Manikandan J, Kayalvizhi K, Yuvaraj Nachimuthu, Jeena R
{"title":"AROA based Pre-trained Model of Convolutional Neural Network for Voice Pathology Detection and Classification","authors":"Manikandan J, Kayalvizhi K, Yuvaraj Nachimuthu, Jeena R","doi":"10.53759/7669/jmc202404044","DOIUrl":"https://doi.org/10.53759/7669/jmc202404044","url":null,"abstract":"With the demand for better, more user-friendly HMIs, voice recognition systems have risen in prominence in recent years. The use of computer-assisted vocal pathology categorization tools allows for the accurate detection of voice pathology diseases. By using these methods, vocal disorders may be diagnosed early on and treated accordingly. An effective Deep Learning-based tool for feature extraction-based vocal pathology identification is the goal of this project. This research presents the results of using EfficientNet, a pre-trained Convolutional Neural Network (CNN), on a speech pathology dataset in order to achieve the highest possible classification accuracy. An Artificial Rabbit Optimization Algorithm (AROA)-tuned set of parameters complements the model's mobNet building elements, which include a linear stack of divisible convolution and max-pooling layers activated by Swish. In order to make the suggested approach applicable to a broad variety of voice disorder problems, this study also suggests a unique training method along with several training methodologies. One speech database, the Saarbrücken voice database (SVD), has been used to test the proposed technology. Using up to 96% accuracy, the experimental findings demonstrate that the suggested CNN approach is capable of detecting speech pathologies. The suggested method demonstrates great potential for use in real-world clinical settings, where it may provide accurate classifications in as little as three seconds and expedite automated diagnosis and treatment.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Regional IP Allocation Techniques Using Drones","authors":"Yang-ha Chun, Moon-Ki Cho","doi":"10.53759/7669/jmc202404047","DOIUrl":"https://doi.org/10.53759/7669/jmc202404047","url":null,"abstract":"Drones, which were initially developed for military applications, have recently been studied and applied to various fields. In this paper, we propose a DANET algorithm that uses a large number of drones to build a wireless communication network infrastructure, and in situations where communication is not possible, such as in disaster areas, we propose a DANET algorithm that uses drones to form a network so that nodes that want to join the network can efficiently acquire IP addresses without collision. In a DANET, a pool of IP addresses is gradually passed to the drones in the next zone in blocks, and the drones in each zone distribute IPs to newly joining nodes, thereby increasing the IP address allocation rate and reducing the IP allocation time to form a temporary but efficient network. Drones assign their own IP addresses through simple Request and Response message exchanges with land-based stations or M-Droin (Mother Droin) in the divided zones that can assign IP addresses. Therefore, DANET can completely eliminate the process of IP collision avoidance (Duplicate Address Detection) and the process of network separation or integration caused by the movement of ships. This paper presents a new possibility for building wireless network infrastructure in unconnected areas such as disaster areas by performing simulations under various conditions to verify the applicability of DANET.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"101 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis","authors":"Sincy John, A. Danti","doi":"10.53759/7669/jmc202404037","DOIUrl":"https://doi.org/10.53759/7669/jmc202404037","url":null,"abstract":"The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"22 S18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
