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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Yuan, Jing Tang, Shaojie Department of Computer Science and Engineering University of North Texas Denton United States Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo United States

ISBN: (纸本)9789819644476

Most of existing studies on submodular maximization focus on selecting a subset of items that maximizes a single submodular function. However, in many real-world scenarios, we might have multiple user-specific functions, each of which models the utility of a particular type of user. In these settings, our goal would be to choose a set of items that performs well across all the user-specific functions. One way to tackle this problem is to select a single subset that maximizes the sum of all of the user-specific functions. Although this aggregate approach is efficient in the sense that it avoids computation of sets for individual functions, it really misses the power of personalization - for it does not allow to choose different sets for different functions. In this paper, we introduce the problem of personalized submodular maximization with two candidate solutions. For any two candidate solutions, the utility of each user-specific function is defined as the better of these two candidates. Our objective is, therefore, to select the best set of two candidates that maximize the sum of utilities of all the user-specific functions. We have designed effective algorithms for this problem. We also discuss how our approach generalizes to multiple candidate solutions, increasing flexibility and personalization in our solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Comparision of Principal Component Analysis, K-means, and Generative Adversarial Network for Compression and reconstruction of medical image

Comparision of Principal Component Analysis, K-means, and Ge...

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Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Anusha Prashanth Shetty Deepthi L Raksha Puthran Surendra Shetty Mohammad Anzil Parveez Shravan Kumar Department of Master of Computer Applications NMAM Insitute of Technology Nitte (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering (AI&ML) Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Medical images are essential in healthcare, but their large file sizes present challenges for storage and transmission. This study proposes a framework for compressing color medical images while preserving key visual information. We apply three compression models: Principal Component Analysis (PCA), K-Means clustering, and Generative Adversarial Networks (GANs), and evaluate their performance based on Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). K-Means with 100 clusters outperforms the other models, achieving the highest compression (CR of 1.20), with excellent image quality (PSNR of 66.77 dB, SSIM of 1.0000). PCA shows moderate performance with a CR of 2.45, while GAN provides the least effective compression and image quality. These results demonstrate that K-Means offers the best trade-off between compression and image fidelity.

关键词： Measurement Image quality Analytical models Image coding PSNR Medical services Generative adversarial networks Image reconstruction Principal component analysis Biomedical imaging

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Comprehensive Analysis of Neural Network Predictions for Structural Instability

Comprehensive Analysis of Neural Network Predictions for Str...

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Communications and computer science (InCCCS), International Conference on

作者： Jayavrinda Vrindavanam Khushi Kumari M Adarsh Pryan Sneha Subramaniam Kartik Tandel Department of Computer Science And Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of Aero Space And Engineering Dayananda Sagar University Bangalore India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

Flutter can be defined as the aeroelastic phenomenon that can occur in the structures of an aircraft like the wings, ailerons and ruddervators. These flutters can cause instability to the air-crafts which can risk the condition of the plane along with its passengers. Hence the aim of the study is to develop a system that can help us in predicting the point of occurrence of the flutter beforehand, as a signal to the pilot in command which can enable them to take the necessary action before anything fatal happens to the aircraft and its fellow passengers. Deep learning models will help us in the real world applications and help us in constructing and enhancing the appropriate model. Some of the models that we will be using are RNN and LSTM. In comparison, RNN has given the highest accuracy of 99.54%.

关键词： Deep learning computer science Accuracy Atmospheric modeling Computational modeling Neural networks aircraft manufacture

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Detection and Quantification of TB Bacilli in Sputum Smear Images

Detection and Quantification of TB Bacilli in Sputum Smear I...

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Women in Computing (InCoWoCo), International Conference for

作者： M Rithani Jeevanantham B Sona RS SyamDev Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of AI \& ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Early and precise detection is essential to stop the spread of tuberculosis (TB) and enhance patient outcomes. This research aims to create an automated system that employs cutting-edge image processing and machine-learning approaches to identify tuberculosis (TB) bacilli in sputum smear microscopy pictures. An automated solution is required to increase diagnostic accuracy and efficiency because traditional diagnostic methods are labor-intensive, time-consuming, and prone to human mistakes. In the end, we optimize the best-performing model to reduce processing time while preserving diagnostic accuracy. Our goal is to assess the performance of several models in terms of training time, testing time, and accuracy. By enabling the use of sophisticated diagnostic technologies in environments with limited resources, the suggested method supports international efforts to combat tuberculosis.

