the increasing complexity and sophistication of cyber threats, traditional machine learning approaches, while successful, frequently struggle with real-time identification due to computational restrictions. In this study, we present an adaptive quantum learning framework for real-time IIoT attack identification that takes advantage of quantum computing’s inherent parallelism and speed. We introduce a novel quantum technique that can quickly and accurately identify both new and existing attack vectors as they emerge in dynamic threat landscapes. the experimental results show improved accuracy rates and a drastic decrease in identification delay compared to classical methods. According to our research, quantum-enhanced learning frameworks have great potential to strengthen IIoT security in the face of more sophisticated cyber-attacks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

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Survey on Wireless Sensor Network Attack Detection using machine learning Approach 7

Survey on Wireless Sensor Network Attack Detection using Mac...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sridhar, B. Santhosh Nivas, B. Raji, K. Nandhini, S. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

this study discusses about the incorporation of cybersecurity in smart microgrids and develops a novel architecture for detecting and preventing data integrity attacks on smart grid based wireless sensor networks (WSN). Intelligent systems that use computer-based algorithms to manage or monitor networking and communication mechanisms are known as cyber physical systems. Due to the existing challenges in cyber system, any cyber incident may have an adverse impact on their economic and physical activity. Power electronics-based smart microgrids are particularly vulnerable to cybe r-attacks due to their inability to change and results in potential consequences in terms of operation and stability. the cyber-physical systems of smart microgrids are briefly analyzed in this research study. the paradigm for detecting cyber-attacks on wireless sensor networks is also explained. Considering that, a Fake Data Injection (FDI) attack undermines the data integrity in one of the most significant threats to smart micro grids is the cyber/communication network, which is thoroughly examined in this study. this study also examines how FDI attacks on smart micro grids physically, economically, and technically. this research study gives illustrations on the design and detection of FDI attacks in intelligent micro grids. Both critical smart grid cyber-security requirements and examples of existing international cyber-security initiatives are provided. the topic of future cyber-security developments in smart microgrids is then examined. © 2023 IEEE.

关键词： Wireless sensor networks

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Estimation of Facial Emotion Based on Landmark Points by Applying Artificial Intelligence and machine learning 7

Estimation of Facial Emotion Based on Landmark Points by App...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Osman Abdalraheem, Mohammed Hassan Hossain, Mohammad Alamgir Ahmed Hamdan, Alfadil Kechadi, M. Tahar Limker, Suresh Jazan University Department of Information Technology Saudi Arabia Jazan University Department of Computer Science Saudi Arabia Sudan University of Science and Technology Department of Computer Science Sudan School of Computer Science University College Dublin Dublin Ireland Aissms Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350304268

In the science of emotion processing, it is hard to figure out how to predict an emotion from naturalistic facial expressions in a multidimensional space. While many studies and works have been done to develop methods for making accurate predictions, relatively little has been done to put the uniqueness of the human face to use in reading and responding to people's expressions of emotion. this work proposes an emotional image retrieval strategy for multimodal emotion detection. this strategy is based on the fact that face features are always there and that the human face has regional redundancy that is independent of face features. Our method uses landmark points to get rid of unnecessary information about facial expressions and focus on the most attractive ones in each frame. It's possible that by making use of the landmarks, we'll be able to predict facial emotions more precisely. this is especially helpful for long-term emotion prediction since it allows us to recover time and space representations by introducing an emotional face. the preliminary results based on a benchmark dataset show that the proposed method exceeds its competitors in terms of prediction accuracy. © 2023 IEEE.

