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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Bean, Joshua Torri, Stephen A. Mississippi State University Department of Computer Science & Engineering United States

ISBN: (纸本)9798350376739

In general, antivirus software has heavily relied on signature-based detection methods. These antivirus products usually reference a database of known malicious signatures, making them naturally susceptible to previously unseen malware samples. One of the easiest ways for malware authors to produce these new malware samples is by applying various obfuscation techniques to the previously existing malware. This paper will survey a newer approach to malware detection through neural networks by gathering results from recent journal articles and conference papers published in various online databases. These artificial intelligence-oriented solutions show promise in resilience to unfamiliar malware variants. Explicitly focusing on obfuscated malware, it will compare detection rates between signature-based and neural network-based malware detection systems and organize results using various NN models and training features. Overall, the survey finds that signature-based methods detect between 50 and 80 percent of obfuscated malware samples, while all but one of the neural network methods detect between 90 and 99 percent. This considerable increase in performance suggests that neural networks might provide an effective alternate or supplemental detection method for antivirus products. © 2024 IEEE.

关键词： antivirus artificial intelligence malware detection neural networks obfuscation

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Label Flipping Attacks on Federated Learning: GAN-Based Poisoning and Countermeasures 2

Label Flipping Attacks on Federated Learning: GAN-Based Pois...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Abdullah, Muhammad Bapu, Vidhatri Akash, K.A. Khan, Abdul Mannan Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350373110

Federated Learning (FL) is a promising approach for training machine learning models in a decentralized manner to preserve data privacy. However, FL is vulnerable to various attacks, including label-flipping attacks where malicious clients manipulate labels to degrade global model accuracy. This research investigates the efficacy of label-flipping attacks using synthetic images generated by a Generative Adversarial Network (GAN). The GAN architecture and learning procedure are adapted to address challenges related to dataset limitations and ensure realistic data generation for the attacks. The study analyzes the impact of label-flipping attacks on FL model performance. The results demonstrate that these attacks can evade conventional defenses and significantly reduce model accuracy. To mitigate this threat, a novel defense mechanism is proposed to identify and isolate poisoned clients based on abnormally high gradient values. This research highlights the security weaknesses of FL and proposes a defense mechanism against label-flipping attacks. This paves the way for more secure and trustworthy FL applications while preserving data privacy. © 2024 IEEE.

关键词： Federated learning

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Exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis 3rd

Exploring Transformer-Based Approaches for Hyperspectral Ima...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Arya, Rajat Kumar Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology-BHU Varanasi India

ISBN: (纸本)9789819743582

Hyperspectral imaging (HSI) is a technique that captures and analyzes multiple spectral bands for each pixel in an image. Convolutional neural networks (CNNs) effectively extract local features for HSI classification but struggle with capturing sequential spectral signatures. To address this, we conducted a comparative study to assess the performance of transformers, which can handle sequential information, in HSI classification. We reviewed a few models in the literature, analyzing their architecture and performance. Using the Indian Pines dataset, we evaluated models like ViT, SSRN-ViT, SpectralFormer, and Double-ViT. Our results show that Double-ViT models outperform transformers for HSI classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Assessing the Significance of Machine Learning in Forecasting Energy Recovery from Waste 4

Assessing the Significance of Machine Learning in Forecastin...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Badal, Nirzona Binta Anjum, Fahmida Nur, Ismail Mahmud Paul, Tithi Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka1219 Bangladesh

