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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Sawadsitang, Suttinee Mahanan, Waranya Niyato, Dusit College of Arts Media and Technology Chiang Mai University Thailand College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350350548

Digital advancements have facilitated vast data storage and sharing capabilities, yet ease of access often neglects data sensitivity, leading to privacy risks and leaks. This is especially critical in healthcare, where staff may inadvertently expose patient information. Such lapses heighten the risk of exploitation, exemplified by widespread phishing and scams targeting personal data. To mitigate these risks, this paper introduces the Optimal data Privacy Protection (ODPP) system. We formulation a mix-integer programming problem subject to k-anonymity approach to generalize numerical data. The ODPP system helps a novice user automates data anonymization while minimizing total degree of information loss. We evaluate the system effectiveness with (i) sample data and (ii) healthcare dataset on stroke, hypertension, and heart disease, highlighting its potential to bolster data privacy across domains. © 2024 IEEE.

关键词： Anonymity

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Intelligent Chatbot for Hospital Recommendation System 2

Intelligent Chatbot for Hospital Recommendation System

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajkumar, K. Ragupathi, T. Karthikeyan, S. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Bangalore562112 India

ISBN: (纸本)9798350371055

The main objective of this project is to create a user-friendly intelligent chatbot for Medical guidance and support. This application has been classified into two different modes based on user preference, those are Online and Offline modes of interaction. In the Online mode of interaction, there is no need for any automation, there will be a real person at the other end answering user queries. Users can even ask for untrained data and will receive instant replies. This application is similar to a normal chat application, but the main disadvantage is that a real person is needed at the other end at all times. In the Offline mode of conversation which is entirely automated the user will be performing an interaction with the chatbot itself. The chatbot must consist of data sets and trained data collection, in this process there is no need for any real person, the chatbot itself will reply instantly based on the trained data. The chatbot will be more accurate when a large number of data is trained. This application is compatible with working in various browsers and varied networks. The Admin of the application plays a significant role. Admin can access all the given functions and is responsible for data training. data training plays a vital role in this application, the trained data will be used by the chatbot while answering user queries i.e. Offline mode. This chatbot application can be accessed by several users at the same time, where IP address plays the main role. The main function of this application is that user can post their corresponding queries and the application will be providing relevant information based on queries. The Latest Modern chatbots are used in applications where simple interaction is needed, for example, service and marketing technology to provide answers such as products, services, and consultancy. If the customer's queries don't match with the trained data sets, the customer will be escalated to manual interaction i.e. Online mode. © 2024 IE

关键词： Automated Intelligent Chat IP Address Offline mode Online mode Trained data

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A Study on Viability of Cryptocurrency Adoption in Qatar: A Thematic Coding Approach of Fifty Reviews

A Study on Viability of Cryptocurrency Adoption in Qatar: A ...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Al-Nassr, Asma Nasser M. N. Alazab, Moutaz Al-Siddiqi, Essa H. M. Oryx Universal College in Partnership with Liverpool John Moores University in Qatar Dept. of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

This study investigates the factors driving cryptocurrency adoption in Qatar, with a focus on creating a comprehensive taxonomy to guide policymakers and stakeholders in this emerging financial landscape. Given Qatar's pivotal role in the Gulf Cooperation Council (GCC) and the broader Middle East and North Africa (MENA) region, the paper is particularly significant. By examining cryptocurrency adoption through the lens of Islamic finance principles and regulatory considerations, the study offers valuable insights into this evolving financial domain. The research employs a thematic coding analysis of 50 literature sources, covering aspects such as regulatory frameworks, Sharia compliance, socio-cultural factors, and technological advancements, ensuring a well-rounded understanding of the topic. The findings reveal a complex interplay of factors influencing cryptocurrency adoption in Qatar, including the need for Sharia-compliant models, robust regulatory frameworks, and the impact of cultural and religious perspectives on financial innovation. Additionally, the study highlights the importance of addressing privacy and security concerns in this context. The research emphasizes the necessity of a balanced approach that tackles regulatory challenges, ensures Sharia compliance, and leverages technological advancements to facilitate successful cryptocurrency adoption. This study significantly contributes to the understanding of cryptocurrency adoption in the Islamic world and provides a strong foundation for future research and policy development in Qatar and similar economies. © 2024 IEEE.

关键词： Decentralized finance

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SAMIC: Segment Anything Model for Integrated Circuit Image Analysis

SAMIC: Segment Anything Model for Integrated Circuit Image A...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Ng, Yong-Jian Tee, Yee-Yang Cheng, Deruo Gwee, Bah-Hwee College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350350821

Circuit annotation is crucial in analyzing integrated circuit (IC) images for hardware assurance. While deep learning algorithms perform well in circuit annotation, they are highly reliant on labeled training data, which are extremely costly to obtain. The Segment Anything Model (SAM) excels in segmenting natural images but performs sub-optimally on IC images due to the domain gap between IC images and natural images. In this paper, we introduce SAMI C which extends the application of SAM for effective annotation of IC images. We curated an extensive dataset of IC images from four different devices and developed a novel training methodology for SAM models in IC image segmentation. Our experiments show that SAMIC outperforms the original SAM model by 36.78 % and improves accuracy by 6.35 % compared to the second-best technique. © 2024 IEEE.

