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Ethnicity Prediction From Audio Samples for Different Languages Using Convolutional Neural 5

Ethnicity Prediction From Audio Samples for Different Langua...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Thoguluva, Sharvaani Ravikumar Gopinath, Suraj Udith, S. Singh, Rimjhim Padam Kanchan, Sneha Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept of Internet Engineering and Computer Science University Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

"Ethnicity Prediction from Voice Samples" deals with prediction of a person's ethnicity from his/her voice samples. Ethnicity Prediction has gained more and more importance and attention in fields like security, recommendations systems, learning apps etc. The proposed work uses a customized Convolutional Neural Network (CNN) based model to predict the ethnicity of the speaker based on combination of varied audio features like Mel-Frequency Cepstral Coefficients (MFCC), pitch, energy, and zero crossing rate. The different voice samples are obtained from the popular Speech Accent Archive Dataset. Augmentation of data by performing time and pitch shifting proved to give a significant boost in the accuracy as it increased the number of voice recordings. The work also compares the performance of the proposed model with other state-of-art models like LSTM. The F-score obtained was 98% for the class Russian, Polish and 90% for the class French, Italian, Spanish i.e. Romance Languages and 80% for mixed language category that comprises of Indian, Italian, German and Polish. © 2024 IEEE.

关键词： Long short-term memory

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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Enabling Precise Diabetic Retinopathy Severity Prediction through Convolutional Neural Networks 10

Enabling Precise Diabetic Retinopathy Severity Prediction th...

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

作者： Shruti, P. Reddy, Tanuja Konda Abhiram, S. Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Amrita Vishwa Vidyapeetam Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Diabetes retinopathy (DR) continues to be a major public health concern, needing efficient and reliable approaches for early identification and severity categorization. In this work, we used deep learning to improve the diagnosis procedure of diabetic retinopathy, which is blindness caused by diabetes. Deep learning algorithms, notably neural networks, have shown extraordinary performance in image analysis tasks, demonstrating their potential to transform medical picture classification. The dataset used was the APTOS-2019 dataset with 3930 retinal images and corresponding classes of severity of the disease. Using a custom-designed CNN architecture, we focused on diabetic retinopathy severity classification, a multiclass classification consisting of 5 classes. The suggested custom model outperformed existing pretrained models in terms of accuracy with training accuracy at a high 98% and testing accuracy of 93%, showing its efficacy in diabetic retinopathy severity categorization. Our discoveries revealed the potential of customized deep learning models to improve diagnostic speed and accuracy, providing a major advancement toward individualized and successful patient treatment in the field of managing diabetic retinopathy. © 2024 IEEE.

关键词： Convolutional neural networks

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Geopolitical Risk Analysis and its Impact on Market Volatility 9

Geopolitical Risk Analysis and its Impact on Market Volatili...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rathinavel, Keshav Indira Kumar, A.K. Sahoo, Anshuman Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350394474

This study addresses the influence of geopolitical risks on financial markets, as noticed in recent events like the COVID-19 pandemic and the Russia-Ukraine war. This study showcases a model combining financial indices with the Geopolitical Risk Index (GPRI), employing Bi-LSTM networks with attention mechanisms to analyze the data. This approach captures the nuanced effects of geopolitical events on market volatility. Focused on the pandemic period, our model offers insights into market dynamics during times of geopolitical unrest and serves as a predictive tool for market behavior in response to global events. © 2024 IEEE.

关键词： Risk analysis

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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kuj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of Data/Data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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Machine Learning-driven Antenna Selection Strategies for Fading MIMO Systems 2

Machine Learning-driven Antenna Selection Strategies for Fad...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrated Modeling of Wireless System Performance under Beaulieu-Xie Fading and κ-μ Co-Channel Interference: A Multi-Dimensional Exploration 27

Integrated Modeling of Wireless System Performance under Bea...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Sharma, Rashmi Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Department of CSE Pune India Madhav Institute of Technology and Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350392319

