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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Harismithaa, L.R. Sadasivam, G. Sudha Psg College of Technology Dept. of Computer Science of Engineering Coimbatore India

ISBN: (纸本)9798331540364

Fetal health care is one of the most crucial concerns in today's world. Evaluating fetal well-being has always been challenging. Proper development and monitoring of the fetus are indispensable for its growth and healthy life. Monitoring and diagnosing abnormalities at the right gestational age is very significant. There are remarkable technical advancements in fetal well-being evaluation, bridging the gap between prenatal medicine and Artificial intelligence. This study provides an intensive review of various modes of non-invasive data that can be used for fetal health monitoring and decision support. Also, existing studies on fetal health care that use deep learning techniques for diagnosis and monitoring are explored. Also, a generic framework designed for an Automated Fetal healthcare Monitoring system using AI approaches is presented. © 2024 IEEE.

关键词： Fetal monitoring

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Metaphor Detection with Context Enhancement and Curriculum Learning

Metaphor Detection with Context Enhancement and Curriculum L...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Jia, Kaidi Li, Rongsheng College of Computer Science and Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798891761148

Metaphor detection is a challenging task for natural language processing (NLP) systems. Previous works failed to sufficiently utilize the internal and external semantic relationships between target words and their context. Furthermore, they have faced challenges in tackling the problem of data sparseness due to the very limited available training data. To address these two challenges, we propose a novel model called MiceCL. By leveraging the difference between the literal meaning of the target word and the meaning of the sentence as the sentence external difference, MiceCL can better handle the semantic relationships. Additionally, we propose a curriculum learning framework for automatically assessing difficulty of the sentence with a pre-trained model. By starting from easy examples and gradually progressing to more difficult ones, we can ensure that the model will not deal with complex data when its ability is weak so that to avoid wasting limited data. Experimental results demonstrate that MiceCL achieves competitive performance across multiple datasets, with a significantly improved convergence speed compared to other models. Our model is available at https://***/Evilxya/***. © 2024 Association for Computational Linguistics.

关键词： Curricula

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Deployment method of wireless sensor networks based on MaOEA/P-GM algorithm

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Soft Computing 2024年 1-10页

作者： Wang, Jun Luo, Dongxu Chen, Weiru Peng, Funan Li, Zhiwu College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Key Laboratory of Industrial Intelligence Technology on Chemical Process Shenyang University of Chemical Technology Shenyang110142 China College of System Engineering Institute Macao University of Science and Technology 00853 China

At present, wireless sensor networks are developing rapidly, but they will also face many challenges. For example, in the deployment problem, many problems such as cost, coverage, connectivity, energy, network life cycle and so on need to be considered, while the traditional multi-objective algorithm does not perform well in dealing with many-objective problems. How to quickly find high-quality solutions is the focus of research today. This paper proposes a deployment method for wireless sensor networks based on MaOEA/P-GM algorithm. This method improves the mutation strategy of the algorithm according to the characteristics of wireless sensor network deployment based on the many-objective algorithm MaOEA/P. Experiments show that using this algorithm in a many-objective environment can quickly get a high-quality solution set with fewer nodes, high coverage, good connectivity, and balanced energy consumption. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Life cycle

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Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

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computer Systems science & engineering 2022年第9期42卷 935-948页

作者： M.Manikandan R.Subramanian M.S.Kavitha S.Karthik Department of Computer Science&Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatore641035India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.

关键词： Cost effectiveness hybrid cloud optimal task scheduling virtual machine resource allocation make span

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Anomaly Detection of Industrial Data Based on Multivariate Multi Scale Analysis 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Lu, Dan Li, Siao Zhao, Yingnan Han, Qilong College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

Anomaly detection stands as a crucial facet within the domain of data quality assurance. Notably, significant strides have been made within the realm of existing anomaly detection algorithms, encompassing notable techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and anomaly detection models founded upon Generative Adversarial Networks (GANs). However, a notable gap lies in the inadequate consideration of interdependencies and correlations inherent in multidimensional time-series data. This becomes particularly pronounced within the context of industrial evolution, where industrial data burgeons in complexity. To address this lacuna, a novel hybrid model has been introduced, synergizing the capabilities of GRU with structural learning methodologies and graph neural networks. The model capitalizes on graph structural learning to unearth dependencies linking data points across distinct spatial dimensions. Concurrently, GRU extracts temporal correlations embedded within data along a single dimension. Through the incorporation of graph attention networks, the model employs a dual-faceted correlation perspective for data prediction. Discrepancies between predicted values and ground truth are utilized to gauge errors. The amalgamation of predictive and scoring mechanisms enhances the model’s versatility. Empirical validation on two authentic sensor datasets unequivocally demonstrates the superior efficacy of this approach in anomaly detection compared to alternative methodologies. A notable augmentation is observed particularly in the recall rate, underscoring the method’s potency in identifying anomalies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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A System Review on Fraudulent Website Detection Using Machine Learning Technique

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SN computer science 2023年第6期4卷 702页

作者： Saraswathi, P. Anchitaalagammai, J.V. Kavitha, R. Department of IT Velammal College of Engineering and Technology Madurai India Department of Computer Science Engineering Velammal College of Engineering and Technology Madurai India

