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Ensemble Machine learning based performance prediction of electrical discharge machining for AISI D6 tool steel

Materials Today: Proceedings 2022年 69卷 720-724页

作者： Matheswaran, P. Nandhini, R. Sasikumar, R. Mathumathi, M. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Die and mould makers frequently employ AISI-D6 steel. First, 32 trials of electrical discharge machining (EDM) are carried out on the aforementioned material. A variety of EDM procedure performance measures, including tool wear ratio (TWR), material removal rate (MRR), and surface roughness (Ra), are taken into account, as well as the pulse time (Ton), pulse current (I), and pulse voltage (V). Operators are guided by process. Increasing MRR and Ra concentrations and decreasing TWR concentrations were found to increase pulse on-time values. As a result of increasing the pulse current, physical removal rate, tool wear ratio and surface unevenness increased. As a result of the increased voltage, the MRR, TWR, and Ra values were all reduced. The outcomes are predicted using ensemble machine learning models. The experimental data used to create estimation models for MRR, TWR, and Ra. In addition, an unknown set of experiments has validated the model-based predictions. © 2023

关键词： Surface roughness

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Developing a Translation Model to Bridge ASL and Indian Regional Languages Using Neural Networks

Developing a Translation Model to Bridge ASL and Indian Regi...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Swetha, M.S. Muneshwara, M.S. Venkatesh, Varnitha Kudremane, Shreyaa S. M. Nair, Ritika S Vismitha, H. Bms Institute of Technology & Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology & Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

In this resarch work, a real-time hand gesture detection system for Ame rican Sign Language (ASL) that enables the deaf-dumb co mmun ity to interact with others by translating sign language into text. The current methods for translating ASL to English primarily use computer vision tools like Media Pipe and OpenPose to recognise hand landma rks and motions. To tackle this issue, we provide a novel convolutional neural network (CNN) architecture that removes the need for manual feature engineering by automatically obtaining discriminative spatial-temporal characteristics from raw video streams. CNNs are more suited to handle the dynamic and highly changeable nature of hand movements because they are very good in simu Itaneously recording spatial and temporal informat ion, unlike LSTM (Long Short-Term Memory) networks, which are frequently emp loyed for sequential data. CNNs are perfect for rea l-t ime applications because they provide more efficient processing and faster inference time. By using multilingual language translators and advanced CNNbased deep learning models, we aim to reduce the communication gaps across India's diverse linguistic minorities. To ensure accurate performance evaluation and successful model learning, the dataset was divided into 70 % for tra inin g and 30 % for testing. Compared to tradit ional methods, the proposed approach has a 95% accuracy rate. With potential uses in public services, healthcare, and education, this program encourages assistive technology by offering a practical tool to improve communicat ion between sign language users and the general public. Through encouraging interlanguage contact, the system helps create a society that is more inclusive. © 2025 IEEE.

关键词： Convolutional neural networks

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Estimation of Clinical Parameters for Glaucoma Detection Using Image Processing and Deep Learning Techniques from the Biomedical Human Eye Images

Estimation of Clinical Parameters for Glaucoma Detection Usi...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Chandrappa, S. Dhasmana, Gulshan Naveen Kumar, H.N. Mathpati, Shilpa Gangodkar, Ninad Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engneering Bengaluru India Vidyavardhaka College of Engineering Department of ECE Mysuru India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (纸本)9798350376470

The technique for distinguishing the essential glaucoma is being exhibited in this segment utilizing a changed neural system with the back spread calculation ideas. The morphological administrators ideas are being utilized for the handling of the cup and disc, in the sense, the cup and the disc zones are discovered from which the proportion is registered, from where the illness can be recognition seeing the proportion. Utilizing an updated highlight extraction process, the highlights of the disc portion and the cup can be discovered, which could be utilized in the idea of cup to plate apportion (cup to disc ratio) recognition, regardless of whether the given test image is glaucomatic or not. Classification is also done using artificial neural networks (ANN) along with the training process. Simulations have been done in the Matlab environment by developing. m codes. Once the developed program is run, the simulation results are observed. From the simulation results, it can be observed that using the computed values of the cup to disc ratio, the severity of the disease can be computed (whether it is normal or moderate or severe glaucoma). Databases have been taken from the hospitals and on-line standard websites. The quantitative results are tabulated in a table. Finally, it was found that after taking a survey of 50 images, 16 were affected with normal, 16 were affected with moderate and the rest were affected with severe case of glaucoma. The Matlab results of the simulation done using coding demonstrated the profoundness with respect to the strategy proposed or developed in this exploration write-up using the concept of neural networks. © 2024 IEEE.

