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Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

computers, Materials & Continua 2022年第6期71卷 5911-5931页

作者： Muhammad Adnan Khan Sagheer Abbas Ali Raza Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming *** is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place,the signal having more strength under the influence of emotions effects the response *** research inmultisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or ***,there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or *** this study,two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or *** was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.

关键词： Multisensory integration sensory enhancement depression emotions

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Mi-Go: Test Framework which uses YouTube as Data Source for Evaluating Speech Recognition Models like OpenAI’s Whisper

arXiv

引用

arXiv 2023年

作者： Wojnar, Tomasz Hryszko, Jaroslaw Roman, Adam Jagiellonian University Faculty of Mathematics and Computer Science Division of Software Engineering Lojasiewicza 6 Krakow30-348 Poland

This article introduces Mi-Go, a novel testing framework aimed at evaluating the performance and adaptability of general-purpose speech recognition machine learning models across diverse real-world scenarios. The framework leverages YouTube as a rich and continuously updated data source, accounting for multiple languages, accents, dialects, speaking styles, and audio quality levels. To demonstrate the effectiveness of the framework, the Whisper model, developed by OpenAI, was employed as a test object. The tests involve using a total of 124 YouTube videos to test all Whisper model versions. The results underscore the utility of YouTube as a valuable testing platform for speech recognition models, ensuring their robustness, accuracy, and adaptability to diverse languages and acoustic conditions. Aditionally, by contrasting the machine-generated transcriptions against human-made subtitles, the Mi-Go framework can help pinpoint potential misuse of YouTube subtitles, like Search Engine Optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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On the Stability of Strategic Energy Storage Operation in Wholesale Electricity Markets (Extended Version)

arXiv

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arXiv 2024年

作者： Navon, Aviad Belikov, Juri Orda, Ariel Levron, Yoash The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Israel The Department of Software Science Tallinn University of Technology Estonia

High shares of variable renewable energy necessitate substantial energy storage capacity. However, it remains unclear how to design a market that, on the one hand, ensures a stable and sufficient income for storage firms, and, on the other hand, maintains stable and affordable electricity costs for the consumers. Here, we use a game theoretic model to study storage competition in wholesale electricity markets. A main result is that these types of games are not necessarily stable. In particular, we find that under certain conditions, which imply a combination of a high share of variable renewable energy sources and low flexibility of conventional power plants, the system will not converge to an equilibrium. However, we demonstrate that a price cap on storage price bids can ensure convergence to a stable solution. Moreover, we find that when the flexibility of conventional power plants is low, while the storage usage for energy balancing increases with renewable energy generation, the profitability of using storage for the sole purpose of energy arbitrage decreases. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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QoE-oriented Soft Caching with Content Recommendation for Edge Computing Networks

QoE-oriented Soft Caching with Content Recommendation for Ed...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Chenyang Chen, Yan Sun, Chuan Jia, Bosen Wang, Xiaofei Taleb, Tarik Leung, Victor C. M. Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen518132 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Nanyang Technological University College of Computing and Data Science Singapore China Unicom Digital Technology Co. Ltd Beijing China Tianjin University College of Intelligence and Computing Tianjin China University of British Columbia Vancouver Canada

ISBN: (纸本)9798350351255

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE. © 2024 IEEE.

关键词： Mobile edge computing

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Robust Feature engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems

arXiv

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arXiv 2024年

作者： Gardezi, Syed Saim Jawed, Soyiba Khan, Mahnoor Bukhari, Muneeba Khan, Rizwan Ahmed Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naïve Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provides an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2024, CC BY-NC-ND.

关键词： Support vector machines

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Real-Time Detection of UAV Small Target's Infrared Imaging via OSTD-YOLOv8

Real-Time Detection of UAV Small Target's Infrared Imaging v...

