检索结果-内蒙古大学图书馆

International Journal of Interactive Mobile Technologies 2023年第16期17卷 82-94页

作者： Sweetline, B. Christina Vijayakumaran, C. Samydurai, A. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu KattankulathurChennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Tamil Nadu KattankulathurChennai India

One of the newest technologies, mobile health, has the potential to support the provision of care for older adults and offer them individualised treatment. This study’s goal is to evaluate the benefits and challenges of personalised mobile health (PMH) for elderly residential care. Virtual worlds are quickly integrating into the landscape of instructional technologies. One of the most well-known of these settings is Second Life (SL). Despite the potential of SL for health professions education, there aren’t many official SL applications for this purpose, and the effectiveness of these applications hasn’t been evaluated to the fullest extent possible. Similarly, it appears that nothing is known about the use of virtual worlds for continuing medical education. In order to better grasp the fundamentals of the aid of MVIs for personal health monitoring (PHM), we were able to pinpoint the key disease regions, sensors, channels, calculations and communication protocols. The main obstacles limiting MVIs’ degree of integration into the international health care system were also identified. The analysis demonstrates that MVIs offer an excellent possibility for the creation of affordable, personalised health systems that meet the unique equipment requirements of a certain field of medicine. © 2023 by the authors of this article. Published under CC-BY.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

An intrusion detection system using ranked feature bagging

引用

International Journal of Information technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

来源：评论

学校读者我要写书评

暂无评论

Mobility Stick with Haptic Feedback for People with Vision Impairments

Mobility Stick with Haptic Feedback for People with Vision I...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Raghul, J. Durga Devi, K. Sreya, M. Maithreye, S. SRM Institute of Science and Technology Department of Electronic and Computer Engineering Chennai Ramapuram India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Ramapuram India

ISBN: (纸本)9798331540685

In this research, haptic motor, Node MCU, ultrasonic sensors are used to address the problem associated with the current walking stick for visually disabled people. These problems include disturbing their main sense of survival which is hearing. Hence, a system containing ultrasonic sensors, haptic motor and Internet of Things has been recommended in this study. This study illustrates that the implementation of haptic motors and ultrasonic sensors can successfully address the major issues that current alternatives face and it has a positive ad significant impact on visually disabled people. Using this method, one can monitor the person's movement and whether they fell down. This system makes use of IoT it integrate the data from sensors and update it onto the website for other person monitoring. © 2024 IEEE.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

引用

Annals of Data science 2024年第6期11卷 1959-1974页

作者： Joel, Linda Parthasarathy, S. Venkatesan, P. Nandhini, S. Department of Mathematics SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai89 India Research and Statistics Sri Ramachandra Medical College and RI Sri Ramachandra University Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Stock price movement forecasting is the process of predicting the future price of a financial and company stock from chaotic data. In recent years, many financial institutions and academics have shown interest in stock market forecasting. The accurate and successful predictions of the future price of stock yield a substantial profit. However, the current approaches are a major challenge due to the dynamic, chaotic, high-noise, non-linear, highly complex, and nonparametric characteristics of stock data. Furthermore, it is not sufficient to consider only the target firms' information because the stock prices of the target firms may be influenced by their related firms. Significant profits can be made by correct forecasting of stock prices, while poor forecasts can cause huge problems. Thus, we propose a novel Island Parallel-Harris Hawks Optimizer (IP-HHO)-optimized Convolutional Long Short Term Memory (ConvLSTM) with an autocorrelation model to predict stock price movement. Then, using the IP-HHO algorithm, the hyperparameters of ConvLSTM are optimized to minimize the Mean Absolute Percentage Error (MAPE). Four different types of financial time series datasets are utilized to validate the performance of the evaluation measures such as root mean square error, MAPE, Index of Agreement, accuracy, and F1 score. The results show that the IP-HHO-optimized ConvLSTM model outperforms others by improving the prediction rate accuracy and effectively minimizing the MAPE rate by 19.62%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

Smart Ear Disorder Management with AI-driven Insights and Recommendations

Smart Ear Disorder Management with AI-driven Insights and Re...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Santhoosh, M.R. Akila, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai26 India

ISBN: (纸本)9798331508685

Otoscopy is an essential diagnostic method for evaluating ear health and detecting diseases such as infections, inflammations, and structural anomalies. The precise classification of otoscopy images is, nonetheless, difficult due to the variety of diseases. This research presents a deep learning methodology utilizing the DenseNet structure for automated categorization of otoscopy images. A curated dataset, including normal anatomy and various diseases, was preprocessed for homogeneity and enriched for diversity. The model was trained utilizing categorical cross-entropy loss, accompanied by hyperparameter optimization to improve performance. The evaluation metrics, comprising accuracy, precision, recall, F1-score, and AUC-ROC, confirmed the model's efficacy. The findings indicate that the suggested method attains a classification accuracy of 99.26%, highlighting its potential to facilitate the rapid and dependable diagnosis of ear diseases in clinical settings. The implementation of sophisticated data augmentation methods mitigates issues associated with restricted datasets, hence ensuring good model generalization. The suggested paradigm shows potential for incorporation into real-time diagnostic tools, addressing deficiencies in accessibility and skill in otological care. © 2025 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Trust and QoS-Driven Query Service Provisioning Using Optimization

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 1827-1844页

作者： K.Narmatha K.Karthikeyan Department of Computer Science and Engineering SRM Institute of Engineering and TechnologyRamapuramChennaiIndia Department of Information Technology Coimbatore Institute of TechnologyCoimbatoreIndia

