Data Extraction Techniques Research Articles

Optimizing Healthcare Data Extraction with AI: A Path to Improved Patient Care

This study analyses the role of AI in enhancing the healthcare system by improving data extraction techniques to address various challenges in the healthcare sector. The roles of artificial intelligence (AI), like natural language processing (NLP) and machine learning (ML) in healthcare, allow the automation of the extraction of unstructured clinical information from records. This results in quicker diagnosis, enhanced treatment outcomes, and reduced paperwork. It has allowed for reduced burnout among healthcare professionals who would otherwise extract this data manually, allowing them to focus on providing better quality care to patients. While AI has been implemented in this sector, much must be done to achieve its full potential.

Utilizing Data Mining Techniques in Geophysical and Biological Analysis for Assessing Environmental Risks

This study aimed to assess environmental risks using data extraction techniques. It focused on geophysical and biological factors and addressed the urgent need for effective risk management strategies to reduce soil erosion, water pollution, and air quality deterioration. A comprehensive dataset was created through the systematic collection of geophysical and biological data including temperature, soil composition, and biological abundance index. It used equipment such as satellite sensors and mountain transmitting stations. Various statistical tools used include decision trees and random forest algorithms. They were used to analyze data and identify important environmental risk factors. The results showed some interesting insights, revealing that the Neural Network has an accuracy of 95.5%, and the Random Forest algorithm predicts risk with an accuracy of 92.0%. It analyzed the classification of environmental hazard zones and identified high-risk zones, such as Zone A, which contains 10,000 people affected by erosion and Zone D, 20,000 people who were at risk from soil contamination. The study concludes that social media can significantly improve understanding and management of environmental risks. Additionally, it provides a useful framework for decision-makers and stakeholders to promote sustainable environmental practices.

Deep adversarial learning models for distribution patterns of piezoelectric plate energy harvesting

This paper presents a novel approach utilizing piezoelectric plate structures with random electrode distribution patterns for energy harvesting applications across various vibration modes. For the first time, leveraging electromechanical Finite Element Analysis (eFEA) and data extraction techniques, we investigate the integration of conditional Generative Adversarial Networks (cGAN)-based dynamic models for this purpose. The cGAN offers an effective technique for generating realistic synthetic data conditioned on input parameters, thereby enabling the creation of diverse and representative datasets for training energy harvesting systems. The integration of eFEA with cGAN opens up new possibilities for optimizing the design and performance of piezoelectric energy harvesters across various applications. Specifically, we explore four distinct cGAN models-based mechanics of energy harvesters by deploying distribution patterns. These models include training data generated by stacking simultaneously mode images, utilizing separate cGAN models for each mode, labeling images by mode, and concatenating all mode images into one. Our study focuses on assessing the effectiveness of these models in minimizing loss in cGAN-based power generation and predicting Structural Similarity Index Measure (SSIM) values, and more importantly, identifying the predicted data point outputs from the generated pixel image extractions. By analyzing the generated data from numerical model and its application in deep learning, we aim to enhance the understanding of the effects of distribution patterns and image processing techniques for optimal power generation and the effectiveness of piezoelectric energy harvesting systems across different vibration modes. The studies explore how different distribution patterns affect the power harvesting efficiency and frequency bandwidth, utilizing the generated datasets.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

A Novel Hybrid Regression Model for Banking Loss Estimation

Given the critical need to identify financial risks in the banking sector early, this study presents a novel approach that uses historical financial ratios from the FDIC database to predict bank failures in the United States. Accurate estimation of potential losses is essential for risk management and decision-making procedures. We present a novel hybrid approach to loss estimation in the context of bank failures in this study. ElasticNet regression and relevant data extraction techniques are combined in our method to improve prediction accuracy. We conducted thorough experiments and evaluated our hybrid approach's performance against that of conventional regression techniques. With a remarkably low Mean Squared Error (MSE) of 0.001, a significantly high R-squared value of 0.98, and an Explained Variance Score of 0.95, our proposed model demonstrates superior performance compared to existing methodologies. The accuracy of our method is further demonstrated by the Mean Absolute Error (MAE) of 1200 units. Our results highlight the potential of our hybrid approach to transform loss estimation in the banking and finance domain, offering superior predictive capabilities and more accurate loss estimations.

(Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review.

An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Data extraction activities associated with evidence synthesis have been described as time-consuming to the point of critically limiting the usefulness of research. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. The goal of the present study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists. We report the baseline results of a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. This review follows PRISMA standards for reporting systematic reviews. The baseline review of social science research yielded 23 relevant studies. When considering the process of automating systematic review and meta-analysis information extraction, social science research falls short as compared to clinical research that focuses on automatic processing of information related to the PICO framework. With a few exceptions, most tools were either in the infancy stage and not accessible to applied researchers, were domain specific, or required substantial manual coding of articles before automation could occur. Additionally, few solutions considered extraction of data from tables which is where key data elements reside that social and behavioral scientists analyze.

Calibrating the Digital Twin of a Laboratory Ball Mill for Copper Ore Milling: Integrating Computer Vision and Discrete Element Method and Smoothed Particle Hydrodynamics (DEM-SPH) Simulations

This article presents a novel approach to calibrating the digital twin of a laboratory mill used for copper ore milling. By integrating computer vision techniques for real-time data extraction and employing DualSPHysics simulations for various milling scenarios, including balls only, balls with ore, and balls with slurry, we achieve a high degree of accuracy in matching the digital twin’s behavior with actual mill operations. The calibration process is detailed for mills with three different diameters, highlighting the adjustments in simulation parameters necessary to account for the absence of ore. Understanding the dynamics between the suspension within the mill and the operation of the grinders is crucial for the future improvement of the grinding process. This knowledge paves the way for optimizing the process, not only in terms of the quality of the end product but primarily in terms of energy efficiency. A profound understanding of these interactions will enable engineers and technologists to design mills and grinding processes in a way that maximizes efficiency while minimizing energy consumption.

El Atlas Histórico del Español: un nuevo recurso para el estudio de la dialectología histórica

Abstract The Historical Atlas of Spanish (AHE) is a free-access tool on the Internet designed to advance the knowledge of dialectal variation in the history of Spanish. The Atlas consists of a database of structural features (with preferential attention to morphosyntax) extracted from dialectal textual corpora. For each of the structures studied, the results are presented in the form of a brief state of the art illustrated with maps and graphs. The Atlas aims to contribute to the study of linguistic variation and change from a dialect perspective and extract significant generalizations about the diffusion of linguistic changes in the physical space. In this article we present the main methodological issues that have been considered in the process of creating the Historical Atlas of Spanish: contents and structure, data sources, techniques for data extraction and analysis, and the computer application for creating the maps and visualizing the results.

The application value of deep learning in the background of precision medicine in glioblastoma.

Introduction: Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current conventional treatment regimen because of different molecular subtypes, in the context of precision medicine, the application of deep learning to identify the salient features of tumors on brain imaging, prognostic predictive assessment combined with clinical data to maximize the benefits of each patient from the treatment regimen is a non-invasive and feasible regimen. Methods: We conducted a comprehensive review of the existing literature on the role of deep learning in glioblastomas, covering molecular classification and diagnosis, prognosis assessment. Results: Data based on a variety of magnetic resonance imaging sequences, genetic information, and clinical combinations enable noninvasive predictive tumor diagnosis of glioblastoma and assess overall survival and treatment response accuracy. For images, standardized image acquisition and data extraction techniques can be effectively translated into learning models for clinical practice. However, it must be recognized that interventions in the treatment of glioblastoma using deep learning are still in their infancy, and the robustness of the model is challenged, as the current total number of glioblastoma samples is insufficient for large-scale experimental methods, which is directly related to the difficulty of application of the model. Conclusion: Compared to radiomics and shallow machine learning, deep learning can be a more robust, non-invasive, and effective approach, providing more valuable information as clinicians develop personalized medical protocols for glioblastoma patients.

