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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Yutong Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China Xi’an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9783031781919

With the development of cities, racial segregation is one of the reasons for social inequality on a large scale, it is also a major factor affecting economic development. However, racial segregation has received comparatively limited attention in research. In the paper, we propose a segregation index independent of any parameters( titled Quantum Walk Convergence Time), which calculates the convergence time statistics for accessing different racial categories through quantum walks on complex networks constructed from urban systems. The magnitude of the time statistics represents the degree of racial segregation. The results generated by our method can also be applied to other social factors, including family situation, income level, and education level. We evaluate our method using large-scale real datasets, including statistics from the U.S. Census Bureau and the Office for National Statistics in the UK. The results demonstrate the close correlation between our method and the degree of racial segregation, showing advantages over traditional random walk methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Continuous time systems

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Fair Machine Learning in Healthcare: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 493-507页

作者： Feng, Qizhang Du, Mengnan Zou, Na Hu, Xia Texas A&M University Department of Computer Science & Engineering College StationTX77843 United States New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States University of Houston Department of Industrial Engineering HoustonTX77004 United States Rice University Department of Computer Science HoustonTX77251 United States

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in ML and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a ML standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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A Machine Learning and Deep Learning based Approach to Generate a Speech Emotion Recognition System 18

A Machine Learning and Deep Learning based Approach to Gener...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sumera Vaidehi, K. Nisha, Qamar Stanley College of Engineering and Technology for Women Department of Artificial Intelligence & Data Science and Computer Engineering Hyderabad India Stanley College of Engineering and Technology for Women Department. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The most common and general medium via which we humans convey or communicate our thoughts, emotions, feelings or ideas artlessly is by speech or articulation. Blending of this artless way of speech with the technological advancements of AI, has given rise to the importance of building emotion recognition systems from speech today. Even more, the speech/articulation emotion recognition system presented here is also to contribute in and facilitate various emerging applications of today like, in detecting persons' physiological state (as in lie detectors), also be used in forensics, medicine. The proposed work identifies/associates an appropriate label/emotion for the respective emotion from speech presented in the form of an audio file (.wav format). About 4240 audio samples are taken. 1440, 2800 samples from RAVDESS and TESS datasets are considered respectively. After this process of data collection, features are separately extracted for each audio dataset mentioned above. Energy, pitch, ZCR, co-efficient of Mel frequency ceptrum (MFCC) are some of the features considered in this study. Furthermore, clubbing and merging of 2 datasets is performed resulting in a total of 4240 rows and 24 columns (features/characteristics including 1class label) of audio samples. The resulting 4240 samples of feature dataset is split/bifurcated into training and testing set by considering 3 different possibilities/instances viz;60%-40% ratio, 70%-30% ratio, 80%-30% ratio. The models namely CNN, Random forest and Support Vector Machine are trained to classify the dataset into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). An attempt to implement the models using two very essential disciplines of AI i.e. Machine Learning and Deep Learning is made here. The accuracy or results are depicted by generating confusion matrices on test data for CNN, RF and SVM models (Each model is trained and test across 3 different ratios viz;60%-40%, 70%-30%, 80%-20%). C

关键词： Classification CNN Confusion matrices Extraction of features MFCC RF Speech/articulation Emotion Recognition SVM

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve data Availability in the Context of Big data

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computers, Materials & Continua 2024年第4期79卷 1561-1579页

作者： Linlin Yuan Tiantian Zhang Yuling Chen Yuxiang Yang Huang Li State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China College of Information Engineering Guizhou Open UniversityGuiyang550025China Guizhou Academy of Tobacco Science Guiyang550025China

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more *** K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big ***,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data *** addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be *** on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data ***,we construct a new information loss function based on the information quantity *** that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss *** addition,to reduce information loss,we improve K-anonymity in two ***,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering *** addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of ***,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information ***,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

关键词： Blockchain big data K-anonymity 2-means clustering greedy algorithm mean-center method

