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A Differential Privacy K-Means Algorithm for Improving Privacy Budget Allocation 22

A Differential Privacy K-Means Algorithm for Improving Priva...

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Proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Sen Liu Jianhua Liu Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

As a privacy protection method with strict mathematical definition, differential privacy has been widely used in various fields of data mining including clustering algorithm. However, the traditional differential privacy k-means algorithm is sensitive to the selection of initial value, and the allocation of privacy budget is relatively single, which reduces the availability of the algorithm. In order to further improve the availability of the differential privacy K-means algorithm, this paper proposes a privacy budget allocation method combining error analysis to optimize algorithm iteration times and merge clustering, and carries out theoretical analysis and experimental verification at the same time. the results show that the algorithm not only satisfies the definition of differential privacy, but also improves the availability of clustering effectively.

关键词： data mining Differential privacy K-means algorithm Privacy protection

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Big data Analytics with Wild Horse Optimizer based Deep learning Model for Healthcare Management

Big Data Analytics with Wild Horse Optimizer based Deep Lear...

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international conference on Inventive Research in Computing Applications (ICIRCA)

作者： M. Vamsikrishna Rajasree RS Gopika G S D. Suganthi Md. Abul Ala Walid Ramu Kuchipudi Department of Information Technology Aditya Engineering College (A) Surampalem Andhra Pradesh India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Karnataka India Department of Computer Science Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

Big data analytics in health service is the procedure of research in huge and different datasets, designed for uncovering hidden patterns, correlations, and trends for making the best decisions in medical field. the health and medical services are developed modern and smart healthcare platforms are made the analysis further robust for the treatment. the proper diagnosis of health records depends on primarily disease recognition and the value of accuracy is decreased if the clinical data quality is worse. But, the existing methods failed to use the learning model for handling heterogeneous healthcare information. therefore, this article introduces Big data Analytics with Wild Horse Optimizer based deep learning (BDAWHO-DL) model for Healthcare Management. the proposed BDAWHO-DL technique investigates the big data in medical field and makes decisions. To do so, the BDAWHO-DL technique exploits attention based long short term memory (ABLSTM) method for data classification purposes. Moreover, the WHO system was used for the optimal hyperparameter tuning procedure of the ABLSTM algorithm to boost the classifier outcomes. the experimental outcomes indicate the promising outcomes of the BDAWHO-DL algorithm over recent approaches.

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Exploring the Potential of machine learning for Early Cattle Disease Diagnosis

Exploring the Potential of Machine Learning for Early Cattle...

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international conference on Inventive Research in Computing Applications (ICIRCA)

作者： Gokul Krishna. R Periyasamy. V S Roshan Khan. B S Mohan Raj. T Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

Dairy farming has long been a common occupation in regions where industrial agriculture is not prevalent, such as India. the dairy industry has seen a rise in productivity as a result of dairy farming modernization, but it is still afflicted by various ailments that can affect cow product output, and poor-quality dairy products not only hamper long-term national economic success. Due to the large number of dairy cattle housed in various dairies, dairy owners and local governments struggle to maintain and monitor their health. A health management method must include continuous monitoring of each cow's health as well as rapid identification and treatment of sick animals. To do this, sensor technology is employed to measure essential animal properties such as heart rate and temperature. this data is then gathered and put into a data mining system, which forecasts any disease-related events. As a result, even routine veterinary exams and animal care may become more expensive. As a result, in this study, the costs of livestock monitoring are avoided.

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A Comparative Analysis of Deep learning Architecture for Accurate Gender Classification Using Vocal data 5th

A Comparative Analysis of Deep Learning Architecture for Acc...

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5th international conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Anand, Khushi Jain, Bhawna Verma, Ananya Gupta, Anushka Chhabra, Niharika Department of Computer Science Engineering Indira Gandhi Delhi Technical University of Women Delhi India

ISBN: (纸本)9783031751639

the human voice contains essential paralinguistic information that is used in many voice recognition applications. From a given voice dataset, gender prediction is considered a pivotal part to be classified using the speech data. this analysis is useful in gender recognition, speaker identification, and the detection of emotions. Several studies have been working on creating an automated system for gender categorization using speech datasets without prior knowledge of the speaker’s gender. the main focus of the study is extracting gender-specific traits from the speech data and creating a model to capture and train on these features. machine learning (ML) and Deep learning (DL), especially Convolution Neural Networks (CNNs), have shown significant performance in this field. the study focused on increasing the accuracy of the models. this study investigates the different CNN models implemented on gender prediction using speech data containing voice spectrograms as input and compares their effectiveness to find the best DL technique. A publicly available dataset is used containing vocal features of both genders. It contains 3,168 voice recordings of male and female speakers, containing 20 acoustic features. three CNN models were tested on this dataset, and the three-layer CNN model fared the best, with an accuracy of 98.21% and a loss of 5.64%. this research improves the performance of deep learning models in voice detection and conducts a comparative analysis among various models, ultimately determining the most effective model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Cross-media Hash Retrieval Using Multi-head Attention Network 25

Cross-media Hash Retrieval Using Multi-head Attention Networ...

