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检索条件"机构=Computer Technology Institute and Department of Computer Engineering and Informatics"

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Remember This Event That Year? Assessing Temporal Information and Understanding in Large Language Models

Remember This Event That Year? Assessing Temporal Informatio...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Beniwal, Himanshu Patel, Dishant Kowsik Nandagopan, D. Ladia, Hritik Yadav, Ankit Singh, Mayank Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar India

ISBN: (纸本)9798891761681

Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and reason about temporal information remains limited, hindering their application in real-world scenarios where understanding the sequential nature of events is crucial. Our study experiments with 12 state-of-the-art models (ranging from 2B to 70B+ parameters) on a novel numerical-temporal dataset, TempUN, spanning from 10,000 BCE to 2100 CE, to uncover significant temporal retention and comprehension limitations. We propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. Our findings reveal that open-source models exhibit knowledge gaps more frequently, suggesting a trade-off between limited knowledge and incorrect responses. Additionally, various fine-tuning approaches significantly improved performance, reducing incorrect outputs and impacting the identification of 'information not available' in the generations. The associated dataset and code are available at https://***/lingoiitgn/TempUN. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Transfer Learning-Based DenseNet with Multi kernel aggregated Channel Attention Block for COVID-19 Classification using CT scan images

A Transfer Learning-Based DenseNet with Multi kernel aggrega...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Department of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350373783

Accurate diagnosis of COVID-19 using chest X-ray imaging is pivotal for large-scale screening and epidemic containment efforts. However, discerning COVID-19 from CT scan images poses formidable challenges due to the heterogeneous and intricate radiographic features, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. Secondly, the limited number of samples also poses a big challenge for deep learning models that require large number of samples to perform efficiently. In this study, we propose a novel approach for COVID-19 classification using a pre-trained DenseNet architecture combined with a MKACAB (multi-kernel aggregated Channel attention Block). Leveraging the expressive power of DenseNet and the attention mechanism's ability to highlight informative features, our model aims to effectively discriminate between COVID-19 and non-COVID-19 cases. Comprehensive experimental evaluations conducted on publicly available dataset attest to the superior performance of our architecture in the domain of COVID-19 classification. © 2024 IEEE.

关键词： COVID-19

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Automatic Detection of Diabetic Retinopathy Using CNN and SVM 2

Automatic Detection of Diabetic Retinopathy Using CNN and SV...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Anser Pasha, C.A. Bhargav, K.S. Akki, Ratan Sanath, R. Vismitha, V. Computer Science and Engineering Department Adichunchanagiri Institute of Technology Chikkamagluru India

ISBN: (纸本)9798350383867

Diabetic retinopathy is an eye disease caused by high sugar levels in blood, leaving the retina damaged. Retina is the part of the eye that detects the light and passes electrical signals to the brain. The optic nerve which connects retina and the brain helps the brain form the image of the objects that we see. Out of 5 diabetic patients, one suffers from diabetic retinopathy. Depending on the level of damage caused to retina, the diagnosed person can go through mild vision loss to total vision lost. Adopting automatic detection of diabetic retinopathy helps early diagnosis and reduce burden on the ophthalmologists enabling them to concentrate on higher priority cases. This paper discusses how CNN and SVM can be used to detect diabetic retinopathy with image processing concepts. ©2024 IEEE.

关键词： Ophthalmology

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Evaluating Neural Network Parameter Obliteration in Catastrophic Forgetting

Evaluating Neural Network Parameter Obliteration in Catastro...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Sehra, Udit Dutta, Rishi Raj Pandit, Mohammad Khalid National Institute of Technology Hamirpur Department of Computer Science & Engineering India

ISBN: (纸本)9798350364866

One of the major weaknesses of Neural Networks is their inability to learn multiple functions (tasks) sequentially. This phenomenon is observed when a data distribution shift is encountered in continual learning, leading to catastrophic forgetting. Such ability is foremost important to develop models with general intelligence. In this paper, we evaluate how different parts of the neural network contribute to overall forgetting by investigating the training dynamics during different methods of continual learning. We evaluate and analyze the learning dynamics in catastrophic forgetting on frequently used classification models on CIFAR-10 and Fashion-MNIST datasets. Our findings reveal a consistent trend in continual learning scenarios, both in domain-incremental and class-incremental settings. Specifically, gamma means demonstrate stability in earlier layers but exhibit increased plasticity in later layers, indicating heightened adaptability to changes in data distributions. © 2024 IEEE.

关键词： Neural network models

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Biomarkers to Predictive Modeling: Leveraging Feature Selection and Ensemble Learning for Ovarian Cancer Prognosis 2024

Biomarkers to Predictive Modeling: Leveraging Feature Select...

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Jaya Rathi, Megha Department of Computer Science & Engineering Jaypee Institute of Information Technology India

ISBN: (纸本)9798400709722

Treatment outcomes and patient survival rates are greatly improved by early identification of ovarian cancer. However, to increase diagnostic accuracy, effective predictive modeling is required due to the biomarkers' variability and complexity. This research proposes a complex methodology that combines feature selection and ensemble learning approaches to create a forecast model for the diagnosis of ovarian cancer. Recursive feature elimination (RFE), a thorough feature selection technique, was originally used to identify important biomarkers in order to lower dimensionality and improve model interpretability. We then used ensemble learning techniques, which fuse together several machine learning models, to boost the prognosis's stability and predictive performance. Using a dataset containing clinical and genetic data from ovarian cancer patients, ensemble techniques called Adaboost, Catboost, Random Forest, and Gradient boosting were put into practice and validated. Performance metrics like precision, accuracy, F1-score, recall and AUC-ROC curve were used to measure the techniques' efficacy. The findings show that our approach not only performs better than conventional single-predictor models, with results that are roughly 99% correct, but it also offers important insights into the biomarkers which are most symptomatic of the course of ovarian cancer. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Workplace Wingman: Enhancing Indoor Autonomy for Efficient Workplace Operations 2

