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6th IEEE international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Panahi, Issa The University of Texas at Tyler Department of Electrical and Computer Engineering TylerTX United States The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798331506520

In modern communication, speech technology plays a vital role, with accent being one of the key features that reflects the nuances of spoken technology. However, differences in accent can make communication difficult, especially in global contexts like language learning and human computer interaction. Accent conversion approaches often solve these issues by transforming the accent of speaker to that of the other speakers, while maintaining linguistic information and speaker identity. This technology holds great applications to speech recognition, language learning, and cross-lingual communication. This paper explores the use of advanced deep learning techniques in accent conversion systems which have improved both the naturalness and intelligibility of converted speech. Additionally, we discuss the methods generally used for performance evaluation of these systems. © 2025 IEEE.

关键词： Deep learning

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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas Sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

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2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Harshavardhan, S. Upender, Patri Ganesh, N. Giftsy, Sharon Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798331529833

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology. © 2025 IEEE.

关键词： Chemical sensors

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Two dimensional DOA estimation of coherent signals based on covariance matrix element transformation 24

Two dimensional DOA estimation of coherent signals based on ...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Qu, Zhiyu Wu, Xu Geng, Zhenya College of Information Science and Engineering Hohai University Jiangsu Nanjing China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing China The 49th Research Institute of China Electronics Technology Group Corporation Heilongjiang Harbin China

ISBN: (纸本)9798400710094

Traditional Direction of Arrival (DOA) estimation algorithms for coherent signals in uniform circular array (UCA), such as mode space transformation, allow for the adaptation of advanced algorithms initially developed for uniform linear arrays to be applied effectively in UCA. However, after mode space transformation, the array will encounter aperture loss and will also be unable to perform two-dimensional DOA estimation effectively. In this paper, we propose a new method that that integrates covariance matrix element transformation with the Multiple Signal Classification (MUSIC) to estimate the DOA of coherent signals in various symmetric antenna arrays, including UCA. The algorithm leverages the phase difference symmetry of the antenna array to decorrelate the incoming signals, effectively restoring the full rank of the covariance matrix. Subsequently, it employs two-dimensional MUSIC for DOA estimation. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Covariance matrix

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Efficient Feature Extraction Methods for Enhanced Mango and Groundnut Disease Classification

Efficient Feature Extraction Methods for Enhanced Mango and ...

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2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Chimate, Yogesh Patil, Sangram Patil, Jaydeep D. Y. Patil Agriculture & Technical University Computer Science & Engineering Kolhapur India D. Y. Patil Agriculture & Technical University School of Engineering & Technology Kolhapur India D. Y. Patil Agriculture & Technical University Computer Science & Enigneering Kolhapur India

ISBN: (纸本)9798331529833

India's agricultural sector is vital to the economy, sustaining millions of farmers, but plant diseases threaten crop productivity and livelihoods. Traditional disease diagnosis methods rely on manual observation, are labor-intensive, and prone to misdiagnosis due to subtle or unclear symptoms. To address these challenges, machine learning techniques have emerged as efficient and accurate alternatives. This study investigates optimized feature extraction techniques for classifying diseases in mango and groundnut crops. Using images of healthy and diseased leaves from Maharashtra's Kolhapur and Ratnagiri districts, we analyze feature extraction methods like SVM, KNN, and RF. Our results show that refining feature extraction improves classification accuracy, achieving a 95% accuracy with RF. These findings highlight the potential of advanced machine learning models to enhance crop health management and support precision agriculture practices. © 2025 IEEE.

关键词： Fruits

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A Scalable and Secure Facial Recognition Attendance System Leveraging CNN Algorithms for Real-Time Accuracy and Efficiency

A Scalable and Secure Facial Recognition Attendance System L...

