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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models 39

Expensive Multi-Objective Bayesian Optimization Based on Dif...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Bingdong Di, Zixiang Lu, Yongfan Qian, Hong Wang, Feng Yang, Peng Tang, Ke Zhou, Aimin Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai 200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Computer Science Wuhan University Wuhan 430072 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

ISBN: (纸本)157735897X

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples efficiently. Besides, we introduce a weighting method based on information entropy to balance different objectives. This method is integrated with a guiding strategy to appropriately balancing different objectives during the optimization process. Experimental results on both synthetic and real-world problems demonstrates that CDM-PSL attains superior performance compared with state-of-the-art MOBO algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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Improving Cancer Prediction in Recommender Systems through Gradient Boosting

Improving Cancer Prediction in Recommender Systems through G...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Pompi Das Sengupta Vikas Kumar Kharbas H Adarsha Department of Management ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The analysis of various styles of most cancers is frequently reliant on accurate prediction models. Recommender systems are powerful equipment that allows the prediction of cancer development and recurrence based totally on information accumulated from affected person information. But, standard feed-forward neural networks (FFNNs) utilized in current cancer prediction fashions are limited via their potential to capture subtle nonlinear relationships among affected person variables. Gradient boosting is a device gaining knowledge of set of rules that makes use of an ensemble of weak selection bushes to produce an effective predictive version. This have a look at examines the capability for boosting algorithms to enhance the predictive ability of the FFNN fashions currently used in recommender structures for predicting most cancers progression and recurrence. The outcomes of our test reveal that boosting algorithms are able to boom model accuracy over FFNNs whilst additionally lowering training time. Moreover, nonlinear relationships between affected person variables are captured extra successfully by means of boosting than with FFNNs. those findings keep crucial implications for the development of powerful predictive models for cancer progression and recurrence.

关键词： Training Accuracy Predictive models Boosting Prediction algorithms Feature extraction Feedforward neural networks Recommender systems Standards Cancer

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Robotic Process Automation for Automating Business Processes: A use case

Robotic Process Automation for Automating Business Processes...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Vivek Bhardwaj Virender Mukesh Kumar Deepak Thakur Vikas Lamba School of Computer Science and Engineering Lovely Professional University Jalandhar India University Center for Research & Development (UCRD) Chandigarh University Punjab India School of Computer Application Lovely Professional University Jalandhar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Numerous advances and changes have been brought about by digitalization in both our daily lives and the business world. RPA is an all-purpose technology for developing specialized agents, or “bots,” that communicate with GUI components to carry out routine, rule-based operations. Anyone can utilize the RPA to automate digital operations. RPA deployment in business processes can help to provide better service and guarantee that processes adhere to more efficiency and accuracy. The objective of this study is to enable human workers to assign their regular generic repetitive tasks to a software bot, freeing them up to concentrate on more challenging and difficult activities. This study presents a methodology for implementing RPA to extract employee information from the XML pages.

关键词： Intelligent automation XML Chatbots Software Reliability Data mining Task analysis

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Distributed Machine Learning System for Intrusion Detection

Distributed Machine Learning System for Intrusion Detection

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Data science and Network Security (ICDSNS), International Conference on

作者： Harshal Patil Yogesh Lohumi K M Tejaswini H C Sudheendramouli Swapnil Parikh Ramya Maranan Balaji Institute of Technology & Management (BITM) Sri Balaji University Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mathematics Sri Siddhartha Institute of Technology Tumakuru India Department of Computer Science and Application GSSS Simha SubbaMahalakshmi First Grade College Mysuru India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

As network systems expand in complexity, the necessity for effective intrusion detection grows. This study compares two distributed machine learning systems utilizing different communication topologies: Parameter Server and Point-to-Point. The former employs centralized parameter management for efficient communication, while the latter facilitates direct node-to-node communication without a central server. Using extensive network traffic data, the models are assessed on performance metrics like accuracy, scalability, and false positive rates. Both systems show promising results, with median accuracy and F1 scores around 93%. However, limitations include potential data homogeneity issues, scalability challenges particularly with the Parameter Server becoming a bottleneck, concerns over model overfitting, and high computational overhead in maintaining models across numerous nodes. Addressing these drawbacks is crucial for improving the reliability of intrusion detection systems in complex network environments.