At present, scams and malicious websites are one of the most widespread and dangerous problems on the website. It brings enormous economic suffering and irretrievable losses to companies and individuals. This approach can strengthen the Internet’s legitimacy and impose sanctions on criminals who engage in prohibited or malicious activities. However, governments still need a derivation to classify websites as dangerous or non-dangerous. However, several malicious and counterfeit goods are published on fraudulent websites to cheat consumers and make high and unfair profits. Due to the proliferation of such fraudulent websites, it is difficult to detect and identify them through manual inspection. Phishing attacks include various attacks, including spoofing malicious-based, DNS-based, data theft, email/spam, web-based delivery, and telephone-based phishing. We propose an integrated machine learning (ML) framework for fraudulent website detection to solve this problem. Artificial neural networks (ANN), support vector machine (SVM), random forests (RF), and K-nearest neighbor (K-NN) are algorithms to detect phishing websites accurately. Some URLs can be used to classify them as appropriate or phishing. Data from publicly available phishing websites can be collected from the UCIrvine ML repository for training and testing. Then, the results can be predicted using the features of the dataset. We conduct an in-depth literature review and propose methods for detecting phishing websites using ML methods. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN DNS KNN ML Phishing RF SVM UCIrvine URL

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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft Computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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Research on Microservice Identification technology Based on API Correlation 29th

Research on Microservice Identification Technology Based on ...

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29th International Conference on Computational and Experimental engineering and sciences, ICCES 2023

作者： Sun, Yang Lu, Dan Han, Qilong Yu, Cheng College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (纸本)9783031425141

Microservices are a new approach to software architecture design that enables complex monolithic applications to be constructed as a set of independent and loosely coupled chunks of services. A critical step in migrating monolithic architectures to microservices-based systems is the identification of the microservices contained in the target application. At this stage, most monolithic applications are highly cohesive and tightly coupled, which makes microservice decomposition difficult. Traditional microservice identification methods rely heavily on a priori knowledge or the intuitive experience of designers. Different identification methods require different data sources as a basis for extracting microservices, making it difficult to develop a uniform specification. This paper proposes a method to automatically identify microservices from a single system through the study of RESTful APIs, by introducing API topic semantic information similarity and response message information similarity as the criteria for microservice classification. The system consists of three parts: (1) Bert-whitening (Su et al., Whitening sentence representations for better semantics and faster retrieval, 2021) and Jaccard’s algorithm (Bennett et al., Proceedings of KDD cup and workshop, San Jose, CA, 2007) were used to calculate the overall similarity of the API, including the candidate topic similarity and the response message similarity. (2) constructing an API similarity graph with APIs as nodes and the overall similarity as the weight. (3) Spectral clustering algorithm is used to calculate scores and identify candidate microservices based on contour coefficients. A comparison with existing microservice classification methods proves the superiority of the method through experiments on six microservice open source projects and the results of the impact of hyperparameters on the quality of the experiments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Enhanced Triple Layered Approach for Mitigating Security Risks in Cloud

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computers, Materials & Continua 2025年第4期83卷 719-738页

作者： Tajinder Kumar Purushottam Sharma Xiaochun Cheng Sachin Lalar Shubham Kumar Sandhya Bansal Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaur135133HaryanaIndia School of Computer Science&Engineering Galgotias UniversityGreater Noida203201Uttar PradeshIndia Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Presidency School of Computer Science Presidency UniversityBangalore560089India Maharishi Markendeshwar Engineering College Maharishi Markandeshwar(Deemed to be)UniversityMullanaAmbala133203India

With cloud computing,large chunks of data can be handled at a small ***,there are some reservations regarding the security and privacy of cloud data *** solving these issues and enhancing cloud computing security,this research provides a Three-Layered Security Access model(TLSA)aligned to an intrusion detection mechanism,access control mechanism,and data encryption *** TLSA underlines the need for the protection of sensitive *** proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard(AES).For data transfer and storage,this encryption guarantees the data’s authenticity and ***,the solution employs the AES encryption algorithm to secure essential data before storing them in the Cloud to minimize unauthorized ***-based access control(RBAC)implements the second strategic level,which ensures specific personnel access certain data and *** RBAC,each user is allowed a specific role and *** implies that permitted users can access some data stored in the *** layer assists in filtering granular access to data,reducing the risk that undesired data will be discovered during the *** 3 deals with intrusion detection systems(IDS),which detect and quickly deal with malicious actions and intrusion *** proposed TLSA security model of e-commerce includes conventional levels of security,such as encryption and access control,and encloses an insight intrusion detection *** method offers integrated solutions for most typical security issues of cloud computing,including data secrecy,method of access,and *** extensive performance test was carried out to confirm the efficiency of the proposed three-tier security *** have been made with state-of-art techniques,including DES,RSA,and DUAL-RSA,keeping into account Accuracy,QILV,F-Measure,Sensitivity,MSE,PSNR,SSIM,and computation time,encryption time,and decryption *** proposed

关键词： Cloud security:data encryption AES access control intrusion detection systems(IDS) role-based access control(RBAC)

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