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A Comparative Study of Algorithmic Efficiency of Feature Selection Algorithm on Microarray 2

A Comparative Study of Algorithmic Efficiency of Feature Sel...

2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Shankari, B Uma Kumar, C Arun Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

A key challenge before classification can take place is feature selection. An effective feature selection method would increase classification accuracy and simultaneously reduce computation costs and time. A variety of filter approaches, along with different search algorithms, were considered in this study. Five traditional classifiers were evaluated on the selected gene subsets: Random Forest, Sequential minimal optimization algorithm, Naive Bayes, Decision Trees, and K-Nearest Neighbour. The datasets chosen for this analysis are the microarray gene expression data of two types of cancers: Acute Lymphocytic Leukaemia (ALL)/Acute Myeloid Leukaemia (AML) and Lung cancer. According to the experimental results, a fuzzy rough subset combined with Genetic Search selects optimal relevant gene subsets and produces significantly good classifier accuracy. Compared to classical classifiers described here, this research finds that Random Forest classifiers yield 94.33% on the raw dataset and 100% classifier accuracy after applying feature selection methods. Utilizing conventional methods like Precision, Recall, F-Score, and Region of Characteristics, MCC Matthews correlation coefficient, results are validated. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Revolutionizing Healthcare With Cloud Computing: The Impact of Clinical Decision Support Algorithm 1

Revolutionizing Healthcare With Cloud Computing: The Impact ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Lenin, J. Komathi, A. Vijayan, Hima Rathinam, Anantha Raman Kasthuri, A. Srinivasan, C. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India S.A. Engineering College Department of Information Technology Tamil Nadu Chennai India Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350330915

The integration of cutting-edge technology, such as cloud computing and Clinical Decision Support (CDS) algorithms, is radically altering the healthcare system. This research digs into how Inference Engines, Bayesian Networks, Machine Learning Algorithms, and Natural Language Processing (NLP) have all played critical roles in reshaping the healthcare industry. Medical professionals may more efficiently use CDS algorithms thanks to cloud-based technologies that streamline data storage, sharing, and processing. Decision-making is aided by Inference Engines because they provide organized insights based on preset criteria. This is where Bayesian Networks come in, with their ability to represent complicated, probabilistic interactions among variables for exact diagnoses and risk assessment. Machine learning algorithms improve predictive analysis by identifying trends in large datasets, paving the way for more individualized approaches to medical care. Chatbots and sentiment analysis are only two examples of how NLP is transforming the doctor-patient relationship by teaching computers to understand human language. Those in the medical field may greatly increase their diagnostic precision, treatment efficiency, and patient outcomes by adopting these innovations. © 2023 IEEE.

关键词： Bayesian Networks Clinical Decision Support Cloud Computing data-driven Healthcare Healthcare Transformation Inference Engines Machine Learning Algorithms Natural Language Processing Patient-Centric Solutions Personalized Medicine

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A Numerical Model Oriented Large-scale Parallel I/O Optimization Method 6

A Numerical Model Oriented Large-scale Parallel I/O Optimiza...

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6th International Conference on data Storage and data Engineering, DSDE 2023

作者： Zhang, Lei Wu, Tao Zhou, Zheng Wang, Dong Chen, Xi School of Computer Science Chengdu University of Information Technology Chengdu China Tsinghua University Department of Earth System Science Beijing China College of Computer Southwest Minzu University Chengdu China

ISBN: (纸本)9798350333145

Numerical models usually contain a large number of data reading and writing, which generally takes a long time. At present, massively parallel technology has accelerated the speed of computing in model, but due to the limitation of computer I/O ability, the acceleration effect is limited, and there is a serious load imbalance during the model operation process, resulting in a waste of computing resources. To deal with that, this paper proposes a new I/O scheme, which can dynamically optimize task allocation during program operation, make full use of computing resources, and increase the mutual coverage of computing and I/O processes, so as to effectively shorten the model running time. Finally, the test in the earth simulator shows that the new I/O scheme improves the operation speed of the regional high-precision air pollution model by 25.45%. © 2023 IEEE.

关键词： Numerical models

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Undersampled Random Forest: A Green Approach to Imbalanced Learning 3

Undersampled Random Forest: A Green Approach to Imbalanced L...

