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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： D'Souza, Jason Kadam, Vedant Shinde, Pranali Saxena, Kumkum Thadomal Shahani Engineering College Information Technology Department Mumbai India Thadomal Shahani Engineering College Artificial Intelligence and Data Science Department Mumbai India Thadomal Shahani Engineering College Computer Engineering Department Mumbai India

ISBN: (纸本)9798350302288

Artificial Intelligence and Machine Learning based models are increasingly being used in developing models for automating recruitment systems in various Multinational Companies as well as small-scale and developing companies and industries. With the advent of Generative Artificial Intelligence models, many companies are turning towards automatic and AI-based hiring software. J.P. Morgan uses HireVue, Deloitte incorporates Hiretual for talent sourcing and engagement, IBM incorporates *** for automated interview scheduling and many more. But in all these systems a major factor to consider is the biasness in the models and how to detect them and efficiently mitigate them. Some models unwillingly sometimes amplify the biases in the models and results in faulty and biased predictions which can lead to the inaccurate matching of profiles to the job description and result in wayward hiring processes. The biasness can be based on the parameters such as gender, address, etc., or biasness transferred from the programmer into the model while creating the model which is called unconscious bias. The models implemented in the research are Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost. The accuracies for the models Random Forest, Decision Tree, KNN Tree, AdaBoost, and XGBoost were 0.80, 0.81, 0.81, 0.78, and 0.84 respectively. © 2023 IEEE.

关键词： Decision trees

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Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement 39

Refine then Classify: Robust Graph Neural Networks with Reli...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhuang, Shuman Wu, Zhihao Chen, Zhaoliang Dai, Hong-Ning Liu, Ximeng College of Computer and Data Science Fuzhou University Fuzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)157735897X

Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data. However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures. Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline. However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness. To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods. Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information. Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges. Additionally, we also provide an improved GNN as classifier. Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE. Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Millibots: Integrating Deep Learning Algorithms for Autonomous Control in Small-Scale Robotics

Millibots: Integrating Deep Learning Algorithms for Autonomo...

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International Conference on Computing and Networking Technology (ICCNT)

作者： A.Angel Sneha B. Diwan Artificial Intelligence and Data Science Department Stella Maris College Of Engineering Nagercoil Department of Computer Science St Joesphs College Of Engineering Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Millibots, miniature robotic platforms, have emerged as pivotal tools in various domains, ranging from medical interventions to environmental monitoring. However, their diminutive size presents formidable challenges in achieving autonomous navigation and control. In this paper, we delve into the realm of millibots and propose a groundbreaking approach to tackle these challenges by integrating deep learning algorithms for autonomous control. Leveraging the power of deep learning, we address the intricate interplay between perception, decision-making, and locomotion in small-scale robotics. Our methodology not only overcomes the limitations inherent to millibots but also unlocks new frontiers in their application. Through a comprehensive review of related work, meticulous experimentation, and insightful analysis, we demonstrate the unparalleled efficacy of our approach. This paper not only accelerates the advancement of small-scale robotics but also envisions a new era of autonomous exploration and innovation.

关键词： Deep learning Technological innovation Reviews Decision making Distance measurement Planning Environmental monitoring Robots Autonomous robots

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A Review on AI-Driven Prediction and Visualization of Kinase Inhibitor Binding Landscapes

A Review on AI-Driven Prediction and Visualization of Kinase...

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Advanced Research in Computing (ICARC), International Conference on

作者： M. A. S. D. Jayasundara R. D. Nawarathna Postgraduate Institute of Science University of Peradeniya Peradeniya Sri Lanka Department of Statistics and Computer Science University of Peradeniya Peradeniya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The development of kinase inhibitors has revolutionized targeted therapy specifically in cancer therapy by blocking aberrant cell signaling pathways. Despite the therapeutic potential, traditional drug discovery for kinase inhibitors is confronted with many challenges including but not limited to the high cost of drug development, lengthy process for drug discovery, and poor specificity due to off-target effects. But with the rise of Artificial Intelligence (AI) came new tools that can help transcend these limitations, promising an alternative route towards accelerating the discovery of kinase inhibitors and optimizing therapeutic strategies. AI-based techniques such as Convolutional Neural Networks and advanced models, including AlphaFold2 (for protein structure prediction) have opened new avenues for investigating kinase-inhibitor binding interactions that are the target for further development of multi-target therapies with minimal side effects. In this context, this comprehensive review summarizes the novel approaches used in AI-empowered prediction and visualization of the binding landscapes of kinase inhibitors, with a strong emphasis on the utilization of convolutional neural networks, AlphaFold2, and molecular dynamics. This review also provides real and practical directions for researchers and practitioners in computational drug design and molecular biology to optimize kinase inhibitor development and therapeutic strategies using deep learning techniques such as Transformer based models for molecular sequences, Protein Interaction Graph Network go under the acronym PIGNet, and Contact Prediction for Ligand-Protein Complexes abbreviated as ConPLex. In the end, it outlines future directions and possible solutions to the challenges faced by AI-mediated drug discovery. This review article will potentially become the reference for scientists and practitioners interested in the application of AI methodologies for kinase inhibitor.

