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Influence of pH value on corrosion behavior of non-equiatomic Fe_(40)Mn_(20)Cr_(20)Ni_(20) high-entropy alloy

Journal of Iron and Steel research International 2025年第4期32卷 1114-1124页

作者： Zhen Wang Jun-feng Duan Jun-wei Qiao Hong-yan Che Xi-kou He Central Iron and Steel Research Institute Co. Ltd.Beijing100083China College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Key Laboratory of Interface Science and Engineering in Advanced Materials Ministry of EducationTaiyuan University of TechnologyTaiyuan030024ShanxiChina

The electrochemical performance and corrosion properties of Fe_(40)Mn_(20)Cr_(20)Ni_(20) high-entropy alloys(HEAs)in a 3.5%NaCl solution with various pH values were thoroughly *** results revealed a close correlation between the corrosion properties of HEAs and the pH *** the one hand,the pH value can affect the semiconducting properties of the passive film on the alloy surfaces,thereby further influencing the protective capacity of the passive *** the other hand,an excess of H+or OH−promotes the growth of localized corrosion,facilitating the formation of a larger number of larger-sized localized corrosion ***,the corrosion of HEAs is most severe in acidic solutions.

关键词： Fe_(40)Mn_(20)Cr_(20)Ni_(20)alloy Corrosion mechanism High-entropy alloy pH value Passive film

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Survival Analysis of Superficial Bladder Cancer patients using DeepSurv and Cox models 2

Survival Analysis of Superficial Bladder Cancer patients usi...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Supriya, K. Anitha, A. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

The advancements in technology help in analyzing and predicting the disease of human life using automation. Out of various technologies, Machine Learning (ML) and Deep learning (DL) provide some promising results to help humankind. Automated technologies help improve the prediction process of the healthcare system, especially in carcinoma detection based on past information. Thus, the proposed model made a survival analysis of the patients who are affected by bladder cancer (BC) based on their past health records. The tumor in the urinary bladder indicates that cancer among patients, when not treated can be spread to the other parts of the human body and cause death. However, the health records provide some linear combinations of the data to analyze the survival rate on treatments such as Placebo and Thiotepa. Thus, the proposed work utilized DeepMLPSurv and CoxPH survival model to make an analysis on the tumour recurrence and predicts the survival rate among the bladder cancer patients. © 2024 IEEE.

关键词： Diseases

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Enhancing User Authentication through Keystroke Dynamics Analysis using Isolation Forest algorithm 2

Enhancing User Authentication through Keystroke Dynamics Ana...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Iyapparaja, M. Karunanithi, Isvarya Sahana Bhat, U. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Authentication in modern era, has evolved significantly to address the increasing complexity and security challenges of our digital world. Traditional methods of authentication, such as passwords and PINS have proven to be vulnerable to various forms of attacks. As a result there is a growing need for innovative and robust authentication techniques. Keystroke dynamics involves the analysis of user typing patterns, including the time interval between keystrokes and the key pressed duration, to establish a unique biometric profile for each individual. Unlike traditional authentication methods keystroke dynamics offer several advantages, such as continuous authentication without the need for additional hardware and the ability to adapt to a user's changing typing behavior. This model will additionally analyze users typing patterns have the capability to identify individuals using the system by considering their typing speed. The first premise involves developing a system that can capture the keystroke dynamics of a user during a training phase and create a unique profile for that user. The second premise involves implementing Mahalanobis distance for Dynamic keystroke Authentication with various machine learning algorithms. This model can maximize the strengths of both methods to improve the accuracy and robustness of the authentication system. Authentication using keystroke dynamics is secured and convenient method and can be used wherever there is requirement of authentication such as baking, e-commerce and secured access control system. From the experiment performed below we can attained that the model functions exceptionally well using Isolation Forest with an accuracy of 90%. © 2024 IEEE.

关键词： Support vector machines

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Fake News Detection Based on Attention Mechanism 6

Fake News Detection Based on Attention Mechanism

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Wang, Zihao Zhao, Tongzhou Wang, Jiageng Li, Shiya School of Computer Science and Engineering & Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Currently, most multi-modal fake news detection methods rely on fusing multi-modal features using a single criterion, overlooking the potential contribution of original multi-modal information in fake news detection and classification. This paper proposes an attention-based multi-criteria evaluation model that strengthens text feature representation on the basis of using multi-criteria. The model utilizes ResNet for extracting image feature representations and weights the output of BERT hidden layers to obtain richer text features. Subsequently, text and image features are inputted into a multi-modal feature fusion module, and the resulting output is concatenated with pooled text and image features before being passed to the final classification module, which then outputs experimental results. Experimental results demonstrate that, compared to other baseline models, the proposed model achieves better performance on Weibo and Twitter datasets. © 2024 IEEE.

