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One-bit DOA Estimation with Array Imperfections: A Learning-based Approach 5

One-bit DOA Estimation with Array Imperfections: A Learning-...

引用

5th National and the 1st International Conference on Applied Research in Electrical engineering, AREE 2025

作者： Azhdari, Yasin Farhang, Mahmoud Shiraz University Dept. of Electrical and Computer Engineering Shiraz Iran

ISBN: (纸本)9798350377170

Low-power and low-complexity Direction-of-Arrival (DOA) estimation is a critical challenge in various signal processing applications. This paper presents a data-driven approach using a neural network framework designed for single-snapshot, one-bit quantized measurements, enabling significant hardware simplification. We explore a Fully Connected (FC) Neural Network architecture (which is identical to a 1D convolutional architecture in the single-snapshot case) trained end-to-end on raw quantized data. The performance of this learned approach is rigorously compared against the well-established MUltiple SIgnal Classification (MUSIC) and Complex-valued Binary Iterative Hard Thresholding (CBIHT) algorithms. Our findings reveal that the proposed neural network achieves superior estimation performance compared to these, especially under low SNR values and in the presence of realistic array imperfections. We observe significant performance gains even without explicitly training the network on data with such imperfections, highlighting the generalization capabilities of the proposed method. © 2025 IEEE.

关键词： Signal to noise ratio

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Semantic Image Retrieval Using Scene Graphs and Natural Language Queries

Semantic Image Retrieval Using Scene Graphs and Natural Lang...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Jaehoon Ko, Byoung Chul Keimyung University Dept. of Computer Engineering Daegu Korea Republic of

ISBN: (纸本)9798331510756

Traditional text-based image retrieval often struggles to provide effective results when there is insufficient information in the search query or when searching with new sentences. This limitation arises because users must input all necessary information for image retrieval, and semantic search is restricted for unregistered sentences. To overcome these challenges, this paper proposes a novel method that converts both sentences and images into graph representations to measure their similarity. Specifically, the approach transforms sentences into semantic graphs and images into scene graphs, then uses a Graph Neural Network to measure the similarity between the two graphs. This enables context-based search and allows image retrieval through natural language queries without prior knowledge of the images. Scene graphs, in particular, can effectively compress and structure the complex information in images, making them highly efficient for similarity analysis. Experiments using the Visual Genome dataset demonstrate that the proposed system improves the efficiency of text-to-image matching and successfully retrieves similar images by capturing the structural meaning of complex scenes. © 2025 IEEE.

关键词： Graph embeddings

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

Advancing Multilingual Speaker Identification and Verificati...

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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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Building Resilient CICD Pipelines: A DevOps Security-First Framework

Building Resilient CICD Pipelines: A DevOps Security-First F...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Ashtagi, Rashmi Belani, Chirag Bhalerao, Prasanna Bhalerao, Yash Chawle, Atharva Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331512965

Continuous Integration and Continuous Deployment are now integrated in automated systems that enhance code stability, and accelerate the deployment process as well as improving monitoring of the whole system in the software development field. This document deals with setting up of a strong CI/CD process involving AWS cloud services and DevOps. The infrastructure is built with the help of Terraform which ensures the scalability and repeatability applying the Infrastructure as Code (IaC) approach. A safe Virtual Private Cloud (VPC) is designed consisting of public and private subnets, load balancing for traffic distribution, and CloudFront CDN to improve geographical distribution. Jenkins is used in CI process to perform disk analysis, OWASP and Trivy vulnerability scanning, and making Docker images. The CD phase use ArgoCD for updates on Kubernetes and integrates Monitoring and observability using Prometheus and Grafana. Notifications are produced to JIRA and email communication for improvement. With the help of the offered cloud services and DevOps tool it is possible to consolidate the SDLC and bring effects in code safety, testing and utilization. This document also provides an overview of methods, instruments, and outcomes, demonstrating how the CI/CD automation influences the development and operation positively. © 2025 IEEE.

关键词： Observability

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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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Conditional Point Cloud Generation from Sketch Using Point-Voxel Representation

Conditional Point Cloud Generation from Sketch Using Point-V...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Hye Rim Ko, Byoung Chul Keimyung University Dept. of Computer Engineering Daegu Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we propose a novel Conditional Generative Model for generating 3D point clouds using sketches as conditions. The model utilizes a sketch-time embedding extracted from sketch and timestep as conditions, applying a diffusion process to noisy 3D point clouds to produce high-quality 3D models. Based on a point-voxel representation, we voxelize the point clouds and effectively integrate the sketch embedding through an improved Feature Aggregation module. Experimental results demonstrate that the proposed model outperforms existing methods in generating more precise 3D point clouds, particularly for objects with complex structures. © 2025 IEEE.

关键词： 3D modeling

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Cyberbullying Detection using Deep Learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

引用

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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Advanced Decision-Making for Autonomous Vehicles Utilizing Deep Q Networks

Advanced Decision-Making for Autonomous Vehicles Utilizing D...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Britto, Akash Ij Abhishek, S. Dhanush, S. Salim, Soja Soniya, B. Sct College of Engineering Dept. of Computer Science Engineering Thiruvananthapuram India

ISBN: (纸本)9798331507541

Autonomous vehicles are poised to revolutionize the transportation industry by offering safer and more efficient navigation in dynamic environments. A critical challenge is managing interactions with other vehicles, pedestrians, and road obstacles, which complicates framing autonomous driving as a supervised learning problem. This study leverages Deep Q-Networks (DQN), a reinforcement learning technique, to control steering, throttle, and braking in autonomous vehicles. The DQN approach enables vehicles to autonomously learn and improve driving strategies, thereby enhancing their ability to navigate complex traffic scenarios effectively. Our objective is to train a model using a Deep Reinforcement Learning (DRL) algorithm, guiding the vehicle to adhere to a predetermined itinerary while adapting to real-time traffic conditions. This work emphasizes the potential of reinforcement learning for enabling continuous learning and adaptation in complex traffic scenarios, laying the groundwork for more reliable and adaptive autonomous driving systems. © 2025 IEEE.

关键词： Deep reinforcement learning

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using Natural Language Processing(NLP) 8

A Machine Learning Approach for Predicting Personality Types...

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

作者： Ganguly, Kinshuk Saha, Subhajit Mondal, Anupam Dey, Monalisa Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

This study explores the use of Natural Language Processing (NLP) approach to predict Myers-Briggs Type Indicator (MBTI) personality types from Twitter texts. Initially, we approached the problem as a 16-class classification task, but encountered significant challenges due to the lack of instances representing certain class labels adequately. To address this, we decomposed the MBTI types into four dimensions and trained individual classifiers for each dimension. Leveraging the best performing classifiers, we constructed a stacking ensemble model to predict personality classes corresponding to each dimension. The results demonstrate improved accuracy compared to the initial 16-class classification approach. Additionally, the performance of ensemble model is found to be better than the individual classifiers. Thus, ensemble approach paired with the separate dimension based classification method can be used for effective prediction of personality types for psychological profiling. © 2025 IEEE.

关键词： Natural language processing systems

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