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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Vijayakumar, T. Ramalakshmi, K. Priyadharsini, C. Vasanthakumar, S. Shaina Sharma, Abhishek Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Alliance College of Engineering and Design Department of Computer Science and Engineering Karnataka Benglure India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India GNA University Department of Computer Science and Engineering Punjab Phagwara144401 India Shri Vaishnav Vidyapeeth Vishwavidyalaya Madhya Pradesh Indore India

ISBN: (纸本)9781665489621

With the tremendous improvement of cloud computing (CC) technique, increasing count of users choosing to outsource image data to the clouds. Cloud resource provider decreases the storing problem on local hardware by outsourcing massive image database to cloud server and exploiting the cloud computation ability to image processing application. Learning effective feature representation and similarity measures are essential for the performance of content-based image retrieval (CBIR). Despite wide-ranging research efforts for decades, it remains a challenge that significantly hinders the success of real-time CBIR system. Since the trail and error hyperparameter selection is a tedious process, metaheuristic optimization can be used. This paper introduces an Evolutionary Optimization Algorithm for Cloud Based Image Retrieval System (EOA-CIRS) technique. The presented EOA-CIRS technique derives a new CBIR model for retrieving highly related images. To obtain this, the EOA-CIRS technique initially employs MobileNetv3 model as a feature extractor, which derives features from the query and database images. Next, the bamboo forest growth optimizer (BFGO) approach was applied as a hyperparameter optimizer to appropriately tune the hyperparameters of the MobileNetv3 approach. Finally, Euclidean distance based similarity measurement is utilized for retrieving the related images. The experimental validation of the EOA-CIRS model on Corel10K dataset demonstrates the effectual efficacy of the EOA-CIRS model over other recent approaches. © 2022 IEEE.

关键词： Search engines

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MIMTrack: In-Context Tracking via Masked Image Modeling 39

MIMTrack: In-Context Tracking via Masked Image Modeling

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Xingmei Nie, Guohao Meng, Jiaxiang Yan, Zining College of Computer Science and Technology Harbin Engineering University China College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)157735897X

Current Siamese and Transformer trackers commonly use various subtask branches like regression and classification to predict object states. Despite the demonstrated success, these subtask branches might introduce location and scale offsets due to discrepancies and misalignment in the respective predictions. To address this, we propose a novel generative tracker, MIMTrack, which defines tracking as a Masked Image Modeling (MIM) process combined with in-context learning (ICL). MIMTrack begins with building the visual prompt image, which consists of a template, a search area, and two target images associated with them. The target image transforms the bounding box into a unified RGB image space as other tracking image. All states prediction are naturally aligned by pixels generation of search target image. In light of this, we perform a MIM process within the visual prompt to reconstruct a masked search target image using the context from other parts. MIM with ICL makes use of implicit cross-relations between template and search area. A single-stream generative framework reduces the offset in the estimation. Furthermore, a latent memory module is introduced as a plugin to enhance pixel generation by leveraging various target appearances over time. The advanced performance observed on leading benchmark datasets highlights the simplicity and effectiveness of our MIMTrack framework. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Photointerpretation

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Explore the Future Earth with Wander 2.0: AI Chatbot Driven By Knowledge-base Story Generation and Text-to-image Model

Explore the Future Earth with Wander 2.0: AI Chatbot Driven ...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

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Support Autonomy: Exploring Player Perspectives on AI-Supported Onboarding in Video Games 25

Support Autonomy: Exploring Player Perspectives on AI-Suppor...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Choong, Lydia Cmentowski, Sebastian Kukshinov, Eugene Tu, Joseph Nacke, Lennart E. Hci Games Group Stratford School of Interaction Design and Business University of Waterloo WaterlooON Canada Industrial Design Eindhoven University of Technology Eindhoven Netherlands Cheriton School of Computer Science University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada

