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Computing (ICOCO), IEEE International Conference on

作者： Ijaz Khan Mohammed Hazli Mohamed Zabil Abdul Rahim Ahmad Nafaa Jabeur Department of Computing College of Computing and Informatics' Univsiti Tenaga Nasional Kajang Malaysia Information Technology Dept. Buraimi University College Al-Buraimi Oman Deparmtne of Computing and Informatics' Universiti Tenaga Nasional (UniTen) Kajang Malaysia Computer Science Dept. German University of Technology Muscat Oman

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier.

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Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance

arXiv

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arXiv 2022年

作者： Chen, Wenkai Liang, Hongzhuo Chen, Zhaopeng Sun, Fuchun Zhang, Jianwei Department of Informatics Universität Hamburg Germany Agile Robots AG Germany State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China

Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the accuracy of a 2D affordance mask, and the generated grasp candidates are restricted to a small workspace. To mitigate these limitations, we firstly construct a novel affordance-based grasp dataset and propose a 6-DoF task-oriented grasp detection framework, which takes the observed object point cloud as input and predicts diverse 6-DoF grasp poses for different tasks. Specifically, our implicit estimation network and visual affordance network in this framework could directly predict coarse grasp candidates, and corresponding 3D affordance heatmap for each potential task, respectively. Furthermore, the grasping scores from coarse grasps are combined with heatmap values to generate more accurate and finer candidates. Our proposed framework shows significant improvements compared to baselines for existing and novel objects on our simulation dataset. Although our framework is trained based on the simulated objects and environment, the final generated grasp candidates can be accurately and stably executed in the real robot experiments when the object is randomly placed on a support surface. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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A proposed framework for adoption green cloud computing in Saudi Arabia 2

A proposed framework for adoption green cloud computing in S...

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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Alharbi, Ebtesam H Alahrbi, Maryam M. Alkhamali, Sahar S. Taibah University College of Computer Science and Engineering Department of Information Systems Saudi Arabia

ISBN: (纸本)9781728154671

Green computing has gained the attention of academia, cloud providers, and governments. The advent of cloud computing has raised sustainability issues (e.g. high power consumption, carbon emissions). Sustainability is the concept of meeting our needs while reducing our impacts on the environment and the life of future generations. It has become a critical concern in the modern environmental life and cloud providers worldwide. Therefore, this paper considers the adaptation of green computing in Saudi Arabia. It proposes a framework that illustrates the factors that affect the adaptation of green computing for current and future cloud computing providers in Saudi Arabia. The framework can be also used as a guideline to ensure that green computing is achieved with best practices. © 2020 IEEE.

关键词： Green computing

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Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data

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computers in Biology and Medicine 2024年 179卷 108902-108902页

作者： Nakagaki, Riku Debsarkar, Shyam Sundar Kawanaka, Hiroharu Aronow, Bruce J. Prasath, V.B. Surya Graduate School of Engineering Mie University 1577 Kurima-machiya Mie Tsu514-8507 Japan Department of Computer Science University of Cincinnati OH45221 United States Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati OH45267 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States

In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas, a type of brain tumor, are classified into astrocytoma, oligodendroglioma, and glioblastoma. Astrocytoma and oligodendroglioma are also called low grade glioma (LGG), and glioblastoma is also called glioblastoma multiforme (GBM). LGG patients frequently have isocitrate dehydrogenase (IDH) mutations. Patients with IDH mutations have been reported to have a better prognosis than patients without IDH mutations. Therefore, IDH mutations are an essential indicator for the classification of glioma. That is why we focused on the IDH1 mutation. In this paper, we aimed to classify the presence or absence of the IDH1 mutation using WSIs and clinical data of glioma patients. Ensemble learning between the WSIs model and the clinical data model is used to classify the presence or absence of IDH1 mutation. By using slide level labels, we combined patch-based imaging information from hematoxylin and eosin (H & E) stained WSIs, along with clinical data using deep image feature extraction and machine learning classifier for predicting IDH1 gene mutation prediction versus wild-type across cohort of 546 patients. We experimented with different deep learning (DL) models including attention-based multiple instance learning (ABMIL) models on imaging data along with gradient boosting machine (LightGBM) for the clinical variables. Further, we used hyperparameter optimization to find the best overall model in terms of classification accuracy. We obtained the highest area under the curve (AUC) of 0.823 for WSIs, 0.782 for clinical data, and 0.852 for ensemble results using MaxViT and LightGBM combination, respectively. Our experimental results indicate that the overall accuracy of the AI models can be improved by using both clinical data and im

关键词： Classification (of information)

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Bootstrapping Human-Autonomy Collaborations by using Brain-computer Interface of SSVEP for Multi-Agent Deep Reinforcement Learning 3

Bootstrapping Human-Autonomy Collaborations by using Brain-C...