关键词： Image annotation

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Correcting Pronoun Homophones with Subtle Semantics in Chinese Speech Recognition 30

Correcting Pronoun Homophones with Subtle Semantics in Chine...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Zhaobo Gan, Rui Yuan, Pingpeng Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9782493814104

Speech recognition is becoming prevalent in daily life. However, due to the similar semantic context of the entities and the overlap of Chinese pronunciation, the pronoun homophone, especially "他/她/它 (he/she/it)", (their pronunciation is "Tā") is usually recognized incorrectly. It poses a challenge to automatically correct them during the post-processing of Chinese speech recognition. In this paper, we propose three models to address the common confusion issues in this domain, tailored to various application scenarios. We implement the language model, the LSTM model with semantic features, and the rule-based assisted Ngram model, enabling our models to adapt to a wide range of requirements, from high-precision to low-resource offline devices. The extensive experiments show that our models achieve the highest recognition rate for "Tā" correction with improvements from 70% in the popular voice input methods up to 90%. Further ablation analysis underscores the effectiveness of our models in enhancing recognition accuracy. Therefore, our models improve the overall experience of Chinese speech recognition of "Tā" and reduce the burden of manual transcription corrections. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Speech recognition

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Smartphone Fundus Photography Enhancement for Retinopathy of Prematurity Disease Diagnosis Using Deep Learning 6

Smartphone Fundus Photography Enhancement for Retinopathy of...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Mutua, Elizabeth Ndunge Kasamani, Bernard Shwabwabo Reich, Christoph School of Computing & Engineering Sciences Strathmore University Nairobi Kenya Institute for Data Science Cloud Computing & It Society Furtwangen University Furtwangen Germany

ISBN: (纸本)9798350345162

Retinopathy of Prematurity (ROP) is the highest cause of childhood blindness globally with babies born preterm having a higher probability of contracting the disease. The disease diagnosis remains an economic burden to many countries, lack of enough ophthalmologists for the disease diagnosis coupled with non-existent national screening guidelines still remains a challenge. To diagnose the disease, a fundus photography is conducted, printout images are analyzed to determine the presence or absence of the disease. With the increase in the development of smartphones having advanced image capturing and processing features, the utilization of smartphones to capture retina image for disease diagnosis is becoming a common trend. For regions where ophthalmologists are few and/or for low resource regions with few or no retina capturing equipment, the use of smartphones to capture retina images for retina diseases is an effective method. This, however, is challenged: different smartphones produce images of different resolutions;some images are darker others lighter and with different resolution. A smartphone retina image capturing has a smaller field of view ranging between 450-900 which is a major limitation. A lens to support a bigger view can be combined with this approach to provide a wide view of 1300. This enlargement however distorts the image quality and may result in losing some image features. To overcome these challenges, this work develops an improved U-Net model to preprocess images captured using smartphones for ROP disease diagnosis. Our focus is to determine the presence or absence of the disease from smartphone captured images. Because the images are captured under a smaller field of view (FOV), we develop an improved U-Net model by adding patches to enhance image circumference and extract all features from the image and use the extracted features to train a U-Net model for the disease diagnosis. The model results outperformed similar recent developments with

关键词： convolutional neural network fundus photography retina vessel enhancement smartphone photography

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels 1

Comparison of Various Computational Models for Accurate Stro...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Arun, Chalana B Anusha, M. Kodipalli, Ashwini Gargi, N. Harish, L. Devamane, Shridhar B Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bangalore India School of Computing Amrita Vishwavidhyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Stroke is one dangerous illness that kills people. Stroke is so called because of the way it strikes people down. Across the globe, it is among the main causes of mortality and disability. It requires early identification and precise measures to be taken in order to prevent stroke and further damages to the brain and other parts of body. Our study investigates the use of machine learning techniques that includes Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Free Classifier, Random Forest Classifier, Naive Bares and multiple Boosting Classifiers. The 'Healthcare dataset Stroke data' is the dataset used, which is preprocessed and then trained upon by Ml. models. For Random Forest Classifier, the maximum accuracy observed was 94.91%, and upon Hyper parameter Tuning- there was rise in accuracy to 95.64%, Evaluation criteria like F1-score, precision, and recall provides additional evidence for the effectiveness of these models: Further in order to explain how each feature contributes differently to each prediction we have made use of two popular techniques for XAT, LIME and SHAP. LIVE and SHIP provides local, interpretable explanations and global explanations respectively. © 2024 IEEE.

关键词： Random forests

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Design of Pseudorandom Sequence Generator Based on Cross-Coupled Chebyshev Lattice 5

Design of Pseudorandom Sequence Generator Based on Cross-Cou...

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Su, Wenyi Zhang, Lu Wang, Yao Zhu, Liping Li, Guodong Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin541004 China Changji University School of Mathematics and Data Science Changji831100 China

ISBN: (纸本)9798350374407

With the widespread adoption of the internet, humanity has entered an irreversible era of information. Highquality pseudorandom numbers play a crucial role in ensuring secure communication, necessitating pseudorandom number generators with excellent performance. Therefore, research on pseudorandom number generators based on chaos theory is of vital importance. In this paper, a pseudorandom sequence generator based on the Cross-Coupled Chebyshev Lattice (CCML) model is proposed. CCML is a typical model in spatiotemporal chaos and exhibits better chaotic characteristics compared to low-dimensional chaotic systems. The generator first utilizes the classical Logistic map to generate the initial state values for each lattice cell. It then employs the Tent map to dynamically perturb the coupling coefficients between the lattice cells, enhancing the chaotic properties of the system. The local function in the model is a transformed Chebyshev map. The generator combines several one-dimensional chaotic mappings to demonstrate the excellent performance of a composite system. The pseudo-random sequences generated by the generator obtain high scores in the $0-1$ test, and the spectral entropy is significantly higher than that of other classical chaotic mappings, reaching a level close to that of white noise. It shows that the generator is capable of generating chaotic sequences with high complexity. © 2024 IEEE.

关键词： White noise

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