Digital Picture Research Articles

Development of an Approach for Image Forgery Detection Using Machine Learning Algorithms

Digital picture fraud detection is an increasing societal necessity due to the importance of verified images. The detection of picture copying, splicing, retouching, and re-sampling forgeries is included. In the absence of digital signatures or watermarks, passive picture authentication may serve as an alternative to active authentication. Passive techniques, every so often recognized as blind techniques, could take place without preceding knowledge of the picture or its reference. Identifying counterfeiting picture or tampering was a research field for long a period of time, triggered via the Internet, online platforms, social messaging platforms, and extensive digital image usage. The rate of failure could be a key factor for examining the alteration of picture or forgery, among other existing methods. The research applies almost six common algorithms related to machine learning in order to extract features from Lightweight, Spatial Exploitation, and Residual deep learning models on benchmark datasets MICC-F220, Columbia, and CoMoFoD. The models of incorporated deep learning could consist of AlexNet, GoogleNet, VGG16, VGG19, SqueezeNet, MobileNetV2, ShuffleNet, ResNet-18, ResNet-50, and ResNet-101 for spatial exploitation. Fine-tuning is applied to the top three deep learning models, optimizing hyperparameters centered on indicators of performance for every single benchmark dataset. Tweaked SqueezeNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results for MICC-F220 dataset. Fine-tuned VGG19, MobileNetV2, and ResNet-50 deep learning models with SGDM Optimizer and SVM v classifier yielded the best results for Columbia dataset. In CoMoFoD dataset, fine-tuned AlexNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results. The proposed approach, utilizing machine learning algorithms and deep learning features, enhanced forgery detection and reduced false positives. Results were validated on benchmark image forgery datasets and compared to current methods.

The Digital Picture as Tools for Teaching Young Learners

Teaching for young learners is more challenging since the characteristics of young learners are quite different from adults. Younger learners are growing up by technology and it has been integrated with their lives. They feel comfortable and confident on using it. The purpose of this study is to examine the teachers’ perception on learning English through technology and to identify the challenges. This study used qualitative descriptive research to perceive the use of technology in teaching young learner including the challenges and to obtain more conclusions, the researcher made an depth interview with a teacher in one of primary school. The results of the research showed that the teachers are mostly agreeing to teach English for young learners by using technology, including digital picture, games, and songs. For the challenges, the teachers faced the lack of students’ concentration and difficulties in saying some words because of mother tongue. This is happened because the students are lack of exposure to English outside classroom. To overcome these challenges, the teacher should be creative making a pleasant classroom atmosphere, actively engaging students to speak English in the classroom and do self-reflection.

The Practice of Interactive Digital Picture Book Design in the Context of Media Convergence

The rapid development of media technology has enabled new media and traditional media to realize a good integration, and people's entertainment is richer in the era of media convergence. Reading is the main way for people to learn independently and enrich their daily life. Under the influence of media convergence, there has been a shift from traditional paper books to e-books and cell phones and other in people's reading carriers. At the same time, people's reading methods have also changed, and interactive reading has gradually broken the limitations of traditional reading way. Nowadays, interactive digital picture books are developing rapidly, and designers can realize the reasonable design of interactive digital picture books and improve people's reading through language sound, contextual stories, digital vision, image interaction, mobile augmented reality design, etc..

Embracing emojis: Bridging the gap in workplace technology adoption and elevating communication effectiveness

An emoji is a small digital picture or icon that is used in electronic messaging to show a feeling, thought, or idea. Emoticons are frequently employed in written communication, such as text messaging, social media platforms and various digital mediums, with the purpose of expressing emotions or providing more meaning to a statement. Adopting emojis in the professional environment has become a modern method to convey emotions, offer feedback, and express appreciation amongst peers. This exploration underlines the intersection of technology – specifically the use of emojis – and its influence on interpersonal interactions and dynamics within the professional setting. This draws attention to the need for effective communication, setting clear expectations, and understanding the implications of technology on workers' attitudes and behaviours. For deeper analysis, this study employs two pivotal frameworks: the theory of operant conditioning and the Technology Acceptance Model (TAM). The former probes into the effects of outcomes, such as the usage of emojis, on employees' behaviours in the office context. The latter, on the other hand, explores the acceptance and perceptions employees have towards such technological innovations. Emphasizing effective communication becomes paramount when we consider the role technology plays in shaping our interactions and viewpoints. While emojis can potentially enhance the clarity and effectiveness of communication, they come with their set of challenges. It's vital to approach their use with discretion, particularly in sensitive scenarios like performance evaluations. In such contexts, the margin for misunderstanding is high, and incorrect interpretations can lead to unintended consequences.

