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9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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Lightweight Attention Based Deep CNN Framework for Human Facial Emotion Detection from Video Sequences

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SN computer Science 2025年第1期6卷 1-12页

作者： Kant, Krishna Shah, Dipti B. Computer Science Dept. P.G Department of Computer Science and Technology Sardar Patel University V.V. Nagar Gujrat Anand 388120 India

Emotions with their intensities are associated with the action of humans which decides the behaviour of an *** recent research has gained enormous attention in the domain of emotion detection due to automatic facial emotion detection. The prime goal of facial emotion recognition (FER) is intended to map different facial expressions from video sequences to specific emotional states. Human facial emotion recognition (HFER) has captivated the attention of researchers for its excellent real time application which serves the society. This research work has proposed a novel lightweight attention based deep convolution neural framework for human facial emotion detection from video sequences on CK + dataset. A dense convolutional network (DenseNet-201) has been applied to strengthen feature propagation to find a solution to the vanishing-gradient problem, and significantly lower the parameter *** provide the model for better visual perceptibility, an attention block is applied between the convolution and dense layer. The proposed model achieves good accuracy of 98.8% while using less memory and processing in comparison to state-of-the-art approaches to reach good performance on CK + data set. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention network Deep convolution neural network DensNet-201 Emotion detection Human face detection Intensity Video surveillance

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A Hybrid Homomorphic Model with RSA Algorithm and Modified Enhanced Homomorphic Encryption Technique

A Hybrid Homomorphic Model with RSA Algorithm and Modified E...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Kamatchi, T.P. Anitha Kumari, K. PSG PTC. Dept. of Computer Computer Networking India PSG CT. Dept. of Information Technology India

ISBN: (纸本)9798350335835

Security for sending secret information over the network is the most demanding in today's reality for a wide range of applications. Many malicious attacks target confidential information. As a result, securing sensitive information on the Internet has become a serious challenge. Cryptographic algorithms are the most secure option when it comes to protecting against malicious assaults. Information can be safeguarded using a variety of approaches. Another big difficulty that still needs to be explored is safeguarding information in the cloud. Thus, the concept of homomorphic encryption technique arises to safely process the data in the encrypted format rather than on plain text. There are a variety of cryptography techniques, but this system utilizes Homomorphic Encryption to combine the new technique with the RSA algorithm, allowing us to conduct arbitrary operations on RSA while maintaining the security of the existing methodology. Our main goal in this system is to assure data security, so a new model suggested a method based on the RSA algorithm and Modified Homomorphic Encryption. © 2023 IEEE.

关键词： Smart city

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Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khan, Sumaiyya Bardekar, Aashish Sipna College of Engineering & Technology Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9798331517953

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content. © 2024 IEEE.

关键词： MATLAB

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A Futuristic Optical and Wireless Convergence Network Towards 6G and Beyond 4

A Futuristic Optical and Wireless Convergence Network Toward...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Molefe, Mlungisi Sibiya, Khulekani Nleya, Bakhe Durban University of Technology Dept. of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Emerging use cases and stringent requirements for 6G and beyond networks require new quantum leaps in mobile communication due to the demands of higher throughput, extensive bandwidth requirements, ultra-low latency and massive number of connected devices. The advancement in wireless communications and the advancement in optical networks will be essential in addressing the demands for future mobile networks by providing an innovative infrastructure for 6G and beyond networks. To support stringent requirements for 6G networks and beyond, new innovative technologies and systems are required that will combine optical and wireless networks. An innovative approach of converging optical and wireless networks promises to improve the network into new heights by creating a smart world with beyond-human cognitive communication capacity and speed, improved network scalability and enhanced energy efficiency. Research has shown that a promising architecture to achieve this goal, and cater for extremely high bandwidth and extremely low latency requirements is based on an All-Photonics Network. Therefore, in this paper, we propose an All-Photonics Network for the future to address bandwidth limitations, latency issues and massive connectivity. Furthermore, we propose an evolved infrastructure or architecture converging optical and wireless networks. Potential benefits for the evolved infrastructure are also discussed. © 2024 IEEE.

