检索结果-内蒙古大学图书馆

Cognitively-inspired intelligent decision-making framework in cognitive IoT network

International Journal of Networking and Virtual Organisations 2024年第2期31卷 87-105页

作者： Jha, Vidyapati Tripathi, Priyanka Department of Computer Applications National Institute of Technology Chhattisgarh Raipur India

Numerous Internet of Things (IoT) applications require brainempowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches. © 2024 Inderscience Enterprises Ltd.

关键词： CIoT cognitive IoT data reduction intelligent decision Internet of Things IoT

来源：评论

学校读者我要写书评

暂无评论

Extraction of emerging trends in quantum algorithm archives

引用

Neural Computing and applications 2024年第29期36卷 17851-17880页

作者： Sood, Sandeep Kumar Singh, Manmohan Bhatia, Munish Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra India

Quantum computing (QC) has been viewed as a groundbreaking development in the modern technological landscape. The field of QC technology has made significant strides in various applications in recent years, making it one of the most researched topics today. QC technology outperforms classical computers by running quantum algorithms (QA), which have demonstrated superiority in real-time decision-making over traditional computing solutions. QA has wide-ranging applications in areas such as cryptography, search, optimization, measurements, quantum system simulation, and solving large-scale linear equations. This current study presents a scientometric analysis of over 2800 QA publications from the Scopus database spanning the last decade (2014–2023). The findings of this study shed light on key research areas in QA, providing insights into geographical analysis, linkages, keyword co-occurrences, prominent institutions, journal co-citations, document co-citations, and future research directions for QA. Furthermore, document co-citation analysis and linkages are conducted for each QA domain. A review of the academic literature reveals significant issues in the QA knowledge domain. The information gathered from this study can help identify relevant applications, research challenges, and major issues, offering a comprehensive overview of QA research. This scientometric study examines the growth and categorization of QA and its domains over time, while also identifying trends in collaboration among leading nations and institutions, prestigious journals, and active research fields. Through collaboration analysis, the top 10 nations and universities supporting QA research were identified, showcasing their global leadership in the discipline. Additionally, the study determined the most highly esteemed journals in the field. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

A beginner's guide to plagiarism detection using blockchain

引用

IEEE Potentials 2024年第5期43卷 9-13页

作者： Saroja, S. National Institute of Technology Department of Computer Applications Trichy620015 India

Plagiarism is a big issue in research. Academic dishonesty is a long-standing issue for higher education service providers. However, the increased availability of information sources that student writers may easily access, as well as essay-writing services aggressively advertised to university students, has resulted in a rise in discussions on this problem in both the media and academic journals. The project aims at using blockchain technology to create a decentralized application for saving the research papers in the private Ethereum blockchain network so that students or scholars in a particular university cannot copy or infringe anybody's intellectual property rights. © 1988-2012 IEEE.

关键词： Blockchains Plagiarism Smart contracts Open source software Security Decentralized applications Memory management

来源：评论

学校读者我要写书评

暂无评论

An artificial bee colony algorithm for the minimum edge-dilation K-center problem

引用

Soft Computing 2024年第13-14期28卷 8497-8511页

作者： Israni, Manisha Sundar, Shyam Department of Computer Applications National Institute of Technology Raipur Raipur492010 India

This paper studies the minimum edge-dilation K-center (MEDKC) problem for edge-weighted, undirected and connected graphs. This problem which is NP-hard holds significant relevance in designing efficient routing schemes for computer networks. To the best of our knowledge, there exists only two variants of genetic algorithm that have been developed for this problem. In this paper, we propose an artificial bee colony algorithm (ABC_MEDKC) for this problem. The proposed ABC_MEDKC incorporates two adaptable neighborhood operators specifically tailored for this problem in which the first neighborhood operator utilizes solution components of another solution, and the second neighborhood operator follows swapping of center vertices with non-center vertices in a mixed strategies of greedy and random approach. On available benchmark instances, computational results of ABC_MEDKC indicate that ABC_MEDKC overall outperforms the existing two variants of genetic algorithm in both solution quality and computational time. ABC_MEDKC also outperforms an existing polynomial-time approximation algorithm developed for this problem in terms of solution quality. ABC_MEDKC finds new values for 17 instances out of 91 instances. In addition, the convergence behavior of ABC_MEDKC and the statistical analysis are also studied. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

The Semantics of COVID-19 Web Data: Ontology Learning and Population

引用

Current Materials Science 2024年第1期17卷 44-64页

作者： Sharma, Sumit Jain, Sarika Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India

Background: The acquisition and exchange of meaningful, integrated, and accurate information are at the forefront of the combat against COVID-19;still, there are many countries whose health systems are disrupted. Moreover, no one is adequately equipped for COVID-19 contingencies. Many organizations have established static information systems to manage the information. This fact presents numerous issues, including delays, inconsistencies, and inaccuracies in COVID-19 information collected for pandemic control and monitoring. Objective: This paper presents a semantic representation of COVID-19 data, a domain ontology to facilitate measurement, clarification, linking, and sharing. We automatically generate a computer-intelligible knowledge base from COVID-19 case information, which contains machineunderstandable information. Furthermore, we have anticipated an ontology population algorithm from tabular data that delivers interoperable, consistent, and accurate content with COVID-19 information. Methods: We utilized the tabula package to extract the tables from PDF files and user NLP libraries to sort and rearrange tables. The proposed algorithm was then applied to all instances to automatically add to the input ontology using the Owlready Python module. Moreover, to evaluate the performance, SPARQL queries were used to retrieve answers to competency questions. Results: When there is an equivalence relationship, the suggested algorithm consistently finds the right alignments and performs at its best or very close to it in terms of precision. Moreover, a demonstration of algorithm performance and a case study on COVID-19 data to information management and visualization of the populated data are also presented. Conclusion: This paper presents an ontology learning/matching tool for ontology and populating instances automatically to ontology by emphasizing the importance of a unit's distinguishing features by unit matching. © 2024 Bentham Science Publishers.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