Art, Images, and Their Media as Sites of Memorialisation

ABSTRACT In this article, I hypothesise that it is in the interaction with digital or material picture objects, or engagement with art dealing with the topic of grief and loss, that the viewer may experience a moment of melancholy—an eruptive and bodily experience of memory and memorialisation. Barthes’ concept of the punctum and Foucault’s heterotopia allow me to consider the potential for printed and digital photographs to become—or, to be translated into—sites of loss for viewers under specific circumstances. I argue that certain photographs have the potential to become sites of loss in their objecthood, either as a printed photograph, or as a technological device displaying a photograph on its screen. This same experience may also be nurtured and investigated in works of art dealing with loss and death.

Crafting a Tailored Digital Picture Storybook to Elevate the Pancasila Student Profile: Insights from Needs Analysis

This research aims to describe the needs analysis results for developing a digital picture storybook to enhance the Pancasila Student Profile of the 1st-grade elementary school students. This type of research is research and development (RD). The development model used is Borg and Gall. The population in the study were 1st-grade students and homeroom teachers of elementary schools in Bantul Regency. The instruments were needs-analysis questionnaires, preliminary observation guidelines, preliminary interview guidelines, and literature. Data were analyzed by descriptive analysis. Based on the research results, it was concluded that students and teachers need digital picture storybooks to increase the Pancasila student profile of the students. The developing digital picture storybook is designed to fit the student's characteristics and the existing resources. It is an Android-based application that provides a story about the Pancasila Student Profile. It uses the Calibry font type with appropriate colors. The digital picture storybook can be operated without the internet. From the results of the needs analysis, schools do not yet have digital storybook facilities. Teachers still rely on textbooks from the government and worksheets from publishers for teaching and learning activities. Digital picture storybook is relevant to the Merdeka Curriculum in the subject of Bahasa Indonesia in phase A (Class I) with reading and viewing elements.

Comparison of three-versus two-dimensional pre-operative planning for total hip arthroplasty

BackgroundA successful clinical outcome for total hip arthroplasty (THA) depends on accurate sizing and positioning of the implants. Using three-dimensional (3-D) pre-operative planning with a computerized tomography (CT) scan has many potential advantages over conventional 2-D planning using radiographs, including potentially more accurate assessments of the size and anteversion of the acetabulum, as well as lateral femoral offset. The purpose of this study was to compare the accuracy of 3-D to 2-D templating with respect to acetabular and femoral size, as well as lateral femoral offset. MethodsPre-operative templating data was collected prospectively from a consecutive series of 290 primary THAs (acetabulum on all, femoral component on 255 of the cases using one specific stem). All cases were initially templated on a digital imaging picture archiving and communication (PACS) system with calibrated images to estimate the acetabular size, femoral size, and lateral femoral offset. The 3-D templating was then performed with software based on a CT scan, and the results were compared to what was surgically implanted. ResultsThe 3-D templating for the acetabulum was accurate 99.7% of the time based on the final implanted component. The 2-D templating for the acetabulum was accurate 39% of the time, with 46% templating larger and 15% templating smaller than the final implanted component. The 3-D templating of the femoral component was accurate 63% of the time, and within one size of final implant in 96% of cases. The 2-D templating of the femoral component was accurate 53% of the time and within one size of final implant in 94% of cases. Lateral femoral offset consistently matched the 3-D plan, with a 100% match rate. The 2-D templated femoral offset was accurate 87% of the time and was changed in 13% of cases after 3-D templating. ConclusionThe CT-based 3-D preoperative planning was superior to 2-D planning for THA with respect to acetabular size, femoral size, and lateral femoral offset. Precise acetabular component sizing conserves bone and allows for a more predictable press fit, while facilitating efficient inventory management. Lateral femoral offset is often difficult to measure on 2-D images, and 3-D templating consistently allows for accurate offset restoration, which is important for normal hip function and stability.

Effect of Digital Wordless Picture Books to Improve Students Ability in Writing Narrative Text at SMP Islam As-Shofa Pekanbaru


 
 
 
 This research aims to introduce teaching media based on technology using digital wordless picture books to improve students’ narrative writing abilities. This teaching media is presented in the form of PDF and e-books. The research design in this research used quasi-experimental. The instrument in this research used writing test. The subject in this research are the eighth-grade students of SMP Islam As-Shofa Pekanbaru. Purposive sampling technique was employed to select the sample. The result of normality test indicate the data is normal distribution and the homogeneity test is homogeneous. The calculation of the Independent Sample T-test shows that the p-value for the posttest data of the experimental group and the control group is p = 0,002 < 0,05. The result of the Independent Sample T-test indicate a that the Digital Wordless Picture Books media is effectively used in improving students’ ability in writing narrative text to the eighth-grade students of SMP Islam As-Shofa Pekanbaru.
 
