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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Ishika Kumar, Anish Kaur, Rupinder Department of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350361155

By decentralising data processing and analysis, edge computing has become a significant paradigm for health monitoring systems. Edge computing allows real-time processing of health data, improving efficiency, scalability, and privacy by bringing computational resources closer to the source of data generation. This review paper offers a thorough analysis of edge computing-based health monitoring systems, covering their introduction, architecture, prospective security issues, and future scope. In addition to the security and access control measures implemented to protect sensitive medical data, we look at how these systems make use of a broad range of edge devices, sensors, and gateways. The potential of edge computing to transform healthcare delivery and enhance patient outcomes is highlighted in this paper through an analysis of current research trends and advancements. © 2024 IEEE.

关键词： Edge computing

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A Diagnosis Method of Non-Small Cell Lung Cancer Whole Slide Images Based on Multi-Dimensional Feature Fusion 5

A Diagnosis Method of Non-Small Cell Lung Cancer Whole Slide...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Hua, Jiajun Ding, Guangtai College of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350385557

The diagnosis of pathological images is the gold standard for lung cancer diagnosis. The pathological image diagnosis method based on deep convolutional neural network has achieved good results. However, for the whole slide image, the vast majority of processing methods are to divide it into small images and predict each small image separately, without considering the global spatial information. Therefore, this paper proposes a whole slice image diagnosis method based on multi-dimensional features fusion. Firstly, the sliding window was used to obtain small images, and the convolutional neural network was used to classify and predict them. Secondly, the prediction result with spatial correlation was formed by the feature fusion of the image patch and the surrounding related image patches, and it was mapped to the global space. On the self-built non-small cell lung cancer whole section image dataset, the performance effects of the three attention mechanisms on the small image classifier are compared, and the constructed feature fusion method is compared with the three feature fusion methods. The results show that the classifier of small images in our proposed method has better performance, with an accuracy of 97.5% and a recall rate of 98.2%. The feature fusion method we designed is more conducive to recognizing the tumor center region and tumor contour in the whole slice image. The proposed method combines image processing methods and deep learning methods, which is helpful to improve the performance of image recognition and provide more effective help for the diagnosis of pathological images. © 2024 IEEE.

关键词： Convolutional neural networks

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One_by_zero@NLU of Devanagari Script Languages 2025: Target Identification for Hate Speech Leveraging Transformer-based Approach 1

One_by_zero@NLU of Devanagari Script Languages 2025: Target ...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Chakraborty, Dola Hossain, Jawad Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798891762015

People often use written words to spread hate aimed at different groups that cannot be practically detected manually. Therefore, developing an automatic system capable of identifying hate speech is crucial. However, creating such a system in a low-resourced languages (LRLs) script like Devanagari becomes challenging. Hence, a shared task has been organized targeting hate speech identification in the Devanagari script. This work proposes a pre-trained transformer-based model to identify the target of hate speech, classifying it as directed toward an individual, organization, or community. We performed extensive experiments, exploring various machine learning (LR, SVM, MNB, GB, and ensemble), deep learning (CNN, LSTM, CNN+BiLSTM), and transformer-based models (IndicBERT, mBERT, MuRIL, XLM-R) to identify hate speech. Experimental results indicate that the IndicBERT model achieved the highest performance among all other models, obtaining a macro F1-score of 0.6785, which placed the team 6th in the task. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Text Recognition in Images and Converting Recognized Text to Speech - Image Processing 10

Text Recognition in Images and Converting Recognized Text to...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Singh, Yadwinder Sharma, Vikrant Singh, Arun Shruti Rakhra, Manik Singh, Prerna Yadidya, Desu Singh, Dalwinder Verma, Vikas Singh, Adwit Lovely Professional University Department of Computer Science and Engineering Phagwara India Graphic Era Hill University Department of Computer Science &engineering Dehradun India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Phagwara India Lovely Professional University Computer Science and Engineering Phagwara India University of Spain Department of Computer Science and Engineering Spain

ISBN: (纸本)9798350382471

This review article provides an overview of recent advancements in image-based text recognition and converting recognized text to speech using image processing techniques. The article covers various techniques for text recognition in images, including Optical Character Recognition, Convolutional Neural Networks (CNN), and Recurrent Neural Networks. Additionally, the process of converting recognized text to speech is discussed, including several Text-to-Speech (TTS) techniques such as concatenative TTS, formant synthesis, and parametric synthesis. © 2023 IEEE.