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3rd IEEE International Conference on Human-Machine systems, ICHMS 2022

作者： Ho, Joshua Wang, Chien-Min Chuang, Chun-Hsiang King, Chung-Ta Feng, Chi-Wei Chou, Tun-Hsiang Chen, Yen-Min Yang, Yu-Hsin Hsiao, Yi-Cheng Institute of Information Science Academia Sinica Taipei115 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan National Tsing Hua University College of Education Hsinchu30013 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan National Taiwan University College of Eecs Taipei106 Taiwan

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Human-Autonomy Teaming (HAT) has become one of the emerging AI trends due to the advances in sophisticated machine design that allows closer cooperation with humans while performing moral, reasonable, and applicable tasks as humans' most exemplary assistants. Based on HAT's pursuing the collective goal and sharing the authority between humans and machines, our research aims at answering whether humans' brain-computer interface (BCI) helps achieve efficient collaborations of human with Reinforcement Learning (RL) agents. How can it efficiently facilitate human-in-the-loop guidance to bootstrap the training of the agents? This study proposes a BCI-based system that interacts with RL agents as a human-in-the-loop teaming integration. The neural responses elicited by the Steady-State Visual Evoked Potential in BCI facilitate the collaboration of learning agents with humans and accomplish this goal in a game simulation environment. The results of our proposed system, NeuroRL, show significant improvement by reducing the non-stationarity of exploitations and explorations in the RL agents. With BCI-assisted human-in-the-loop, the rewards can be optimized during the early investigations to achieve more efficient convergence in the training. The novel design proposed in this study can extend the development of the emerging HAT field and knowledge-based RL systems for various applications in dynamic environments. © 2022 IEEE.

关键词： Reinforcement learning

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Enhancing the Uplink of Cell-Free Massive MIMO Through Prioritized Sampling and Personalized Federated Deep Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mendoza, Charmae Franchesca Kaneko, Megumi Rupp, Markus Schwarz, Stefan Technische Universität Wien Institute of Telecommunications Sustainable Radio Access Networks Christian Doppler Laboratory Digital Twin Assisted AI Vienna1040 Austria National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan Department of Computer Science University of Tokyo Tokyo Japan Technische Universität Wien Institute of Telecommunications Vienna1040 Austria

Effective power control is key to solving the inter-user interference problem that degrades performance in cell-free massive multiple-input multiple-output (MIMO) systems. Motivated by its ability to operate online and model-free, without relying on training datasets, we leverage deep reinforcement learning (DRL) for uplink power control, aiming to maximize the guaranteed rate. We propose a fully centralized single-agent framework and two distributed schemes that employ several agents for improved scalability, leveraging prioritized experience replay to enable fast adaptation to the dynamic changes of the wireless environment. We investigate the performance of two multi-agent system architectures: (1) centralized training, decentralized execution (CTDE), where each agent forwards its RL experience to a central trainer, and (2) personalized federated learning (FedPer), where the training is performed locally at each agent, and only the base layer of the local deep neural network (DNN) model is forwarded periodically for aggregation at a server. We focus on the realistic scenario of dynamic device (de-)activation, combined with user mobility. Numerical evaluations demonstrate that the proposed FedPer with prioritized sampling achieves near-optimal rate and power performance while incurring the least amount of communication overhead. © 2015 IEEE.

