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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models 62

A Curious Case of Searching for the Correlation between Trai...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Dang, Cuong Le, Dung D. Le, Thai FPT Software AI Center Viet Nam College of Engineering and Computer Science VinUniversity Viet Nam Department of Computer Science Indiana University United States

ISBN: (纸本)9798891760998

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to predict the attack success rate effectively, and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://***/CaptainCuong/RobustText_ACL2024. © 2024 Association for Computational Linguistics.

关键词： Adversarial machine learning

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A Comparative Analysis of Machine Learning Algorithms for Agricultural Drought Forecasting 3

A Comparative Analysis of Machine Learning Algorithms for Ag...

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3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Vrindavanam, Jayavrinda Babu, Tina Gandiboina, Harika Jayadev, Gopika School of Engineering Dayananda Sagar Univeristy Department of Ai and Ml Bengaluru India School of Engineering Dayananda Sagar Univeristy Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665482684

The occurrence of drought is a climatic feature and is a phenomenon that happens over time. Depending on the severity, it can last for a short or long time. Farming households are trying to meet due to rising agricultural operating costs that hinder the country's development. This study aims to forecast the severity of the drought over time. Drought scores vary from 0 to 5, with 0 and 5 indicating the least and highest intensity drought conditions. This is done using weather and soil data of a region consisting of Precipitation, Surface Pressure, Humidity, Temperature, Wind Speed, and Soil data. The main reasons for the cause of drought are first identified. These features are used to train the multivariate time series models like Prophet, VAR (Vector Auto-Regression), LSTM (Long short-term memory), and Comparison of actual v/s predicted values. The results were promising. The study has done an analysis comparing different machine learning algorithms for agricultural drought forecasting and it was found that the LSTM model performed better than VAR and Prophet models. © 2022 IEEE.

关键词： Drought

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Chaotic Water Wave Optimization and Support Vector Machine of Role-Based Access Control Policies in Enterprise IAM

Chaotic Water Wave Optimization and Support Vector Machine o...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Priyanka, K. Harshini, Chikka Aluvala, Srinivas Abbas, Haider Mohammed Ram Kumar, Ramaswamy Padmanathan Nitte Meenakshi Institute of Technology Department of Ise Bengaluru India Department of Cse Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

The access control state of a system is complex, and Machine Learning (ml) based access control decision engines have demonstrated advantages of policies. However, struggle to effectively implement Role-Based Access Control (RBAC) policies because require large enterprises Identity and Access Management (IAM) system with a high volume of users and access logs. This paper, proposed Chaotic Water Wave Optimization (CWWO) algorithm combined with a Support Vector Machine (SVM) to classify users based on selected features, leading to more accurate and secure role to assignment. The dataset used is obtained from amazon and pre-processing is done using Min-max normalization, which scales the feature to a specific range, ensuring that different features are on same scale. Feature selection is performed using the CWWO algorithm, which helps in selecting relevant feature and reduce dimensionality. The classification using SVM for binary classification, which assigns relevant feature and handle large amounts of data, achieving better accuracy. The proposed ensemble model technique for ship motion detection is achieving better outcomes such as high accuracy of 0.98, precision of 0.97, F1-score of 0.96 and recall of 0.98 and respectively. The existing technique such Random Forest (RF) and Decision Tree (DT) are evaluated of proposed method. © 2024 IEEE.

关键词： Random forests

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A Beetle Antennae Search-Based Sparrow Search Algorithm for Network Intrusion Detection

A Beetle Antennae Search-Based Sparrow Search Algorithm for ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Baig, Ayub Reddy, R. Archana Muhsin Hassan, Muntather Subramanya, S.G. Manu, Hajari Department of Infornation Technology Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying multiple, varied attack patterns which often lead to poor performance in detecting critical threats. In order to improve detection accuracy, identify numerous attacks, developing an efficient system becomes crucial which handles imbalanced data. Thus, this paper presents an Adaptive network IDS that employs a hybrid approach by combining the Improved Beetle Antenna Search (BAS) Algorithm and the Sparrow Search Algorithm (SSA) to enhance detection accuracy and adaptability in identifying cyber threats on NSL-KDD and CSE-CIC-IDS2018 datasets. Additionally, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is utilized to reduce the False Positive Rate (FPR) and increase the entire efficiency of the system by effectively overcoming the challenges such as imbalanced data and low detection rate. Experimental evaluations on benchmark intrusion datasets such as NSL-KDD and CSE-CIC-IDS2018 using proposed IBAS-SSA + Bi-LSTM demonstrate higher detection results with accuracy of 98.12% and 98.64% respectively, when compared to other traditional IDS methods. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning Based air Quality Forecasting Using Satellite Data

Deep Learning Based Air Quality Forecasting Using Satellite ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： K Navya Sree Mancharla Rakesh Reddy Saraswathi. S Department of Computer science and engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies.

