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Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages 1

Advancing Multilingual Speaker Identification and Verificati...

引用

1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Sritharan, Braveenan Thayasivam, Uthayasanker Dept. of Computer Science & Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9798891762145

Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems. © 2025 Association for Computational Linguistics.

关键词： Speech recognition

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Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion 10

Synergizing Insights for Precise Rice Leaf Disease Diagnosis...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pally, Shelly Chelsy Jenefa, J. Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350384369

Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them. © 2024 IEEE.

关键词： Plant diseases

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Gender Classification from Thermal Images using CNN

Gender Classification from Thermal Images using CNN

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Gurram, Harika Kethineni, Keerthi Kommineni, Deva Harshini Soumya, Kakani VRSEC Dept. Computer Science & Engineering Vijayawada India

ISBN: (纸本)9798350361537

In this study, two deep learning architectures - AlexNet and InceptionV3 - are used to evaluate gender categorization using thermal imagery. One of the most promising techniques for classifying gender is thermal imaging, which offers distinct insights into physiological traits. Divided into separate training and testing subsets, the dataset is made up of carefully preprocessed thermal pictures. InceptionV3, known for its complex design combining inception modules, and AlexNet, a pioneering convolutional neural network (CNN), are refined on the training data to adjust to the complexities of gender categorization. Then, in order to measure the performance of the trained models, they are rigorously evaluated on the testing set. Specifically, their accuracy is evaluated. Experimental findings show that InceptionV3 performs better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This significant disparity highlights how much better InceptionV3 is at identifying subtle thermal patterns and characteristics, which improves gender categorization accuracy. The work makes a substantial contribution to the rapidly developing area of thermal imagingbased gender categorization by highlighting how crucial it is to use advanced deep learning architectures in order to improve performance and accuracy. Subsequent investigations might delve into novel methodologies, including multi-modal fusion or sophisticated methods, to enhance precision and resilience in tasks using thermal-based gender categorization. © 2024 IEEE.

关键词： AlexNet Convolutional Neural networks Deep learning Gender classification Inception V3 Thermal images

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A Comprehensive Approach to Song Popularity Forecasting 2

A Comprehensive Approach to Song Popularity Forecasting

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Kishore, N. Santhiya, P. Mohamed Askar Asib, M. Kirubaaharan, S. Kongu Engineering College Dept. Computer Science India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Assessing the potential success of a song based on its acoustic characteristics is a crucial task within the music industry. Traditionally, this has been approached by leveraging audio features independently. In this study, we take a novel approach by jointly exploiting audio features, framing as a regression on prediction task. Our methodology employs a wide architecture, enabling a comprehensive analysis. Additionally, we enhance feature sets by incorporating information about track release years. Evaluation on the Million Song Dataset, using Billboard Hot 100 inclusion as a metric for song success, reveals that our approach surpasses baseline methods and those utilizing low- or high-level features in isolation. Notably, the inclusion of release year alongside mood and vocal features further enhances predictive accuracy, highlighting the efficacy of our comprehensive model in gauging song popularity. © 2024 IEEE.

关键词： Music

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Event Identification and Chronology Model of Ramayana 8

Event Identification and Chronology Model of Ramayana

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Paul, Apurba Mishra, Rishabh Padia, Nayan Das, Dipankar IEM Kolkata UEM Kolkata Dept. of Computer Science & Engineering Kolkata India IEM Kolkata Dept. of Computer Science & Engineering Kolkata India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

The paper introduces an event chronology model for Hindu mythological texts, with a focus on the Ramayana's Bala-kanda. It captures and structures sentence-level events based on their chronological order to better understand narrative progression and character interactions. A manually crafted dataset of 1217 events, involving 214 characters across 75 chapters, is used to generate a knowledge graph. The graph organizes events by sequence, linking characters, actions, and chapters within the storyline. The event chronology is mapped by establishing relationships between events, ensuring they are connected in the proper sequence to reflect the narrative flow. This approach enables a deeper understanding of how each event contributes to the overarching story, providing insights into both the individual actions of characters and their significance within the broader mythological narrative. Transformer-based models (BERT, DistilBERT, and RoBERTa) were utilized for predicting subsequent events, with RoBERTa showing the highest accuracy in preserving the correct event chronology. This structure is key to analyzing the sequential development of the Ramayana's storyline. © 2025 IEEE.

关键词： event chronology Hindu mythological texts knowledge graph Ramayana sentence-level event extraction transformer models

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Classification and Structuring of DL-Based Method for Finding Alzheimer's Disease through MRI Images 5

Classification and Structuring of DL-Based Method for Findin...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tripathi, Tripti Kumar, Rakesh Mmmut Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350383522

Alzheimer's disease (AD) is a type of neurological disorder that progresses over a period and affects a significant number of people worldwide. Early detection of AD is necessary for rapid intervention and improved outcomes for patients. In this paper, we compare the performance of four widely used Deep Convolutional Neural Network (DCNN) architectures: CNN, VGG16, InceptionV3, and DenseNet121. The study focuses on the use of DCNNs for AD identification. Our dataset is collected from the Kaggle website and preprocessed such that the inputs are identical for every model. Several criteria are used to assess the DCNN architecture's performance, including recall, accuracy, precision, and f1-score. By looking at individual architecture's predictive capabilities, we may discover further the benefits and drawbacks of AD detection jobs. Also, transfer learning techniques employ pre-trained weights from large neuroimaging datasets to enhance model performance and generalization abilities. This study contributes to the increasing body of knowledge in AD research by identifying the ideal DCNN architecture for fast and accurate AD diagnosis. © 2024 IEEE.

