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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Vignesh, T. Kanimozhi, K.V. Sathish, R. Kumar, R. Prasanna Jeyavathana, R. Beaulah Ezhumalai, P. Srm Institute of Science and Technology Department of Master of Computer Application Chennai Ramapuram India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India J.N.N Institute of Engineering Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Amrita School of Engineering Amritha Vishwa Vidyapeetham Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Kattankulathur India R.M.D Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665480352

In the computerized world the uniqueness of information security is fundamental. Biometric frameworks utilizing individual physiological or conduct ascribes which are turning out to be ordinarily normal in giving IT insurance and getting to powerless places, for example, air terminals, organizations, medical care, the military, and so forth A multimodal biometric framework that offers an individual personality base on different physical or interior attributes is liked to permit a biometric framework to work effectively in assorted applications and conditions. To additionally further develop security, multi-modular biometric confirmation could be utilized by defeating the requirements looked by unimodal biometric validation frameworks. The proposed work clarifies facial, unique finger impression and iris-based calculations, particularly in multimodal biometrics. In this first work a coded multi-work acknowledgment of the face under unusual lighting conditions has been executed. Face picture is taken as a contribution to this proposed work. Strategies dependent on normalization or enlightenment is proposed to accomplish great outcomes under lighting changes. The Gabor highlights, LBP elements and step similarity qualities are then gotten from pre-handled facial pictures. The removed attributes are combined with Z score level combination and primary handling. The facial pictures are perceived by the ANFIS classifier as indicated by the combined keys. The trial discoveries show that the proposed technique is showing improvement over the current framework. It doesn't, in any case, yield compelling shading face picture acknowledgment execution. Therefore, the determined attributes are consolidated at the level of the match to make the multi-biometric design and perform keyage utilizing a combination subject to thickness. LVQ is in this way used to coordinate with the example. The discoveries closed, comparative with current K -Closest Neighbor calculations (KNN), Support

关键词： Support vector machines

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An Improved Vision Transformer Architecture-Based computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiang-Rui Huang Huai-Jhan Ke Szu-Yu Kuo Liang-Bi Chen Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species.

关键词： computer vision Electric potential Accuracy Computational modeling computer architecture computer applications Flowering plants Network architecture Transformers Consumer electronics

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An Improved Vision Transformer Architecture-Based computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Huang, Xiang-Rui Ke, Huai-Jhan Kuo, Szu-Yu Chen, Liang-Bi Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (纸本)9798331521165

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Investigating the Effectiveness of Image Restoration Algorithms in Digital Image Processing

Investigating the Effectiveness of Image Restoration Algorit...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sidhu, Amritpal Kantak, Ganesh Kumar Naveena, S. Sathiya Shinde, Gitanjali R. Singh, Vaishali Zaidi, Taskeen Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vivekananda Global University Department of Mechanical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper examines the effectiveness of image healing algorithms for virtual photo processing. The research makes a specialty of different forms of picture recovery algorithms, inclusive of side-retaining methods and regression-based totally methods and their outcomes on photo nice. It proposes a quantitative assessment framework based on a hard and fast of goal metrics for the overall performance of these algorithms. The assessment framework includes simulations to acquire sensible dimension consequences. Outcomes have shown that the regression-based techniques outperformed the brink-keeping techniques in phrases of picture constancy and sharpness. The paper concludes that the proposed quantitative evaluation framework may be used to assess the performance of different picture restoration algorithms in digital picture processing. © 2023 IEEE.

关键词： Image reconstruction

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Understanding and Developing the Integration of Encapsulating Security Payload in Wireless Network Security

Understanding and Developing the Integration of Encapsulatin...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Haroon, Syed Jangid, Pradeep Kumar Singh, Jagtej Naveena, S. Sathiya Manisha, P. Singh, Vaishali Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India

ISBN: (纸本)9798350329773

The integration of Encapsulating security Payload (ESP) right into a wireless community safety device calls for careful know-how and improvement. ESP is a crucial security protocol which offers an authentication header, an integrity takes a look at, and encryption of payload information. It really works via encapsulating IP datagram's inside sub-protocols with cryptographic functionality, taking into consideration the secure transport of information throughout the network. At some stage in the mixing system, ESP should be configured such that it could offer the desired degree of security at the same time as meeting performance necessities. This typically entails the configuration of suitable cryptographic algorithms, key sizes, and cryptographic mode settings, as well as other parameters. As soon as integrated, ESP ought to be examined to make sure that it meets the desired requirements. This could be done by using evaluating its overall performance in opposition to a variety of attacks, or by means of going for walks a dedicated penetration testing suite. After success integration and trying out, ESP has to be monitored to detect any changes in its protection posture. Additionally, any recognized issues ought to be remediated in a timely manner to decrease the impact of any safety risk. © 2023 IEEE.