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2024 IEEE URUCON, URUCON 2024

作者： Dahri, Fida Hussain Laghari, Asif Ali Dahri, Nisar Ahmed Abro, Ghulam E Mustafa Estrela, Vania V. De Oliveira, G.G. Iano, Y. Chuma, Euclides L. Southeast University School of Computer Science and Engineering Nanjing China Shenyang Normal University Software College Shenyang China University Teknologi Malaysia Faculty of Educational Sciences and Technology Johor Bahru Malaysia King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Aviation and Space Exploration Dhahran Saudi Arabia Department of Telecommunications Niteroi Brazil School of Electrical and Computer Engineering Campinas Brazil School of Technology Limeira Brazil

ISBN: (纸本)9798350355383

Unmanned aerial vehicles (UAVs) have boosted modern living. Tiny, frail high-density targets, low resolution, complicated backgrounds, noise, and poor real-time exposure performance have augmented due to UAV firms. Realtime recognition in high-altitude (HA) infrared thermal images is intricate. Our fresh OSTD-YOLOv8 is a multi-class target recognition tactic to tackle these issues and spot and classify objects on the HIT-UAV dataset. YOLOv8 is an effective advanced object detection model. Our OSTD-YOLOv8 model detects small objects in HA thermal images with 90% precision, 91.5% recall, 90.5% F1-score, 89.1% AP, and 98% mAP. Our unique approach delivers effective object recognition in difficult thermal imaging conditions. Consequently, this distinctive UAV-focused HA, thermal, target detection scheme can be used. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Latency and Secrecy Performance Analysis in Backscatter-Enabled Vehicular Cloudlet Networks

Latency and Secrecy Performance Analysis in Backscatter-Enab...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Van-Long Tien Bui, Thi My Phan, Van-Binh Ha, Dac-Binh School of Computer Science Duy Tan University Da Nang Viet Nam Quy Nhon AI Center FPT Software Science Avenue Binh Dinh Province Quy Nhon City Viet Nam Kien Giang Vocational College Faculty of Basic Sciences Viet Nam School of Engineering and Technology Duy Tan University Da Nang Viet Nam

ISBN: (纸本)9798331530839

In this work, we investigate the joint secrecy and latency performance of a multi-user backscatter communication (BC)-enabled vehicular network in the presence of a passive eavesdropper. Specifically, a number of vehicles intend to offload their tasks to a vehicle equipped with a mobile edge computing (MEC) server by utilizing dedicated BC communication and time division multiple access (TDMA) schemes. Meanwhile, a passive mobile eavesdropper attempts to intercept information from each user without any attacks. In order to investigate the performance of this system, we derive the closed-form expressions of the successful computation probability (SCP) and secrecy successful computation probability (SSCP) in two corresponding scenarios: with and without eavesdropper. In addition, the impacts of key parameters on the system's behavior to gain insight into its performance are also examined. Finally, the numerical results using Monte-Carlo simulations are provided to validate our analysis. © 2024 IEEE.

关键词： Time division multiple access

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Recognition of Handwritten Tamazight Characters Using ResNet, MobileNet and VGG Transfer Learning 23

Recognition of Handwritten Tamazight Characters Using ResNet...

引用

23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Manai, Elyes Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage. © 2024 IOS Press. All rights reserved.

关键词： Transfer learning

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Analyses of Parametric Relation in Ant Colony Approach for Robot Path Planning Problem

Analyses of Parametric Relation in Ant Colony Approach for R...

引用

IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Sudeep Sharan Anh Tong Ngoc Minh Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

One of the most challenging areas of robotics research is the mobile robot vehicle in a complicated obstacle environment. Recently, algorithms for autonomous mobile vehicles have largely been influenced by nature-based algorithms. The swarm behavior of many different organisms, such as ants, fireflies, honey bees, and foxes, is simulated by several algorithms in mobile robot research, and the results are highly interesting and inspiring. In this paper, we took the inspired algorithm as an ant colony which is used to solve the path planning problems for any mobile vehicle/robots. While studying and using the ant colony approaches to solve the problem, we found that the parameter selections are one of the important expects to make the results of any application worse or better. Therefore, we decided to perform the study on the parametric relation in an ant colony approach. The importance of utilization of the Ant Colony optimization (ACO) approach is to control the parameters. In this paper, we analysed/investigated the relationship between the different control parameters of the ACO algorithm and their performance in the resolution of path planning problem. Simulation is performed with different parameter values and by the result analysis, a selection of parameter is presented.

关键词： Ant colony optimization Path planning Organisms Mobile robots

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A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM

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Intelligence-Based Medicine 2025年 11卷 100206-100206页

作者： Naveed, Sajid Husnain, Mujtaba Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects. © 2025 The Authors

关键词： Drug response recommendation system Drug SMILES Gene expression Glioblastoma K-mer fragmentation LightGBM

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