ISBN: (纸本)9798400713941

Video game onboarding faces the challenge of teaching game mechanics in a fun and engaging way. Artificial intelligence (AI) solutions have become a quick fix to help users understand technology. However, little is known about how AI supports player onboarding in video games. To address this knowledge gap, this research explores player perspectives on AI-supported onboarding. We conducted a qualitative user study (n = 20) to investigate player expectations, attitudes, and concerns about AI-supported learning experiences. Players learn primarily through the lived experience of a game and value personalized guidance during onboarding. Participants emphasized the importance of maintaining control over how AI is used during onboarding and the freedom to choose their support level. Our results suggest that players want future AI-supported onboarding systems to prioritize their agency, encourage active learning, and maintain transparency throughout the learning process. We contribute to game design research by proposing balanced, player-centric AI-supported onboarding experiences in video games. © 2025 Copyright held by the owner/author(s).

关键词： Active learning

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Smart Containers for Dieticians: Enabling Efficient Nutritional Monitoring through IoT 1

Smart Containers for Dieticians: Enabling Efficient Nutritio...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sridhar, T. Chandramma, R. Chokkanathan, K. Shreenidhi, H.S. Somashekhara Reddy, D. Mahesh, T.R. Alliance University Alliance College of Engineering and Design Bangalore India University Faculty of Engineering and Technology Bengaluru India Madanapalle Institute of Technology & Science Department of Ai Madanapalle India University Bengaluru India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Faculty of Engineering and Technology Kanakapura India

ISBN: (纸本)9798350359756

The IoT, refereed as Internet of Things is certainly changing people's lives. IoT has infiltrated practically every aspect of human life. With IoT, things are becoming smarter. The kitchen is such a space that has absorbed a great deal of technological progress in recent years and is one important part of our home. This project describes how to build and implement a smart kitchen using the internet of things. This paper explains about a smart container that will store daily staple foods like cereal grains (rice or any other grains). The suggested system comprises an integrated microcontroller board and an ultrasonic sensor to measure the container's level. The website developed can be used to monitor the change in the level of the container. © 2023 IEEE.

关键词： Containers

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Predicting Employee Under Stress Using Machine Learning

Predicting Employee Under Stress Using Machine Learning

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Thonduru Anjum Javeriya M Kabilan Golla Manoj Yarragonda Bhavya Sree Rongala Venkata Satya Jagadish Mahmood Hussain Mir Saswati Debnath B. M. Girish Bachelor of Technology in Computer Science and Engineering Alliance College of Engineering and Design Bengaluru Karnataka Bachelor of Technology in Information Technology Alliance College of Engineering and Design Bengaluru Karnataka Bachelor of Technology in Electrical and Electronic Engineering Alliance College of Engineering and Design Bengaluru Karnataka Department of Computer Science and Information Technology Alliance College of Engineering and Design Bengaluru Karnataka Department of Mechanical Engineering Alliance College of Engineering and Design Bengaluru Karnataka

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Because of COVID-19, businesses have had to change how they operate, like having people work from home or having fewer employees in the office. This “new normal” is sticking around for a while. Workers have had to adjust to these changes, which can make them feel tired and Pressure. Stress happens when people feel overwhelmed and uncertain. There are lots of things that can cause stress for workers, like getting paid less, having too much work to do, feeling lonely while working from home, working longer hours, not having a good place to work, not feeling like part of a team, feeling like they can’t do their job well, and worrying about getting sick. If bosses can recognize when workers are starting to feel stressed, they can help fix the problem, which can make things better for both the worker and the company.