关键词： MATLAB

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Spectral Unmixing Using Shape-Driven Blind Deconvolution in Convolutional Autoencoder 13

Spectral Unmixing Using Shape-Driven Blind Deconvolution in ...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Banda, Nithish Reddy Galla, Akhil Samrat, B. Ghorai, Mrinmoy Indian Institute of Information Technology Sri City Computer Science and Engineering Department India

ISBN: (纸本)9798350395570

This research paper involves the integration of spectral shape information into a convolutional autoencoder (CAE) for blind unmixing in hyperspectral image analysis. We propose a novel approach that incorporates shape-based similarity measures in the CAE to capture and represent underlying spectral characteristics. Extensive experiments on benchmark datasets demonstrate improved quality in blind unmixing. The impact of architectural choices is also investigated, providing insights for optimal configuration at the end. This research contributes to advancing blind unmixing techniques in the perspective of shape-driven information and offers a promising avenue for enhancing accuracy and efficiency in hyperspectral image analysis © 2023 IEEE.

关键词： Convolution

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Efficient Low Light Video Enhancement Based on Improved Retinex Algorithms

Efficient Low Light Video Enhancement Based on Improved Reti...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Lee, Sung-Ling Yang, Shih-Hsuan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350313154

Videos shot in low-light environments suffer from low contrast and high noise. In this paper, an improved zero-reference low-light enhancement technique for videos based on the Retinex model is presented. The proposed method improves the existing Retinex approaches in several aspects. First, the image features extracted by the VGG network are employed as a part of the input to the generator of the Retinex parameters for increasing temporal stability. Second, a deformable convolution kernel is used to enhance the spatial correlation. Third, the optical flow between frames is approximated as a combination of affine linear transformations for reducing complexity. Compared with the state-of-the-art low-light enhancement algorithms, the proposed method achieves more favorable and stable image qualities in PSNR and SSIM with short processing time. © 2023 IEEE.

关键词： Linear transformations

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Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters 38

Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Dom...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zhao, Dong Zang, Qi Wang, Shuang Sebe, Nicu Zhong, Zhun School of Artificial Intelligence Xidian University Shaanxi China Department of Information Engineering and Computer Science University of Trento Italy School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Presently, pseudo-labeling stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-labels. However, we identify two key limitations within this paradigm: (1) under relatively severe domain shifts, most selected reliable pixels appear speckled and remain noisy. (2) when dealing with wild data, some pixels belonging to the open-set class may exhibit high confidence and also appear speckled. These two points make it difficult for the pixel-level selection mechanism to identify and correct these speckled close- and open-set noises. As a result, error accumulation is continuously introduced into subsequent self-training, leading to inefficiencies in pseudo-labeling. To address these limitations, we propose a novel method called Semantic Connectivity-driven Pseudo-labeling (SeCo). SeCo formulates pseudo-labels at the connectivity level, which makes it easier to locate and correct closed and open set noise. Specifically, SeCo comprises two key components: Pixel Semantic Aggregation (PSA) and Semantic Connectivity Correction (SCC). Initially, PSA categorizes semantics into "stuff" and "things" categories and aggregates speckled pseudo-labels into semantic connectivity through efficient interaction with the Segment Anything Model (SAM). This enables us not only to obtain accurate boundaries but also simplifies noise localization. Subsequently, SCC introduces a simple connectivity classification task, which enables us to locate and correct connectivity noise with the guidance of loss distribution. Extensive experiments demonstrate that SeCo can be flexibly applied to various cross-domain semantic segmentation tasks, i.e. domain generalization and domain adaptation, even including source-free, and black-box domain adaptation, significantly improving the performance of existing state-of-the-art methods. The code is available at https://***/DZhaoXd/SeCo. © 2024 Neural information

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Identifying Insider Cyber Threats Using Behaviour Modelling and Analysis 21

Identifying Insider Cyber Threats Using Behaviour Modelling ...

