As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

/pdf/data-harmonization-for-heterogeneous-datasets-a-systematic-2o48aus7g3.pdf

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

Does data cleaning improve heart disease prediction

Wireless sensor networks are very suitable for remote sensing and monitoring tasks. Sensor nodes being battery operated devices and other limited resources present many challenges for protocols in WSNs. Researches are being continuously conducted with the purpose of combating these issues. Clustering in WSNs aim to distribute the energy load of the nodes by assigning some aggregator nodes or cluster heads. This paper presents a new criteria for CH selection which uses concepts of node density and residual energy. The purpose is to have a load balance to prolong network life.

Energy efficient cluster head selection technique for homogeneous wireless sensor networks

A database is a collection of information that is organized for easy storage, retrieval and update. This information is represented in many forms like text, table, image, chart and graph etc. here we concentrate on information that is stored in the form of images. Content based image retrieval (CBIR) technique explores various methodologies in extracting implicit knowledge, patterns and relationships found in the images from the collection of images. This paper focuses on various techniques that were proposed in earlier literature.

/pdf/a-study-on-content-based-image-retrievalsystems-4bpaxogofr.pdf

A Study on Content Based Image RetrievalSystems

/pdf/an-energy-efficient-technique-for-improved-network-lifetime-318xjmxp.pdf

An Energy Efficient Technique for Improved Network Lifetime in Wireless Sensor Network (WSN) Through Energy, Distance, and Density-Based Clustering

Sensor nodes are characterized with limited resources of processing, memory, and battery. These features motivate the researchers to propose power-aware communication protocols. Clustering is used to help for this purpose. It is used to organize the massive number of deployed sensors in the network to minimize energy consumption. Different categories of clustering techniques were proposed. One of these categories is density-based clustering which mainly depends on measuring the density around nodes before grouping them into clusters. This paper proposes an Energy-Efficient Density-based clustering technique which aims to balance the energy consumption among all clusters. This is done by the adaptation of the transmission range of cluster heads to use a suitable value according to the density around it. Simulation results for the proposed technique shows its effectiveness as it achieves less power consumption and more network lifetime when compared with other density-based clustering techniques.

Energy efficient density-based clustering technique for wireless sensor network

Clinical records contain massive heterogeneity number of data types, generally written in free-note without a linguistic standard. Other forms of medical data include medical images with/without metadata (e.g., CT, MRI, radiology, etc.), audios (e.g., transcriptions, ultrasound), videos (e.g., surgery recording), and structured data (e.g., laboratory testresults, age, year, weight, billing, etc.). Consequently, to retrieve the knowledge from these data is not trivial task.Handling the heterogeneity besides largeness and complexity of these data is a challenge. The main purpose of this paperis proposing a framework with two-fold. Firstly, it achieves a semantic-based integration approach, which resolves theheterogeneity issue during the integration process of healthcare data from various data sources. Secondly, it achieves asemantic-based medical retrieval approach with enhanced precision. Our experimental study on medical datasetsdemonstrates the significant accuracy and speedup of the proposed framework over existing approaches.

https://ijci.journals.ekb.eg/article_33955_1e9d15072033562141243b98f78ab369.pdf

Semantic-based Approach for Solving the Heterogeneity of Clinical Data

/pdf/an-evaluation-framework-for-selecting-cloud-service-provider-29ujfobd.pdf

An evaluation framework for selecting cloud service provider in neutrosophic environment and Modified Generative Adversarial Network

Noura A.Semarya,Kariman M.Mabrouka,, Hatem Abdul-Kaderb a Information Technology dept., Faculty of Computers and Information, Menoufia University, Menoufia 32511, Egypt b Information system dept., Faculty of Computers and Information, Menoufia University, Menoufia 32511, Egypt kariman.mamdouh@ci.menofia.edu.eg Abstract 3D models unauthorized modification is an attractive research problem nowadays due to the widespread availability of technologies and designs on the internet. In this paper, we propose a blind fragile watermarking scheme in the spatial domain for 3D model authentication based on the curvature features of the 3D model and chaos sequence. First, we compute the curvature feature for every vertex of the 3D model, after that, the vertices are classified into 3 classes: flat, peak, and In-between vertices using K-means clustering algorithm. We suggested two methods for embedding the watermark based on the least significant bit (LSB) substitution technique; Cluster Type-based Embedding (CTE) and Cluster Size-based Embedding (CSE). The proposed methods employ a Chaos sequence generator to generate a Chaos sequence that is used to generate the embedded watermark, where the tampering region can be verified and located by the Chaos sequence-based watermark check. Many assessment methods are employed to evaluate the proposed method with various unauthorized attacks like rotation, translation, scaling, cropping, and noise addition. The experiment results show an improvement of embedding imperceptibility as well as tempered regions detection compared to existing literature works.

/pdf/a-blind-fragile-watermarking-for-3d-triangular-model-based-vgwaj9da.pdf

A blind fragile watermarking for 3D triangular model based on curvature features and chaos sequence

H. M. Abdul-kader, Rashed Salem, Safa'a Said Saleh Information Systems Dept Menoufia University Shebin Elkom Eygpt Abstract— The demand for real-time database is increasing. Indeed, most real-time systems are inherently distributed in nature and need to handle data in a timely fashion. Obtaining data from remote sites may take long time making the temporal data invalid. This results in large number of tardy transactions with their catastrophic effect. Clustering the database sites nodes can help distributed real-time database systems to face the challenges meeting their time requirements. Reducing the large number of network sites into many clusters with smaller number of sites will effectively decrease the response time, resulting in better meeting of time constraints. In this paper, we introduce a clustering algorithm for distributed real-time database that depend on both the communication time cost and the timing properties of data. The results show the effectiveness of the proposed approach via achieving lower communication time, higher database performance and better meeting of timing requirements. Keywords—clustering; database; real-time; distributed systems

https://ijci.journals.ekb.eg/article_33957_2506b47d05c5cdbfa26a9bdaa0363dcb.pdf

Clustering Algorithm for Distributed Real-Time Database sites

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hatem Ahmed

Author Tools

Chat about Author