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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Axcellin. T S. Kamalakkannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Traffic flow prediction (TFP) for intelligent transportation system is essential for effective transportation planning and city development. Conventional models often have difficulty with the complex nature of traffic data, which includes nonlinear patterns, spatial dependencies, and external influences such as weather or road conditions. Prediction accuracy may be significantly increased by utilizing an ensembling strategy, which combines the capabilities of many models. The goal of the study is to improve TFP accuracy by integrating the results of different machine learning (ML) models. The study uses Random Forest(RF) Naïve Bayes(NB), K-Nearest Neighbor(KNN), Extreme Gradient Boosting(XGBoost) and Support vector machine(SVM) as prediction models and based on the prediction probabilities and the score level fusion is performed with RF and XGBoost ensembling. The new fusion procedure uses a weighted average technique in which weights are assigned dynamically depending on individual model performance criteria including accuracy, precision, recall, and F1-score. Higher-performing algorithms are given greater significance in the final forecast, resulting in a dynamic and data-driven assembly strategy. The findings show that the efficiency of the ensemble of RF-XGBoost is higher compared to the contrasted methods. The model also uses feature selection approaches like mutual information to find the most significant traffic-related characteristics, lowering computational cost and increasing forecast efficiency.

关键词： Radio frequency Accuracy Computational modeling Heuristic algorithms Transportation Forestry Predictive models Feature extraction Vectors Data models

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Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

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computer Systems science & Engineering 2023年第5期45卷 1869-1886页

作者： V.Divya S.Sendil Kumar V.Gokula Krishnan Manoj Kumar School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia Department of Electrical and Electronics Engineering S A Engineering CollegeChennaiTamilnaduIndia Department of Computer Science and Information Technology CVR College of EngineeringMangalpallyHyderabadTelanganaIndia School of Computer Science University of Petroleum and Energy StudiesDehradunIndia

Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.

关键词： Deep learning optimization algorithm signal conducting system schizophrenia convolutional neural network mayfly optimization algorithm

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Optimized Hybrid Cryptographic Technique Using Symmetric and Asymmetric Encryption for Cloud Computing

Optimized Hybrid Cryptographic Technique Using Symmetric and...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： R. Suresh T.R. Nisha Dayana Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Cloud Computing (CC) is an expanding computing concept in which computing resources are given as Internetbased services. Security and confidentiality of cloud storage services are critical and pose a difficulty in cloud computing owing to loss of control over data and reliance on the cloud computing provider. While there is a big number of transmitting data in the cloud system, the risk of data access by attackers raises. CC requires encryption to maintain the privacy and security of data stored, processed, and transported on the cloud. Encryption ensures that confidential information saved in the cloud stays private and accessible only to authorized parties. The field of cryptography is continuously evolving to deal with current challenges in cybersecurity, confidentiality, and computing power. Optimizing encryption algorithms entails enhancing security, performance, and efficiency while reducing resource usage. This study suggests a optimized framework for encryption using RSA, an asymmetric key cryptography for key exchange and advanced / Encryption Standard (AES), a symmetric key cryptography for bulk message encryption to improve the efficiency of encryption. The suggested models's performance evaluated with RSA with Data Encrytpion Standard(DES) and RSA with BlowFish algorithms and the efficiency of RSA with AES is found to more in terms of Execution time, Throughput, memory usage and latency.

关键词： Cloud computing Data privacy Memory management Public key Throughput Data models Encryption Blockchains computer security Standards

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GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G

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Digital Communications and Networks 2023年第2期9卷 422-435页

作者： Nilesh Kumar Jadav Riya Kakkar Harsh Mankodiya Rajesh Gupta Sudeep Tanwar Smita Agrawal Ravi Sharma Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabadGujaratIndia Centre for Inter-Disciplinary Research and Innovation University of Petroleum and Energy StudiesP.O.Bidholi Via-Prem NagarDehradunIndia

The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply chains,and smart industries without any human ***,MTC has to cope with significant security challenges due to heterogeneous data,public network connectivity,and inadequate security *** overcome the aforementioned issues,we have proposed a blockchain and garlic-routing-based secure data exchange framework,i.e.,GRADE,which alleviates the security constraints and maintains the stable connection in ***,the Long-Short-Term Memory(LSTM)-based Nadam optimizer efficiently predicts the class label,i.e.,malicious and non-malicious,and forwards the non-malicious data requests of MTC to the Garlic Routing(GR)*** GR network assigns a unique ElGamal encrypted session tag to each machine partaking in ***,an advanced Encryption Standard(AES)is applied to encrypt the MTC data ***,the InterPlanetary File System(IPFS)-based blockchain is employed to store the machine's session tags,which increases the scalability of the proposed GRADE ***,the proposed framework has utilized the indispensable benefits of the 6G network to enhance the network performance of ***,the proposed GRADE framework is evaluated against different performance metrics such as scalability,packet loss,accuracy,and compromised rate of the MTC data *** results show that the GRADE framework outperforms the baseline methods in terms of accuracy,i.e.,98.9%,compromised rate,i.e.,18.5%,scalability,i.e.,47.2%,and packet loss ratio,i.e.,24.3%.

关键词： Garlic routing Blockchain I2P LSTM Artificial intelligence Onion routing

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Pragmatic Analysis in Supervised Machine Learning Methods for Heart Disease Risk Prediction

Pragmatic Analysis in Supervised Machine Learning Methods fo...