关键词： convolutional neural networks deep learning image processing machine learning natural language processing speech synthesis Text recognition

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Chaotic Convolutional Long Short-Term Memory Network for Respiratory Motion Prediction 4

Chaotic Convolutional Long Short-Term Memory Network for Res...

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4th International Conference on Image Processing and Vision engineering, IMPROVE 2024

作者： Ghasemi, Narges Nabavi, Shahabedin Moghaddam, Mohsen Ebrahimi Shekofteh, Yasser Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Science Viterbi School of Engineering University of Southern California United States

ISBN: (纸本)9789897586934

One of the challenges of treating lung tumors in radiation therapy is the patient's respiratory movements during the treatment, which lead to tumor motion. The goal of respiratory motion prediction is to predict the movements of lung tissues and lung tumors during the breathing cycle. Predicting respiratory movements allows radiation to be directed only at the tumor, minimizing exposure to healthy tissue and reducing the risk of side effects. Using 4D CT images, we can find the next position of the lung tumor and make a 4D radiation therapy plan. As obtaining 4D CT scans is harmful to the patient due to radiation, the aim of this study is to construct a 4D CT during a respiratory cycle using only a 3D image. In this paper, a Chaotic Convolutional Long Short-Term Memory network is proposed, which utilizes chaotic features in respiratory signals to predict pulmonary movements more accurately. The innovation of this method is paying attention to chaotic features of respiratory signals, which leads to better interpretability of the presented model. The obtained results show that the proposed method has a higher learning speed and better performance compared to previous models, which generate 4D CT scans. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Radiotherapy

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Machine Learning-driven Optimal Crop Selection in Response to Market Demand 1

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Rawat, Ankit Patwal, Akash Kumar, Ankit Manav Kumar, Sandeep Department of Computer Science and Engineering Tulas Institute Dehradun India

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

Sixty percent of Indians work in the field of agriculture, and traditional farming methods are used extensively in these endeavors (Patel and Rane in Crop recommendation system, 2023 [1]). This preference for conventional techniques has made it more difficult to maximize crop yields, which has hindered many farmers’ ability to gain financial profit from the crop yield. Choosing crops with little knowledge of the most profitable crop based on the environment as well as market conditions makes it harder for the farmer to gain the maximum amount of profit. In our study, we have used machine learning in the field of agriculture to provide improved information to the farmers to make them capable of making well-informed decisions that will help them gain more profit from their cultivated crops. Our research utilizes information such as soil nutrients, weather, and climate data, and market demand for the crop to build an intelligent system that can recommend the crop for the most optimal gains. In this research, we have utilized the machine learning algorithms such as Random Forest, Decision Trees, and Naive Bayes to improve accuracy of our recommendation system. The aim of this research is to increase the profit of farmers by providing them with the most optimized option of the crop to cultivate with precision. Our research highlights the optimal use of machine learning in the domain of agriculture and how it can be used to increase the profitability of farmers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Agriculture Crop recommendation system Machine learning NB—Naive Bayes Python RF—Random Forest

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A Novel Three-Way MAGDM Model Under Q-Rung Orthopair Hesitant Fuzzy Environment 10

A Novel Three-Way MAGDM Model Under Q-Rung Orthopair Hesitan...