关键词： Adaptation models Accuracy Scalability Computational modeling Intrusion detection Machine learning Telecommunication traffic Data models Topology Servers

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Recent Advancements on Intelligent Multi-focus Image Fusion

Recent Advancements on Intelligent Multi-focus Image Fusion

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Prabhishek Singh Aayushi Rana Aditya Joshi Mohd Shuaib Manoj Diwakar Amit Kumar Mishra School of Computer Science Engineering & Technology Bennett University Greater Naida India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of CSE Graphic Era Deemed to be University Dehradun India Dehradun India Department of CSE Graphic Era Hill University Dehradun India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Recently deep learning approaches are more popular for multi-focus image fusion. In this review article, non-conventional multi focus image fusion techniques recently published were analyzed in detail and current advances as well as impending trends were discussed. The push for ever increasing image quality in multi focus imaging has raised the desire for non-conventional techniques as possible substitutes to conventional techniques. In this review, we systematically examine state of the art research in deep learning models, hybrid methodologies combining multiple transform domains, and adaptive thresholding strategies. We conducted a comparison evaluation of visual data against different strategies utilizing the complementary fusion capabilities of each. In addition, quantitative metrics of these values were used to objectively measure the performance of each strategy. In the paper, important ongoing developments, such as integration of deep learning techniques with transformer methods and adaptive fusion filters to enhance image details while avoiding artefacts are stressed. The implications of this review for academics include a comparison of the progress in the field to date and the potential of non classical techniques to realize higher accuracy and reliability in image fusion. The results suggest that further investigation of more sophisticated hybrid and adaptive techniques will yield improved diagnostic quality in a clinical setting.

关键词： Deep learning Image quality Visualization Adaptation models Filters Accuracy Reviews Transforms Market research Image fusion

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Beyond Anchors: Optimal Equality Constraints in Cooperative Localization 4th

Beyond Anchors: Optimal Equality Constraints in Cooperative ...

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4th EAI International Conference on Industrial IoT Technologies and applications, Industrial IoT 2020

作者： Zhang, Ping Cheng, Fei Lu, Jian The Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China The School of Information Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9783030710606

Although anchors are the most common in cooperative localizations, they are not the optimal in the class of equality constraints which provide the global reference information for deriving absolute locations. Using Cramér-Rao lower bound (CRLB) to evaluate the localization accuracy, this paper derives the optimal equality constraints that achieve the lowest CRLB trace under given constraint number, and analyzes the feasibility of constructing the optimal constraints before knowing the node ground truth locations. Simulations compare the performance between the anchor-type constraints and the optimal ones, and suggest a cooperative localization algorithm by using the optimal equality constraints. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Singular value decomposition

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Comparative Evaluation of Fraud Detection in Online Payments Using CNN-BiGRU-A Approach

Comparative Evaluation of Fraud Detection in Online Payments...

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

作者： Prabhakar K M. S. Giridhar Amrita Tatia Trupti Mandar Joshi Subharun Pal Upendra Singh Aswal Department of Computer Science and Engineering School of Engineering and Technology CMR University Bangalore India Department of Electrical and Electronics Engineering Lakireddy Bali Reddy College of Engineering Jntuk Kakinada Mylavaram Andhra Pradesh India Indira School of Business Studies Pune Maharashtra India Masters of Business Administration Indira School of Business Studies Pune Maharashtra India Department of Computer Science and Engineering Indian Institute of Technology Jammu Jammu & Kashmir India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Online payment systems are now ubiquitous in people's daily lives. However, with this increased convenience comes a new security risk. Due to the nature of cybercrime involving an online payment service, which is often characterized by specialization, diversification, concealment, industrialization, scenario, and cross-regional elements, online payment security is notoriously difficult to implement and maintain. Effective and comprehensive online payment fraud detection is crucial. The first three stages of the proposed technique are preprocessing, feature selection, and model training. Remove Null Value, Data Transform, Resample, and Normalize Data are all preprocessing techniques used to improve the quality of raw data. Feature selection makes use of PCA and IG as a means of analysis. CNN-BiGRU-A is used to train the models after information gain is used to choose relevant features. When compared to the two most common alternatives, BiGRU and CNN, the proposed technique performs better.