关键词： Integration

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Binju Saju Sahil Barwal V. Asha Shubham Kumar Neethu Tressa Nikhil T Department of Computer Science-AI Engineering Adi Shankara Institute of Engineering & Technology Kalady India Department of Master of Computer Applications New Horizon College of Engineering Bengaluru India

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%.

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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An approach Human Resource Information Systems (HRIS): A Hierarchical Deep Learning Framework for Predicting Employee Satisfaction

An approach Human Resource Information Systems (HRIS): A Hie...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Hannah, V. Betsy Tripathi, Pijush Kanti Elizarova, Lidiya Belidhe, Sandeep Rathnamma, M.V. Dineshkumar, P. St. Peter's College of Engineering and Technology Department of Master of Business Administration Tamilnadu India Haldia Government College Purba Medinipur Department of Geography West Bengal India Kuban State University Krasnodar Russia DevSecOps Telangana India Klm College of Engineering for Women Department of Computer Science and Engineering Andhra Pradesh Kadapa India Sona College of Technology Department of Information Technology Tamilnadu Salem India

ISBN: (纸本)9798331508685

The change from traditional techniques of human resource information management to urban computerization approaches was received with discontent by certain government personnel. Still, in this era of computerized governance, this shift is crucial since it improves the efficiency and effectiveness of human resource tasks. In light of this emerging tendency, the government has developed an HRMIS program specifically for use by government agencies. Human Resource Management Information System (HRMIS) is an integrated e-government service that acts as a public sector database and gives government workers a unified interface to do HR-related tasks. The suggested approach consists of two parts: preprocessing and training the model. Transferring data from an unstructured format to one that machine learning systems can work with is the goal of this phase. Data encoding and cleaning are part of the process, as is principal component analysis (PCA) for dimensionality reduction. It was the HDL that we used for training the model. We outperform state-of-the-art alternatives like CNN and DNN with our proposed model, which obtains an average accuracy rate of 93.58 percent. © 2025 IEEE.

关键词： Dimensionality reduction

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions

arXiv

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arXiv 2024年

作者： Eli, Aimina Ali Ali, Abida Master of Science in Information Technology School of Science & Technology Emporia State University Emporia KS USA Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Medical image analysis has emerged as an essential element of contemporary healthcare, facilitating physicians in achieving expedited and precise diagnosis. Recent breakthroughs in deep learning, a subset of artificial intelligence, have markedly revolutionized the analysis of medical pictures, improving the accuracy and efficiency of clinical procedures. Deep learning algorithms, especially convolutional neural networks (CNNs), have demonstrated remarkable proficiency in autonomously learning features from multidimensional medical pictures, including MRI, CT, and X-ray scans, without the necessity for manual feature extraction. These models have been utilized across multiple medical disciplines, including pathology, radiology, ophthalmology, and cardiology, where they aid in illness detection, classification, and segmentation tasks. This study offers a thorough examination of applications for deep learning in medical image processing, addressing both its achievements and obstacles. It underscores the relevance of deep learning in enhancing diagnostic precision in tasks including tumor detection, organ classification, and disease progression prediction. Additionally, we investigate sophisticated deep learning methodologies, such as generative adversarial networks (GANs) for picture creation and augmentation, alongside multimodal and federated learning for the integration of heterogeneous data sources and the improvement of model generalizability. Notwithstanding the encouraging outcomes, the domain continues to encounter obstacles like the necessity for extensive labeled datasets, model understanding, and regulatory impediments. We examine methods to tackle these issues and propose future avenues to enhance the influence of deep learning in clinical practice, especially regarding ethical considerations and real-time implementation. As deep learning advances, its incorporation into healthcare is poised to augment medical picture analysis and boost patient outcomes wo

关键词： Convolutional neural networks

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