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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Jagadam, Naveen Goyal, Mayank Kumar Ravulakollu, Kiran Kumar Sharan, Bhagwati Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Ceo N2force Technologies Inc. NapervilleIL60563 United States School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Technology Woxsen University Hyderabad India School of Engineering and Sciences Srm University Department of Computer Science and Engineering AP Amaravati India

ISBN: (纸本)9789380544472

Video game development process is different from other software projects while yet being related to them in software engineering. It is distinctive in that it incorporates the work of teams from several fields (art, music, acting, programming, etc.), and that it seeks to create a compelling gaming experience by using prototypes and iterations. This research study focuses on game development and how it is accomplished utilizing a game engine. The process of creating/developing games from a concept is known as game development. Game engines are software programs that help game developers create games. The literature study discusses about the various game engine. The utility of the 'Unity' gaming engine is investigated in this paper. Unity may be utilized in different fields as study subjects, but there can be certain frequent benefits & obstacles of utilizing it in different case studies. Students learn which game engine to use based on their specific requirements. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Search engines

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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A Deep Learning Method to Translate Phonocardiogram (PCG) Signals to Electrocardiogram (ECG) Waveforms 13

A Deep Learning Method to Translate Phonocardiogram (PCG) Si...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Chen, Wei Li, Zhi Zhang, Guanglie College of Computer Science and Software Engineering Shenzhen University Shenzhen China City University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China

ISBN: (纸本)9798350315196

In clinical practice, an electrocardiogram (ECG), which analyzes the electrical activity of the human heart, is a common cardiovascular diagnostic test. The ECG data can be stored and diagnosed effectively. However, precise positioning of the ECG electrodes is necessary for a reliable ECG tracing. This has an impact on how ECG monitors are used to check on patients at home. On the other hand, the Phonocardiogram (PCG) is a standard method for quantifying the properties of heart sounds in order to monitor and assess cardiac activity. With the advancement of wearable sensors, it is now simpler to record the heart's sound. We proposed a deep learning approach to translating PCG signals to ECG waveforms. This technique can be utilized to monitor cardiovascular disease in consumer wearable device development. The proposed method is evaluated on a benchmark dataset of normal and anormal cardiac data. Experimental results show that our proposed approach is reasonable and effective. The generated ECG outputs can achieve a high accuracy, and the root mean squared error (RMSE) is 0.053. © 2023 IEEE.

关键词： Electrocardiograms

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Research on Multi-Blockchain Electronic Archives Sharing Model

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computers, Materials & Continua 2023年第9期76卷 3921-3931页

作者： Fang Yu Wenbin Bi Ning Cao Jun Luo Diantang An Liqiang Ding Russell Higgs School of Information Engineering Qingdao Binhai UniversityQingdao266555China School of Computer and Software Dalian Neusoft University of InformationDalian116023China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China Archives Management Department QingdaoWest Coast New Area ArchivesQingdao266427China Editorial Research Department QingdaoWest Coast New Area ArchivesQingdao266427China School of Mathematics and Statistics University College DublinDublinIreland

The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric *** at the problem of dynamic permission management in common access control methods,a new access control method based on smart contract under blockchain is proposed,which improves the intelligence level under blockchain ***,the Internet attribute access control model based on smart contract is *** the dynamic access of heterogeneous devices,the management contract,permission judgment contract and access control contract are designed;Secondly,the access object credit evaluation algorithm based on particle swarm optimization radial basis function(PSO-RBF)neural network is used to dynamically generate the access node credit threshold combined with the access policy,so as to realize the intelligent access right management ***,combined with the abovemodels and algorithms,the workflow of electronic archives sharing and utilization model of multi blockchain is *** experimental results show that the timeconsuming of the process increases linearly with the number of continuous access to electronic archives blocks,and the secure access control of sharing and utilization is feasible,secure and effective.

