检索结果-内蒙古大学图书馆

Securing Sensitive Data: How Blockchain Transforms Privacy in Healthcare, Law, and Education

Securing Sensitive Data: How Blockchain Transforms Privacy i...

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bale, Ajay Sudhir Arulananth, T.S. Saranya, S. Gadag, Tejashwini Sathiamoorthy, J. Ponnuru, Sudhakiran New Horizon College of Engineering Dept. of Ece Karnataka Bengaluru India Mlr Institute of Technology Department of Electronics and communication Engineering Telangana Hyderabad500043 India Bms Institute of Technology & Management Dept. of Mathematics Bengaluru India School of Computer Science Engineering Reva University Bengaluru India Rmk Engineering college Department of Computer science and Design Kavarapettai India

ISBN: (纸本)9798331543617

In the field of healthcare, law, and education, Blockchain technology (BT) is emerging as a transformative force. This paper helps to explore the usage of BT to enhance the privacy and security in the above said domains. In healthcare industry, blockchain offers privacy and secure management of the vital electronic health records (EHR) and patient health records (PHR), boosting the data integrity and access control. In the law sector, blockchain-enabled smart contracts are providing new possibilities for the transactions automation, transparency, and security of the transactions, thoroughly facing the challenges in terms of jurisdiction and compliance with regulations like GDPR. In the field of education, blockchain technology is improvising the record-keeping, degree verification, and collaborative learning environments. The paper helps to examine the features like the effectiveness, efficiency, and security aspects of blockchain implementations in these sectors, focusing on both the potential benefits and the challenges imposed that need to be addressed for the widespread adoption in real time. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

An automatic question generation system using rule-based approach in bloom’s taxonomy

引用

Recent Advances in computer science and Communications 2021年第5期14卷 1477-1487页

作者： Gnanasekaran, Deena Kothandaraman, Raja Kaliyan, Kannan Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science Engineering Dhaanish Ahmed College of Engineering Chennai India Department of Computer Science Engineering AudiSankara College of Engineering and Technology Nellore Gudur India

Background: In this competing era, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot in the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. To generate questions is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Objective: The proposed system is to generate the test questions that are mapped with bloom’s taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest bloom’s cognitive levels. Methods: Proposed an Automatic Question Generation (AQG) system which, automatically gener-ates the assessment questions from the input file Dynamically. Results & Conclusion: The outputs are dynamic to create a different set of questions at each execu-tion. Here, the input paragraph is selected from the computer science domain and their output efficiency is measured using precision and recall. © 2021 Bentham science Publishers.

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Approaches for Improving Traffic Flow Predictions in Smart Transportation Systems 1

引用

8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Kanakaprabha, S. Ganeshkumar, G. Sureshkumar, A. Udhayakumar, T. Nisha, M. Poornaprakash, P. Department of Computer Science and Engineering Rathinam Technical Campus Anna University Coimbatore India Department of Information Technology Rathinam Technical Campus Anna University Coimbatore India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering Anna University Coimbatore India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Traffic flow extrapolation is a vital aspect of intellectual transportation systems, as it facilitates the smooth and efficient management of traffic. However, traditional traffic flow prediction methods have several limitations, including a lack of accuracy and the inability to handle large and complex data. In this study, advanced machine learning techniques are suggested for enhancing the precision and efficiency of traffic flow predictions in intelligent transportation systems. We collect and preprocess a large and diverse dataset of historical traffic flow data, and use techniques such as feature engineering, model selection, model evaluation, model tuning, and model ensemble to develop a robust-based traffic flow prediction system. The system suggested in this research integrates KNN, Random Forest, and ARIMA machine learning algorithms to forecast traffic flow considering various factors including weather, road conditions, and traffic volume. The outcomes show that the projected system significantly enhances the accuracy of traffic flow predictions compared to traditional methods and can handle a wide range of traffic scenarios and conditions. This research demonstrates the potential of advanced machine learning approaches for improving traffic flow predictions in smart transportation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

A Novel Gradient Boosted Energy Optimization Model(GBEOM)for MANET

引用

computer Systems science & engineering 2023年第7期46卷 637-657页

作者： Neenavath Veeraiah Youseef Alotaibi Saleh Alghamdi Satish Thatavarti Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaA.P521180India Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramA.P522502India

Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move *** MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the *** different nodes are combined and presented to achieve energy-efficient data transmission and classification of the *** route identification and routing are established based on the data broadcast by the network *** transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the ***,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery *** this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery ***,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing *** WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in *** balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth ***,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing *** proposed GBEOM exhibits∼4%improved performance in MANET routing.

关键词： Manet routing load balancing clustering gradient boosting

来源：评论

学校读者我要写书评

暂无评论

Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

引用

Neural Computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Predictive Analysis of Zero-Day Vulnerabilities Using Deep Neural Networks 3

Predictive Analysis of Zero-Day Vulnerabilities Using Deep N...

