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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Onesinus Saut Parulian Indra Budi Aji Omega Joel Patria Ariyanto Adi Nugroho Nanang Susanto Totox Mariyanto Nurul Jannah Salsabila Muhammad Haris Lindung Parningotan Manik Computer Science Department Universitas Nusa Mandiri Jakarta Indonesia Computer Science Department Research Center for Data and Information Sciences Badan Riset dan Inovasi Nasional Universitas Nusa Mandiri Jakarta Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

Rapid development of software significantly facilitates developers in all phases of software development. Developers can easily leverage frameworks, content management systems (CMS), and libraries available in each programming language. With the large and rapid addition of frameworks, CMS, and libraries, developers must be able to choose precisely which ones to use based on the requirements of their software development projects. The utilization of machine learning for segmentation or clustering enables developers to identify groups of frameworks, CMS, and libraries by leveraging features obtained from static analysis, such as the size of codebases, complexities, structures, and other code characteristics. One algorithm that can be used for segmentation is K-means clustering. However, the availability of datasets is crucial as it can offer information about the code characteristics of each software. The use of machine learning on datasets can be an ongoing research area in software development related to utilizing code characteristics in software analysis. This study provides a novel dataset for the domains of software development and machine learning. The dataset we created contains 30 instances and 68 features that can be utilized for related or future research. This research utilizes the Silhouette score to determine the optimal $\boldsymbol{k}$ value.

关键词： computer languages Technological innovation Codes Systematics Machine learning Static analysis Libraries Complexity theory Software tools Software development management

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Detection of TB from Chest X-ray: A Study with EfficientNet

Detection of TB from Chest X-ray: A Study with EfficientNet

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rama, A. Rajakumar, M.P. Mythili, N. Arunmozhi, S. Mohammed, Mazin Abed Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India St.Joseph's College of Engineering Department of Artificial Intelligence and Data Science TN Chennai600119 India St.Joseph's Institute of Technology Department of Computer Science and Engineering TN Chennai600119 India Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798350315127

The lung is one of the prime organs, and any disease in the lung causes mild to severe breathing problems;untreated lung disease will lead to several complications. Tuberculosis (TB) is a lung ailment that needs premature recognition and handling. The primary objective is to employ the deep-learning (DL) based TB detection using chest $X$-rays. Various stages of the proposed scheme consist of (i) data collection and resizing, (ii) DL-supported feature extraction, (iii) binary classification and five-fold cross-validation, and (iv) comparison with earlier results and confirming the merit of the scheme. This research implements EfficientNet (EN) variants to classify the chosen $\mathrm{X}$-rays into healthy/TB classes using the SoftMax classifier. The proposed scheme with EN_B2 (ENB2) has been successful in providing an accuracy of $96{\% }$ as far as detection accuracy is considered when compared to other methods. The superiority of the suggested strategy is also confirmed by an analysis using the most recent technology, which confirms the worth of the proposed system on the chosen $\mathrm{X}$-ray imagery. © 2023 IEEE.

关键词： Deep learning

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Machine Learning Development in Solving Critical Medical Problems 6

Machine Learning Development in Solving Critical Medical Pro...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Fatima, Shaista Sangeetha, G. Ponmurugan, P. Arularasan, A.N. Prabhu Chakkaravarthy, A. Denis, R. Chinnasamy, A. Department of Electronics and Communication Engineering MJ College Telangana Hyderabad500053 India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Tamil Nadu Thiruvallur India Department of Research Meenakshi Academy of Higher Education and Research Tamil Nadu Chennai600078 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Tamil Nadu Chennai600123 India Department of Networking and Communications School of Computing College of Engineering and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Mount Carmel College Autonomous Karnataka Bengaluru560052 India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur campus Tamil Nadu 603203 India

ISBN: (纸本)9798350398496

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry. © 2023 IEEE.

关键词： Machine learning

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

arXiv

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arXiv 2024年

作者： Aly, Salah A. Bakhiet, Ali Balat, Mazen Computing & Data Science College Badya University October 6th City Giza Egypt Computer Science Department Culture & Science October 6th City October 6 City Giza Egypt Computer Science Department E-JUST Alexandria Egypt

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology. © 2024, CC0.