关键词： attention mechanism component fake news detection multi-criteria evaluation

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunakumari, B.N. Swain, Vikas Sharan, K. Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru560064 India

ISBN: (纸本)9798350370249

Efficiently finding similar items in large datasets is a crucial task with applications in various domains. This abstract presents a survey of algorithms and techniques designed for this purpose. Notable approaches include Minhashing, which employs hash signatures to estimate set similarity;Locality-Sensitive Hashing (LSH), a family of hash functions grouping similar items into common buckets;Cosine Similarity for Vector Spaces, suitable for data represented as vectors;Random Projection, a dimensionality reduction technique preserving pairwise distances;Word Embeddings, leveraging pre-trained embeddings for measuring similarity in text data;Bloom Filters, space-efficient structures for set-membership testing;Tree-based Methods like KD-trees and Ball-trees for efficient nearest neighbor searches;and SimHash, a method generating hash codes to represent similarity. To choose an algorithm the factors such as data characteristics, dataset size, and the desired balance between accuracy and computational efficiency should be considered. Experimentation with different techniques is recommended to determine the most effective approach for specific use cases. © 2024 IEEE.

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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Automatic Modulation Recognition with Breast Cancer Classification for Medical Internet of Things Applications 1

Automatic Modulation Recognition with Breast Cancer Classifi...

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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Nirmalrani, V. Poornima, D. Gowri, S. Chandrasekaran, Kumaresan Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350359756

Medical Internet of Things (MIoT) and healthcare have lately converged, which has led to novel techniques to identifying and treating medical troubles. This research gives a completely unique method that utilizes the Internet of Things (IoT) to combine Automatic Modulation Recognition (AMR) with Breast Cancer (BC) categorization. The loss of included answers arises from the reality that most current research ignores BC type in desire for both AMR or BC type. A unique Medical Internet of Things-based totally Breast Cancer (MIoT-BC) category method addresses the project. In order to facilitate automatic modulation identity, the proposed gadget first statistics indicators from clinical gadget and wireless verbal exchange channels. After that, it uses DBNs (deep belief networks) to categorize BCs. The MIoT-primarily based strategy ensures the secure transmission and instantaneous analysis of scientific facts, which improves the precision and timeliness of diagnosis. There are some of blessings to the use of MIoT for BC categorization, such as less difficult get entry to to health information, faster diagnostic instances, and the option for faraway monitoring. In addition, the potential for misclassification and human errors is reduced, making this a useful tool inside the realm of medical diagnostics. DBNs are used to give a dependable and correct type version for BC categorization. Several healthcare packages have found achievement with DBNs due to their suitability for processing excessive-dimensional, complicated scientific facts. Differentiating benign from malignant breast lesions is an area where their capability to extract sizeable facts from enter facts robotically shines. MIoT-primarily based processes the usage of DBNs enhance BC classification accuracy in comparison to traditional techniques, consistent with this paper. Evaluation criteria, such as sensitivity, specificity, accuracy, overall performance and F1 rating, measure the effectiveness of the suggeste

关键词： Internet of things

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment 5

Enhanced Machine Learning Approach for Detecting Spammers in...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bhavana, K. Sai Abhinandu, B. Sriyesh, P. Sai Rama, K. Satheesh Kumar, S. Institute of Aeronautical Engineering Dept. Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798331518097

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities. © 2024 IEEE.

关键词： Adversarial machine learning

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Recognition of Plant disease using Deep Learning Approaches

Recognition of Plant disease using Deep Learning Approaches

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Seshaiah, M. Shravya, D.K. Tarun, K.H. Vandana, R. Varshitha, R. S J C Institute of Technology Department of Computer Science and Engineering Chickballapur India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359688

Agriculture is a crucial turning point in the evolution of human civilization. Plant disease outbreaks have caused the biggest decline in agriculture, which has a substantial impact on the economy of the country and lowers the output of high-quality meals. Plant diseases have the power to eliminate species diversity and significantly reduce food output. Due to their invisible, often misconstrued symptoms and lack of readily available experience, plant diseases can be challenging for farmers to identify. This can delay identification and negatively impact crop productivity. The identification accuracy of image classification algorithms has significantly increased as a result of the early detection of various plant diseases using various detection methods. Deep learning methods have recently increased the precision of disease. Convolutional neural network, or CNN, models are used in the study that is being presented to identify disorders in plant leaves. The study leverages a dataset from Plant Village comprising 54,343 images categorized into 39 classes, each representing different plant species. For training, testing, and validation, we employed the previously trained Inception-V4 model. To achieve high accuracy in leaf disease detection, the Enhanced Deep Super Resolution Network (EDSR), bicubic interpolation models, and the Super Resolution Convolutional Neural Network (SRCNN) are used. © 2024 IEEE.

关键词： Plant diseases

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Personalised Emotion Detection from Text Using Machine Learning 6th

Personalised Emotion Detection from Text Using Machine Learn...

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6th EAI International Conference on computer science and engineering on Digital Transformation in Organizations: New Challenges, COMPSE 2022

作者： Bhavya, A.V. Dhanush, R.H. Sangeetha, J. Jose, Arun Cyril Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031347498

This research discusses an emotion recognition system, which is an important component of many effective computing technologies for natural language processing, based on open-source platforms with automatic speech recognition (ASR) and text analysis, and which is used for user-based customised sentiment analysis. PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser for text automatic analysis are used to design the proposed framework. Further, the dataset that is used for testing and training the model is from International Survey on Emotion Antecedents and Reactions (ISEAR). This research yields a user-dependent system that will function as a tailored assistant for identifying emotional responses and discovering innovative applications. The suggested model greatly outperforms the prior models, with an efficiency of 81% and an f-measure of 89%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Emotion Recognition

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