关键词： Sharing and utilization of electronic archives dynamic permission management PSO-RBF neural network credit evaluation

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年第3期18卷 1308-1320页

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： Predictive Models Feature Extraction Business Adaptation Models Accuracy Long Short Term Memory Transformers Process Control Computational Modeling Process Monitoring Predictive Process Monitoring Remaining Time Prediction Process Mining Incremental Event Log Business Processes Event Log Prediction Model Prediction Accuracy Dynamic Changes Effects Of Characteristics Long Short Term Memory Static Model Prediction Framework Continuous Updating Incremental Process Reconstruction Scheme Neural Network Training Set Convolutional Neural Network Change Process Hidden Layer Combination Of Features Recurrent Neural Network Flow Control Neural Units Continuous Bag Of Words Mean Absolute Error Values Concept Drift Improve Prediction Accuracy Construction Of Prediction Model Dynamic Nature Of Processes Real Life Scenarios Prediction Task Incremental Learning

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PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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An Ultra-wideband Wide-beam Magneto-electric Dipole Antenna For Target Detection

An Ultra-wideband Wide-beam Magneto-electric Dipole Antenna ...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Zhitao Ye Wanchen Yang Dongxu Chen Haonan Tian Li Wei College of Computer Science and Software Engineering Hohai University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

This manuscript presents an ultra-wideband magneto-electric (ME) dipole antenna that owns wide half-power beamwidth (HPBW) in both the E- and H-plane. Firstly, by adopting two $\Pi$-shaped parasitic patches, the antenna features wider HPBW in both the E- and H-plane. Meanwhile, by selecting a pair of $\Gamma$-shaped differentially-fed probes, the impedance bandwidth of the antenna is obviously widened, moreover, it also effectively improve the pattern skewness in the E-plane. Furthermore, by using larger square patches, the impedance bandwidth of the antenna is further broadened. The results indicate that the antenna has a small footprint of $0.24 \lambda_{0} \times 0.32 \lambda_{0}$ (@8.8GHz), a very large $10-\mathrm{dB}$ impedance bandwidth of $81 \%$ (from 8.8 to 20.8 Hz), and very wide HPBW of $103^{\circ}-154^{\circ}$ and 81° 108° for the H -plane and E-plane from 9 GHz to 16 GHz. Due to its characteristics of ultra-wide band, wide beam, and small size, it has promising prospects for development in the field of target detection.

关键词： Microwave antennas Dipole antennas Magnetoelectric effects Bandwidth Object detection Terahertz radiation Ultra wideband antennas Impedance Probes Antenna radiation patterns

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ao Song Zhou Lei Shengbo Chen College of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments

关键词： Semantics Training data Production Learning (artificial intelligence) Knowledge representation Information filters Data models Internet Information technology Recommender systems

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Decentralized Self-Auditing Multiple Cloud Storage in Compressed Provable Data Possession

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yang, Yang Chen, Yanjiao Xiong, Ping Chen, Fei Chen, Jing Zhongnan University of Economics and Law School of Information Engineering Wuhan China Zhejiang University College of Electrical Engineering Hangzhou China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Wuhan University School of Cyber Science and Engineering Wuhan China

As cloud storage becomes popular, more and more users tend to outsource their data to powerful cloud severs. To prevent a single point of failure, users prefer to store data on multiple cloud servers from different cloud service providers. However, after outsourcing data to cloud servers, users lose the control of their data, which may incur many serious security issues, such as abnormal data tampering and deleting. It is necessary for users to audit multiple cloud storage aperiodically. In this paper, we aim to design a decentralized self-auditing solution for multiple cloud storage, in which cloud servers can audit the integrity of each other, and thus no third-party entity is required. Firstly, based on basic algebra, our protocol realizes decentralized self-auditing multiple cloud storage that only involves encrypted user data. Secondly, we design a proof exchanging mechanism, making any number of cloud servers form the same final integrity proof with a linear number of interactions. Thirdly, our solution can achieve cloud dynamics, which can freely enable and disable a cloud server to provide storage service. At last, security proof and performance evaluation show that the proposed protocol has provable security and high efficiency. © 2004-2012 IEEE.

关键词： Cloud security

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Attention-guided CenterNet deep learning approach for lung cancer detection

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computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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