The growing advancements with the Internet of Things(IoT)devices handle an enormous amount of data collected from various applications like healthcare,vehicle-based communication,and smart *** research analyses cloud-based privacy preservation over the smart city based on query ***,there is a lack of resources to handle the incoming data and maintain them with higher privacy and ***,a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment.A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards *** handling privacy,other inter-connected metrics like efﬁciency are also essential,which must be considered to fulﬁl the privacy ***,this work provides a query-based service model and clusters the query to measure the relevance of frequently generated ***,a Bag of Query(BoQ)model is designed to collect the query from various *** is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get theﬁnal *** processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Opti-mizer(GWO).It is used to attain global and local solutions regarding privacy *** iterative data is evaluated without any over-ﬁtting issues and computational complexity due to the tremendous data handling *** on this analysis,metrics like privacy,efﬁciency,computational complexity,the error rate is *** simulation is done with a MATLAB 2020a *** proposed model gives a better trade-off in contrast to existing approaches.

关键词： Cloud computing IoT privacy security grey wolf optimization

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach With Sparse Autoencoder for Alzheimers Disease Classification 23th

A Deep Learning Approach With Sparse Autoencoder for Alzheim...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Oommen, Deepthi K. Arunnehru, J. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Tamilnadu Chennai India

ISBN: (纸本)9783031648120

Alzheimer's Disease is a progressive neurodegenerative condition distinguished by a steady deterioration in cognitive abilities. Given the empirical inquiries conducted, it is imperative to take age into account as a fundamental criterion when selecting participants. Younger individuals are more vulnerable to the transient aspect compared to those who are older. The current work focused on selecting young-onset subjects, as early diagnosis can significantly impact patients' lives. The identification of this disease at an early stage using traditional means poses significant difficulties. Deep Learning (DL) has emerged as a highly effective approach for enhancing the performance of diagnostic processes and boosting forecast accuracy. The research utilized deep learning techniques and neuroimaging approaches to autonomously identify and categorize the condition based on its phases of mild cognitive impairment. The suggested study is conducted through a series of three sequential phases. Prior to further analysis, the 3D input visuals need to undergo image preprocessing with Weiner filtering in order to remove noise and smooth the image. Following, Transfer learning models are utilized to extract features, which are then compressed through the use of cascaded Auto Encoders. The ultimate stage involves the utilization of a fine tuned Deep Neural Network (DNN) for categorizing of the phases of AD into five classes. The combination of the ResNet-18 and sparse autoencoder with the deep neural network model yielded a remarkable accuracy rate of 95.62%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Web Security through Machine Learning: A Random Forest Approach to Malicious URL Detection 4

Enhancing Web Security through Machine Learning: A Random Fo...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Praba, M.S. Bennet Duddukunta, Karthik Reddy Bezawada, Vishnu Sai Addanki, Sai Vikas Srm Institute of Science and Technology Department of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Detecting malicious URLs by employing the Random Forest algorithm alongside URL parsing techniques. The dataset comprises URLs categorized into phishing, benign, and defacement classes. By parsing these URLs, various features are extracted, such as domain name, abnormality indicators, security status, URL shortening presence, and IP address presence. These features are utilized to train the Random Forest classifier. The Random Forest model learns to differentiate between different URL types based on the extracted features. A feature importance analysis is conducted to identify the most influential features affecting classification decisions. The effectiveness of the approach is evaluated by assessing the accuracy of the Random Forest classifier on training, testing, and out-of-bag (OOB) data. The model's ability to accurately classify URLs aids in identifying potential security threats, including phishing attacks, website defacement, and other online risks. By automating the detection of malicious URLs, the model contributes to enhancing web security measures. This model presents a comprehensive methodology for detecting malicious URLs through the integration of machine learning and URL-parsing techniques. The evaluation of the Random Forest classifier's accuracy on multiple datasets demonstrates its effectiveness in proactively identifying and addressing security vulnerabilities in online environments. This approach provides organizations with a valuable tool for bolstering online security and mitigating cyber threats. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Enhanced Cardiovascular Disease Detection from Phonocardiogram Signals Using Deep Learning & Wavelet-Based Denoising 4

Enhanced Cardiovascular Disease Detection from Phonocardiogr...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Guru, R. Pavithra Kumar, C. Varun Sekhar, M. Chandra Vamshi, Maloth Srm Institute of Science & Technology Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Heart beat reveals crucial information about the heart condition at an early stage. The AI based diagnosis systems plays a crucial role for classifying heart sounds in CVD diagnosis. To accurately extract information from heart auscultation regarding a heart health condition, it's essential that the heart sound signals remain uncontaminated by noise. This ensures that misclassification between normal and abnormal situations can be avoided. PCG signal contains crucial details regarding the functioning of heart and this information is used in diagnosis to detect various kinds of heart related diseases. Also, this PCG signals are very much distorted with noise which makes the denoising of the signal important for accurate detection of cardiovascular diseases (CVD). In this study, an automated CVD detection technique is introduced which detects various heart conditions from the PCG signal and also, we integrate a denoising technique to the diagnosis system which denoises the PCG signal if found to have any kind of internal or ambient noises. The findings presented in this paper suggest that our proposed architecture exhibited very good accuracy in detecting five classes of heart diseases with considerable denoising capabilities across various datasets featuring different noise levels and characteristics. This method is rooted on VGG based deep neural network model for detection of cardiac disease and the PCG denoising is done through wavelet denoising technique, represents a groundbreaking advancement aiming to increase the precision of automated CVD detection systems in identifying cardiac diseases within noisy environments. © 2024 IEEE.

关键词： Phonocardiography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：