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25th international conference on pattern recognition (ICPR)

作者： Li, Zhixin Ling, Feng Xu, Chuansheng Zhang, Canlong Ma, Huifang Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9781728188089

the cross-media hash retrieval method is to encode multimedia data into a common binary hash space, which can effectively measure the correlation between samples from different modalities. In order to further improve the retrieval accuracy, this paper proposes an unsupervised cross-media hash retrieval method based on multi-head attention network. First of all, we use a multi-head attention network to make better matching images and texts, which contains rich semantic information. At the same time, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. therefore, this method can capture the potential correlations between different modalities and within the same modality, so as to make up for the differences between different modalities and within the same modality. Secondly, the method is unsupervised and does not require additional semantic labels, so it has the potential to achieve large-scale cross-media retrieval. In addition, batch normalization and replacement hash code generation functions are adopted to optimize the model, and two loss functions are designed, which make the performance of this method exceed many supervised deep cross-media hash methods. Experiments on three datasets show that the average performance of this method is about 5 to 6 percentage points higher than the state-of-the-art unsupervised method, which proves the effectiveness and superiority of this method.

关键词： convolutional neural network multi-head attention network unsupervised learning collaborative learning cross-media retrieval

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Facial expression recognition Using machine learning 5

Facial expression recognition Using Machine Learning

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5th international conference on Intelligent Computing in data Sciences, ICDS 2021

作者： Chouhayebi, Hajar Riffi, Jamal Mahraz, Mohamed Adnane Yahyaouy, Ali Tairi, Hamid Sciences Faculty Dhar EL Mahraz Sidi Mohamed Ben Abdellah University LISAC Department of Computer Science Fez Morocco

ISBN: (纸本)9781665442381

Facial expression recognition (FER) plays an important role in computer vision. In this paper, we compare the result of two proposed methods of representing Facial landmarks detection and Feature Extraction. the first method is based on image processing (for example, histogram equalization, thresholding, color conversion, morphological operations, etc.) and the second one used the Dlib library to detect facial landmarks. We examine each feature descriptor by considering two classifications methods such as Support Vector machine (SVM) and the Multi-layer Perceptron (MLP) with three facial expression databases(10k US Adult Faces database, the MUG Facial Expression and personal database) to classified three different facial expressions: happiness, surprise and *** Experimental results demonstrate that the first proposed method shows 91.5% accuracy and more than 96% accuracy for the second method. © 2021 IEEE.

关键词： Support vector machines

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5th international conference on New Trends in Information and Communications Technology Applications, NTICT 2021

5th International Conference on New Trends in Information an...

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5th international conference on New Trends in Information and Communications Technology Applications, NTICT 2021

ISBN: (纸本)9783030934163

the proceedings contain 13 papers. the special focus in this conference is on New Trends in Information and Communications Technology Applications. the topics include: Energy-Saving Adaptive Sampling Mechanism for Patient Health Monitoring Based IoT Networks;ETOP: Energy-Efficient Transmission Optimization Protocol in Sensor Networks of IoT;the Extent of Awareness of Faculty Members at Albaydha University About the Concept of Educational Technology and their Attitudes Towards It;Audio Compression Using Transform Coding with LZW and Double Shift Coding;the Effect of Speech Enhancement Techniques on the Quality of Noisy Speech Signals;Image Steganography Based on DNA Encoding and Bayer pattern;Panoramic Image Stitching Techniques Based on SURF and Singular Value Decomposition;Lossless EEG data Compression Using Delta Modulation and Two Types of Enhanced Adaptive Shift Coders;automatic Classification of Heart Sounds Utilizing Hybrid Model of Convolutional Neural Networks;hybrid Approach for Fall Detection Based on machine learning;three N-grams Based Language Model for Auto-correction of Speech recognition Errors.