关键词： AD detection Alzheimer's disease CNN computational efficiency Deep Convolutional Neural Networks DenseNet121 InceptionV3 performance evaluation transfer learning VGG16

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Enhanced ConvLSTM with Hierarchical Attention Scheme for Unusual Activity Detection in Classrooms or Laboratories

Enhanced ConvLSTM with Hierarchical Attention Scheme for Unu...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Shrivas, Ansh Mahendra Kumar Singh, Dushyant Jain, Rishabh Chandel, Shreyansh Singh Sahu, Abhimanyu Mnnit Computer Science and Engineering Dept. Allahabad India

ISBN: (纸本)9798350364866

Surveillance camera systems have been essential in enhancing security protocols, serving as crucial tools for monitoring and deterring unauthorized or unusual activities across various settings. This paper addresses the significant vulnerability of educational spaces to numerous security threats, such as unauthorized access, theft, vandalism, and risks to the safety of students and staff. In an age where both physical and digital security are critically important, there is an increasing demand for proactive surveillance solutions that can quickly and accurately identify anomalies and potential security breaches. Deep learning techniques have demonstrated remarkable effectiveness in identifying patterns, anomalies, and subtle deviations in behavior within real-time video feeds. We have developed an enhanced ConvLSTM model incorporating spatial and temporal attention mechanisms to extract essential features from raw video footage, which are then processed by the backbone network. The architecture is designed to selectively focus on the most valuable features, thereby reducing computational complexity and enhancing the model's detection performance. The model is fine-tuned to detect and classify activities into usual and unusual categories. The proposed design is trained on videos from the UCF-Crime dataset and fine-tuned on a custom dataset created from YouTube videos, which includes videos of actions that are unusual or anomalous in educational environments. Our approach achieved approximately 99% accuracy on the UCF-Crime dataset, surpassing detection baselines (85.53% to 88.56%) and outperforming other architectures such as C3D, ConvLSTM, and CNN+RNN models with and without attention mechanisms. Evaluation on a custom YouTube dataset yielded a detection accuracy of 82.66%. © 2024 IEEE.

关键词： Deep learning

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Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-Based Heterogeneous System with Algorithm-based Fault Tolerance 18

Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Abid, Marzan Binte Shaha, Shuvo Kabir, Upama Arefin, Md. Fahim University of Dhaka Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350331011

A heterogeneous high-performance computing (HPC) system is an aggregation of CPUs and GPUs through high-speed interconnection. As graphics processing units (GPUs) can conduct rapid mathematical and vector operations due to their hardware properties, GPU-based heterogeneous systems can optimize algorithms that show data parallelism. Since the dynamic programming (DP) solution of the 0/1 knapsack algorithm is inherently data-parallel, this paper optimized the algorithm to maximize the advantages of a GPU-based parallel environment. Furthermore, this research identified that the algorithm was well suited to four distinct GPU-launch approaches. Among them, the "single-cell calculation by single thread with a single kernel launch"approach outperforms others by reducing the complexity from O(n∗w) to O(n). Besides, these HPC systems are susceptible to the slightest of errors. Checkpoint recovery (CP/R) is the most commonly used fault tolerance and recovery system, which suffers from huge checkpoint and recovery overhead. To reduce this overhead in CPU-GPU-based heterogeneous systems, an algorithm-based fault tolerance (ABFT) technique has been proposed for a particular class of algorithm: 0/1 knapsack. The experimental results verify the feasibility of the proposed ABFT technique and demonstrate that the system can recover from failures with minimal overhead. © 2024 IEEE.

关键词： Graphics processing unit

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Cyberbullying Detection using Deep Learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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Integrated Framework for Equitable Healthcare AI: Bias Mitigation, Community Participation, and Regulatory Governance 14

Integrated Framework for Equitable Healthcare AI: Bias Mitig...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Dept. of Computer Science and Engineering Chandigarh University Punjab Mohali India Dept. of Computer Science and Engineering MRIIRS Faridabad India

ISBN: (纸本)9798331531935

Artificial intelligence (AI) has revolutionized healthcare by enhancing diagnostic precision, personalizing treatments, and optimizing clinical workflows. However, algorithmic biases arising from imbalanced training data disproportionately affect marginalized populations, perpetuating health disparities. For instance, a widely-used predictive model underprioritized Black patients by equating healthcare costs with illness severity, restricting access to essential care. To address these challenges, this paper introduces an integrated framework combining adversarial debiasing, reinforcement learning (RL)-driven threshold optimization, synthetic data augmentation, and community engagement strategies. Empirical evaluations using the MIMIC-III dataset demonstrate near-perfect Area Under the ROC Curve (AUC = 0.994) alongside significant reductions in demographic parity (DP = 0.000) and error rate differences across subgroups. While the RL-optimized threshold ensures no missed diagnoses, it increases false positives, highlighting the trade-offs between fairness and precision. This work underscores the importance of synthesizing technical, regulatory, and community-driven approaches to promote equitable AI deployments. By embedding participatory design principles and aligning with emerging governance frameworks like the European Union’s AI Act, the proposed methodology offers a scalable pathway toward transparent, accountable, and high-impact healthcare AI solutions. © 2025 IEEE.

关键词： Patient treatment

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