引用

3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Sharma, Vikalp Khare, Neha Yadav, Santosh Kumar Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

Staying one step ahead of developing threats to national security requires rapid innovation in predictive analytic approaches. This is because it is projected that the complexity of cyberthreats will continue to rise in the coming years. This paper provides a unique technique for zero-day vulnerability prediction analysis using deep neural networks (DNN). This approach was developed thanks to the characteristics of DNN. The use of historical data on anticipated weak points is at the core of our method, which was developed with the goal of providing a proactive reaction to the issues presented by elusive threats. This strategy's major purpose was to provide a proactive reaction. The recommended technique provides an all-encompassing approach and is made up of three reliable components. Convolutional neural networks (CNNs)are used for feature extraction, LSTM networks for temporal analysis, and Random Forests, or RFs, for group learning. This method, which draws on a wide variety of academic fields, provides for a complete and trustworthy prognostic analysis. These parameters include, in addition to accuracy and precision, false positive rate (FPR), mean time to detection (MTTD), area under the receiver operating characteristic curve (AUC), recall, and F1 score. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Review of dehazing techniques: challenges and future trends

引用

Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Microarchitecturally Exploring Fault-Tolerance and Timing on Silicon on Chip

Microarchitecturally Exploring Fault-Tolerance and Timing on...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Vaishali Khan, Gulista Ojha, Ananta Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper presents a micro architectural exploration of fault-tolerance and timing behavior on silicon on chip. Silicon on chip represents a full-size mission in designing reliable hardware with excessive timing accuracy because of its demanding situations resulting from layout variability, disorder densities, clock jitter, and different assets of noise. To improve the reliability of those devices, this work investigates the effect of redundant hardware systems, dynamic voltage and frequency scaling, and gentle-blunders mitigation strategies. The reliability and accuracy of the proposed systems were evaluated in two fundamental kinds of subthreshold versions: random device versions and systematic tool variations because of overlaying and doping defect styles. Effects illustrate that the redundancies and voltage scaling strategies can increase the robustness of the gadget against subthreshold variations, providing processors with better accuracy and reliability. © 2024 IEEE.

关键词： Hardware Microarchitectural Redundant Subthreshold Systematic Systems

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

引用

computers, Materials & Continua 2022年第4期71卷 629-649页

作者： Savita Khurana Gaurav Sharma Neha Miglani Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Nitin Goyal Information Technology Department Seth Jai Parkash Mukand Lal Institute of Engineering and TechnologyRadaurHaryanaIndia Computer Science&Engineering Department Seth Jai Parkash Mukand Lal Institute of Engineering and TechnologyRadaurHaryanaIndia Computer Engineering Department National Institute of TechnologyKurukshetraHaryanaIndia Computer Science&Engineering Department Lovely Professional UniversityJalandharPunjabIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia

COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory *** is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,*** standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of *** proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest *** analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July *** model has been developed to obtain the forecast values till September *** study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the *** India,the cases are rapidly increasing day-by-day since mid of Feb *** prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second *** empower the prediction for future validation,the proposed model works effectively.

关键词： Covid-19 machine learning neuralprophet model poisson distribution prediction random forest model

来源：评论

学校读者我要写书评

暂无评论

Movie Recommendation System Using Euclidean Distance

Movie Recommendation System Using Euclidean Distance

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Muneeb Sami Hussain, Zunnurain Amaad, Muhammad Irtaza Hasan, Muhammad Zulkifl Khalid, Aqsa Chuhan, Saad Hussain Awan, Rimsha Yaqub, Muhammad Atif Ashraf, Farhan Information Technology University Department of Computer Engineering Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology India National College of Business Administration and Economics Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350373783

In this study, we pivot from the commonly used cosine similarity to the Euclidean distance as the foundation for our film recommendation algorithm. Initially, data pertaining to users, films, and ratings are loaded using the panda's library. The algorithm then computes normalized ratings, tabulates the number of films in each genre, and visualizes this distribution. The frequency of ratings for each genre is similarly plotted. A dictionary is maintained to store movie IDs for each genre. These values are then integrated into the normalized rating matrix. For each genre, the normalized ratings of movies are cataloged in a lexicon. Using the normalized ratings matrix and movie IDs, a Data Frame is generated. Subsequently, the movie and ratings datasets are merged. A pivot table captures the number of films each user has watched per genre. The study also be happy with their purchase if these recommendations are tailored to their needs. As a result, the client would utilize this application once more. Because of the significant money gained determines the average number of films a user watches in each genre. This pivot table is refined to display movies in each genre that a user has watched, but only those exceeding the genre's average viewership. Instead of cosine similarity, the Euclidean distance is computed to gauge the similarity between movies, offering a fresh perspective on recommendation systems. © 2024 IEEE.

关键词： ContentBased Filtering Data Normalization Euclidean Distance Genre Analysis Machine Learning Algorithms Movie Recommendation System Personalized Recommendations Similarity Measurement User Preferences User Rating Analysis

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：