检索结果-内蒙古大学图书馆

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

来源：评论

学校读者我要写书评

暂无评论

MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

引用

computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

来源：评论

学校读者我要写书评

暂无评论

Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of Algorithm for Person Re-Identification Using Extended Openface Method

引用

computer Systems science & engineering 2023年第1期44卷 545-561页

作者： S.Michael Dinesh A.R.Kavitha Anna University Chennai600025India Department of Computer Science&Engineering SRM Institute of Science&TechnologyChennai600026India

Deep learning has risen in popularity as a face recognition technology in recent ***,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per *** also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)***-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the *** research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face *** proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye *** extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face *** system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.

关键词： Constrained local model enhanced constrained local neuralfield enhanced hog scattered gated expert network support vector machine

来源：评论

学校读者我要写书评

暂无评论

Cooperative Channel and Optimized Route Selection in Adhoc Network

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 1547-1560页

作者： D.Manohari M.S.Kavitha K.Periyakaruppan B.Chellapraba Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119TamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035TamilnaduIndia Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107TamilnaduIndia Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is *** uses have expanded in lockstep with its *** to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is *** this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the conﬁdence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization *** that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated *** simulation work is validated in this section using the MATLAB ***,steps show how the node acts as a conﬁdent forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between *** cal-culate the conﬁdence of the node at the start of the network by combining the reliability report for theﬁrst hop and the reliability report for the secondary *** then refer to the same node as the conﬁdent node in order to operate as a *** a result,we witness an increase in the leftover energy in the *** percentage of data packets delivered has also increased.

关键词： Adhoc Network conﬁdent forwarder one-hop optimized route selection secondary report channel selection

来源：评论

学校读者我要写书评

暂无评论

A new bi-level deep human action representation structure based on the sequence of sub-actions

引用

Neural Computing and Applications 2025年第2期37卷 985-1008页

作者： Akhlaghian Tab, Fardin Ramezani, Mohsen Afshoon, Hadi Seyedi, Seyed Amjad Moradyani, Atefeh Department of Computer Engineering Faculty of Engineering University of Kurdistan Sanandaj Iran Department of Computer Science University of Kurdistan Sanandaj Iran

Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of sub-actions. A technique is used to segment actions on the time axis based on body movements via an energy diagram. After dividing actions into sub-actions, a novel bi-level deep structure is used to extract their features. Then, the sequence of sub-action features is modeled by a deep network to create the action model. As extracted sub-actions have fewer variations in execution manner, their representation is more stable, and modeling their sequence would be an efficient model. Experimental results on UCF-YouTube, UCF-Sport, and Human Motion DataBase (HMDB) datasets indicate the sustainable performance of this method. Overall, the accuracy of the proposed method is 0.972 on average, while the value for the second-best method is 0.925. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human form models

来源：评论

学校读者我要写书评

暂无评论

Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

引用

Artificial Intelligence in Agriculture 2024年第1期11卷 23-33页

作者： Thanawat Phattaraworamet Sawinee Sangsuriyun Phoempol Kutchomsri Susama Chokphoemphun Department of Electrical and Computer Engineering Faculty of Science and EngineeringKasetsart University Chalermphrakiat Sakonnakhon Province Campus SakonnakhonThailand Department of Computer and Information Science Faculty of Science and EngineeringKasetsart University Chalermphrakiat Sakonnakhon Province Campus SakonnakhonThailand

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus ***,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus *** current study introduced the concept of smart learning in this setting to increase interest and motivation for *** neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each *** scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as *** performance of the model was presented in terms of accuracy,F1-score,precision,and recall *** results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of *** best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception *** addition,the number of total parameters was reduced by approximately 1.80–2.19 *** findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.

关键词： Lotus Park Classification Smart learning park Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Hybrid Color Texture Features Classification Through ANN for Melanoma

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

来源：评论

学校读者我要写书评

暂无评论

An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

引用

Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

来源：评论

学校读者我要写书评

暂无评论

Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

引用

Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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：