关键词： Proteins Drugs Inhibitors Reviews Computational modeling Medical treatment Predictive models Drug discovery Convolutional neural networks Artificial intelligence

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Mathematical Model of Information Processing with Relative Priority and Different Service Intensity 19

Mathematical Model of Information Processing with Relative P...

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19th International Asian School-Seminar on Optimization Problems of Complex Systems, OPCS 2023

作者： Moiseeva, Svetlana Pakulova, Ekaterina Haustova, Natalia National Research Tomsk State University Department of Probability Theory and Mathematical Statistics Tomsk Russia Institute of Computer Science and Information Security Southern Federal University Taganrog Russia

ISBN: (纸本)9798350331134

This paper proposes a model of information processing with priorities. The peculiarity of the model is that the system receives two flows of different types of data of different intensity. Thus we may operate with two classes of customers. The first class of the customers are priority. In this paper, we consider a system with a relative priority. It means that the arrived priority customer does not push out a customer of the second class, if the last one is servicing by a server. The service time of customers is random with an exponential probability distribution. Each type of customer has its own service duration. It is supposed that each class of customers has its own queue. The queue sizes are the same. Limiting the length of queues is the reason for the system to work with losses. It means that if at the time of receipt of a customer the server is idle, then a customer immediately gets up for service;if the server is busy servicing another customer, and there is a place in the queue, the newly received customer enters the waiting queue, otherwise it is lost. We suggest a system of equations for stationary probabilities and an original numerical implementation of finding the system's probability distribution. We also obtain the characteristics of the QoS parameters of customers - the probability of receiving a customer for instant service, the probability of getting into the system regardless of priority, the loss probability for priority customers, the loss probability for non-priority customers, the probability of refusing to service a customer of any class, the probability that the server is currently serves the customer of the first class, the probability that the server is currently servicing the customer of the second class. © 2023 IEEE.

关键词： Quality of service

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Sentiment Analysis of IMDB Movie Reviews

Sentiment Analysis of IMDB Movie Reviews

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： Abhijaat Pandey Roobal Yadav Aryaki Pathak Neeli Shivani Bhavika Garg Ankur Pandey Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering (Data Science) G.L. Bajaj Imstitue of Technology and Mangement Greater Noida India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Sentiment analysis is very important and feedback from the customers or the people plays a big role in many fields. If done manually for a huge amount of data, it will waste lots of time and effort. The purpose of our study is to make the sentiment analysis of large datasets as easy and precise as possible. We used Machine Learning and Natural Language Processing to conduct sentiment classification on IMDb movie reviews. Sentiment analysis of movie reviews is important as it not only guides the production team on how to improve the quality of their content but also tells the people if they should spend their precious time as well as money on the film. It gives them a fair idea of the type and quality of the film beforehand. Our findings are specific to IMDb movie reviews, but their applications are not just limited to movie reviews. We have used the following classification algorithms to classify the reviews as either negative or positive: K Nearest Neighbors, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and Linear Discriminant Analysis. Most accurate of these was Support Vector Machine which gave us an accuracy of 88%. We also used the famous BERT Model which was recently published by Google researchers. It is very rightly said that the BERT Model is a game changer in the field of NLP. We achieved an accuracy of 93.97% using the BERT Model. We conclude that the BERT Model gave us the highest accuracy as compared to the six classifiers that we implemented.

