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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Wu, Shangxuan Luan, Wendi Wang, Yong Zeng, Dan Shen, Qiaomu Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore Singapore School of Artificial Intelligence Sun Yat-Sen University Guangdong Zhuhai China

ISBN: (纸本)9798400713958

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires significant manual effort. This paper fills the gap by integrating automated insight mining with interactive data visualization and striking a proper balance between them to facilitate insight discovery and exploration. Specifically, we regard data insights as a special type of data and further present InsightMap, a novel visualization approach that uses the map metaphor to provide a quick overview and in-depth exploration of different data insights, where a metric is proposed to measure the similarity between different insights. The effectiveness and usability of InsightMap are demonstrated through extensive case studies and in-depth user interviews. © 2025 Copyright held by the owner/author(s).

关键词： Video analysis

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Developing a More Adaptive and Dynamic Trust Evaluation System Using a Task Role Based Access Control Module (T-RBAC)

Developing a More Adaptive and Dynamic Trust Evaluation Syst...

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Data science and Business Systems (ICDSBS), International Conference on

作者： J. Umamageswaran Anish Shivakumar Achanta Srinivas Varshith Prabu M Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Tamil Nadu India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Due to the increasing adoption of cloud computing, which is adopted for its flexibility and cost-effectiveness, huge amounts of sensitive information are entrusted to cloud storage systems. However, a crucial point of contention in this growth is ensuring data security and confidentiality. Traditional cryptographic access control techniques provide a level of protection but are insufficient for guaranteeing complete trust. This paper deals with the persistent trust issues with access control methods, therefore, decreasing the security risks and increasing data owners' confidence regarding the data stored. We propose a new Task-Role based access model(T-RBAC), tailored to the cloud environment, with a holistic framework for trust evaluation, so that trust is dynamically evaluated with broad criteria for adaptation to different contexts and scenarios. Unlike most of the existing trust-based modules, the T-RBAC system combines real-time contextual factors with machine learning algorithms that predict trends of trustworthiness to aid in decision-making. The high security offered by our framework is also balanced with flexibility and scalability to allow seamless integration into existing cloud storage systems. The model will maintain long-term reliability through continuous monitoring and trust recalibration. Through extensive simulations and real-world case studies in healthcare, finance, and critical infrastructure, the computational efficiency and robustness of the T-RBAC system are measured. The T-RBAC framework, enhanced to counter the shortcomings of static RBAC models and existing modules of trust, represents an important leap forward in the security of clouds. The proposed model provides a balanced means of securing sensitive data, while enabling data owners to make better-informed decisions, thereby making way for innovative trust management in cloud computing

关键词： Access control Cloud computing Adaptation models Machine learning algorithms Computational modeling Scalability Medical services Data models Real-time systems Context modeling

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Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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Blockchain-Based Two-Stage Federated Learning With Non-IID Data in IoMT System

Blockchain-Based Two-Stage Federated Learning With Non-IID D...

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作者： Lian, Zhuotao Zeng, Qingkui Wang, Weizheng Gadekallu, Thippa Reddy Su, Chunhua The University of Aizu Department of Computer Science and Engineering Aizuwakamatsu965-8580 Japan Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210093 China City University of Hong Kong Department of Computer Science SAR Hong Kong Hong Kong Vellore Institute of Technology School of Information Technology and Engineering Vellore632014 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon

The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry. IoMT devices can detect patient signs and provide treatment guidance and even instant diagnoses through technologies, such as artificial intelligence (AI) and wireless communication. However, conventional centralized machine learning approaches are often difficult to apply within IoMT devices because of the difficulty of large-scale collection of patient data and the potential risk of privacy breaches. Therefore, we propose a blockchain-based two-stage federated learning approach that allows IoMT devices to train a global model collaboratively without gathering the data to a central server. Specifically, to address the problem of poor training performance on non-independent identically distributed (non-IID) data, we design a blockchain-based data-sharing scheme that can significantly improve the model's accuracy without threatening user privacy. We also design a client selection mechanism to further improve the system's efficiency. Finally, we validate the feasibility and effectiveness of our system through simulation experiments on three popular datasets (i.e., MNIST, Fashion-MNIST, and CIFAR-10). © 2014 IEEE.