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2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Kumar, Aakash Sharma, Jiya Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering India

ISBN: (纸本)9798331529833

The facial recognition attendance system represents a modern approach to attendance tracking, designed to improve efficiency, accuracy, and security in various organizational settings. advanced biometric technologies are leveraged to capture and encode facial features, creating unique digital identifiers for each individual. The methodology employs cutting-edge facial recognition algorithms powered by convolutional neural networks (CNNs) with a 99.5% recognition accuracy rate. These algorithms extract and encode facial vectors with sub-millisecond latency, which are then compared in real-time against a secure, encrypted database of preregistered individuals. Upon a successful match, the system automatically records the attendance, ensuring an accurate and efficient process. With an intuitive web interface built on ReactJS and a backend supported by *** and MongoDB, the system provides seamless access to attendance records and administrative functionalities. The entire attendance recording process is optimized for speed, typically completing within two seconds per individual. Additionally, advanced anti-spoofing measures and integration support with existing HR management systems redefine traditional attendance management practices with a scalable, robust, and secure solution. (Abstract) © 2025 IEEE.

关键词： Efficiency

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A Novel NSGA-II Approaches for Combating advanced Persistent Threats with Machine Learning 12th

A Novel NSGA-II Approaches for Combating Advanced Persistent...

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12th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA 2024

作者： Soni, Monika Pawar, Avinash M. Khubalkar, Deepti Tanjeja, Ankit Kumar Mishra, V.A. Waykole, Tushar Satish Auricle Global Society of Education and Research Bikaner India Bharati Vidyapeeth’s College of Engineering for Women Maharashtra Pune India Pune India Information Technology Arya College of Engineering Jaipur India Computer Engineering VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601387

advanced persistent threats (APTs) are becoming more advanced, requiring the creation of strong cybersecurity measures. This study suggests a new method to address advanced persistent threats (APT) by utilizing machine learning, particularly by implementing the "Non-dominated Sorting Genetic Algorithm" II (NSGA-II). The proposed model attains a true-positive rate (TPR) of 98.62% and a precision of 97.59%. Extensive experiments were conducted using a diverse dataset containing APT and benign data samples to assess the effectiveness of NSGA-II compared to other popular machine learning algorithms such as random forest, isolation forest, and LightGBM. The NSGA-II model consistently surpassed other algorithms in terms of true-positive rate (TPR) and precision, demonstrating its superiority in detecting and mitigating advanced persistent threats (APT). The study explores the structure and functionality of NSGA-II, highlighting its ability to enhance the efficiency of machine learning models for APT detection. The algorithm's efficiency in exploring the solution space, balancing multiple objectives, and identifying nondominated solutions greatly contributes to its success in dealing with the complex nature of APT. This study emphasizes the practical implications of using NSGA-II for APT detection in real-world situations. The model shows a high level of accuracy in differentiating malicious activities from normal network behavior, offering an effective defense against APT. The suggested method employing NSGA-II for APT detection provides a promising solution to the changing cybersecurity threat environment. The high true-positive rate (TPR) and precision rates of NSGA-II exceed those of random forest, isolation forest, and LightGBM, demonstrating its potential as a strong and effective tool in addressing advanced persistent threats (APT) and improving cybersecurity resilience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Towards Hybrid Meta-Heuristic Analysis for the Optimization of Fundamental Performance in Robotic Systems

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international Journal of advanced computer Science and Applications 2025年第4期16卷 1136-1149页

作者： Dabbaghi, Boudour Hamidi, Faiçal Aoun, Mohamed Jerbi, Houssem Research Laboratory MACS LR16ES22 University of Gabes Gabes Tunisia Laboratory of Information-Communication and Knowledge Sciences and Techniques University of Western Brittany Lorient France Department of Industrial Engineering College of Engineering University of Hail Hail1234 Saudi Arabia

This paper examines the concept of implementing a hybrid optimization approach through combining analytical and meta-heuristic approaches to improve the performance of practical engineering systems. Designed in support of artificial intelligence strategy, the proposed approach ensures high stability and efficiency under actuators saturation constraint. This is a well-known and sensitive problem in robotics and control. Specifically, this paper deals with the problem of computing the stability region for controlled systems. While addressing this issue, research approaches take into consideration the fact that actuator saturation may occur. It is imperative to maintain this propriety and ensure the reliability of design control systems, particularly those developed to control robot actuators. Models of the studied systems are based on differential algebraic representations and polytypic regions in state space. The developed technique combines LMI with an improved meta-heuristic based optimization approach that fast searches and enlarge domains of attraction for robot actuators. The direct Lyapunov theory is used to analyze and validate stability key performance. A numerical example study has been conducted to validate the proposed approach’s efficacy and efficiency. A comparative benchmarking study has been carried out to highlight the main concepts and results of this study. © (2025), (Science and Information Organization). All rights reserved.