关键词： Deep reinforcement learning

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Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation

arXiv

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arXiv 2024年

作者： Zeng, Guangyang Zeng, Qingcheng Li, Xinghan Mu, Biqiang Chen, Jiming Shi, Ling Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen518172 China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose a two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination;In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimate. We prove that the proposed estimate owns the same asymptotic statistical properties as the ML estimate: The first is consistency, i.e., the estimate converges to the ground truth as the point number increases;The second is asymptotic efficiency, i.e., the mean squared error of the estimate converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Advanced AI Techniques for Autonomous Moving Object Detection and Tracking

Advanced AI Techniques for Autonomous Moving Object Detectio...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： G. Meenakshi Sundaram Nalluru Mourya Sai Eatesh Manjiri Ulhas Karande Warish Patel B Kannadasan Harshal Patil Department of Electronics and Communication Engineering Aditya college of Engineering Surampalem Andhra Pradesh India Computer science and business systems R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Padm. Dr. V. B. Kolte College of Engineering Malkapur Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of Civil Architectural Engineering and planning Kannadasan Balakrishnan Research Foundation Kalieswari nagar Peraiyur Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving objects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods based solely on image information face substantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuable resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional Neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to object tracking, the study delves into the capabilities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Object Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study’s findings contribute significantly to the advancement of AI techniques, enhancing the capabilities of autonomous systems for object detection and tracking in complex and dynamic real-world environments.

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Enhancing Predictive Accuracy in Cardiovascular Disease Diagnosis: A Hybrid Approach Using RFAP Feature Selection and Random Forest Modeling

Enhancing Predictive Accuracy in Cardiovascular Disease Diag...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Vikram Pasupuleti Bharadwaj Thuraka Chandra Shikhi Kodete V Priyadarshini Koti Mani Kumar Tirumanadham Vahiduddin Shariff School of Technology Eastern Illinois University Charleston IL USA School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA Department of CSE SRKR Engineering College Bhimavaram India Department of CSE Sir C R Reddy College of Engineering Eluru India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cardiovascular disease remains one of the leading causes of death all over the world and is characterized by ailments affecting cardiovascular structures, such as the heart failure, arrhythmia, and coronary artery diseases. It has multiple roots based on genetic influences on the disease, meals, drinks, physical inactivity, or presence of other diseases including hypertension, and diabetes. Heart diseases are still rampant in the world despite the development in the research on diseases and in healthcare. This is mainly because of the increasing average life span of individuals as well as increased cases of urbanization and adoption of poor-quality diets and leading inactive or sedentary lives. Such problems further escalate due to shortcomings in healthcare provision and preventive options by socioeconomic differences, especially affecting the needy populace. Hence, there is a rising focus on using complex technologies such as artifices and machine intelligence, along with big data analytics and predictive models, in handling these issues. These methodologies utilize computational methods and large-scale data to enhance risk assessment, optimize treatment pathways, and alleviate the overall burden of cardiac disease on healthcare systems and individuals. The concentration of this research is on the systematic application of rigorous data preprocessing, class imbalance correction using techniques such as SMOTE, and outlier management using methods such as IQR, in order to improve the effectiveness of predictive modelling. This study intends to improve predictive accuracy and model generalizability, thereby contributing to improvements in healthcare decision-making and patient care outcomes, by utilizing the RFAP hybrid technique for feature selection and Random Forest for modelling.

关键词： Accuracy Systematics Computational modeling Urban areas Medical services Predictive models Feature extraction Socioeconomics Random forests Standards

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CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans

CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Pl...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Deepanjali Chowdhury Ahana Roy Sreenivasan Ramasamy Ramamurthy Nirmalya Roy Department of Electrical and Computer Engineering Texas A&M University College Station United States Howard County Public School System Maryland United States Department of Computer Science Bowie State University Bowie United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

Managing a work-life balance has always been challenging, especially after the recent trend of working from home, which has made maintaining one's fitness and diet regime strenuous. Failing to adhere to a fitness and diet plan has been shown to cause long-term effects on a person's health including obesity and shortened lifespans. Currently, people plan their fitness and diet plans around their schedule, but these tasks are challenging to maintain. Users tend to give up these plans due to a lack of time and planning. To help with better planning for a fitness and diet regime, we propose, CHARLIE, a chatbot that works around a user's schedule to intelligently recommend diets and fitness goals based on their calendar. CHARLIE is a recommendation system that suggests the users' a certain meal and fitness activities based on the amount of time they have left in their day and the number of calories they lose.

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