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10th International Conference on Fuzzy Systems and Data Mining, FSDM 2024

作者： Chen, Yuanyuan Ma, Xiuqin Qin, Hongwu Wang, Yibo College of Computer Science and Engineering Northwest Normal University China

ISBN: (纸本)9781643685694

Q-rung orthopair hesitant fuzzy set (q-ROHFS) is a powerful instrument for addressing uncertainty problems. Nevertheless, the classification methods of three-way multi-attribute group decision-making (TWD-MAGDM) under this new model have been seldom researched, and the current TWD-MAGDM method in a hesitant fuzzy environment fails to consider the psychological behavior and fuzzy correlation of decision-maker, resulting in not enough distinction among classified objects. To resolve this issue, we present a novel TWD-MAGDM classification model for a q-rung orthopair hesitant fuzzy (q-ROHF) environment. Firstly, this paper considers the fuzzy correlation by allocating weight through Shapely and combines the prospect theory and Gaussian function to develop a preference function that can accurately describe the loss and gain. Based on this function, it presents a relative utility function that can more accurately measure the utility. Secondly, we provide a conditional probability that considers psychological factors and has enhanced recognition capabilities. Finally, a novel TWD-MAGDM classification model for q-ROHF is provided based on the new relative utility function and conditional probabilities. We subsequently verify the efficacy of the proposed approach. © 2024 The Authors.

关键词： Fuzzy sets

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Sustainable Water Management: A Regression-Based Approach to Predicting Demand and Distribution for an Indian Geography 8

Sustainable Water Management: A Regression-Based Approach to...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mahajan, Kshitij Satish Chidhananda, R.S. School of Computer Science and Engineering Rv University Bengaluru India

ISBN: (纸本)9798331505462

Urban water management in developing nations has provided substantial challenges in achieving the sustainable goals required to meet the needs of the ever-growing population. Effective water management is critical for urban sustainability, particularly in developing Indian cities facing severe water shortages. Using data from Telangana's water management system for 2022, this research makes use of two widely practiced regression models to check the feasibility for certain Indian geographies. Evaluation of the performance of these models aims to identify the more suitable approach of the two, given that the dataset is characterized by variability in geography and consumption patterns. This study aims to provide data for water management facilities by predicting key metrics that can be expected based on historic datasets. The findings highlight the potential of advanced machine learning techniques in developing geographies for sustainable water management solutions. Furthermore, the paper draws attention to the challenges that can be posed by the fluctuating water data variability of Indian cities and advo-cates for integrating predictive analysis into water management practices. © 2024 IEEE.

关键词： Sustainable city

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Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations

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SN computer science 2025年第2期6卷 1-13页

作者： Dangi, Dharmendra Dixit, Dheeraj Kumar Bhagat, Amit Rao, Durgesh Gupta, Jeetendra Kumar Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science and Engineering MITS Madhya Pradesh Gwalior India Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Applications Atal Bihari Vajpayee Vishwavidyalaya Chhattisgarh Bilaspur India

Human motion detection is a critical application of biomechanics, leveraging computer vision to analyse and interpret human activities. This technology is essential across various fields, including sports training, where it aids in monitoring and optimizing athlete performance, and security, where it plays a role in tracking movements in sensitive areas. In this study, we employ two distinct datasets: the Movi dataset and the MoCap CMU dataset. To address the challenge of limited data, we utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for data augmentation, generating new, synthetic images to enhance the diversity and quantity of the training data. Subsequent analysis was conducted using multiple deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), VGG16, AlexNet, and AlexNet with K-Fold Cross Validation. The application of these models yielded promising results, with accuracies of 87.5%, 89.9%, 90.2%, 91.2%, and 98.3%, respectively. The findings demonstrate the effectiveness of DCGAN in improving model performance through data augmentation, as well as the potential of these deep learning models to accurately detect and classify human motion. This research contributes to advancements in biomechanics and its applications in sports science and security domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomechanics DCGAN Deep learning Human motion detection K-Fold cross validation Transfer learning

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Integrating Deep Learning Frameworks for Automated Medical Image Diagnosis 8th

Integrating Deep Learning Frameworks for Automated Medical I...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. The study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. The process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. The study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. The research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP International Federation for Information Processing 2025.

关键词： Clinical research

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