A Systematic Literature Review on Digital Forensic Investigation on Android Devices

Mobile forensics, particularly in the Android ecosystem, is a rapidly evolving field that demands continuous advancements to address the growing complexity and diversity of mobile devices. This article emphasizes the importance of developing techniques for digitally analyzing Android smartphones, which dominate the smartphone market. The primary objective of this research is to contribute to the development of effective forensic investigation strategies tailored specifically for Android mobile devices, providing insights into the tools and methods used for this purpose. The objective of this study is to improve the precision and effectiveness of forensic examinations pertaining to Android mobile phones. It discusses the fundamental functionality of mobile devices as a source of digital evidence and provides an overview of tools and methodologies for collecting and analyzing such evidence. The importance of comprehending the hardware and software architecture of Android handsets in order to choose the right forensic tools is also highlighted in the article. Furthermore, it proposes future enhancements for Andriller, a popular digital forensic tool, to improve its effectiveness in Android forensic investigations. These enhancements include advancements in data extraction techniques, compatibility with new Android versions, support for additional data types, integration with advanced analysis methods, and addressing identified limitations. Additionally, the paper stresses the need for robust methodologies for conducting cloud forensics on Android devices, particularly in the context of data stored in cloud storage services. The proposed work aims to enhance the capabilities of Andriller and improve the efficiency of digital forensic investigations on Android devices.

Development of supporting platform for the fine flow characteristics of reactor core

This study presents the Supporting platform for reactor fine flow characteristics calculation and analysis (Cilian platform), a user-friendly tool that supports the analysis and optimization of pressurized water reactor (PWR) cores with mixing vanes using computational fluid dynamics (CFD) computing. The Cilian platform allows for easy creation and optimization of PWR's main CFD calculation schemes and autonomously manages CFD calculation and analysis of PWR cores, reducing the need for human and computational resources. The platform's key features enable efficient simulation, rapid solution design, automatic calculation of core scheme options, and streamlined data extraction and processing techniques. The Cilian platform's capability to call external CFD software reduces the development time and cost while improving the accuracy and reliability of the results. In conclusion, the Cilian platform exemplifies an innovative solution for efficient computational fluid dynamics analysis of pressurized water reactor (PWR) cores. It holds great promise for driving advancements in nuclear power technology, enhancing the safety, efficiency, and cost-effectiveness of nuclear reactors. The platform adopts a modular design methodology, enabling the swift and accurate computation and analysis of diverse flow regions within core components. This design approach facilitates the seamless integration of multiple computational modules across various reactor types, providing a high degree of flexibility and reusability.

Methods for using Bing's AI-powered search engine for data extraction for a systematic review.

Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently.

RESEARCH ON EFFICIENT INDEXING OF LARGE-SCALE GEOSPATIAL DATA BASED ON MULTI-LEVEL GEOGRAPHIC GRID

Abstract. With the implementation of unified natural resource management in China, national geographic conditions monitoring data have been identified as fundamental data for natural resource survey and monitoring. The efficiency of information extraction from massive spatio-temporal data to support natural resource management has emerged as a critical indicator for maximizing the value of geographic conditions monitoring data and enhancing data-driven decision management. Traditional spatial indices are computationally intensive, and when confronted with immense data volume or uneven data scale, issues such as extensive index computations and poor scale adaptability arise, impeding the efficient retrieval of complex geospatial data. In response to the need for efficient indexing of massive geospatial monitoring data at a scale of 100 million, a multi-level geographic spatial index framework based on geographic grids is proposed. Within the geographic conditions spatio-temporal database, a three-level spatial index of "zone-grid-space" is constructed, utilizing massive land cover data for analysis and testing. The results demonstrate that the multi-level spatial index method exhibits excellent scale adaptability, and grid coding dimensionality reduction and numerical operations effectively reduce the computational load of spatial retrievals of complex vector patches. This method significantly improves the retrieval efficiency of large-scale national geographic conditions data, providing an efficient technique for lightweight information extraction of large-scale monitoring geospatial data within spatial computing systems. The method holds reference value for on-demand retrieval, analysis, and decision-making of natural resource spatio-temporal big data.

Usage of Web Scraping in the Pharmaceutical Sector

INTRODUCTION: Web scraping is a technique that provides organizations with the ability to analyse large amounts of information and gather new information. 
 OBJECTIVES: Find a group that is a health check, a full body test, a blood test, and so on. In this way, the pharmaceutical industry should consider how to improve information, information storage, information retrieval, and capture. For example, the healthcare system may decide to standardize the assessment of speech and allow information to be shared across organizations to improve treatment outcomes in web scraping applications.
 METHODS: Web scraping is based on the pharmaceutical industry. From here, we get information about pharmacies, such as drug names in different categories or drug sales. However, we are dealing with diseases and common medicines. Using this information, we can find the most common viruses. There are many factors to consider when creating a junk website for the pharmaceutical industry, such as drug names, tablet categories, and syrups found in the pharmaceutical industry.
 RESULTS: As is clearly visible from the output, there are columns for drug names, manufacturers, drug types, and prices. This is the information we get from a website called Net meds, a pharmacy site. With the help of this information, we learn which drugs are most needed, and then we can find the most common diseases today.
 CONCLUSION: The results of this web scraping can be very useful and powerful. However, the industry's success in web scraping and data extraction techniques depends on the availability of clean chemical data.