关键词： COVID-19 Machine learning Companies Signal processing Stress Business

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Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms 8

Analysis of Sleep apnea Considering Electrocardiogram Data U...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Rajabrundha, A. Lakshmisangeetha, A. Balajiganesh, A. Electronic System Design Lab Velammal Engineering College India Department of Computer Science Engineering Velammal Engineering College India Velammal Engineering College Chennai India

Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employed to diagnose sleep disorders by using either in-home or laboratory-based comprehensive tests. The untreated OSA leads to deterioration in health, performance consequences with severity including daytime sleepiness, motor vehicle accidents, workplace errors, cardiovascular morbidity, and mortality. The pre-processed, interpolated and segmented ECG signal is considered for the examination of OSA. This paper focuses on three types of deep learning classifiers-based prediction models for detection of apnea from the ECG signal. The accuracy value of Long Short Term Memory model (LSTM) is 85 percent and classifier's ability to distinguish between normal and apnea events is *** Gated Recurrent Unit (GRU) classifier and Convolution Neural Network (CNN) model have an f1- score value of 0.80. The proposed LSTM model provides the optimal performance in comparison to other deep learning models used for classification with respect to area under the curve (AUC) and accuracy metrics. © Published under licence by IOP Publishing Ltd.

关键词： Electrocardiograms

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An Efficient Rainfall Forecasting System using Machine Learning Methods 2

An Efficient Rainfall Forecasting System using Machine Learn...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Kalaivani, K. Vijayakumar, N.C. Poovizhi, P. Selvapandian, D. SNS College of Engineering Department of Computer Science and Design Coimbatore India School of Commerce and Economics Presidency University Bengaluru India Dr.N.G.P. Institute of Technology Department of Information Technology Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397635

Precipitation expectation is hugely critical in day-to-day existence standard just as for water asset the board, stochastic hydrology, and rain run-off displaying and flood hazard relief. Machine Learning (ML) strategies can operate computational techniques and anticipate precipitation by extracting and integrating the obscured information from the linear and non-linear trends of previous atmosphere information. Different devices and strategies for estimating precipitation are at present reachable;however, there is as yet a paucity of precise outcomes. Earlier techniques are impending short at whatever point monstrous datasets are utilized for precipitation estimate. In this research, a few models and strategies were applied to anticipate the precipitation information Nellore Station, AP State, India. The relative review was led zeroing in on creating and contrasting a few ML models, assessing various situations and time skyline, and gauging precipitation utilizing two kinds of techniques. The anticipation approach uses four distinct ML calculations, which are Bayesian-Linear-Regression (BLR), Boosted-Decision-Tree-Regression (BDTR), Decision-Forest-Regression (DFR) and Neural-Network-Regression (NNR). Then again, the precipitation was anticipated on various time skyline by utilizing distinctive ML models which is strategy 1 (M1): Predicting Rainfall by Autocorrelation-Function (ACF) and technique 2 (M2): Predicting Rainfall by forecasting Error. The outcomes show that, two distinct strategies have been applied with various situations and diverse time skylines, and M1 displays a preferably high exactness over M2 utilizing BDTR demonstrating. © 2023 IEEE.

关键词： Rain

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Deep Rank-Based Average Pooling Network for Covid-19 Recognition

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computers, Materials & Continua 2022年第2期70卷 2797-2813页

作者： Shui-Hua Wang Muhammad Attique Khan Vishnuvarthanan Govindaraj Steven L.Fernandes Ziquan Zhu Yu-Dong Zhang School of Mathematics and Actuarial Science University of LeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Biomedical Engineering Kalasalingam Academy of Research and Education626126Tamil NaduIndia Department of Computer Science Design&JournalismCreighton UniversityOmahaNebraskaUSA Science in Civil Engineering University of FloridaGainesvilleFloridaFL 32608USA School of Informatics University of LeicesterUK

(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection *** the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy *** method firstly introduced an improved multiple-way data ***,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid ***,a novel DRAPNet was proposed based on NRAPM and inspired by the ***-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test *** sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,*** precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,*** F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,***,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious *** RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.

关键词： COVID-19 rank-based average pooling deep learning deep neural network

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A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

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Chinese Journal of Electronics 2025年第4期23卷 706-711页

作者： Jiarui Zhou Zexuan Zhu Zhen Ji College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.

关键词： Memetics Metabolomics Metaheuristics Urban areas Liver Prediction algorithms Search problems Physiology Vectors Classification algorithms

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