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Integrated Word2Vec-Based Speech Annotations for Enhanced EEG Decoding of Speech Intentions

Integrated Word2Vec-Based Speech Annotations for Enhanced EE...

引用

International Winter Workshop on Brain-computer Interface (BCI)

作者： Se-Na Jang Hyeong-Yeong Park Seong-Wook Kim Seong-Hyun Yu Minji Lee Ji-Hoon Jeong Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. The results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation.

关键词： Support vector machines Annotations Semantics Speech enhancement Linguistics Electroencephalography Brain-computer interfaces Decoding Linear discriminant analysis Automatic speech recognition

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Sentiment Analysis of Customer Feedback and Reviews in E-Commerce Systems

Sentiment Analysis of Customer Feedback and Reviews in E-Com...

引用

International Conference on New Trends in Computing Sciences (ICTCS)

作者： Olla Bulkrock Abdallah Qusef Ahmed BaniMustafa Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Natural Language Processing (NLP) is an effective tool for analyzing consumer satisfaction with products and services typically sold by e-businesses. This study explores the use of sentiment analysis to understand customer feedback and ratings using a dataset acquired by Amazon. That dataset covers online purchases over a period of eight years. The study aims to examine the factors that impact sentiment polarity throughout different periods and understand changes in consumer feelings and reviews towards online products and services. The applied method comprehensively studies sentiment, extracting features using the Term Frequency-Inverse Document Frequency (TF-IDF) and other text analysis and data processing procedures. Visualization is also applied to illustrate the trends, patterns, and changes in customer satisfaction over time and per product and line of products. The findings underscore the importance of sentimental research in discerning customer sentiments on online e-commerce platforms and offer practical recommendations for enhancing online and improving customer satisfaction.

关键词： Sentiment analysis Text analysis Reviews Social networking (online) Mood Customer satisfaction Market research Feature extraction Multilingual Electronic commerce

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Data-Driven Decision-Making in IT Projects: A Regression Application in software Estimation

Data-Driven Decision-Making in IT Projects: A Regression App...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Olla Bulkrock Ahmed BaniMustafa Abdallah Qusef Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

IT-related projects are among the most challenging to manage due to their inherent complexity, uncertainty, and the intangibility of their outcomes. However, a data-driven decision-making approach can help tackle these challenges and increase the chance of IT project success. Machine learning techniques can be used to predict uncertain aspects of IT projects, such as the estimation of time, cost, and effort, when applied to historical data collected from previous or similar projects. This approach can allow the project manager to make informed decisions based on facts rather than human judgment or hunch. In this work, we review the potential of data-driven approaches in the management of IT projects, and we demonstrate a use case that applies three regression algorithms, including polynomial regression, linear regression, and partial least squared (PLS), to predict software effort estimation based on the NASA93 public dataset. The best results were achieved using polynomial regression which scored 82% in R 2 , 81% in MAPE, 152 in MAE, 276 in RMSE and 76387 in MSE metrics.

关键词： Measurement Analytical models Reviews Computational modeling Decision making software algorithms Estimation Project management Polynomials software

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Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

Deep UAV Path Planning with Assured Connectivity in Dense Ur...

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IEEE Symposium on Network Operations and Management

作者： Jiyong Oh Syed M. Raza Lusungu J. Mwasinga Moonseong Kim Hyunseung Choo Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of IT Convergence Software Seoul Theological University Bucheon Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

关键词： Cellular networks 5G mobile communication Scalability Autonomous aerial vehicles Deep reinforcement learning Path planning

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia Nguyen, Duy H. N. An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798331507022

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance. © 2025 IEEE.

关键词： Convex optimization

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Ensemble Method For Fault Detection & Classification in Transmission Lines Using ML

Ensemble Method For Fault Detection & Classification in Tran...

引用

Annual IEEE Systems Conference

作者： Muhammad Hayyan Bin Shahid Akramul Azim Dept. Of Electrical Computer and Software Engineering Ontario Tech University Ontario Canada

Faults in a transmission line (TL) are the most common faults faced by almost every power station. Suppose these faults are not detected in time. In that case, they can result in multiple losses, such as a loss in an estimated power generation w.r.t predicted time and financial losses. In order to investigate the fault, the systematic approach of an engineer would be first to detect whether there is a fault or not. If a fault is detected in the transmission line, it should be classified as soon as possible. The following classifications would help the maintenance team identify the fault type: line fault, line-to-line fault, double line fault, triple line fault, single-line-to-ground fault, double line-to-ground fault, three-phase fault, and no fault. This paper proposes that the ensemble method, using the Machine Learning (ML) technique, will help the engineers detect and classify the faults in the transmission line. The investigation also trained and tested multiple ML classifiers to inform better recommendations. The shared research will help the user find the best possible ML results for predicting faults in the transmission line. Hence early and accurate fault detection will enhance safety and reliability and reduce interruption and downtime.