关键词： Lung cancer

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Juan Carlos González-Quesada Francisco Javier Cabrerizo Enrique Herrera-Viedma Witold Pedrycz Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada Spain Department of Electrical & Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus.

关键词： Hands Protocols Decision making Buildings Resource management Optimization Cybernetics

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Bridging Accuracy and Efficiency: The Role of PINNs in Immune Response Simulations

Bridging Accuracy and Efficiency: The Role of PINNs in Immun...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fernandes, Thiago E. Dos Santos, Rodrigo W. Lobosco, Marcelo Federal University of Juiz de Fora Graduate Program on Computational Modelling Juiz de Fora Brazil Federal University of Juiz de Fora Institute of Exact Sciences Department of Computer Science Juiz de Fora Brazil

ISBN: (纸本)9798350386226

Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. Computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system's solutions, offering substantial computational efficiency for this application. © 2024 IEEE.

关键词： Pathogens

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Causal Reasoning in Large Language Models using Causal Graph Retrieval Augmented Generation

Causal Reasoning in Large Language Models using Causal Graph...

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Conference on Human System Interactions, HSI

作者： Chamod Samarajeewa Daswin De Silva Evgeny Osipov Damminda Alahakoon Milos Manic Centre for Data Analytics and Cognition La Trobe University Australia Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Computer Science Virginia Commonwealth University Richmond USA

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

Large Language Models (LLMs) are leading the Generative Artificial Intelligence transformation in natural language understanding. Beyond language understanding, LLMs have demonstrated capabilities in reasoning tasks, including commonsense, logical, and mathematical reasoning. However, their proficiency in causal understanding has been limited due to the complex nature of causal reasoning. Several recent studies have discussed the role of external causal models for improved causal understanding. Building on the success of Retrieval-Augmented Generation (RAG) for factual reasoning in LLMs, this paper introduces a novel approach that utilizes Causal Graphs as external sources for establishing causal relationships between complex vectors. This method is empirically evaluated using two benchmark datasets across the metrics of Context Relevance, Answer Relevance, and Grounding, in its ability to retrieve relevant context with causal alignment. The retrieval effectiveness is further compared with traditional RAG methods that are based on semantic proximity.

关键词： Measurement Limiting Large language models Semantics Benchmark testing Cognition Vectors

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Stabilizing Adversarial Training for Generative Networks

Stabilizing Adversarial Training for Generative Networks

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IEEE International Conference on Big data

作者： Walter Gerych Kevin Hickey Thomas Hartvigsen Luke Buquicchio Abdulaziz Alajaji Kavin Chandrasekaran Hamid Mansoor Emmanuel Agu Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA School of Data Science University of Virginia Charlottesville VA College of Computer and Informational Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science Worcester Polytechnic Institute Worcester MA Department of Computer Science University of Victoria Victoria BC Canada

Generative modeling is a powerful technique that involves creating machine learning models capable of creating new data similar to the data it was trained on. Generative Adversarial Networks (GANs) are a leading approach for generative modeling. However, GAN training is known to be a notoriously difficult task. GAN convergence issues are largely caused by the supports of the real and generated distributions being disjoint. To tackle this open problem, we propose a novel GAN pre-training process that effectively aligns the supports of the generated and real data prior to applying traditional adversarial GAN training. The key component of our method, called AlignGAN, is learning a mapping between the input data distribution and a latent representation defined over a hypersphere, regularized by a One Class Classifier. This successfully encourages the generator to produce samples throughout the support of the real data, while not generating samples outside the support. We maintain support alignment through low-bandwidth noise convolutions and additional One Class regularization, leading to continued stable GAN training. We validate our approach against leading stabilization methods on three benchmark datasets, showing AlignGAN routinely produces the best results.

关键词：

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LLM4Decompile: Decompiling Binary Code with Large Language Models

arXiv

引用

arXiv 2024年

作者： Tan, Hanzhuo Luo, Qi Li, Jing Zhang, Yuqun Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong

Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4DecompileRef models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and ex-ecutability while complementing conventional tools for optimal results. © 2024, CC BY.

关键词： Binary codes

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