关键词： Training Solid modeling Image segmentation Accuracy Tuberculosis Microscopy Computational modeling Neural networks Machine learning Testing

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Novel Recoverable Audio Mosaic Technique Using Segmental and Hierarchical Permutations

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IEEE Internet of Things Journal 2024年第3期11卷 4857-4871页

作者： Tsai, Morris C.-H. Huang, Scott C.-H. Wu, Hsiao-Chun National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan

Mosaic is a prevalent signal-processing approach to hide or protect critical information from users. The conventional mosaic schemes simply abolish or add artificial noise to the original signal content a sender wants to conceal. Thus, they are not recoverable simply by use of a key which can be represented as a very short sequence compared to the concealed signal content. In this work, we extend our previous effort in recoverable image mosaicing to design a novel audio mosaicing approach using hierarchical permutations. Besides, we establish the mathematical relationship between the popular signal-quality metric, namely, signal-to-noise ratio (SNR), and our previously proposed signal-destructuring metric, namely, Kullback-Leibler divergence of discrete cosine transform (DCT-KLD), so that the mosaicing or signal-destructuring effect in terms of DCT-KLD and the general signal-quality measure in terms of SNR can be translated into each other. As a result, one can easily judge if the mosaicked signal reaches the concealability which is equivalent to the maximum SNR to eliminate the intelligibility of an utterance. The new relationship between DCT-KLD and SNR we develop can thus be very useful to qualify an audio mosaic method without any need of human listening test. © 2014 IEEE.

关键词： Signal to noise ratio

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FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings 38

FasMe: Fast and Sample-efficient Meta Estimator for Precisio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tan, Xiao Wang, Yiqin Shen, Yangyang Shen, Dian Wang, Meng Duan, Peibo Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing China College of Design and Innovation Tongji University Shanghai China Department of Data Science & AI Monash University Melbourne Australia

Precision matrix estimation is a ubiquitous task featuring numerous applications such as rare disease diagnosis and neural connectivity exploration. However, this task becomes challenging in small sample settings, where the number of samples is significantly less than the number of dimensions, leading to unreliable estimates. Previous approaches either fail to perform well in small sample settings or suffer from inefficient estimation processes, even when incorporating meta-learning techniques. To this end, we propose a novel approach FasMe for Fast and Sample-efficient Meta Precision Matrix Learning, which first extracts meta-knowledge through a multi-task learning diagram. Then, meta-knowledge constraints are applied using a maximum determinant matrix completion algorithm for the novel task. As a result, we reduce the sample size requirements to O(log p/K) per meta-training task and O(log |G|) for the meta-testing task. Moreover, the hereby proposed model only needs O(p log ϵ−1) time and O(p) memory for converging to an ϵ-accurate solution. On multiple synthetic and biomedical datasets, FasMe is at least ten times faster than the four baselines while promoting prediction accuracy in small sample settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey 1

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, ai, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated. © 2023 IEEE.

关键词： COVID-19

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAai Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans 38

Apathetic or Empathetic? Evaluating LLMs' Emotional Alignmen...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Jen-Tse Lam, Man Ho Li, Eric John Ren, Shujie Wang, Wenxuan Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Institute of Psychology Tianjin Medical University China Tencent AI Lab China

Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a careful and comprehensive survey, we collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. Categorizing the situations into 36 factors, we conduct a human evaluation involving more than 1,200 subjects worldwide. With the human evaluation results as references, our evaluation includes seven LLMs, covering both commercial and open-source models, including variations in model sizes, featuring the latest iterations, such as GPT-4, Mixtral-8x22B, and LLaMA-3.1. We find that, despite several misalignments, LLMs can generally respond appropriately to certain situations. Nevertheless, they fall short in alignment with the emotional behaviors of human beings and cannot establish connections between similar situations. Our collected dataset of situations, the human evaluation results, and the code of our testing framework, i.e., EmotionBench, are publicly available at https://***/CUHK-ARISE/EmotionBench. © 2024 Neural information processing systems foundation. All rights reserved.

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Fake Account Detection Using ANN Based Model in Machine Learning

Fake Account Detection Using ANN Based Model in Machine Lear...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Y. Alekya Rani Ganja Srividya Allam Balaram Kotha Harish Kumar Ajmeera Kiran Manda Silparaj Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad Dept of CSE Stanley College of Engineering & Technology for Women Hyderabad Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer science Engineering (Data science) CVR College of Engineering Hyderabad Department of Computer Science and Engineering (CS) CVR College of Engineering Hyderabad

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In the digital age, the proliferation of fake accounts on various platforms poses significant challenges in terms of cybersecurity, misinformation, and online harassment. To combat this issue, this project aims to develop a robust fake account detection system using machine learning techniques. The project involves the collection of user data, feature engineering,data preprocessing, and the selection of suitable machine learning algorithms. The model is trained and evaluated using relevant metrics to distinguish between real and fake accounts effectively. Once deployed, this system continuously monitors user activities and alerts administrators when fake accounts are detected, enabling timely and legal compliance. The success of this project depends on the quality and quantity of data as well as the ability to adapt and improve the detection methods over time, in a constant battle against evolving fake account creation techniques.

关键词： Measurement Machine learning algorithms Social networking (online) Law Artificial neural networks Forestry Information age

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