关键词： Accuracy Atmospheric measurements Prevention and mitigation Urban areas Weather forecasting air pollution Pollution measurement Forecasting Public healthcare Diseases

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An Improved Wind Driven Optimization and Factored Conditional Restricted Boltzmann Machine for Electric Load forecasting 2

An Improved Wind Driven Optimization and Factored Conditiona...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Deepa, N. Adnan, Myasar Mundher Naveen, I.G. Kumar, Gotte Ranjith Palakurthy, Deepthi Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University School of Computer Science & Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai & Ml Hyderabad India

ISBN: (纸本)9798350372892

Now-a-days, because of the expanding effects of both human behavior and the environment, the consumption of electric power is increasing rapidly in our century. It is used in decision-making and power grid operation;electric load forecasting is significant. The complex signal that is electric load profile arises from the nonlinear and stochastic behavior of users. The suggested model is a comprehensive framework that includes modules for training, forecasting, feature selection, and data preprocessing and optimization. An enhanced version of the mutual information approach, the Distance-Based Synthetic Minority Oversampling Technique (D-SMOTE) used for data pre-processing and feature selection module. It is used to choose abstractive features from historical data. The Factored Conditional Restricted Boltzmann Machine (FCRBM), a deep learning model enhanced by learning to estimate the future electric load, is used for training and forecasting module. The optimization module used for fine tuning the adjustable parameters of model is based on Improved Wind Driven Optimization (IWDO) algorithm. The evaluation metric used in this research is Mean Absolute Percentage Error (MAPE). The average MAPE value of IWDO algorithm is 0.4125% better than that of existing methods like Temporal Inception Convolution Network using Multi-Head Attention (TICN-Att), Residual Convolutional Neural Network (RCNN) and Multilayered Long Short-Term Memory (ml-LSTM) and Feature Preprocessing, Multi-Layer Perceptron and Genetic Wind-Driven Optimization (FPP-mlP-GWDO) algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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An Alternative Deep Learning Approach for Early Diagnosis of Malaria

An Alternative Deep Learning Approach for Early Diagnosis of...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jayavrinda Vrindavanam E Mary Jasmine Gaurav Kamath Anirudh Narayanan Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bengaluru India Department of Computer Science and Engineering Christ University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine Learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep Learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria.

关键词： Deep learning Accuracy Malaria Microscopy Convolutional neural networks Blood Residual neural networks

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A Trustworthy Product Rating System Based on the Blockchain with E-Commerce Business Model

A Trustworthy Product Rating System Based on the Blockchain ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kranthi, Alapati Smitha Ranjith Kumar, Gotte Tejasri, Nimmagari Raviprakash Siddhanti, Prateeksha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women Dhulapally It Department Hyderabad India Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Nitte Meenakshi Institute of Technology Yelahanka Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

This article covers the difficulties that customers and retailers encounter while searching for and getting real items at reasonable costs in the internet marketplace. While the development of e-commerce has made it simpler than ever for people to purchase from the comfort of their own homes, it has also resulted in an explosion of phoney reviews and rating manipulation, causing shoppers to lose trust in both the reviews they read and the rating systems themselves. This article offers a method based on blockchain technology to provide e-commerce enterprises with a trustworthy and transparent product rating system. The recommended method for evaluating things in the paper is based on blockchain technology, which the study believes provides e-commerce enterprises with a trustworthy and transparent rating system. According to the outcome analysis, 90% of users thought the blockchain-based grading system was easier to use and more straightforward than prior ones. © 2023 IEEE.

关键词： Blockchain technology Decentralization E-commerce Legitimate products Market disruptions Product Grade System Product quality Smart contract

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Enhancing Radiology Report Interpretation with Large-Scale Language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

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A Novel Approach to Skin Cancer Detection using CNN, ResNet, and Hybrid Feature Extraction 2

A Novel Approach to Skin Cancer Detection using CNN, ResNet,...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kiran, T.Srinivasa Ravi Madhuri, Y.Sudha Venkata Bala Annapurna, P. Lakshmanarao, A. Murthy, B.S.N. Parvathaneni Brahmayya Siddhartha College of Arts & Science Department of Computer Science AP Vijayawada India India Aditya University Department of Ai&ml Surampalem India Aditya University Department of It Surampalem India Bonam Venkata Chalamayya Engineering College Andhra Pradesh Odalarevu533210 India

ISBN: (纸本)9798350368413

Skin cancer is a prevalent and potentially life-threatening condition, making early and accurate detection crucial for effective treatment. This work proposes a novel hybrid approach to skin cancer detection, integrating both deep learning and traditional ml techniques. Utilizing a dataset from Kaggle, the process began with initial classification using a Convolutional Neural Network (CNN) and achieved an accuracy of 90%. Features were then extracted using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and ResNet, adding multiple layers of feature richness. These features were combined into a comprehensive hybrid feature vector and analyzed using ml models, including Random Forest, XGBoost, and AdaBoost. The Random Forest model achieved an accuracy of 96%, while XGBoost obtained an accuracy of 95. 60%. AdaBoost reached an accuracy of 95%. The proposed method demonstrated significant improvements in detection accuracy and robustness, highlighting its effectiveness in enhancing skin cancer detection. © 2024 IEEE.

关键词： Lung cancer

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