检索结果-内蒙古大学图书馆

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Gok, Gorkem Boyaci, Aytug Ulas, Mustafa Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey Air Force Academy National Defence University Department of Computer Engineering İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from 'Fast Learning Network' to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big Data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

AsPeer: Method to Self and Peer Assessment in Large Online Design Classes

International Journal of Emerging Technologies in Learning

引用

International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

引用

computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

来源：评论

学校读者我要写书评

暂无评论

Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

引用

Iran Journal of computer science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for sustainable internet research

引用

International Journal of computers and Applications 2022年第6期44卷 501-502页

作者： Haldorai, Anandakumar Sharif, Muhammad Ravana, Sri Devi Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering COMSATS University Islamabad Wah Cantt Pakistan Department of Information System Faculty of Computer Science & Information Technology University of Malaya Malaysia

来源：评论

学校读者我要写书评

暂无评论

An Explainable Predictive Model for Diabetes Detection Using Shapley Additive Explanations Approach

An Explainable Predictive Model for Diabetes Detection Using...

引用

International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Khan, Andleeb Yadav, Saundarya Nand, Parma Khanday, Akib Mohi Ud Din Bhushan, Bharat Jamil, Akhtar Hameedkhan, Alaa Ali Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan Department of Computer Engineering Faculty of Engineeringand Natural Sciences Istinye University Istanbul34396 Turkey

ISBN: (纸本)9783031709234

The chronic and incurable disease of diabetes that is not only marked with increasing prevalence but is also linked to other life-threatening diseases like cardiovascular diseases, tissue injuries and retinopathy. With this the early detection and diagnosis of the disease becomes essential. This paper, proposes a novel explainable predictive model for diabetes detection using Shapley additive explanations approach which will not just predict the disease but also highlight the most contributing features contributing to our result. Through the utilization of PIDD dataset, diabetes has been predicted by using three machine learning classifiers i.e. Random Forest, XGBoost and Support Vector Machine. The proposed explainable model outperforms the existing studies by giving an accuracy of 82%. The results were then interpreted using Shapley Additive Explanations (SHAP) which further explained that the most contributing features throughout the study in each model were ‘Glucose’, ‘BMI’ and ‘Age’ among others. This explanation is meant to aid the medical community in their diagnosis and effective recommendations to the patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 2363-2378页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mofreh A.Hogo Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Electrical Engineering Department Faculty of EngineeringBenha UniversityBenha13518Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Women from middle age to old age are mostly screened positive for Breast cancer which leads to *** over the past decades,the overall sur-vival rate in breast cancer has improved due to advancements in early-stage diag-nosis and tailored *** all hospital brings high awareness and early detection technologies for breast *** increases the survival rate of *** traditional breast cancer treatment takes so long,early cancer techniques require an automation *** research provides a new methodol-ogy for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best ***,after successful learning of Convolutional Neural Network(CNN)algorithms,data augmentation is used to enhance the representation of the feature *** it uses BreastNet18 withﬁne-tuned VGG-16 model for pre-training the augmented *** feature classiﬁcation,Entropy controlled Whale Optimization Algorithm(EWOA)is *** features that have been optimized using the EWOA were utilized to fuse and optimize the *** identify the breast cancer pictures,training classiﬁers are *** using the novel probability-based serial technique,the best-chosen characteristics are fused and categorized by machine learning *** main objective behind the research is to increase tumor prediction accuracy for saving human *** testing was performed using a dataset of enhanced Breast Ultrasound Images(BUSI).The proposed method improves the accuracy com-pared with the existing methods.

关键词： Deep learning classiﬁcation data augmentation feature extraction the fusion of features breast cancer optimization classiﬁcation

来源：评论

学校读者我要写书评

暂无评论

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

引用

computers, Materials & Continua 2023年第1期74卷 141-152页

作者： Nidhi Agarwal M.Gokilavani S.Nagarajan S.Saranya Hadeel Alsolai Sami Dhahbi Amira Sayed Abdelaziz Department of Information Technology KIET Group of InstitutionsDelhi201206India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for WomenNew DelhiDelhi110006India Department of Computer Science and Engineering KL UniversityVaddeswaramAndhra Pradesh522502India Department of Electronics&Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial *** nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor *** goal of any NL scheme is to improve the localization accuracy and reduce the localization error *** this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for *** presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the *** addition,the IAOAB-NLS model is stimulated by the behaviour of *** IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the *** guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS *** resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

关键词： Aquila optimizer node localization WSN intelligent models unknown nodes anchor nodes

来源：评论

学校读者我要写书评

暂无评论

Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

引用

2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Data Analysis Investigation: Papua is The Most Unsafe Province in Indonesia for Aviation: An Exploratory Data Analysis Study from KNKT-Database Accidents and Incidents (1988-2021)

引用

Journal of engineering science and Technology Review 2022年第3期15卷 158-164页

作者： Passarella, Rossi Nurmaini, Siti Doctoral Engineering Department Faculty of Engineering Universitas Sriwijaya Indonesia Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Indonesia

Due to its high population and growing middle class, Indonesia is a locale for a promising expansion of commercial aviation businesses. Furthermore, being a large archipelago, mobility of people and goods via air travel appears to be the most convenient option available. Nevertheless, aviation safety has remained an issue of concern in Indonesia since 2007, after the EU banned Indonesian aircraft flights in the European airspace. This was done as a response to the multiple accidents that earned Indonesian aviation a global reputation for being unsafe. We conducted the investigation based on a reputable report produced by KNKT, known as the National Transportation Safety Board, to establish a more comprehensive context of this situation. Since 1988, all accident data reports have been gathered and archived in the KNKT database, which was then utilized to further investigate the issues confronting Indonesian airlines by evaluating the data using the EDA approach. According to the results, Papua Island is the least safe region for aviation, with 140 accidents (21.9 percent of the total accidents) in Indonesia over the past 33 years. Moreover, the noteworthy results reveal that 45.71 percent of the accidents or incidents occur during the landing phase. This finding informed us regarding the overall geography of Papua, which lacks transportation roads, prompting the government to open the airport for mobilization. Papua has 217 airports, accounting for 42.3 percent of all Indonesian airports, with 196 being small airports at an average altitude of 452.15 meters. Human factors account for 63 percent of accidents in Papua, while technological factors account for 19 percent. The government may use the study's findings to pressure operators to enhance safety in addition to revitalizing aviation safety supporting technologies. © 2022. School of science, IHU. All rights reserved.

关键词： Database systems

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：