关键词： Data privacy

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Reliability Analysis of DC EV Charging Stations for E-Mobility 50

Reliability Analysis of DC EV Charging Stations for E-Mobili...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Masum, Mahmudul Islam Chen, Sipeng Kiran, Dhruva Sankalp Hassan Franco, Kenny Behnamfar, Milad Amaan, Adil Tariq, Mohd Sarwat, Arif School of Computing and Information Sciences Florida International University Miami United States Department of Electrical and Computer Engineering Florida International University Miami United States Harman International Whitefield Bangalore560066 India

ISBN: (纸本)9781665464543

This research paper discusses the reliability analysis of Direct Current (DC) Electric Vehicle (EV) charging stations, focusing on their crucial role in advancing sustainable transportation. Utilizing Continuous Markov Processes, the methodology assesses the dependability of key components in DC charging systems, such as inverters, resonant circuits, transformers, and rectifiers. Real-world operational data and failure rates derived from MIL-HDBK-217 are employed for analysis, and the study utilizes the Markov process to model the system’s dynamic behavior, presenting results that affirm the high reliability (0.9993) of the DC charging infrastructure over a ten-year period. The impact of different Mean Time to Repair (MTTR) on system reliability is considered. The reliability analysis is conducted using dedicated software designed for Markov modeling and system reliability assessment. © 2024 IEEE.

关键词： Markov processes

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Predicting venous thromboembolism(VTE)risk in cancer patients using machine learning

Health Care Science

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Health Care science 2023年第4期2卷 205-222页

作者： Samir Khan Townsley Debraj Basu Jayneel Vora Ted Wun Chen-Nee Chuah Prabhu R.V.Shankar Department of Electrical and Computer Engineering University of CaliforniaDavisCaliforniaUSA Department of Computer Science University of CaliforniaDavisCaliforniaUSA School of Medicine Davis HealthUniversity of CaliforniaSacramentoCaliforniaUSA

Background:The association between cancer and venous thromboembolism(VTE)is well-established with cancer patients accounting for approximately 20%of all VTE *** this paper,we have performed a comparison of machine learning(ML)methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population,identified important features(clinical biomarkers)for ML model predictions,and examined how different approaches to reducing the number of features used in the model impact model ***:We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical ***:Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778±0.006(mean±SD)when trained on a set of 15 *** result is comparable with the model performance when trained on all features in our feature pool[0.779±0.006(mean±SD)with 29 features].Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%.We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based ***:From these findings,we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer *** results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML *** a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system,we were able to achieve an equivalent performance using a subset of only 15 features through stra

关键词： binary classification cancer machine learning pipeline VTE

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COMPLEX DYNAMICS OF A HOST-VECTOR DYNAMICS OF DENGUE INFECTION INCORPORATING OPTIMAL CONTROL STRATEGY WITH COST-EFFECTIVENESS: A FRACTIONAL-ORDER DERIVATIVE METHOD

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Journal of Mathematical sciences (United States) 2025年 1-30页

作者： Patra, Sathi Jana, Soovoojeet Adak, Sayani Majee, Suvankar Kar, T.K. Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur West Bengal Howrah 711103 India Department of Mathematics Ramsaday College Amta West Bengal Howrah 711401 India Centre for Soft Computing Research Indian Statistical Institute West Bengal Kolkata 700108 India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Karnataka Bengaluru 560035 India