Retrospective study of propionic acidemia using natural language processing in Mayo Clinic electronic health record data

BackgroundPropionic acidemia (PA) is a rare autosomal recessive organic acidemia that classically presents within the first days of life with a metabolic crisis or via newborn screening and is confirmed with laboratory tests. Limited data exist on the natural history of patients with PA describing presentation, treatments, and clinical outcomes. ObjectiveTo retrospectively describe the natural history of patients with PA in a clinical setting from a real-world database using both structured and unstructured electronic health record (EHR) data using novel data extraction techniques in a unique care setting. Design/MethodsThis retrospective study used EHR data to identify patients with PA seen at the Mayo Clinic. Unstructured clinical text (medical notes, pathology reports) were analyzed using augmented curation natural language processing models to enhance analysis of data extracted by structured data fields (International Classification of Diseases 9th or 10th revision [ICD-9/−10] codes, Current Procedural Terminology [CPT] codes, and medication orders). De-identified health records were also manually reviewed by clinical scientists to ensure data accuracy and completeness. The index date was defined as the patient's date of PA diagnosis at the Mayo Clinic. Results were reported as aggregate descriptive statistics relative to patients' index dates. Complications, therapeutic interventions, laboratory tests, procedures, and hospitalization encounters related to PA were described at and within 6 months of the patient's index date, and from medical history available before the index date. ResultsIn total, 13 patients with PA were identified, with visits occurring from 1998 to 2022. Age at diagnosis ranged from birth to 3 years; age at initial evaluation at the Mayo Clinic ranged from 3 days to 28 years. The mean number of Mayo Clinic outpatient visits was 31 (median duration of care, 2 years). PA-related complications were documented in 85% of patients and included nutritional difficulties (46%), metabolic decompensation events (MDEs; 38%), neurologic abnormalities (38%), and cardiomyopathy (7%). One pair of affected siblings had mild symptoms and no complications or MDEs. All 5 patients with a history of MDEs presented with developmental delays. Among patients with MDEs, the mean frequency of outpatient clinical care visits was 10 per year, and 3 patients required inpatient hospitalization (mean duration, 16 days). The incidence of severe complications was higher among patients with MDEs than those without MDEs. Of the patients with MDEs, 2 experienced crises while receiving treatment at the Mayo Clinic, with 9 total MDEs occurring between the 2 patients. Symptoms at presentation included hyperammonemia (78%), fever and/or decreased nutritional intake (67%), hyperglycemia/hypoglycemia (56%), intercurrent upper respiratory infection and/or lethargy (44%), constipation (33%), altered mental status (33%), and cough (33%). ConclusionsThis study highlights the range and frequency of clinical outcomes experienced by patients with PA and demonstrates the clinical burden of MDEs.

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The disorder of human thinking is a very complex problem caused by heavy-duty in daily life. We need some future and optimal classifier selection by using different techniques for depression data extraction using EEG. Intelligent decision support is a decision-making process that is automated based on some input information. The primary goal of this proposed work is to create an artificial intelligence-based fuzzy decision support system (AI-FDSS). Based on the given criteria, the AI-FDSS is considered for classifier selection for EEG under depression information. The proposed intelligent decision technique examines classifier alternatives such as Gaussian mixture models (GMM), k-nearest neighbor algorithm (k-NN), Decision tree (DT), Nave Bayes classification (NBC), and Probabilistic neural network (PNN). For analyzing optimal classifiers selection for EEG in depression patients, the proposed technique is criterion-based. First, we develop a general algorithm for intelligent decision systems based on non-linear Diophantine fuzzy numbers to examine the classifier selection technique using various criteria. We use classifier methods to obtain data from depression patients in normal and abnormal situations based on the given criteria. The proposed technique is criterion-based for analyzing optimal classifier selection for EEG in patients suffering from depression. The proposed model for analyzing classifier selection in EEG is compared to existing models.

