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SSRN 2023年

作者： Thirumalraj, Arunadevi Aravinda, K. Revathi, V. Balasubramanian, Prabhu Kavin New Horizon College of Engineering Outer Ring Rd near Marathalli Karnataka Bengaluru560103 India Department of Computer Science Engineering K.Ramakrishnan College of Technology Tamil Nādu Trichy India Department of Data Science and Business Systems SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur India

The efficiency of brain-computer interface (BCI) schemes is severely hindered by the presence of duplicated data in multichannel electroencephalogram (EEG) signals. A channel selection technique improves the BCI's classification accuracy by eliminating unnecessary channels. The use of deep learning (DL) to create a BCI for motor imagery (MI) has quickly become a promising new way for the brain to communicate directly with computers and other electronic devices. In this research, we present a technique that uses CycleGANs to generate synthetic data in cycles. The transfer technique allows for the conversion of created signals from standard signals. The accuracy of the produced data is enhanced by adding a feature discriminator and incorporating the class activation mapping attention technique into the original CycleGAN generator. In addition, backpropagation is utilised to improve the produced signals' contour information, and the minimal feature representations. Furthermore, the optimisation performance of the basic grey wolf optimisation algorithm is enhanced by using four strategies to fine-tune the algorithm's hyper-parameters: variation. The results demonstrated an efficiency boost when visual presentation information was incorporated in the training data for BCI Competition IV 2a and 2b. The accuracy of the projected perfect was enhanced by 10.14% when training data was presented visually. To get the best results from your model, we don't advocate utilising any data other than motor imagery time for training. © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Simulated Annealing for Optimal Path Selection and Scheduling in Energy Routing for Smart Grids

Simulated Annealing for Optimal Path Selection and Schedulin...

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International Conference on computer and Applications (ICCA)

作者： Assala Nacef Lemia Louail Djamila Mechta Lina Benchikh Saad Harous LRSD laboratory Faculty of Sciences University Ferhat Abbas setif 1 setif Algeria Université de Lorraine CRAN F-54000 Nancy France Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah UAE

Technological advancements in electronics and power generation devices have led to the development of smart grids and the energy internet. Energy routing is a critical step in creating a reliable and functional energy system capable of harnessing the full potential of renewable energy. One of the major challenges in energy routing is the optimal path determination problem, which involves finding the path with the least power loss. An optimal path can alleviate network congestion and minimize energy costs for grid users. In this paper, we present an innovative path discovery protocol based on simulated annealing. We evaluate the efficiency of our protocol by comparing it to two other existing methods in the literature using simulation. The findings highlight the effectiveness of our approach in achieving optimal results for both large and small networks.

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Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology

arXiv

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arXiv 2024年

作者： Hussain, Zeshan Lam, Barbara D. Acosta-Perez, Fernando A. Riaz, Irbaz Bin Jacobs, Maia Yee, Andrew J. Sontag, David Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Division of Hematology and Oncology Department of Medicine Beth Israel Deaconess Medical Center Division of Clinical Informatics Department of Medicine Beth Israel Deaconess Medical Center Department of Industrial and Systems Engineering University of Wisconsin-Madison United States Division of Hematology and Oncology Department of Medicine Mayo Clinic AZ United States Department of Computer Science McCormick School of Engineering Northwestern University United States Division of Hematology and Oncology Department of Medicine Massachusetts General Hospital United States

Purpose: Evaluate how clinicians approach clinical decision-making when given findings from both randomized controlled trials (RCTs) and machine learning (ML) models. Methods: We designed a clinical decision support system (CDSS) that displays survival curves and adverse event information from a synthetic RCT and ML model for 12 patients with multiple myeloma. We conducted an interventional study in a simulated setting to evaluate how clinicians synthesized the available data to make treatment decisions. Participants were invited to participate in a follow-up interview to discuss their choices in an open-ended format. Results: The cohort contained 32 physicians. When ML model results were concordant with RCT results, physicians had increased confidence in treatment choice compared to when they were given RCT results alone. When ML model results were discordant with RCT results, the majority of physicians followed the ML model recommendation in their treatment selection. Perceived reliability of the ML model was consistently higher after physicians were provided with data on how it was trained and validated. Follow-up interviews revealed four major themes: (1) variability in what variables participants used for decision-making, (2) perceived advantages to an ML model over RCT data, (3) uncertainty around decision-making when the ML model quality was poor, and (4) perception that this type of study is an important thought exercise for clinicians. Conclusion: ML-based CDSSs have the potential to change treatment decisions in cancer management. Meticulous development and validation of these systems as well as clinician training are required before deployment. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

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Mobile Information systems 2022年第1期2022卷

作者： Omer, Saman M. Ghafoor, Kayhan Z. Askar, Shavan K. Department of Technical Information System Engineering Erbil Technical Engineering College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Computer Science College of Basic Education University of Raparin Kurdistan Region Ranya Iraq Department of Computer Science Knowledge University Kurdistan Region Erbil44001 Iraq Department of Software and Informatics Engineering Salahaddin University-Erbil Kurdistan Region Erbil44001 Iraq

In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms. © 2022 Saman M. Omer et al.

