International Journal of Information Technology最新文献_第9页

Spatio-temporal Weber Gradient Directional feature for visual and audio-visual phrase recognition systems 用于视觉和视听短语识别系统的时空韦伯梯度方向特征

International Journal of Information Technology Pub Date : 2024-08-12 DOI: 10.1007/s41870-024-02138-9

Salam Nandakishor, Debadatta Pati

{"title":"Spatio-temporal Weber Gradient Directional feature for visual and audio-visual phrase recognition systems","authors":"Salam Nandakishor, Debadatta Pati","doi":"10.1007/s41870-024-02138-9","DOIUrl":"https://doi.org/10.1007/s41870-024-02138-9","url":null,"abstract":"Visual phrase recognition needs lip movement related visual features, while audio-visual phrase recognition requires both acoustic and visual features. In this work, we propose a novel visual feature; Spatio-temporal Weber Gradient Directional (SWGD) to effectively represent the micro-patterns of lip movements. The proposed visual feature is obtained by using micro-texture information; local differential excitation, gradient orientation, and gradient directional information. Experiments are conducted using standard OuluVS database. Polynomial kernel based support vector machine (SVM) classifier is employed, as it provides relatively better performance. The SWGD extracted from (2times 5times 3) video block size provides higher performance of 73.9%. Additionally, we explore twelve distinct local descriptors commonly employed in face recognition and utilize them for the first time in a comparative study of phrase recognition. SWGD performs better than these twelve distinct features but has higher dimension of 4320. By reducing the dimension to 100 using the soft locality preserving map (SLPM), performance improved from 73.9 to 81.3%. The dimensionally reduced SWGD (SWGD(_{text {SLPM}})) outperforms other state-of-the-art visual features mentioned in this paper. This shows the benefit of the salient micro-texture information considered in the proposed feature but neglected in state-of-the-art features. We observe that the SWGD(_{text {SLPM}}) feature has high discriminative ability to represent distinct lip movement patterns for different phrases. Mel-frequency cepstral coefficient (MFCC) based audio phrase recognizer performance degrades as the signal-to-noise level decreases. Including the SWGD(_{text {SLPM}}) visual feature and Glottal MFCC (GMFCC) excitation source feature improves performance by 3.6%, reflecting noise robustness.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942767","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}

引用次数: 0

Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks 使用双变量深度神经网络在 mapreduce 中进行糖尿病慢性病预测的综合正常判别分析

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02139-8

R. Ramani, D. Dhinakaran, S. Edwin Raja, M. Thiyagarajan, D. Selvaraj

{"title":"Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks","authors":"R. Ramani, D. Dhinakaran, S. Edwin Raja, M. Thiyagarajan, D. Selvaraj","doi":"10.1007/s41870-024-02139-8","DOIUrl":"https://doi.org/10.1007/s41870-024-02139-8","url":null,"abstract":"This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers.","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942776","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}

引用次数: 0

A privacy-preserving approach for detecting smishing attacks using federated deep learning 利用联合深度学习检测网络钓鱼攻击的隐私保护方法

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02144-x

Mohamed Abdelkarim Remmide, Fatima Boumahdi, Bousmaha Ilhem, Narhimene Boustia

引用次数: 0

A novel approach for the enhancement of payload capacity in pixel value differencing image steganography schemes 提高像素值差分图像隐写术方案有效载荷容量的新方法

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02114-3

C. D. Nisha, Thomas Monoth

引用次数: 0

Intuitionistic fuzzy approach for reliability analysis of NSP system under time varying failure rates 用直觉模糊法分析故障率随时间变化的非接触式传感器系统的可靠性

International Journal of Information Technology Pub Date : 2024-08-11 DOI: 10.1007/s41870-024-02059-7

S. C. Malik, A. D. Yadav, Masum Raj

引用次数: 0

QoS aware task scheduling and congestion avoidance in fog enabled car parking systems 雾功能停车场系统中的 QoS 感知任务调度和拥堵避免

International Journal of Information Technology Pub Date : 2024-08-10 DOI: 10.1007/s41870-024-02090-8

M. K. Dhananjaya, Kalpana Sharma, Amit Kumar Chaturvedi

引用次数: 0

Optimizing avian species recognition with MFCC features and deep learning models 利用 MFCC 特征和深度学习模型优化鸟类物种识别

International Journal of Information Technology Pub Date : 2024-08-10 DOI: 10.1007/s41870-024-02108-1

Raviteja Kamarajugadda, Rahul Battula, Chaitanya Borra, Harsha Durga, Venkat Bypilla, Seelam Srinivasa Reddy, Farzana Fathima Khan, Shrimannaraya Bhavanam

引用次数: 0

Machine learning for computation of droop controller coefficients to improve the frequency nadir and ROCOF of a stand-alone microgrid 通过机器学习计算下垂控制器系数，改善独立微电网的频率低点和 ROCOF

International Journal of Information Technology Pub Date : 2024-08-10 DOI: 10.1007/s41870-024-02100-9

Swathy Nair, K. Manickavasagam, S. N. Rao

引用次数: 0

Privacy enhanced course recommendations through deep learning in Federated Learning environments 在联合学习环境中通过深度学习提高课程推荐的隐私性

International Journal of Information Technology Pub Date : 2024-08-10 DOI: 10.1007/s41870-024-02087-3

Chandra Sekhar Kolli, Sreenivasu Seelamanthula, Venkata Krishna Reddy V, Padamata Ramesh Babu, Mule Rama Krishna Reddy, Babu Rao Gumpina

引用次数: 0

A synergistic fusion of shallow and deep generative model to enhance machine learning efficacy and classification performance in data-scarce environments 浅层生成模型与深度生成模型的协同融合，提高数据稀缺环境下的机器学习效率和分类性能

International Journal of Information Technology Pub Date : 2024-08-09 DOI: 10.1007/s41870-024-02120-5

K. Bhat, S. Sofi

引用次数: 0