This paper proposes simulation and analysis to discuss how the fading affects the wireless signal waves on different fading levels. Four types of fading: (i) no fading, (ii) a low level of fading, (iii) moderate level of fading and (iv), high level of fading was considered for simulating the signal amplitude over time. Selected fading levels were 'added' to model real world wireless scenarios in which both environmental and interference conditions are at play. The results indicated that in the absence of fading, the amplitude of the signal wave was continuous and not changed significantly;however, from low to severe fading, there could be directly proportional soaring fluctuations in its proper amplitude corresponding to lose of signal strength. Together, these findings underscore the importance of adaptive signal processing techniques and robust modulation strategies in suppressing the degradative effects of fading against the desirable characteristics of proper signal integrity and reliable performance. The findings highlight one reality in communications: understanding fading is critical for designing ultrareliable wireless systems that perform consistently across a variety of operational settings. The knowledge extracted based on this study would further add to novel way of designing robust strategies for Signal resilience and Communication reliability emerged in future wireless networks. © 2024 IEEE.

关键词： Cochannel interference

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Delta-γ Modeling of Wireless System Resilience in Multipath Fading and Interference Environments 7

Delta-γ Modeling of Wireless System Resilience in Multipath...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kunj Bihari Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350350067

In this paper, we pursue the resilience of wireless communication systems in multipath fading and interference environments utilizing Delta- γ modelling. Furthermore, fading phenomena such as Rayleigh/Rician/Nakagami channels can be simulated to examine the impact on important performance metrics like SNR, BER and throughput. Different types of interference scenarios, covering co-channel and adjacent channel interferences are also studied to understand impact on system capacity as well as reliability. This article investigates the use of adaptive modulation techniques, multiple-input multiple-output (MIMO) strategies and advanced interference management schemes by extensive *** results illustrate the importance of adaptive strategies in overcoming fading and interference which leads to an increase in system resilience as well as a robust communication link. Metrics for the outage probability and mean time to failure (MTTF) can inform how reliable systems are across operational scenarios. This work reconciles theory with practice to provide a systematic way of controlling the operational parameters in wireless networks under varying conditions. These lessons will be crucial in steering the design of forthcoming wireless systems that must more reliably satisfy a broader set of application requirements. © 2024 IEEE.

关键词： Multipath fading

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Optimal Resource Allocation in Dynamic Fading Channels via Meta-Learning Techniques 2

Optimal Resource Allocation in Dynamic Fading Channels via M...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this paper, we address the optimally dynamical resource allocation problem for fading channels utilizing meta-learning methods with an application to improve efficiency and performance in wireless communication systems. Extensive experiments are conducted and our meta-learning model has demonstrated the ability to adapt itself dynamically in various channel conditions, leading to improvement of Signal-to-Noise Ratio (SNR), reduction of Bit Error Rate (BER) and enhancement on throughput when compared with traditional methods. Visualizations like performance analysis comparisons, heatmaps or 3D plots are used to show how the model can be leverage as effectively manager of resources where they are and when they suit best. The performance improvements demonstrated here are statistically significant testifying for the scalability of our work. The findings of this study demonstrate the major benefits that can be achieved by meta-learning to overcome immanent issues in fast-changing wireless conditions and its potential to propel future communication technologies forward. © 2024 IEEE.

关键词： Contrastive Learning

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Dynamic Resource Allocation and Security Strategies in Cooperative Relaying Systems with Eavesdropping Threats 27

Dynamic Resource Allocation and Security Strategies in Coope...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Upadhyay, Abhay Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Deemed University Centre for Internet of Things Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9798350392319

The dynamic resource allocation and security strategies for cooperative relaying systems proposed here provide significant performance improvements along with robust security features over traditional methods. The method improves throughput, energy efficiency and secrecy capacity by dynamically provisioning resources according to real-time network conditions in conjunction with its interference-suppression techniques such adaptive beamforming and artificial noise. The system is quite good at preventing eavesdropping, and it has such high signal-to-noise ratios that there is a very low error rate. The use case results show that the integrated framework optimizes resource consumption while mitigating security threats, thereby offering a trade-off between existing solutions thus solving some of their shortcomings. Our work demonstrates the opportunity for efficient and secure communication in dynamic environments. © 2024 IEEE.

关键词： Energy efficiency

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