关键词： Bandwidth

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Mental State Report From Social Media Activity

Mental State Report From Social Media Activity

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ajit, Akshay Mathew, Crispin Denny, David Johns Devananth, H. Dhanya, P.M. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798331540685

This paper is about making user experience with social media better, by developing the complete analysis tool that would parse textual data from different platforms like Instagram, Facebook or Reddit. By developing a custom browser extension, the user-generated content is captured in real-time and can also be processed subsequently. The project comprises two key components: Emotion Detection and Hate Speech Detection. The Emotion Detection system uses sophisticated Natural Language Processing NLP techniques, such as BERT models to identify users' emotional state through their social media posts. This information is then used to develop a daily mental state report by which users are able to gain insights into their emotional health over time. Further, the Hate Speech Detection system leverages machine learning algorithms to detect and mark words that may be harmful or offensive in user- generated content. This feature enhances positive and inclusive Internet environment. The project goes beyond the analysis;it includes a pragmatic component suggesting specific tasks for users depending on their mental state reports. These recommendations are aimed at promoting positive interaction and enhancing the user's overall well-being. This project offers a comprehensive insight into users' emotional experiences on social media using state-of-the-art NLP and machine learning approaches. The combination of task recommendations and text summarization increases interaction among users, creating a pleasant atmosphere where people can enjoy using social media. The browser extension is a multipurpose tool that does not merely analyze but provides some improvements to the user's mental health and online activity. © 2024 IEEE.

关键词： Emotion Recognition

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Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer

Enhancing Brain Tumor Classification Using Data-Efficient Im...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yang, Jiafeng Rusak, Alena Belozubov, Alexander ITMO University Software Engineering and Computer Technology Dept. Saint-Petersburg Russia

ISBN: (纸本)9798350395044

Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tumor MRI images. We use a combination of three publicly available brain tumor datasets classified into four categories and appropriately modify existing transformer variants, including adjustments to patch size, number of categories, dropout, etc. The results indicate that, due to the condition of insufficient data volume, most transformer variants are still inferior to the benchmark model VGG-16. The DeiT model with knowledge distillation has significantly improved various indices on the test set, verifying its effectiveness in the classification of brain tumor MRI images under limited data. The article supports the application of transformer-based models to medical image analysis. Follow-up work will verify the generalization performance of the model on other datasets and explore methods to reduce the dependence of the transformer on the data, making it more applicable to tasks such as medical images classification. © 2024 IEEE.

关键词： Distillation

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Quality Assessment for 3D Coloured Point Clouds in Autonomous Vehicles 1

Quality Assessment for 3D Coloured Point Clouds in Autonomou...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatia, Sagar Brindha, M. National Institute of Technology Dept. of Computer Science and Engg. Tiruchirappalli India

ISBN: (纸本)9798350365016

This paper proposes a novel no-reference (NR) technique for evaluating the quality of 3D colored point clouds, notably for autonomous vehicle applications. Our technique intends to provide an automated and dependable framework for precisely measuring point cloud data quality by using color information, hence improving the assessment process's capacity to detect small deviations and abnormalities. Unlike existing approaches that rely heavily on geometric data, our methodology incorporates color information, which considerably enhances discrepancy detection and gives a thorough quality rating. Our proposed technique uses sophisticated deep learning models, specifically Convolutional Neural Networks (CNNs), to enhance the feature extraction and regression stages. By employing CNNs, we improve the model's capacity to detect detailed patterns in point cloud data, hence enhancing the accuracy and dependability of quality predictions. The technique is intended to be applicable across several domains, supporting high-fidelity applications such as entertainment, industrial quality control, and autonomous cars. It guarantees the consistency and dependability of input data, allowing for more accurate environmental perception and decision-making. The study also benchmarks against full-reference (FR) 3D-QA measures and compares the proposed NR methodology to existing methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Dataset Genreratoin for Semantic Segmentation from 3D Scanned Data Considering Domain Gap 9

Dataset Genreratoin for Semantic Segmentation from 3D Scanne...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Wada, Marin Adachi, Miho Ueda, Yuriko Miyamoto, Ryusuke Graduate School of Science and Technology Dept. of Computer Science Japan School of Science and Technology Meiji University Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350311402

An autonomous moving scheme with semantic information extracted from images captured by the monocular camera was proposed, providing accurate results of semantic segmentation. A training dataset must accommodate the moving environment to train a classifier for autonomous moving. However, preparing a large-scale dataset composed of images having pixel-wise manually-annotated class labels is impractical. We generated datasets automatically from 3D point clouds, for reducing manpower. The dataset had significant problems: domain gap and shadows. Therefore, style transfer is incorporated in the proposed scheme to bridge the gap to resolve this problem. Moreover, pseudo shadows were added to improve the classification accuracy of testing images with shadows. Experimental results using 3D point clouds and testing images taken around Tsukuba City showed that classification accuracy was improved by the proposed scheme. The classification accuracy of the sidewalk, the most significant class for autonomous moving at the Tsukuba Challenge, was 95.7%. © 2023 IEEE.

关键词： Semantic Segmentation

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