Dengue is a resurging ailment infecting an estimated 100–140 million individuals annually. To understand the transmission mechanism of dengue, we define a nine-compartmental (six compartments for humans and three compartments for vectors) model incorporating fractional-order derivatives. Three different control measures, namely insecticide control for mosquitoes, treatment, and public awareness campaigns for the human population, are considered for the mathematical model to be proposed. We assess the existence and uniqueness of the solution and calculate the basic reproduction number (R0) using the next-generation matrix approach. It has been established that the proposed system has two equilibria viz: disease-free and endemic equilibrium, depending on R0. The stability of these equilibria has been investigated, considering R0 as the threshold parameter. Also, we have found that at R0=1, the system produces a transcritical bifurcation. Through numerical simulation with feasible parameter values, we demonstrate that employing controls decreases the infection, and all controls lead to a quicker removal of the disease. A cost-effectiveness analysis has been performed utilizing infection averted ratio (IAR) and incremental cost-effectiveness ratio (ICER) methods to determine the most cost-effective policy. Finally, the model has been calibrated using real dengue cases in Cambodia, one of the most dengue-prone countries, resulting in short-term predictions showing that the fractional-order system is more appropriate. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cost-effectiveness analysis Dengue Fractional-order calculus Short-term prediction of dengue Transcritical bifurcation

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Age-Aware Edge Caching and Multicast Scheduling Using Deep Reinforcement Learning

Age-Aware Edge Caching and Multicast Scheduling Using Deep R...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Seyedeh Bahereh Hassanpour Ahmad Khonsari Masoumeh Moradian Aresh Dadlani Galymzhan Nauryzbayev School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Computing Science University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The temporal nature of data in Internet of Things (IoT) networks necessitates periodic updates of cached content at edge devices, while multicasting dynamic content can enhance network efficiency. This paper addresses the challenge of joint cache updating and multicast scheduling in a cache-enabled, queue-equipped small base station (SBS) with limited cache capacity, which accesses a macro base station (MBS) to download (update) uncached (cached) content and serves requests through multicasting. We formulate a two-stage optimization problem to minimize the average age of information (AAoI) per request, subject to constrained average queueing delay and access rate. The first stage employs the Lyapunov drift-plus-penalty method at the SBS to schedule multicasting and downloading (updating) uncached (cached) content. The second stage, implemented at the MBS, leverages deep reinforcement learning (DRL) to determine the content replacement policy. Simulation results show that the DRL-based cache replacement policy yields up to 50%, 59%, and 60% improvements in AAoI compared to the maximum age, least-recently-used, and least-frequently-used baseline policies, respectively.

关键词： Wireless communication Base stations Schedules Simulation Multicast communication Deep reinforcement learning Delays

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Analysis of Cancer Gene Recognition System Based on Hybridaization of PSO and GSA Optimal Methodologies 1

Analysis of Cancer Gene Recognition System Based on Hybridai...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Rajkannan, K. Venkatesan, A. Thirunnavukkarasan, M. Karthikeyan, M. Rajakumar, P. Kumar, A. Suresh School of Computing Science and Engineering Galgotias Universtiy Delhi NCR India School of Electronics and Communication Engineering Galogotias University Delhi NCR India Vellore Institute Technology Vellore India Galogotias University Department of Civil Engineering Delhi NCR India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India

ISBN: (纸本)9798350341737

Rapid Growth of Gene expression from DNA or RNA can be recorded using a variety of approaches. The expensive microarray DNA technique generates high-dimensional data from a modest sample size. The problem with handling such a heavy dataset is that the learning model will become overfit. In order to resolve this issue, a significant reduction in the data source's dimensions is required. Machine learning has grown in popularity in genetic research during the past few years. Machine learning-based gene selection techniques have been proposed to improve the precision of dimensionality reduction. In recent years, a great deal of research has been done on the effectiveness of machine learning-based gene selection. Semi-supervised, unsupervised, and supervised feature selection methods are the three types available. A single experiment enables the analysis and investigation of hundreds and thousands of genes. Cancer, anemia, and other diseases linked with modern-day genes can be detected using this technology. By analyzing gene expression, it can identify the genes that are expressed differently, which may be responsible for the development of some diseases. It uses a variety of prediction models to show the differences between normal and abnormal genes. © 2023 IEEE.

关键词： Gene expression

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