引用

computers, Materials & Continua 2024年第5期79卷 3111-3138页

作者： Sujithra Sankar S.Sathyalakshmi Department of Computer Applications Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia Department of Computer Engineering Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce ***,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and *** paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present *** study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction *** the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the *** original dataset is used in trainingmachine learning models,and further used in generating SHAP values *** the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based *** new integrated dataset is used in re-training the machine learning *** new SHAP values generated from these models help in validating the contributions of feature sets in predicting *** conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making *** this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the *** study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of *** proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area un

关键词： Explainable AI machine learning clinical decision support systems thyroid cancer association-rule based framework SHAP values classification and prediction

来源：评论

学校读者我要写书评

暂无评论

Title-Based Topic Modeling on E-learning Web Content Titles Using BERTopic Model 5th

Title-Based Topic Modeling on E-learning Web Content Titles ...

引用

5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Lalitha, T.B. Sreeja, P.S. Department of Computer Applications Hindustan Institute of Technology and Science Chennai India Department of Computer Applications Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819747108

In today’s information-rich digital age, the volume of web content available to users has become overwhelming, making it challenging for individuals to find relevant and personalized content. Recommendation systems have emerged as a transformative solution, catering to individual users by offering customized suggestions aligned with their unique interests. This research explores a novel approach that utilizes topic modeling techniques on web content titles for recommendation purposes. Topic modeling, a subfield of natural language processing (NLP) is utilized to automatically identify latent topics within a large corpus of text. The proposed work begins by collecting a diverse dataset of web content titles across the domains. It employs a combination of other state-of-the-art topic modeling algorithms like BERTopic modeling and statistical model to uncover the underlying topics in the titles. By leveraging this approach on web content titles, aim to extract meaningful themes and categorize the content efficiently. Then preprocess the data to remove irrelevant information, ensuring that the subsequent topic modeling process yields accurate and meaningful results. This approach not only expedites the recommendation process but also conserves computational resource. Once the topics are identified, associate them with appropriate metadata, such as user preferences, and content types. This step forms the foundation of our content-based recommendation approach. Then maps the user’s interests to the most relevant topics, enabling us to present a tailored list of web content titles. By recommending content based on underlying themes rather than just keywords, this approach surpasses traditional methods, ensuring more accurate and diverse suggestions for users. The results demonstrate the system’s ability to provide highly personalized recommendations, enhancing user satisfaction and engagement. By delving into the semantic structure of content rather than relying solely on

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Employment dynamics: Exploring job roles, skills, and companies

引用

IEEE Potentials 2025年第1期44卷 23-29页

作者： Saroja, S. Jinwal, Swati National Institute of Technology Department of Computer Applications Trichy620015 India Citi Bengaluru560001 India

Are you curious about which jobs are trending, which skills to learn for a better career, and which job is most relevant to your skills along with your probability of selection? This research article addresses these questions comprehensively. The first phase employs web scraping techniques to gather data from prominent job websites, constructing a current dataset. This dataset captures emerging job roles, in-demand skills, geographic job distributions, experience requirements, and related insights. In the subsequent phase, a job recommendation system is developed. Leveraging natural language processing (NLP) techniques such as term frequency–inverse document frequency (TF-IDF) vectorization and cosine similarity, the system matches user-provided skills with job descriptions to recommend relevant job opportunities. Additionally, the recommendation system estimates the probability of selection for each candidate. This project serves as a valuable resource for understanding contemporary job market dynamics, facilitating informed career decisions, and providing insights into emerging job trends and skill requirements crucial for success in the evolving job market. © 1988-2012 IEEE.

关键词： Commerce

来源：评论

学校读者我要写书评

暂无评论

Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

引用

Recent Patents on Engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham Science Publishers.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

An ensemble-based stegware detection system for information hiding malware attacks

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4401-4417页

作者： Monika, A. Eswari, R. Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Information hiding techniques like steganography are used by hackers to obfuscate malicious attack codes to carry malware scripts and deliver to crypto-miners in on-demand platforms like Cloud. Stegware is a type of information-hiding malware that employs steganography to avoid detection by modern malware detection systems. This work proposes a Stegware detection system that recognizes obfuscated payloads from input images and verifies whether the obfuscated payload is the target of any stegware attack. The proposed system detects the stegware in four phases: Obfuscated Payload Detection phase that detects the presence of any obfuscated item concealed inside the digital medium;Obfuscated Payload Extraction phase that decodes stego-repository images to extract the data that are steganographically obfuscated inside the input;Obfuscated Payload Classification phase that detects whether the extracted data is legitimate or malicious stegware file, using binary classifier;Calculation of malicious percentage phase that uses fuzzy C-means clustering algorithm to calculate the quantum and frequency of malicious activities. The proposed system is experimentally tested on real dataset and analyzed with existing models. The simulation results illustrate that the proposed Stegware detection system detects the steganographically-hidden attacks and identifies malicious activities in percentage terms as compared to other models. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Malware

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：