Workplace Wingman: Enhancing Indoor Autonomy for Efficient W...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Potdar, Komal Mahajan, Yash Thakare, Ajinkya Amrutkar, Ruturaj Joglekar, Pushkar Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798331519056

This paper presents the 'Workplace Wingman,' an innovative autonomous system designed for workplace environments. Our solution integrates advanced features to optimize indoor operations. The system navigates curved roads with precision, maneuvers through roads with left and right turns, adjusts speed at stops, and employs a line-following mechanism for efficient path traversal. Furthermore, obstacle avoidance capabilities ensure safe navigation using Arduino technology. These features collectively enhance workplace efficiency, productivity, and safety. Our research contributes to the advancement of indoor autonomy, offering practical solutions to meet the evolving demands of modern workplaces. Innovatively, our system achieves autonomy without relying on Lidar, cameras, or Raspberry Pi, thus providing a cost-effective alternative without compromising performance or functionality. In addition to our development, we have reviewed and analyzed relevant research to enrich our understanding and inform our approach. The system's integration of diverse technologies demonstrates its adaptability and effectiveness in real-world workplace scenarios. © 2024 IEEE.

关键词： Motion planning

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Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Model

Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Mo...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sasidaran, Keerthana Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

Hate speech detection is crucial for secure online environments, especially with recent policy changes on social media. This study enhances the Llama 2 model by fine-tuning it with a comprehensive Twitter dataset to improve hate speech detection accuracy. The system's frontend allows users to upload or record audio, upload videos, or provide YouTube URLs, with processing managed by the Google Speech Recognition API and the Whisper library. The backend integrates advanced speech recognition and video processing technologies. The refined Llama 2 model achieved an F1 score of 0.94, with accuracy, precision, and recall metrics surpassing other state-of-the-art models. These results demonstrate the model's high effectiveness and reliability in hate speech detection. This research highlights the potential of transformer-based models in enhancing online safety and inclusivity. Future work will focus on expanding the dataset, improving model generalization, supporting additional languages, and enhancing system scalability to better handle evolving hate speech forms. © 2024 IEEE.

关键词： Social networking (online)

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Quantification of Reliability in Object-Oriented Software: Functionality Perspective 1

Quantification of Reliability in Object-Oriented Software: F...

引用

1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Yadav, Anuj Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science Lucknow India

ISBN: (纸本)9798350373042

Object-oriented design and development are now commonly used in software development environments. Important design concepts including inheritance, coupling, cohesion, and encapsulation are supported by this method. The work in progress aims at offering a functionally-based, robust estimating technique for object-oriented design. Functionality plays a critical part in producing high-quality products within predetermined time and cost limits. This paper introduces an approach for quantifying functionality and reliability, acknowledging the paramount importance of functionality during the design phase. Statistical analyses affirm functionality as a significant factor influencing software reliability. Building on the proposed mapping of object-oriented design features to functionality, a multiple regression equation has been constructed to calculate the functionality of design hierarchies. It is clear that usefulness has a favourable effect on object-oriented systems' dependability. In order to determine dependability based on functionality, an additional multiple regression equation is created. Lastly, experimental trials are used to validate the suggested model. © 2024 IEEE.

关键词： Software reliability

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Ovulytics: A Machine Learning Approach for Precision Diagnosis of PCOD, PCOD and Infertility 1

Ovulytics: A Machine Learning Approach for Precision Diagnos...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jha, Tanushree Sirisha, M. Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCDS) and Polycystic Ovarian Disease (PCOD) are common endocrine disorders that affect a significant percentage of women of reproductive age, often leading to complications such as infertility. Early detection and differentiation of these conditions are important for timely intervention and effective management. This research proposes a machine learning-based solution to classify and predict the occurrence of PCDS, PCOD, and PCDS with infertility using Random Forest, Logistic Regression, and XGBoost. Each algorithm is trained and tested to predict the level of presence of PCDS, PCOD, and infertility-related PCDS. Among the models, Random Forest demonstrates superior performance. However, the importance of our work extends beyond performance metrics. This study illustrates how machine learning can be applied to complex, real-world healthcare challenges, such as early diagnosis and differentiation of overlapping conditions like PCDS and PCOD. The ability to leverage machine learning in this context aids earlier detection of these conditions, allowing for timely interventions that can have the potential to prevent further complications, such as infertility, cardiovascular diseases, and metabolic disorders. The potential impact of this research is substantial, offering scalable, non-invasive, and cost-effective diagnostic solutions, thus improving outcomes for millions of women worldwide and transforming reproductive healthcare practices. © 2024 IEEE.

关键词： Logistic regression

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Smart Lock Security System 2

Smart Lock Security System

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Ravikumar, Pushpa Arpitha, C.N. Hemanth, N.G. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

With the percentage of single-parent households rising quickly, criminal vulnerability is becoming more of a social issue The main issue is often worry over stranger visiting, especially in the case of single female families. This project focuses on the design strategy for an embedded real-time surveillance system based on a Raspberry Pi for the purpose of detecting intruders. Surveillance technology is reinforced to provide vital security for our daily lives, along with related control and alert functions. Our creative integration of cameras and motion detectors into a web application is the foundation of the suggested security system. Remote sensing cameras and motion detectors are managed and operated by Raspberry Pi. The goal of this research is to create a system that can quickly identify outsiders and respond by taking pictures and sending them to a wireless module that is based on the owner. If owner wish to add the other members to home, then he need to save the person photo in database. © 2024 IEEE.

关键词： Remote sensing

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