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Classification of Sarcoma Based on Genomic data Using Machine Learning Models

Procedia Computer science 2025年 252卷 317-330页

作者： Pratham Gala Yash Pandloskar Shubham Godbole Fayed Hakim Pratik Kanani Lakshmi Kurup Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Dwarkadas J. Sanghvi College of Engineering Mumbai India

The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever; they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers.

关键词： Sarcoma Classification Stacking Ensemble Model Genomic data

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Classification of Sarcoma Based on Genomic data Using Machine Learning Models 4

Classification of Sarcoma Based on Genomic Data Using Machin...

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

作者： Gala, Pratham Pandloskar, Yash Godbole, Shubham Hakim, Fayed Kanani, Pratik Kurup, Lakshmi Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Dwarkadas J. Sanghvi College of Engineering Mumbai India

The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever;they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers. © 2025 The Authors.

关键词： Adversarial machine learning

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Drug Recommendation System Using Patient Reviews Based on Sentimental Analysis 2

Drug Recommendation System Using Patient Reviews Based on Se...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kumar, C Siva Sagar, P.Lakshmi Kumar, Golla Dinesh Reddy, Chereddy Harshitha Krushna, Bandlamudi Sai Reddy, Chikkem Mahidhar Sree Vidyanikethan Engineering College Dept. of CSSE AP Tirupati517102 India Department of Data Science A.P Tirupati India Sree Vidyanikethan Engineering College AP Tirupati517102 India

ISBN: (纸本)9798331505745

The integration of digital technology in healthcare has been greatly facilitated by the internet. It has made access to vital data quite easy, with which solutions created help in improving patient outcomes. This leads to an improvement in patient care and medical procedures. Vigilance is required to avoid the dissemination of information that may be incorrect or dangerous, and thus harm patients and healthcare providers alike. The amount of patient-generated data involved is huge, and this has become quite central to the drug recommendation system in the healthcare industry. It is envisioned that medication recommendations by these systems are highly precise to increase efficacy and accuracy of treatment. Using Natural Language Processing tools, the suggested approach will make a prediction about the best drug, using ratings and reviews made by the patients. It would be advisable to apply some NLP approaches to the input provided by the patient to extract some useful insights and information that might help in prescription decisions. Examples of some machine learning methods applied for enhancing accuracy and dependability of such drug recommendations include the XGBoost classifier and LightGBM model. These algorithms further process and analyse data to bring out the relationships and patterns not otherwise evident. They therefore provide crucial help toward the formation and establishment of an effective or reliable drug-related recommendation system and greatly improved the treatment of patients. © 2025 IEEE.

关键词： Drug infusion

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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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A New Holistic Approach for Prosthesis Polymeric Aortic Valve Design

A New Holistic Approach for Prosthesis Polymeric Aortic Valv...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Müge Yaren Yaşartürk Emirhan Yığıt Reza Daryanı Emre Cenk Ersan Mustafa Serdar Çelebı Dept. of Computational Science and Engineering Istanbul Technical University Istanbul Turkey Dept. of Computer Engineering Istanbul Beykent University Istanbul Turkey Dept. of Mechanical Engineering Istanbul Technical University Istanbul Turkiye Dept. of Computational Science and Engineering Istanbul Technical University Istanbul Turkiye

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

Aortic valve insufficiency is a life-threatening condition. The primary treatment approach is the replacement of the native valve with prosthetic valves in severe cases. However, current prostheses often lead to complications such as durability issues, the need for lifelong anticoagulation medicine usage, and tissue rejection. As a result, multiple surgical interventions are frequently required for prosthesis replacement. This study highlights the importance of a comprehensive prosthetic design process by encompassing patient-specific design, careful material selection, and advanced three-dimensional fabrication, which is essential for addressing these challenges. To enhance the mechanical durability of polymeric heart valve prostheses, we introduce a framework that combines fluid-structure interaction (FSI) simulations to obtain hemodynamic properties prior to advanced structural analyses. While our methodology is based on determining hemodynamic loads with FSI simulations and transferring them to an additional finite element model (FEM) that captures the detailed mechanical properties of valve tissue, the results presented in this study are obtained using more traditional loading conditions in FEM to ensure the validity and comparability of our findings. These results serve as a foundation for future studies incorporating full FSI-driven structural simulations. These analyses lead to the creation of a geometrically optimized design, fabricated with a digital light processing-based (DLP) method. Subsequently, mechanical testing was conducted to evaluate the effects of this manufacturing technique on material behavior, producing more realistic material model coefficients for use in numerical simulations. Consequently, different ratios of material rigidity in fiber-reinforced models are investigated with FEM analysis. An important novelty of this approach is to be able to detect stress accumulations by taking hemodynamic indices into account to optimize the stress di

关键词： Analytical models Valves Finite element analysis Hemodynamics Numerical models Polymers Heart valves Stress Prosthetics Load modeling

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset 11

Comparative Analysis of Machine Learning Techniques for Arab...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Attar, Hani Sharrab, Yousef Smadi, Saja Jbaily, Safa Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Isra University Department of Data Science and Artificial Intelligence Amman Jordan Yarmouk University Dept. Computer Science Irbid Jordan

ISBN: (纸本)9798331509293

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency;indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications. © 2025 IEEE.

关键词： Logistic regression

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-Computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Anjali Deshpande Pratiksha Deshmukh Dept. of Computer Science and Engineering COEP Technological University Pune Dept. of AI & DS PICT Pune Dept. of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Natural Language Processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications.

关键词： Surveys Law Reviews Semantics Focusing Named entity recognition Linguistics Natural language processing Labeling Global communication

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