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International Conference on Inventive Computation Technologies (ICICT)

作者： G. Ezhilvani G. Thailambal Department of Computer science VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Heart disease has remained the leading cause of death globally over the previous two decades. In impoverished nations, inadequate resources and access to skilled medical personnel and diagnostic equipment make it difficult to properly diagnose and treat cardiac disease. One of the most important aspects of treating cardiac disorders is analyzing large datasets and using data to predict, prevent, and treat conditions such as heart attacks. It is typical for medical staff at the hospital to have differing degrees of training and experience when it comes to treating cardiac disease, which can have fatal consequences. The occurrence of heart disease can be predicted with the aid of several health parameter analysis. Various machine learning methods, like Decision Trees (DT), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), can be utilized for the heart disease prediction. This research delves into cardiac disease prediction by employing different algorithms, parameters, and outcomes.

关键词： Heart Logistic regression Machine learning algorithms Cardiac disease Nearest neighbor methods Bayes methods Decision trees Medical diagnostic imaging Diseases Overfitting

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Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games

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IEEE Access 2023年 11卷 18157-18182页

作者： Hsueh, Chu-Hsuan Ikeda, Kokolo Wu, I-Chen Chen, Jr-Chang Hsu, Tsan-Sheng Japan Advanced Institute of Science and Technology School of Information Science Ishikawa Nomi923-1211 Japan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan Academia Sinica Institute of Information Science Taipei11529 Taiwan

The AlphaZero algorithm achieved superhuman levels of play in chess, shogi, and Go by learning without domain-specific knowledge except for game rules. This paper targets stochastic games and investigates whether AlphaZero can learn theoretical values and optimal play. Since the theoretical values of stochastic games are expected win rates, not a simple win, loss, or draw, it is worth investigating the ability of AlphaZero to approximate expected win rates of positions. This paper also thoroughly studies how AlphaZero is influenced by hyper-parameters and some implementation details. The analyses are mainly based on AlphaZero learning with lookup tables. Deep neural networks (DNNs) like the ones in the original AlphaZero are also experimented and compared. The tested stochastic games include reduced and strongly-solved variants of Chinese dark chess and EinStein würfelt nicht!. The experiments showed that AlphaZero could learn policies that play almost optimally against the optimal player and could learn values accurately. In more detail, such good results were achieved by different hyper-parameter settings in a wide range, though it was observed that games on larger scales tended to have a little narrower range of proper hyper-parameters. In addition, the results of learning with DNNs were similar to lookup tables. © 2013 IEEE.

关键词： Reinforcement learning

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Ensuring Access Control Reliability and Security of Lightweight Blockchain-Based IOT Cloud-Based Electronic Medical Records Sharing

Ensuring Access Control Reliability and Security of Lightwei...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： S.J. Sujitha P Tamil Selvi Dept. of Computer Science Vels Institute of Science Technology and Advanced Studies

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

EMRs, or electronic medical records, and their accessibility have undergone a significant transformation as a result of the healthcare industry’s growing adoption of cloud computing & Internet of Things, or IoT, technology. In order to address the crucial safety and effectiveness concerns in sharing of fog-assisted Internet of Things based on the cloud EMRs, this research proposes a novel approach for constructing a lightweight control system for access based on blockchain. The outsourcing of secured electronic medical records, or EMRs, has come to be a vital component of the contemporary healthcare landscape, mostly due to the collection of data from several healthcare application and Internet of Thing devices. This strategy has unmatched benefits, such as increased accessibility, efficient interprofessional collaboration, and a significant decrease in computational operation expenses. The goal of this research project is to maximize the effectiveness of EMR sharing while simultaneously strengthening the security of sensitive medical data. The suggested framework leverages blockchain technology to provide an intuitive and safe access control mechanism for the Internet of Things, with assistance from Fog. By doing this, the system delivers strong resistance to unwanted access, transparent access, and data integrity. Privacy Preservation using cutting-edge encryption methods to safeguard medical data *** Protection implementing access control on blockchain to raise the general security of the system. Increasing Efficiency Making use of Fog-assisted IoT to reduce computing expenses and optimize data-sharing procedures.

关键词： Access control Cloud computing Medical services Blockchains Outsourcing Encryption Internet of Things

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Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

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computers, Materials & Continua 2022年第5期71卷 2599-2616页

作者： Muhammad Ibrahim Faisal Jamil YunJung Lee DoHyeun Kim Big Data Research Center Department of Computer Science and StatisticsJeju National UniversityKoreaJeju-si63243Korea Department of Computer Engineering(and Advanced Technology Research Institute) Jeju National UniversityJeju 63243Korea

In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security *** IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,*** these applications,fitness applications have been widely considered for smart fitness *** users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness ***,scheduling such a huge number of requests for fitness exercise is a big ***,the user fitness data is critical thus securing the user fitness data from unauthorized access is also *** overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling *** proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource *** performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection *** obtained results confirm the efficiency of the proposed scheduling *** investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource *** Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches *** obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

关键词： Load balancing resource scheduling task scheduling Internet of things blockchain fitness applications

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Shadocnet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal 48

Shadocnet: Learning Spatial-Aware Tokens in Transformer for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Xuhang Cun, Xiaodong Pun, Chi-Man Wang, Shuqiang University of Macau Department of Computer and Information Science China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology China

ISBN: (纸本)9781728163277

Shadow removal improves the visual quality and legibility of digital copies of documents. However, document shadow removal remains an unresolved subject. Traditional techniques rely on heuristics that vary from situation to situation. Given the quality and quantity of current public datasets, the majority of neural network models are ill-equipped for this task. In this paper, we propose a Transformer-based model for document shadow removal that utilizes shadow context encoding and decoding in both shadow and shadow-free regions. Additionally, shadow detection and pixel-level enhancement are included in the whole coarse-to-fine process. On the basis of comprehensive benchmark evaluations, it is competitive with state-of-the-art methods. © 2023 IEEE.

关键词： Document shadow removal transformer

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