Accurate estimating energy recovery from trash is vital for optimizing the Waste-to-Energy (WTE) mechanisms essential in tackling global waste management processes and energy sustainability issues. This paper analyzes a synthetically expanded dataset generated with the help of SMOTE techniques to compare the performance of four machine learning (ML) models, Decision Tree Regression, Random Forest Regression, CatBoost Regression, and XGBoost Regression. Here, the dataset contains important waste parameters like composition, moisture content, and treatment procedures that help the models forecast energy output with high precision - datasets and evaluate additional machine learning techniques to boost prediction accuracy in industrial WTE systems further. Performance indicators like MAE, RMSE, MAPE, and R2 scores have been assessed here to identify each model's accuracy and computational efficiency. The final result of the analysis states that the ensemble based models, more precisely XG-Boost and CatBoost, outperformed the simpler ones like Decision Tree, where CatBoost achieved the best R2 value of 0.9893 and the minimum MAPE of 12.90 percent. Though using little extra storage, CatBoost showed great performance. The obtained results bring useful insights into efficient model selection for WTE applications. Further studies shall therefore be exclusively focused on validating this study's results in real life conditions. © 2025 The Authors. Published by Elsevier B.V.

关键词： CatBoost Comparative Analysis Energy Recovery Prediction Machine Learning Waste-to-Energy

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An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid 11

An Analysis Conducted Retrospectively on the Use: Artificial...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kaushik, Ruchi Sharma, Kanta Prasad Malik, Sidharth Shanbhog, Manjula Delhi Ncr Campus Department of Computer Science Ghaziabad India Gla University Department of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350350357

The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. © 2024 IEEE.

关键词： Ultrasonography

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An In-depth Exploration of ResNet-50 for Complex Emotion Recognition to Unraveling Emotional States

An In-depth Exploration of ResNet-50 for Complex Emotion Rec...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Praveen, T.N.V.S. Sivathmika, Dasaradhi Jahnavi, Goparaju Bolledu, Jaswitha Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350396485

Emotional Recognition is an important task in computer vision and has many applications, including humanrobot interaction, virtual assistants, and mental health monitoring. In recent years, deep learning models have shown great promise in accurately recognizing emotions from images and videos. This paper proposes the ResNet-50 model for emotional recognition. ResNet-50 is a popular deep learning architecture that has been successfully applied to image classification tasks, and can be adapted for emotional recognition. Using ResNet-50 for emotional recognition offers several advantages over existing approaches. Firstly, ResNet-50 is a well-established architecture that has been extensively used in many computer vision tasks, which makes it a reliable choice. Secondly, ResNet-50 has a deep architecture with many layers that allows it to capture more complex patterns and features from images, which can lead to better emotion recognition performance. Finally, ResNet-50 is relatively efficient in terms of computational resources, which means it can be trained and deployed on a variety of hardware, including low-power devices such as smartphones and embedded systems. ResNet-50 for emotional recognition has several advantages over existing approaches, including reliability, better performance, and efficient use of computational resources. These advantages make ResNet-50 a promising candidate for emotion recognition tasks in a wide range of applications. © 2023 IEEE.

关键词： Emotion Recognition

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AI-Driven Quadrocopter Propeller Acoustic Health Monitoring based on Deep Learning 8

AI-Driven Quadrocopter Propeller Acoustic Health Monitoring ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Ahmed, Asif Rahman, S. M. Rizwanur Chowdhury, Nur Mohammad Rahman, Md Jillur Uddin, Mohammed Fahim Khan, Md Tanvir Rahman Technische Universität Chemnitz Faculty of Computer Science Chemnitz Germany Louisiana Tech University Department of Computer Science Ruston United States University of Asia Pacific Department of Computer Science & Engineering Dhaka Bangladesh Maharishi International University Department of Computer Science IA United States

ISBN: (纸本)9798350367904

In daily rescue missions, Unmanned Aerial Vehicles (UAVs) are vital. Finding propeller damage in UAVs is a critical first step in guaranteeing safe operation and endurance. Because a faulty UAV might endanger people nearby, coexisting with humans and UAVs brings additional concerns. In Addition, by promptly and effectively identifying these problems, the risk to human life can be minimized to the greatest extent possible by providing neighbors with an early warning. The main purpose of this study is to develop an inspection model to classify healthy and broken propellers through audio sound generated by the drone propeller. In addition, different audio data augmentation mechanisms are used to increase the data volume size. We have applied two approaches to train the model: passive learning and active learning. Furthermore, four different CNN-based architectures are used to develop the acoustic inspection models which were trained with three different datasets to find the model's performances and their training accuracy and loss. In the passive deep learning approaches, the ResNet-50 inspection model has attained the best accuracy which is 88.54% by using the entire dataset. In contrast, the ResNet-50 inspection model obtained 85.86% model accuracy in the active deep learning approach, the highest accuracy among the iterations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Incorporation of Two-Fold Trust in Group Recommender System to Handle Popularity Bias