关键词： Robots

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Rarh Rainfall Analytics: Machine Learning Insights from NASA Data

Rarh Rainfall Analytics: Machine Learning Insights from NASA...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Murmu, Saachin Sain, Priyabrata Karmakar, Rahul The University of Burdwan Department of Computer Science Bardhaman India

ISBN: (纸本)9798331513894

Predicting rainfall is an essential part of meteorology and is crucial for solving problems in agriculture, water resource management, and disaster relief. In regions with significant climatic variability, such as the Rarh region of India, accurate rainfall forecasting is essential to support sustainable development and ensure agricultural productivity. This research focuses on predicting rainfall over the Rarh region of India, an area known for its diverse climatic conditions and agricultural reliance. By leveraging NASA's comprehensive satellite datasets, this study employs advanced ML techniques to increase the accuracy of rainfall forecasting in this region. Integrating various meteorological variables into the dataset enables the discovery of intricate patterns and relationships that traditional methods might not detect. This research adopts a systematic machine learning pipeline involving feature selection, correlation matrix analysis to understand relationships among features, 10 CV, hyperparameter passing, and three splitting ratios for training and validation. Five ML classifiers are employed to analyze performance among which Naïve Bayes gave the best accuracy score of 0.99. © 2025 IEEE.

关键词： Rain

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Effectiveness of the Programmed Visual Contents Comparison Method for Two Phase Collaborative Learning in computer Programming Education: A Case Study 10th

Effectiveness of the Programmed Visual Contents Comparison M...

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29th international conference on Database Systems for advanced Applications (DASFAA)

作者： Thanh Ha Nguyen Sun, Yi Nishida, Takeshi Wang, Xiaonan Ohtsuki, Kazuhiro Kiyomitsu, Hidenari Kobe Univ Kobe Hyogo Japan Kobe Inst Comp Kobe Hyogo Japan

ISBN: (纸本)9789819609130;9789819609147

This paper introduces the Programmed Visual Content Comparison (PVCC) for collaborative programming learning. The PVCC is based on comparing two displayed visual objects produced by programming. This feature enables candidates to take the tests in a lighthearted and fun manner because they only judge "which is more difficult." The test based on the PVCC method works well to assess a candidate's programming abilities related to Panoramic Understanding of Programming (PUP). We conducted a collaborative learning experiment with students in a programming class using the PVCC method. This experiment method contains two phases of activity. First, each candidate answers a problem with the reason for the judge. In the second (collaborative phase), candidates discuss their answers and reasons in a group and then make the group answer with its explanation. The result from the experiment suggests a potential that collaborative learning facilitated by the PVCC method is beneficial for students in programming education by improving understanding and problem-solving skills in programming.

关键词： computer science education programming training collaborative learning software engineering

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Game theory Presented to Italian High School Students in Connection with Infinity Computing 4th

Game Theory Presented to Italian High School Students in C...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Marotta, Corrado Mariano Melicchio, Andrea University of Calabria CS Arcavacata di Rende87036 Italy

ISBN: (纸本)9783031812460

Game theory is a rather vast discipline, the purpose of which is to analyze the strategic behaviors of decision-makers (players), or to study the situations in which different players interact pursuing common, different or conflicting objectives. The first purpose of this paper is to discuss the possibility of approaching elementary game theory in high schools. The second objective is to analyze the students’ response in terms of learning, but also of liking. Is it possible "giocare con la teoria dei giochi" (Engl. transl: play with game theory) in the classroom? The third objective is to highlight the possible links between elementary game theory and the grossone-based system introduced by Y.D. Sergeyev. Once again with the didactic aspect of providing high school students with an "easy" and stimulating approach to modern research fields in mathematics. In particular, we describe the approach to a little cycle of lessons of two classes and the performances of a conclusive students’ class test. We will see a great students response, above all regarding the connection between game theory and the grossone system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Game theory

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