关键词： Support vector machines Sentiment analysis Accuracy Reviews Production Motion pictures Software systems Classification algorithms Software measurement Random forests

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Enhancing Skin Lesion Diagnosis with Ensemble Learning 4

Enhancing Skin Lesion Diagnosis with Ensemble Learning

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4th International Conference on computer science and Blockchain, CCSB 2024

作者： Liu, Xiaoyi Yu, Zhou Tan, Lianghao Yan, Yafeng Shi, Ge Ira A. Fulton Schools of Engineering Arizona State University Tempe United States University of Illinois at Chicago Department of Mathematics Statistics and Computer Science Chicago United States W. P. Carey School of Business Arizona State University Tempe United States Stevens Institute of Technology Department of Computer Science Hoboken United States Independent Researcher Tempe United States

ISBN: (纸本)9798350356502

Skin lesions are an increasingly significant medical concern, varying widely in severity from benign to cancerous. Accurate diagnosis is essential for ensuring timely and appropriate treatment. This study examines the implementation of deep learning methods to assist in the diagnosis of skin lesions using the HAM10000 dataset, which contains seven distinct types of lesions. First, we evaluated three pretrained models: MobileNetV2, ResNet18, and VGG11, achieving accuracies of 0.798, 0.802, and 0.805, respectively. To further enhance classification accuracy, we developed ensemble models employing max voting, average voting, and stacking, resulting in accuracies of 0.803, 0.82, and 0.83. Building on the best-performing ensemble learning model, stacking, we developed our proposed model, SkinNet, which incorporates a customized architecture and fine-tuning, achieving an accuracy of 0.867 and an AUC of 0.96. This substantial improvement over individual models demonstrates the effectiveness of ensemble learning in improving skin lesion classification. © 2024 IEEE.

关键词： Federated learning

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Estimating the Causal Effects of T Cell Receptors

arXiv

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arXiv 2024年

作者： Weinstein, Eli N. Wood, Elizabeth B. Blei, David M. Data Science Institute Columbia University New YorkNY United States Jura Bio BostonMA United States Department of Computer Science Department of Statistics Columbia University New YorkNY United States

A central question in human immunology is how a patient’s repertoire of T cells impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR repertoire sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders, such as a patient’s environment and life history, by using the patient’s immature, pre-selection TCR repertoire. The pre-selection repertoire can be estimated from nonproductive TCR data, which is widely available. It is generated by a randomized mutational process, V(D)J recombination, which provides a natural experiment. We show formally how to use the pre-selection repertoire to draw causal inferences, and develop a scalable neural-network estimator for our identification formula. Our method produces an estimate of the effect of interventions that add a specific TCR sequence to patient repertoires. As a demonstration, we use it to analyze the effects of TCRs on COVID-19 severity, uncovering potentially therapeutic TCRs that are (1) observed in patients, (2) bind SARS-CoV-2 antigens in vitro and (3) have strong positive effects on clinical outcomes. © 2024, CC BY-NC-ND.

关键词： T-cells

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images

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computers in Biology and Medicine 2024年 183卷 109266-109266页

作者： Bouzarjomehri, Narjes Barzegar, Mohammad Rostami, Habib Keshavarz, Ahmad Asghari, Ahmad Navid Azad, Saeed Talatian Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening. Contrast-Enhanced Spectral Mammography (CESM or CM) is used in conjunction with DM to detect and identify hidden abnormalities, particularly in dense breast tissue where DM alone might not be as effective. In this work, we explore the effectiveness of each modality (CM, DM, or both) in detecting breast cancer lesions using deep learning methods. We introduce an architecture for detecting and classifying breast cancer lesions in DM and CM images in Craniocaudal (CC) and Mediolateral Oblique (MLO) views. The proposed architecture (JointNet) consists of a convolution module for extracting local features, a transformer module for extracting long-range features, and a feature fusion layer to fuse the local features, global features, and global features weighted based on the local ones. This significantly enhances the accuracy of classifying DM and CM images into normal or abnormal categories and lesion classification into benign or malignant. Using our architecture as a backbone, three lesion classification pipelines are introduced that utilize attention mechanisms focused on lesion shape, texture, and overall breast texture, examining the critical features for effective lesion classification. The results demonstrate that our proposed methods outperform their components in classifying images as normal or abnormal and mitigate the limitations of independently using the transformer module or the convolution module. An ensemble model is also introduced to explore the effect of each modality and each view to increase our baseline architecture's accuracy. The results demonstrate superior performance compared with other similar works. The best performance on DM images

关键词： Image enhancement

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