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Apple Variety Classification: Leveraging CNN and Random Forest for Precision

Apple Variety Classification: Leveraging CNN and Random Fore...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Yashu Vinay Kukreja Tejinder Pal Singh Brar Vishal Jain Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer application Chandigarh Group of Colleges Landran Mohali India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida U.P. India

An important difficulty in computer vision and agricultural technology is the automatic classification of apple cultivars based on their shape, texture, and colour properties. In this article, we suggest a unique method for tackling this problem that combines Random Forest classifiers and Convolutional Neural Networks (CNNs). In total, 5,510 high-resolution photos of the five well-known apple cultivars Gala, Cortland, Pink Lady, Honeycrisp, and Fuji were gathered in our extensive collection. In the first step of our process, a CNN automatically extracts distinguishing features from these photos. Four convolutional layers with ReLU activation make up the CNN architecture, which is followed by layers with maximum pooling to reduce spatial dimension. The Random Forest classifier then uses the output probabilities from the CNN to determine the final classification. We rigorously experimented to show the accuracy and resilience of our model in identifying these apple types. Deep learning and ensemble approaches work together to successfully capture minute variations in shape, texture, and colour. The agriculture and food industries stand to benefit significantly from this research in terms of automated quality control, precise inventory management, and increased customer satisfaction. Our method opens up new directions for future study in automated fruit classification and agricultural automation while demonstrating the potential of contemporary machine learning techniques to simplify difficult agricultural operations.

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Non-uniform sampling based frequency shifting fringe projection technique 14

Non-uniform sampling based frequency shifting fringe project...

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14th International Conference on Information Optics and Photonics, CIOP 2023

作者： Qi, Zhaoshaui Hu, Rui Feng, Zeyuan Liu, Xiaolin Hao, Yifeng Pang, Jingqi Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University No.127 YouYi West Road BeiLin District Shaanxi Xi'an710129 China School of Computer Science Northwestern Polytechnical University No.127 YouYi West Road BeiLin District Shaanxi Xi'an710129 China

ISBN: (纸本)9781510671744

Recently, the frequency-shifting (FS) projection technique provides a promising alternative to the traditional phase-shifting (PS) fringe projection profilometry (FPP). It solves the absolute phase retrieval problem directly in a completely different fashion, without any phase unwrapping. However, due to the Nyquist sampling theorem (NST), a constant frequency-shift only supports for a limited effective field (EF). It only covers a limited region of the fringe pattern, and the phase retrieval quality degenerates dramatically for those out of the EF. This issue tends significant especially for a high-resolution fringe pattern, limiting the practical application in real life. To this end, we introduce the non-uniform sampling strategy into frequency-shifting technique. With a temporally varying frequency-shift, the EF can be improved significantly, achieving globally high quality across all regions in the pattern for the retrieved phase. Simulation and real experimental results have validated the efficacy of the proposed technique. © 2023 SPIE.

关键词： Profilometry

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Guru Prasad M S Praveen Gujjar J Arnav Kotiyal Prasanna Kumar H R Raghavendra M Devadas Afsar Jahan Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Dept. of Information Science & Engg. Visvesvaraya Technological University PESITM Shivamogga India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Computer Application Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models.

关键词： Analytical models Technological innovation Data analysis Navigation Data visualization Machine learning Feature extraction

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