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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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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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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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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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Integrated Circuit Image Feature Extraction Based on K-Means Clustering Combined with Harris

Integrated Circuit Image Feature Extraction Based on K-Means...

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

作者： Qing Liu Yubo Zhou Huiling Li Kun Chen Limin Zhao Xiangbing Li Yuxiang Zhao Jinfang Yang Jinbing Zhang School of Electronic Information & Electrical Engineering Tianshui Normal University Tianshui China Engineering Research Center of Integrated Circuit Packaging and Testing Ministry of Education Tianshui China Tianshui Huatian Technology Co. Ltd Tianshui China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

To effectively extract corner point feature information from IC chip images, a technique of extracting corner point feature information from IC defective chip images combining K-means clustering algorithm and Harris algorithm is proposed. Firstly, the traditional Harris algorithm is improved from the perspective of adapting to the processing of IC chip images; secondly, the IC chip images are detected using Harris corner point detection and then combined with K-means based clustering algorithm to achieve the optimal detection effect of the image target; finally, the proposed algorithm is compared with Moravec algorithm, ORB algorithm, SIFT algorithm and Finally, the proposed algorithm is compared with Moravec, ORB, SIFT and traditional Harris corner detection algorithms, and the performance of the algorithm is further explained by objective evaluation using direct statistics and common evaluation indexes such as the running time of the algorithm. the experimental results show that this method can achieve the corner point feature detection of IC chips, so that the obtained feature areas not only contain as many stable corner points as possible, but also do not overlap each other, and the method can effectively extract the real corner points in the image, which has strong applicability.

关键词： Integrated circuits Feature detection Clustering algorithms machine learning Feature extraction Approximation algorithms Production facilities data mining Indexes Surface treatment

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A Deep learning Based Plant Analysis for Ayurvedic Applications

A Deep Learning Based Plant Analysis for Ayurvedic Applicati...

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Innovative Trends in Information Technology (ICITIIT), international conference on

作者： Priyanka Vemulakonda Tanishq Rachamalla Radhesyam Vaddi Syed Afreen Dept. of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Vijayawada India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Ayurvedic plants are rich sources of therapeutic compounds, yet accurately identifying the plants posses significant challenges. the introduction of a deep learning approach by researchers had already taken place. A deep learning approach for the automated recognition of Ayurvedic plant species. Our approach strategically combines specialized datasets to create a comprehensive collection covering one or three distinct Ayurvedic leaves. Comprising over 9000 displaying images under diverse and realistic conditions this data set Sequentially supports the testing of CNN and Mobile NetV2 architectures. Convolution neural network for spectral feature extraction and Mobile NetV2 architecture optimized through depthwise separable convolutions and transferred learning enhancing efficiency and accuracy. Comparative analysis reveals that Mobile NetV2 achieves over 81% testing accuracy by surpassing the CNN model by 13% which means CNN model achieves 68%. this underscores the value of transferable representatives in addressing data set complexities. these findings the potential of our approach to reliability distinguish visually similar ayurvedic species, a crucial capability of practical ayurvedic applications while significant progress has been made, further optimizations in architecture tuning and data search augmentation can enhance generalization performance. In somebody hopes can provide so precise and robust platform for unlocking the Ayurvedic treasures of plant species through the integration of machine learning and botanical heritage.

关键词： Deep learning Analytical models Accuracy Convolution Neural networks Market research Reliability

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Custom Object Detection for Fresh and Rotten Fruit Classification

Custom Object Detection for Fresh and Rotten Fruit Classific...

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international conference on Advanced Information Technologies (ICAIT)

作者： Khine Zin thant Minn Khant Paing Win Win thant Hmway Hmway Tar Aung Nway Oo University of Information Technology Yangon Myanmar Faculty of Computer Science University of Information Technology Yangon Myanmar

ISBN: (数字)9798331510046

ISBN: (纸本)9798331510053

Automated grading and processing of fruits is a complex task which requires the creation of effective fruit vision detection systems. this paper presents an introduction of Fruit detection and recognition system for grading apples, bananas and oranges though incorporation of deep learning methodologies. For this study, two separate databases of fruits including the Kaggle and Google databases were carefully prepared. the images were annotated with precision and augmented using the Roboflow web-based platform, categorizing each image into one of six classes: fresh apple, rotten apple, fresh orange, rotten orange, fresh banana, rotten banana. After data collection, the next step involved was data partitioning into training and validation datasets through a data splitting technique. Regarding the model training process, the YOLOv8 and YOLOv5 model was used. the evaluation that was conducted on the trained model was impressive, as the system returned a high value for accuracy, precision, recall, F1-score, and mAP. the proposed system not only can enhance fruit grading efficiency but also has a wide range of applications in other agricultural fields. Moreover, this study helps toward some significant concerns in agricultural industry, particularly in the area of fruit recognition. It aims to improve the automation the automation of post-harvest fruit quality selection, ensuring more efficient and sorting and grading processes. this extensive review is quite beneficial as it provides insights into the future direction on how to better improve automatic fruit picking technology for better efficiency and optimization.

关键词： Training YOLO Industries Deep learning Automation Accuracy databases machine vision data models Internet

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