关键词： Image enhancement

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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A Novel Generalized Conjugate Gradient Framework: Unlocking New Optimization Methods and Insights

SSRN

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SSRN 2025年

作者： Onuoha, Oluwaseun Biodun Bamigbola, Olabode Matthias Omole, Ezekiel Olaoluwa Moghrab, Issam A.R. Ibrahim, Sulaiman Mohammed Halilu, Abubakar Sani Raso, Mario Mathematical Sciences Department Adekunle Ajasin University Akungba Nigeria Mathematics Department University of Ilorin Nigeria Mathematics Programme Department of Physical Sciences Landmark University Omu-Aran Nigeria Information Systems and Technology Department Kuwait Technical College Kuwait Department of Computer Science College of Arts and Sciences University Central Asia Naryn Kyrgyzstan School of Quantitative Science Universiti Utara Malaysia Kedah Sintok06010 Malaysia Faculty of Education and Arts Sohar University Sohar311 Oman Department of Mathematics Sule Lamido University Kafin Hausa Nigeria Department of Computer Science Sapienza University of Rome Italy Numerical Optimization Research Group Bayero University Kano Nigeria

Conjugate Gradient (CG) methods are efficient techniques in addressing large-scale, unconstrained optimization problems because of their efficiency and minimal storage requirement. This paper introduces a new family of generalized CG methods by linearly combining terms from the numerators and denominators of ten existing CG methods to propose a novel update parameter. We derive two new methods from this general framework and conduct a comprehensive numerical simulation against established methods. Numerical experiments on benchmark optimization problems show that one of the newly developed methods consistently surpasses conventional methods in both the speed of convergence and robustness. The second method competes favorably with existing methods. These findings indicate that the new class of generalized CG methods offers a promising direction for further research and practical applications in optimization techniques. © 2025, The Authors. All rights reserved.

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Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

arXiv

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arXiv 2023年

作者： Chen, Longfei Liu, Qianyu Zhang, Chenyang Huang, Yangkun Peng, Zhenhui Zeng, Haipeng Sun, Zhida Ma, Xiaojuan Li, Quan The School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging China The Department of Computer Science University of Illinois Urbana-Champaign United States Tandon School of Engineering New York University United States The School of Artificial Intelligence Sun Yat-Sen University China The School of Intelligent Systems Engineering Sun Yat-Sen University China The College of Computer Science and Software Engineering Shenzhen University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations. © 2023, CC BY.

关键词： Music

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Federated Quality Profiling: A quality evaluation of patient monitoring at the Edge

Federated Quality Profiling: A quality evaluation of patient...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Alramzana Nujum Navaz Mohamed Adel Serhani Hadeel T. El Kassabi Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates University Department of Computer Science and Software Engineering Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage the processing and analytics are moved close to where the data is located (e.g., the Edge). Data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, can mislead clinical judgments and cause incorrect actions, if not managed properly. In this paper, inspired by Federated Learning (FL), we propose a novel approach using Federated Data Quality (FDQ) Profiling to assess DQ at the edge considering a global quality profile aggregated based on local profiles. We conducted experiments to evaluate the effect of FDQ profiling on DQ improvement considering the assessment of outlier detection and unbalanced data. The results demonstrated that the improved DQ has a positive impact on the accuracy of four conventional machine learning models.

关键词： Wireless communication Patient monitoring Data privacy Image edge detection Data integrity Machine learning Sensors

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AR Intelligent Real-time Method for Cultural Heritage Object Recognition

AR Intelligent Real-time Method for Cultural Heritage Object...

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International Conference on Advanced Information and Communication Technologies (AICT)

作者： Oleh Pisnyi Ivan Kit Khrystyna Lipianina-Honcharenko Jürgen Sieck Anatoliy Sachenko Maciej Dobrowolski Grygoriy Sapozhnyk Master's degree in Computer Science West Ukrainian National University Ternopil Ukraine Department for Information-Computing Systems and Control West Ukrainian National University Ternopil Ukraine Hochschule für Technik und Wirtschaft Berlin Berlin Germany Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland

In a world of rapid technological changes and growing interest in cultural heritage, the development of innovative tools for travelers and history enthusiasts becomes incredibly important. Even in the digital age, interactive ways of exploring historical landmarks remain relevant and attractive. In this research, an AR Intelligent Real-time Method for Cultural Heritage Object Recognition has been developed. The method is implemented through a chatbot, which, when using a smartphone's camera, recognizes objects of cultural heritage and provides descriptions of these objects. The chatbot opens up new possibilities for travelers, allowing them to enjoy history and cultural heritage using intelligent tools and augmented reality.

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Advanced Signal Processing For IoT Device Communication In Smart Homes 3

Advanced Signal Processing For IoT Device Communication In S...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Malini, M. Jashva, Munigeti Benjmin Banu, M.Sheerin Jayanthi, N. Umarani, C. Babu, T.Harish Akshaya College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Malla Reddy University Department of Cse Telangana Hyderabad India R.M.K. Engineering College Department of Information Technology Chennai India Rmk College of Engineering and Technology Department of Physics Chennai India Alagappa Chettiar Government College of Engineering and Technology Department of Cse TamilNadu Karaikudi India Sethu Institute of Technology Electrical and Electronics Engineering India

ISBN: (纸本)9798331542375

This work focuses on intelligent home ventilation systems since they provide a fresh approach to the problem. By using renewable energy sources and cloud-based ANFIS architecture, the approach seeks to lower the energy consumption. The study focuses on the creation of a cloud-based cooling system able to maximize energy economy without sacrificing the degree of comfort inherent in the construction. The most important problem is not enough study has been done on how to include renewable energy sources into house cooling control systems. The described method combines a ANFIS system functioning in the cloud with solar power. This system may change the cooling power output in response to occupancy levels, humidity, and temperature as external factors. Conversely, unlike conventional cooling systems, the results imply that 10% of energy consumption might be cut without sacrificing the degree of comfort inside the structure. It is shown via the ANFIS approach that cooling power can be precisely controlled - a essential ability to maximize the use of ecologically friendly energy sources. © 2025 IEEE.

关键词： Smart homes

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