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SN computer science 2024年第2期5卷 259页

作者： Richa Bedi, Punam Gupta, Shivani Kumar, Ranjeet Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science University of Delhi Delhi New Delhi India School of Computer Science Engineering and Technology Vellore Institute of Technology Tamilnadu Chennai India School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India

In recent years, there has been a rise in work which focuses on various issues of recommender systems other than accuracy. Popularity bias is one factor that causes the list of recommendations to deviate from the user’s expectation and causes biased list to generate. This paper proposed an approach that handles the popularity bias in trust aware group recommender system using trust propagation. The proposed work considers two factors, novelty and serendipity, to evaluate the presence of bias. By incorporating trust, along with novelty, diversity, and unexpectedness, the diverse list of recommendation that maintains accuracy should be generated. Trust propagation combines trustworthy user’s opinion indirectly involves enhancing the accuracy. The trust propagation and randomness in trust propagation ensures the trade-off between the accuracy and bias of the recommendations. A prototype is developed using the tourism dataset and compared using traditional approach of recommendation using various accuracy measures and coverage. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Diversity Novelty Popularity bias Recommender system Relevancy

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Leveraging LLM Reasoning with Dual Horn Programs 27th

Leveraging LLM Reasoning with Dual Horn Programs

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27th International Symposium on Practical Aspects of Declarative Languages, PADL 2025

作者： Tarau, Paul Department of Computer Science and Engineering University of North Texas Denton United States

ISBN: (纸本)9783031849237

Dual Horn clauses mirror key properties of Horn clauses. We revisit Dual Horn clauses as enablers of a form of constructive negation that supports goal-driven forward reasoning and is valid both intuitionistically and classically. In particular, we explore the ability to falsify hypotheses in the context of a background theory expressed as a Dual Horn clause program. With Dual Horn clause programs, by contrast to negation as failure, the variable bindings in their computed answers provide explanations for the reasons why a statement is successfully falsified. We devise a compilation scheme from Dual Horn clause programs to Horn clause programs, ensuring their execution with no performance penalty and we design the embedded SymLP language to support combined Horn clause and Dual Horn clause programs. As a (motivating) application, we cast LLM reasoning chains into propositional Horn and Dual Horn clauses that work together to constructively prove and disprove goals and enhance Generative AI with explainability of reasoning chains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Application programs

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Automating Documentation Maintenance: Implementing Hyperlink Testing via GitHub Actions 1

Automating Documentation Maintenance: Implementing Hyperlink...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Charan, N. R V College of Engineering Computer Science Department Bangalore560059 India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518578

This paper addresses the testing phase of the Software development life cycle, mainly focusing on identifying the broken links in documentation. Broken links cause serious issues that can bring down the integrity of the documentation, so it is required to regularly check for broken links in documentation. Current methods for detecting broken hyperlinks in documentation often rely on manual checks, which can be time-consuming and prone to human error. This paper introduces a practical approach for automatically detecting broken hyperlinks in documentation files using GitHub Actions. The goal is to automate the identification of links with 404 error (i.e. Page not found) and invalid path reference issues, ensuring high-quality documentation in collaborative software projects. By incorporating this method into continuous integration and continuous Deployment workflows, it can automatically detect broken hyperlinks before integrating and publishing new documents, thus maintaining the documentation integrity. © 2025 IEEE.

关键词： Integration testing

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