关键词： emotion prediction facial expression landmark point LBP method

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Accelerating Alzheimer's Research with machine learning Models for Improved Detection 7

Accelerating Alzheimer's Research with Machine Learning Mode...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kavitha, G. Sudha, K. Jayasutha, D. Sivaraman, V. Nalini, M. Siva Subramanian, R. Rajalakshmi Institute of Technology Dept of CSE India R.M.D Engineering College Dept of CSBS India Hindusthan Institute of Technology Dept of CSE India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept of CSE India S A Engineering College Dept of CSE India R.M.K. College of Engg and Tech Dept of CSE India

ISBN: (纸本)9798350340600

the tenacious and fatal neurological disorder Alzheimer's disease poses a growing challenge to modern healthcare. this condition that worsens with time and is characterized by memory loss, cognitive decline, and a loss of independence has a significant effect on both people and society. the relevance of Alzheimer's disease is becoming more and more clear in both the medical and social settings as the world's population continues to age. Alzheimer's disease, which is characterized by the buildup of aberrant protein aggregates in the brain, causes a series of deteriorating alterations that impair cognitive performance. this study investigates the crucial area of Alzheimer's disease prediction, highlighting the value of early detection, better patient treatment, and the revolutionary use of machine learning algorithms. Our goal in this research project is to investigate the use of several machine learning models for the early identification of Alzheimer's disease. An Alzheimer's disease dataset obtained from the Kaggle repository is used, along with four different ML models: Random Forest, Decision Tree, K-NN, and XG Boost. For the purpose of building and assessing these models, this dataset is divided into training and testing subsets. the results obtained from the ML models are then evaluated using a variety of validity scores, such as accuracy, recall, precision, and F1-Score, each of which has a specific function in computing model performance. From the results it is clear, the models from Random Forest and XG Boost stand out for consistently good performance across measures, giving them excellent options for Alzheimer's disease prediction. © 2023 IEEE.

关键词： Patient treatment

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Detection of Brain Stroke Disease Using Deep learning Techniques 7th

Detection of Brain Stroke Disease Using Deep Learning Techni...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Faruki, Tasnim Akter, Ayesha Hasan, Md. Mainul Sardar, Abdul Malek Amer, Asef Jennifer, Sanjeda Sara Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka1212 Bangladesh

ISBN: (纸本)9783031733239

Early stroke detection is essential for effective treatment and prevention of long-term disability. Deep learning methods have shown promising results in detecting various medical conditions, including stroke. through this study, a strategy for identifying brain stroke disease using deep learning techniques and image preprocessing is provided. the methodology involves collecting a diverse and balanced dataset of brain scans, preprocessing the data to extract relevant features, training a deep learning model, tuning hyperparameters, and evaluating the model. Two transfer learning models have been used, MobileNet and ResNet. Among them, MobileNetV3Small has the highest validation accuracy which is 88%. For ResNet50 the overall accuracy is 99% and for MobileNetV3Small it is 96%. the proposed methodology can potentially improve the accuracy and efficiency of stroke diagnosis and enable early intervention, leading to better patient outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Advances in Frontier Research on Engineering Structures - Proceedings of the 7th international conference on Civil Architecture and Structural Engineering, ICCASE 2023

Advances in Frontier Research on Engineering Structures - Pr...

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7th international conference on Civil Architecture and Structural Engineering, ICCASE 2023

ISBN: (纸本)9781643684123

the proceedings contain 77 papers. the topics discussed include: review of structural health monitoring in bridges;innovation research on construction safety management of civil engineering based on machine learning;analysis of influence of geotechnical dynamics characteristics on tunnel engineering design;the study on nonlinear flutter of long-span suspension bridge;study on the reasonable clear distance of ultra-shallow buried tunnel group through expressway;experimental study on the construction site of the subgrade of the engineering waste soft soil solidified material;static load test of single beam of a simply supported to continuous small box beam skew bridge;study on the method of divisional filling for high fill embankment based on settlement control;numerical simulation research on coupling mechanism of shield-support-surrounding rock in complex loess geology;research on the influence of external water pressure on the construction of shield tunneling in complex loess geology;and analysis of surrounding rock disturbance in shallow buried double-side biased small clear distance tunnel.

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