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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Early-cycle prediction of battery aging onset across chemistries

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Journal of Energy Chemistry 2025年第6期105卷 233-242页

作者： Xijun Tan Nathan Zeng Yixiang Deng Shuguo Sun Bo Rui Jun Xu Department of Mechanical Engineering University of Delaware Energy Mechanics and Sustainability Laboratory (EMSLab) University of Delaware Dover-Sherborn High School Department of Computer and Information Sciences University of Delaware Department of Biomedical Engineering University of Delaware Data Science Institute University of Delaware

Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary energy storage ***,the inherent electrochemo-mechanical-thermal complexities of battery aging present significant challenges for physics-based models and machine-learning models,often leading to reduced predictive *** study developed a comprehensive dataset comprising 20 lithium nickel manganese cobalt oxide(NCM)/graphite cells(0.5-1 C) from our lab and 162 commercial lithium iron phosphate(LFP)/graphite cells(3-6 C) from the public database,with knee point observed between 100 and 1000 *** proposed a new strategy to extract novel features with strong physical context from early-cycle voltage curves,enabling precise knee point predictions across the chemistries without the need for extensive cycling *** model achieved a mean absolute percentage error(MAPE) of 7% for knee point prediction using five selected ***,the model yielded 8% MAPE with only one single feature across the initial 200 cycles,and 10% MAPE when applying five features across the initial 50 cycles,spanning different battery *** work highlights the potential of integrating multi-chemistry datasets with data-driven modeling to forecast aging patterns across diverse battery chemistries,advancing battery longevity and reliability.

关键词： Battery aging Knee point Machine learning Modeling

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Applying Deep Neural Networks and NLP Techniques for Sentiment Analysis in Social Media data 2

Applying Deep Neural Networks and NLP Techniques for Sentime...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Nathiya, N. Raguraman, P.J. Srinivasan, K. Paavai College of Engineering Computer Science and Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Excel Engineering College Computer Science and Engineering Namakkal India Paavai College of Engineering Artificial Intelligence and Data Science Namakkal India

ISBN: (纸本)9798350349221

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks - like transformers or recurrent neural networks - with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation. © 2024 IEEE.

关键词： Sentiment analysis

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Implementation and Comparison of Path Planning Algorithms for Autonomous Navigation

Implementation and Comparison of Path Planning Algorithms fo...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Kherudkar, Ratnesh Tiwari, Shreyansh Vedantham, Udayini Chouti, Nithish Prabhu Prasad, B.M. Vanahalli, Manjunath K. Pavan Kumar, B.N. Computer Science & Engineering IIIT Karnataka Dharwad580009 India Data Science and Artificial Intelligence IIIT Karnataka Dharwad580009 India Computer Science & Engineering IIIT Andhra Pradesh Sri City517646 India

ISBN: (纸本)9798331505776

In autonomous navigation, efficient path planning is critical for ensuring robust and reliable performance in dynamic environments. This paper presents a comparative study of two widely-used path planning algorithms: Dynamic Window Approach (DWA) and A∗ algorithm, among other leading algorithms, implemented using Robot Operating System (ROS) on the TurtleBot3 platform. The DWA algorithm focuses on real-time obstacle avoidance and local path optimization, while A∗ provides a global path solution based on heuristic search. Through a series of experiments in simulated and real-world scenarios, the effectiveness of the algorithms in terms of computational efficiency, path optimality, and adaptability to dynamic obstacles has been evaluated. The findings highlight the strengths and limitations of each approach, providing valuable insights for the development of autonomous systems in various applications. This study aims to contribute to the ongoing efforts in enhancing the autonomy and reliability of mobile robots. Notable enhancements in terms of travel distance, time and overall path have been recorded, and the combined approach shows significant improvement in Path Distance (approximately 3.53%), Travel Time (approximately 6.62%, and Travel Distance (approximately 30.17%. © 2024 IEEE.

关键词： Robot Operating System

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