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21st OITS International Conference on information technology, OCIT 2023

作者： Akash, Tej Jayan, Alaka Gaur, Nimit Reddy, Saddikuti Vishnu Vardhan Manjith, B.C. Indian Institute of Information Technology Kottayam Department of Computer Science & Engineering Kerala India

ISBN: (纸本)9798350358230

Prevention of insider threats is challenging as credible associates of the organisation are involved and have access to confidential information. User behaviour modelling is crucial for detecting these anomalies. The suggested approach leverages a GRU-based autoencoder to model user behaviour based on users' session activities and identify anomalous events based on day-wise data. The technique consists of two steps. It initially calculates the reconstruction error using the GRU-based autoencoder on the nonanomalous data to determine the threshold used to identify outliers apart from regular data points. Then, anomalies are allocated to the identified outliers. The model creates substantial reconstruction errors for any unseen behaviour or abnormal pattern. © 2023 IEEE.

关键词： Statistics

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A Secured Framework for Emergency Care in the E-Healthcare System 2nd

A Secured Framework for Emergency Care in the E-Healthcare S...

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Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022

作者： Ansari, Aman Ahmad Mishra, Bharavi Gera, Poonam Department of Computer Science and Engineering LNM Institute of Information Technology Rajasthan Jaipur India

ISBN: (纸本)9789819935680

The e-healthcare systems provide many services to the patients and the users, including patient health record management, remote patient monitoring, and emergency response system. Nowadays, many e-healthcare systems include emergency care, but the services can only be available to registered patients. Ansari et al. recently proposed a secure and privacy-preserving framework for e-healthcare systems. The emergency phase of the framework provides emergency care only to patients with Wireless Body Area Network (WBAN). This paper extends the above framework to include unregistered patients for emergency care and registered patients without WBAN. The proposed scheme uses hash functions and elliptic curve cryptography (ECC) to provide mutual authentication and security, and it is evaluated using the AVISPA tool and traditional security analysis. The result of security analysis and simulation results demonstrate that the suggested scheme is protected from all known attacks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Data-driven COVID-19 Prediction Mechanisms: Recent Advancement and Open Issues 2

Data-driven COVID-19 Prediction Mechanisms: Recent Advanceme...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ali, Rajab Selwal, Arvind Sharma, Deepika Sabha, Ambreen Central University of Jammu Department of Computer Science and Information Technology Samba181143 India

ISBN: (纸本)9781665456302

COVID-19 pandemic has spread throughout the world and has resulted in several casualties since 2019. Moreover, it impacted on every aspect of society including the socio-culture, health, and economic sector of the countries. The accurate and timely detection of covid-19 disease may help to tackle the problem of further spread as well as saving numerous lives. The emergence of a computer vision-based paradigm has facilitated the more effective prediction of covid-19 disease thus making the job of doctors easier. This study explicates a focused investigation of state-of-the-art (SOTA) data-driven models for COVID-19 prediction (COV-19). A novel taxonomy is presented for the classification of COV-19 prediction approaches. This research work analyzes the image datasets with X-ray and CT scans of both the normal and COV-19 patients. A brief illustration of evaluation metrics for automatic COV-19 prediction systems is also expounded. Proposed study indicates that significant success has been achieved for the accurate and efficient detection of the COV-19 disease. However, designing of efficient deep-level models that need comparatively lower training overhead is a major open issue. Besides, insufficient datasets for learning deep convolutional neural networks (DCCN) model is a bottleneck for the researchers. Additionally, the generalization of the data-driven robust model that effectively detects multiple and unknown new variants of COVID-19 is a multifarious open research challenge. © 2023 IEEE.

关键词： COVID-19

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Rice Leaves Disease Diagnose Empowered with Transfer Learning

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computer Systems science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

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