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OYEN: A User-Centric LLM-Based Bilingual Healthcare Chatbot

OYEN: A User-Centric LLM-Based Bilingual Healthcare Chatbot

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international conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Owen Chin Nurul Syafidah Jamil Zanariah Zainudin Nor Azizah Hitam Noraini Ibrahim Ahmad Hakimi Ahmad Sa'ahiry Department of Digital Economy and Technology Universiti Tunku Abdul Rahman (UTAR) Perak Malaysia Faculty of Computer Information Systems Higher Colleges of Technology Abu Dhabi United Arab Emirates Department of Computer and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Perak Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

the expanding healthcare sector requires creative solutions to connect patients with vital information. Language barriers exacerbate the challenge, as many chatbots are predominantly English-based, limiting accessibility for non- English speakers. therefore, this work introduces OYEN, a Large Language Model (LLM)-based chatbot designed for patients with bilingual (Mandarin and English) healthcare guidance. Early chatbot architectures relied on statistical Natural Language Processing (NLP) methods and keyword pattern recognition. However, the rise of LLMs with learning capabilities revolutionized chatbot development since 2018. Transformer-based LLMs became dominant due to their exceptional performance in modeling natural text. Developed for healthcare institutions in Malaysia offering Traditional Chinese Medicine (TCM) services, OYEN accurately responds to open-ended TCM-related questions. To ensure OYEN's capability to be user-language centric, responding in the language of the user's input (if a user inputs Mandarin, the response will be in Mandarin and vice versa for English language), a similarity search mechanism using a vector database which is known as Retrieval Augmented Generation (RAG) is integrated. this advanced technique enhances OYEN's ability to retrieve and present relevant TCM information.

关键词： Limiting Generative AI Large language models Medical services Chatbots Transformers data models Vectors pattern recognition Information and communication technology

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Research on Automation Control of Intelligent Logistics Assistant

Research on Automation Control of Intelligent Logistics Assi...

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machine learning and Computer Application (ICMLCA), international conference on

作者： Ziwen Li Dun Lin Kaitao Deng Weijia Li Yezhao Li Libin Feng College of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai Guangdong China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

this paper investigates an intelligent logistics assistant based on the YOLOv5 algorithm, using a three-wheel structure design with a master control system of STM32F407ZGT6. the task information is displayed through the LCD display, and the system is equipped with an MG996 robotic arm servo to control the clamping of the material while the servo of the material tray realizes the rotation of the material tray. To ensure the stability of the attitude, the system uses HW101 gyroscope for attitude correction, while the laser distance measurement module is responsible for the accurate calibration of the position. openMV vision camera runs the YOLOv5 algorithm to realize the real-time recognition and processing of the environment, and the RFID scanning module is used to obtain and identify the task code. For motion control, Emm _ V5.0closed-loop stepper motors drive three omnidirectional wheels for flexible movement, while a gimbal closed-loop stepper motor is used to regulate the rotation of the robotic arm. the lifting slide rail cooperates with the transmission belt to realize the lifting and lowering transmission of the robotic arm. through the synergistic work of the above modules, the designed intelligent logistics robot has an efficient automated material handling capability.

关键词： YOLO Accuracy Wheels Laser stability Manipulators Robot sensing systems data models Mobile robots Servomotors Logistics

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thermal Characterisation of Unweighted and Weighted Networks 25

Thermal Characterisation of Unweighted and Weighted Networks

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25th international conference on pattern recognition (ICPR)

作者： Wang, Jianjia Wu, Hui Hancock, Edwin R. Shanghai Univ Sch Comp Engn & Sci Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9781728188089

thermodynamic characterisations or analogies have proved to provide powerful tools for the statistical analysis of network populations or time series, together with the identification of structural anomalies that occur within them. For instance, classical Boltzmann statistics together with the corresponding partition function have been used to apply the tools of statistical physics to the analysis of variations in network structure. However, the physical analogy adopted in this analysis, together with the interpretation of the resulting system of particles is sometimes vague and remains an open question. this, in turn, has implications concerning the definition of quantities such as temperature and energy. In this paper, we take a novel view of the thermal characterisation where we regard the edges in a network as the particles of the thermal system. By considering networks with a fixed number of nodes we obtain a conservation law which applies to the particle occupation configuration. Using this interpretation, we provide a physical meaning for the temperature which is related to the number of network nodes and edges. this provides a fundamental description of a network as a thermal system. If we further interpret the elements of the adjacency matrix as the binary microstates associated with edges, this allows us to further extend the analysis to systems with edge-weights. We thus introduce the concept of the canonical ensemble into the thermal network description and the corresponding partition function and then use this to compute the thermodynamic quantities. Finally, we provide numerical experiments on synthetic and real-world data-sets to evaluate the thermal characterisations for both unweighted and weighted networks.

关键词： Statistical Physics

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