关键词： Training Power transmission lines Systematics Fault detection Mean square error methods Maintenance engineering Safety

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Deep Word Embedding and Language Model based Sentimental Analysis of COVID-19 Tweets

Deep Word Embedding and Language Model based Sentimental Ana...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Sobia Qaiser M. Usman Akram Dept. of Computer and Software Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The great Covid-19 pandemic affected billions of people's lives personally and socially. The research involves the analysis of public's views and opinions shared on Twitter social media platform related to Covid-19 pandemic and its detrimental or non-detrimental effects on public's mental health by using machine learning algorithm. The main purpose of this research includes analyzing public views related to Covid-19 pandemic by classifying the Tweets collected from the Twitter social platform. The proposed approach combines deep word embedding with MiniLM as an encoder to produce word vectors of high-dimensionality to preserve the words' semantic information. The resultant word vectors were used to train the model for the classification of the tweet in five sentiments i.e. Positive, Extremely Positive, Negative, Extremely Negative, and Neutral. The methodology is tested using publicly available Kaggle dataset as well as privately collected tweets. The comparative evaluation of the models revealed that MiniLM outperformed existing BERT based counterparts and attained highest accuracy of 93% with the Kaggle dataset. This analysis can assist the medical health authorities to monitor health information, conduct, and plan interventions to lower the pandemic effect and can help government to take precautionary measures.

关键词： COVID-19 Machine learning algorithms Pandemics Social networking (online) Blogs Semantics Government Mental health Vectors Monitoring

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A Robust Scheduling Algorithm for Overload-Tolerant Real-Time Systems

A Robust Scheduling Algorithm for Overload-Tolerant Real-Tim...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Amin Avan Akramul Azim Qusay H. Mahmoud Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

A real-time system is overloaded when all the tasks in a workload cannot meet their deadlines, and hence a robust algorithm is essential to maximize the number of tasks that meet their deadlines with the minimum number of miss rates and context switching. Although the Rate Monotonic (RM), Earliest Deadline First (EDF), and Least Laxity First (LLF) algorithms optimally perform and schedule tasks on a non-overloaded system, they have deficient performance when the system is overloaded. Therefore, we propose a new scheduling algorithm for uniprocessor and partitioned multiprocessor systems to address the overload situation. Since the proposed scheduling algorithm operates like EDF non-overloaded conditions, the proposed algorithm is optimal for non-overloaded systems. In addition, the proposed algorithm is robust against overloading situations as it executes the maximum possible tasks in the overload situation instead of missing deadlines of many tasks or burdening context switching to the system. The proposed algorithm allocates a processor to tasks based on the possibility of executing the task. The experimental results demonstrate that the proposed scheduling algorithm maximizes the number of tasks that meet their deadlines in overload conditions without a domino effect and context switching. In addition, the proposed algorithm achieves the lowest miss rate without context switching and the highest efficiency and processor utilization in the overloaded system compared with RM, EDF, and LLF.

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GPU Accelerated Modelling and Real-time Rendering of Fluid Motion

GPU Accelerated Modelling and Real-time Rendering of Fluid M...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： William Valentine Ramakrishnan Mukundan Dept. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes a fluid rendering pipeline that uses OpenGL-4 shaders to employ the parallel processing capabilities of the GPU. The fluid’s surface mesh is produced using tessellation shader stages where the input patches are assigned tessellation levels based on the fluid heightmap’s curvature. The curvature is stored using a texture buffer object which allows access by shaders, thus allowing the tessellation calculations to be carried out in parallel. Use of this adaptive tessellation method increases both the simulation’s framerate as well as its capacity to handle a greater number of primitives. Furthermore, it more optimally distributes the mesh’s vertices to effectively increase the level of detail without using more primitives. Polygon culling using the geometry shader further optimises the number of primitives used to define the fluid surface. The Phong-Blinn model is used for surface lighting. We propose two GPU-based fluid surface flow visualisation methods. Texture buffer objects can be used to store and update a surface texture. Alternatively, particle positions are updated each frame using the geometry shader and stored in a buffer object using transform feedback. These flow visualisation techniques are particularly effective for communicating the swirling motion of vortices.

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