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3rd International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Kamalov, Firuz Elnaffar, Said El Khatib, Ziad Cherukuri, Aswani Kumar Jonnalagadda, Annapurna Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates Department of Computer Science Canadian University Dubai Dubai United Arab Emirates School of IT & Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331532925

The increasing size of machine learning models and the datasets used for training has resulted in significantly higher computational demands. Modern large language models, in particular, consume vast amounts of energy during training, raising serious environmental concerns. In this paper, we propose a low-energy classifier, the Undersampled Random Forest (URF), designed specifically for imbalanced data. The URF method leverages undersampling in combination with a parallelizable random forest architecture. This approach reduces the size of the training data and accelerates computation, leading to more energy-efficient training processes. Moreover, the proposed method consistently achieves high recall rates on minority class instances, making it both effective and environmentally conscious. © 2024 IEEE

关键词： Adversarial machine learning

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Hu, Yongquan Gowen' Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Gfloyd' Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The University of Tokyo Tokyo Japan Tsinghua University Beijing China Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia CSIRO's Data61 Sydney Australia University of New South Wales Sydney Australia

ISBN: (纸本)9798400713941

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： artificial intelligence computer vision context aware multimodal survey system vision based interface visual data

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Collaborative List-and-Pairwise Filtering from Implicit Feedback : Extended Abstract 39

Collaborative List-and-Pairwise Filtering from Implicit Feed...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Yu, Runlong Liu, Qi Ye, Yuyang Cheng, Mingyue Chen, Enhong Ma, Jianhui University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology Hefei China Rutgers University Management Science and Information Systems Rutgers Business School Newark United States

ISBN: (纸本)9798350322279

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback. © 2023 IEEE.

关键词： Collaborative filtering

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A study on the impact of pre-trained model on Just-In-Time defect prediction 23

A study on the impact of pre-trained model on Just-In-Time d...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Guo, Yuxiang Gao, Xiaopeng Zhang, Zhenyu Chan, W.K. Jiang, Bo Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350319583

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone's pre-training model is similar, the training resources that need to be consumed are closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data. © 2023 IEEE.

关键词： Forecasting

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Real-time Anomaly Detection in Cyber-Physical Systems Using Fourier Transform Features and Explainable AI 51

Real-time Anomaly Detection in Cyber-Physical Systems Using ...

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51st International Conference on computers and Industrial Engineering, CIE 2024

作者： Kamal, Md Sarwar Nimmy, Sonia Farhana Alharbi, Wafa Tajwar, Ahnaf Hasan, Nazia School of Computer Science University of Technology Sydney UltimoNSW2007 Australia School of Business UNSW Canberra CampbellACT2612 Australia Department of Computer Science and Engineering BRAC University Dhaka1212 Bangladesh Department of Business Administration East West University Dhaka1212 Bangladesh

In critical Cyber-Physical Systems (CPS), such as healthcare and transportation, the need for robust and interpretable anomaly detection is paramount to prevent failures and ensure system reliability. In this paper, we present a novel anomaly detection method that uses the Fourier transform (FT) for feature extraction and incorporates a novel explainable artificial intelligence (XAI) approach based on cooperative game theory, specifically employing the core *** FT is used to transform time series data from CPS into the frequency domain to isolate crucial features that indicate normal and abnormal behavior. These features include username regularity, credential changes, email verification a nd m o re. T he t ransformation p rocess h ighlights t he frequency components associated with irregular patterns, facilitating accurate anomaly detection. The XAI approach interprets the interactions between the different m odel c omponents a s a s trategic g ame, w here t he i nfluence of each feature is akin to a player's contribution to a coalition. By using the kernel, we ensure that the distribution of explanatory weight between features (players) is such that no subset of features would be better off forming an alternative explanatory coalition. This method provides a mathematically rigorous, quantifiable explanation of each feature's role in the model's predictions, promoting deeper confidence and understanding of AI-driven *** proposed method (FT + XAI) has a precision of 0.92, recall of 0.89, and accuracy of 0.95, outperforming K-means (0.75, 0.65, 0.78), Isolation Forest (0.85, 0.82, 0.87), and LSTM (0.9, 0.94, 0.93). It also has a lower false positive rate (0.03) and false negative rate (0.02). © 2024 computers and Industrial Engineering. All rights reserved.

关键词： Frequency domain analysis

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MMSciBench: Benchmarking Language Models on Multimodal Scientific Problems

arXiv

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arXiv 2025年

作者： Ye, Xinwu Li, Chengfan Chen, Siming Tang, Xiangru Wei, Wei Department of Computer Science Yale University United States Department of Computer Science Brown University United States School of Data Science Fudan University China Datawiz LLC United States

Recent advances in large language models (LLMs) and vision-language models (LVLMs) have shown promise across many tasks, yet their scientific reasoning capabilities remain untested, particularly in multimodal settings. We present MMSciBench, a benchmark for evaluating mathematical and physical reasoning through text-only and text-image formats, with human-annotated difficulty levels, solutions with detailed explanations, and taxonomic mappings. Evaluation of state-of-the-art models reveals significant limitations, with even the best model achieving only 63.77% accuracy and particularly struggling with visual reasoning tasks. Our analysis exposes critical gaps in complex reasoning and visual-textual integration, establishing MMSciBench as a rigorous standard for measuring progress in multimodal scientific understanding. The code for MMSciBench is open-sourced at GitHub1, and the dataset is available at Hugging Face2 © 2025, CC BY.

关键词： Visual languages

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