FVI-BD: Multiple File Extraction using Fusion Vector Investigation (FVI) in Big Data Hadoop Environment

— The Information Extraction (IE) approach extracts useful data from unstructured and semi-structured data. Big Data, with its rising volume of multidimensional unstructured data, provides new tools for IE. Traditional Information Extraction (IE) systems are incapable of appropriately handling this massive flood of unstructured data. The processing capability of current IE systems must be enhanced because to the amount and variety of Big Data. Existing IE techniques for data preparation, extraction, and transformation, as well as representations of massive amounts of multidimensional, unstructured data, must be evaluated in terms of their capabilities and limits. The proposed FVI-BD Framework for IOT device Information Extraction in Big Data. The unstructured data has cleaned and integration using POS tagging and similarity finding using LTA method. The features are extracted using TF and IDF. The Information extracted using NLP with WordNet. The classification has done with FVI algorithm. This research paper discovered that vast data analytics may be enhanced by extracting document feature terms with synonymous similarity and increasing IE accuracy.

Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model.

e13592 Background: The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. However, creating digital pathology algorithms requires large volumes of training data, often on the order of thousands of histopathology slides. This becomes problematic for rare diseases, where imaging datasets of such size do not exist. This makes it impossible to train digital pathology models for these rare conditions. However, recent advances in generative deep learning models may provide a method for overcoming this lack of histology data for rare diseases. Pre-trained diffusion-based probabilistic models can be used to create photorealistic variations of existing images. In this study, we explored the potential of using a deep generative model created by OpenAI for the purpose of producing synthetic histopathology images, using chondrosarcoma as our rare tumor of interest. Methods: Our team compiled a dataset of 55 chondrosarcoma histolopathology images from the annotated records of Dr. Henry Jaffe, a pioneering authority in musculoskeletal pathology. We built a deep learning image-generation application in a Jupyter notebook environment, iterating upon OpenAI’s DALL-E application processing interface (API) with python programming language. Using the chondrosarcoma histology dataset and NVIDIA GPUs, we trained the deep learning application to generate multiple synthetic variations of each real chondrosarcoma image. Results: After several hours, the deep learning model successfully generated 1,000 images of chondrosarcoma from 55 original images. The synthetic histology images retained photorealistic quality and displayed characteristic cellular features of chondrosarcoma tumor tissue. Conclusions: Deep generative models may be useful in addressing issues of data scarcity in rare diseases, such as chondrosarcoma. For example, in situations where existing imaging data is insufficient for training diagnostic computer vision models, diffusion-based generative models could be applied to create training datasets. However, further exploration of ethical considerations and qualitative analyses of these generated data are needed.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

DESIGN OF A FRAMEWORK FOR DATA EXTRACTION AND ANALYSIS FROM ANDROID-EMBEDDED SMARTPHONES

Mobile forensic acquisition is a critical aspect of modern criminal investigations, as mobile devices have become an essential part of our daily lives. Yet, forensic investigators aiming to extract data from current mobile devices in a forensically sound manner have faced substantial difficulties due to the robust security features of these devices, such as encryption. Conventional forensic data-collecting techniques are no longer viable, necessitating the development of new techniques that work by evading security measures and taking advantage of system flaws. The CLOUD Act is a US law that gives foreign governments the authority to demand information from US-based Cloud Service Providers or to eavesdrop on conversations, which makes it more difficult to do forensic analysis on mobile devices. To address these issues and evaluate contemporary forensic data extraction approaches in light of the contentious regulation of encryption and governmental access to encrypted devices, this article suggests a new model for mobile forensic acquisition. One of the major challenges faced by forensic investigators is the constantly evolving nature of mobile devices and their software. As manufacturers release new models with different security features and operating systems, forensic experts must adapt their techniques to keep up with these changes. Additionally, the use of encryption is becoming more widespread among consumers, which makes it more difficult for forensic experts to access data on these devices. Therefore, it is essential that forensic investigators stay up to date with the latest advancements in mobile technology and develop new techniques for data extraction that can keep pace with these advancements.