检索结果-内蒙古大学图书馆

5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rao, Gouravelli Akshith Prakash, L.N.C.K. Suryanarayana, G. Joshua, Pathi Varun Reddy, Katta Nithin Kumar Karnati, Ramesh Computer Science and Engineering Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Sentiment analysis is part of text mining, which means extracting data from comments on specific products and people’s opinions or sentiments. It involves considering opinions, analysis, and suggestions to customers about Amazon Alexa products by classifying a review as positive, negative, or neutral. This work is to explore different learning algorithms to solve sentiment analysis on Amazon products. The proposed work is a comparative analysis of existing learning algorithms that can help businesses in real-time decision-making by providing insights into customer preferences, needs, and choices which in turn results in the growth of the company and enhancement of customer satisfaction. The existing works provided the accuracy of classification to a level of 80%. Algorithms used in previous projects are SVM, Naïve Bayes, Random Forest, and other learning algorithms. In this work, logistic regression, Gradient boosting, Multinominal NB, and Gaussian NB are used along with previously implemented algorithms for sentiment analysis. Deep learning algorithms such as Long Short-Term Memory (LSTM) and ANN with dropout and without dropout layers are also implemented and compared with machine learning algorithms. Word2Vec is employed with LSTM to increase the accuracy of the representation. Based on the evaluation of their metrics, accuracies are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

引用

International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

来源：评论

学校读者我要写书评

暂无评论

Integrating ERNIE 3.0 and biterm topic model for rumor detection on weibo

Integrating ERNIE 3.0 and biterm topic model for rumor detec...

引用

2024 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhai, Xiaojie Lu, Yiqing College of Computer Science Beijing Information Science and Technology University Beijing100192 China College of Management Science and Engineering Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510689275

In response to the underutilization of contextual semantic and thematic features, as well as the sparsity of textual features in Weibo rumor detection, a method integrating ERNIE 3.0 (Enhanced Representation through Knowledge Integration) and BTM (Biterm Topic Model) for rumor detection on Weibo is proposed. This method first employs the ERNIE 3.0 to extract contextual semantic features from Weibo text, then utilizes the BTM to extract thematic features from the text. The two types of features are concatenated and input into the TextCNN (Text Convolutional Neural Network) model to extract hidden features from the textual semantic representation for rumor detection. Experimental results demonstrate that the proposed method outperforms various mainstream rumor detection methods in terms of accuracy, precision, recall, and F1 score, proving the effectiveness of the proposed method in enhancing the effectiveness of rumor detection on Weibo. © 2025 SPIE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

引用

Intelligent Automation & Soft Computing 2023年第3期35卷 3845-3860页

作者： P.N.Ramesh S.Kannimuthu Computer Science and Engineering Karpagam Institute of TechnologyCoimbatore641105India Information Technology Karpagam College of EngineeringCoimbatore641032India

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difﬁculty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and toﬁnd learners’learning ***ﬁltering(CF)and con-tent-basedﬁltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation *** sequence similarity measure is used toﬁnd the top k neighbors based on the sequence of problems solved by the *** experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关键词： Recommender systems skill level navigation pattern programming online judge collaborativeﬁltering content-basedﬁltering

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation in Non-Cooperative Game Models with Reinforcement Learning

Resource Allocation in Non-Cooperative Game Models with Rein...

引用

2024 China Automation Congress, CAC 2024

作者： Wang, Linyao He, Qiyong Chen, Kai Li, Xiuxian Liang, Li Wang, Youqing College of Information Science and Technology Beijing University of Chemical Technology Beijing China College of Electronics and Information Engineering Tongji University Shanghai China College of Electronics and Information Engineering SRIAS Tongji University Shanghai China

ISBN: (纸本)9798350368604

This study proposes a non-cooperative game model that includes a two-layer supply and demand relationship and employs reinforcement learning for its *** study takes the water resource allocation in Pujiang County, Jinhua City, Zhejiang Province, as a case study, constructing a non-cooperative game model with reservoirs, water plants, and factories as the main entities. It simulates the strategic interactions among various water users to optimize the water resource allocation plan. This model takes into account the scarcity of water resources, the dynamic changes in demand, and the fairness of distribution. By introducing the CommFormer reinforcement learning algorithm, the model achieves policy optimization in complex and dynamic environments, reduces computational complexity, and speeds up decision-making response time. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

引用

Journal of Communications and information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

来源：评论

学校读者我要写书评

暂无评论

A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

引用

computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

来源：评论

学校读者我要写书评

暂无评论

Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

引用

computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

来源：评论

学校读者我要写书评

暂无评论

Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

引用

computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Approach for Early Detection of Leaf Infection 5th

A Comprehensive Approach for Early Detection of Leaf Infecti...

引用

5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Priya, L. Kiruthika, A. Kumar, P. Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819788606

The agriculture industry is fundamental to the foundation of a country and is essential to the promotion of economic prosperity. It is crucial to ensure that Monitoring the health and detecting leaf infections in plants is crucial for maintaining sustainable agriculture. The actions taken to identify leaf infection represent a significant challenge in the agricultural industry. Manually recognizing plant diseases by analysing leaf images can be a time-consuming undertaking. In response to this challenge, continuous advancements in technology, particularly in image detection, have become instrumental. These advancements enable the accurate and prompt recognition of leaf infections and pests, facilitating the development of early treatment strategies. The paper objective is to precisely recognize diseases in leaf images. The essential steps in this procedure involve pre-processing, training, and recognition. Artificial Intelligence emerges as a ground breaking innovation, empowering the training of machines with image datasets to autonomously identify diseases in crops. Using this proposed methodology this system aims to improve the speed, accuracy, and overall effectiveness in identifying and managing crop infections across diverse agricultural settings through the utilization of machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agricultural economics

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：