 
 
 

Development of Flash Card Media for Early Reading Student

This research aims to study how first-gradestudents improve their reading skills by using digital picture word cards. This study uses the ADDIE development model and is a development research. The sampling technique in this study used simple random sampling by using the Slovin formula in determining the sample. The one-group posttest technique used in this study the experimental class followed the same experimental methodology. Normality and homogeneity tests were used in qualitative and quantitative data analysis methodology. The results of the analysis showed that with a percentage of 92.73%, the professional assessment of learning media design. With a percentage of 90%, the expert assessment of the learning media content is included in the very correct category. With a proportion of 95%, the linguist's assessment of educational media is included. With excellent criteria, the overall validation level of linguists, design experts, and material experts is 92.58%. This digital flash card media turned out to be feasible to use as a learning tool and can improve students' reading skills. The results showed a significant difference between students' initial reading test scores on the pretest and posttest. This conclusion is reinforced by the results of the Piered Sample Test which shows the variance in students' initial reading using digital picture word card media between pretest and posttest with a significance level of 0.000 <0.05. Therefore, it can be said that the use of digital flash card media can help children in elementary schools acquire basic reading skills.

Development and research of deep neural network fusion computer vision technology

Abstract Deep learning (DL) has revolutionized advanced digital picture processing, enabling significant advancements in computer vision (CV). However, it is important to note that older CV techniques, developed prior to the emergence of DL, still hold value and relevance. Particularly in the realm of more complex, three-dimensional (3D) data such as video and 3D models, CV and multimedia retrieval remain at the forefront of technological advancements. We provide critical insights into the progress made in developing higher-dimensional qualities through the application of DL, and also discuss the advantages and strategies employed in DL. With the widespread use of 3D sensor data and 3D modeling, the analysis and representation of the world in three dimensions have become commonplace. This progress has been facilitated by the development of additional sensors, driven by advancements in areas such as 3D gaming and self-driving vehicles. These advancements have enabled researchers to create feature description models that surpass traditional two-dimensional approaches. This study reveals the current state of advanced digital picture processing, highlighting the role of DL in pushing the boundaries of CV and multimedia retrieval in handling complex, 3D data.

Medical Image Processing using Deep Learning Techniques in Big Data Perspective

Artificial intelligence and machine learning will be the driving forces behind the next computing revolution. These technologies rely on the ability to identify trends from historical information and predict future outcomes. One of the best machine learning techniques, deep learning is employed in a variety of applications, including object recognition, picture categorization, image analysis, and clinical archives. Image and video data are necessary for both diagnosing the patient's illness and determining its severity. Convolutional neural networks are efficient gears for digital picture classification and image understanding. The production of medical photographs has ex-ponentially increased as a result of the proliferation of digital devices and the development of camera technology, which creates Bigdata. Massive, difficult-to-manage volumes of structured, unstructured data are referred to as "Big data". The more data processed for analysis, the greater will be the analytical accuracy and also the greater would be the confidence in our decisions based on the analytical findings. In this paper, we proposed a novel method for early detection of pneumonia disease using deep learning techniques along with the big data storage and big data analytics to achieve more better performance. The results show that, the model achieved 91.16% of accuracy and 93.22% of F1-score.

Study on Copy-Move Forgery Detection in Medical Images using Handcrafted Features

Abstract: Medical image analysis and diagnosis are significantly hampered by digital picture alteration and fabrication. Copymove forgery, which involves copying a section of a picture and pasting it somewhere else in the same image to generate a fake copy, can result in incorrect diagnoses and even damage patients. Using handmade characteristics, we suggest a reliable technique in this study for identifying copy-move forgery in medical photos. Our method uses sophisticated feature extraction methods and image processing techniques to find duplicated portions within a picture. The integrity of medical image analysis is improved by experimental findings showing the efficacy of our suggested strategy in spotting copy-move forgery in medical pictures.

Development of a System for Transmitting Medical Data and Diagnostic Images via the Internet for Hospitals in Thailand

This research project aims to advance healthcare data and diagnostic imaging delivery by employing a digital Picture Archiving and Communication System, also known as PACS, alongside a web application that is internet accessible. The study explores two main areas, which are User Interface design and system functionality. The User Interface design merges the aesthetic of traditional paper documentation with the advantages of electronic displays to enhance both readability and overall user experience. The system functionality has been assessed and confirmed to offer secure and efficient transmission of medical diagnostic data as well as radiographic images. By complying with the Personal Data Protection Act of 2019 or PDPA, the system ensures the safe handling of confidential personal information. Test results show that the system meets performance standards and has been well-received by users. The project serves as a timely solution for today's medical industry, focusing on the speed and security of diagnostic data and image transmission.

Content-Based Image Retrieval using Hard Voting Ensemble Method of Inception, Xception, and Mobilenet Architectures

Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low-level attributes and human visual perception has become pivotal in refining Content-Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.

Smartphone-Enabled Fluorescence and Colorimetric Platform for the On-Site Detection of Hg2+ and Cl- Based on the Au/Cu/Ti3C2 Nanosheets.

Smartphone-assisted fluorescence and colorimetric methods for the on-site detection of Hg2+ and Cl- were established based on the oxidase-like activity of the Au-Hg alloy on the surface of Au/Cu/Ti3C2 NSs. The Au nanoparticles (NPs) were constructed via in-situ growth on the surface of Cu/Ti3C2 NSs and characterized by different characterization techniques. After the addition of Hg2+, the formation of Hg-Au alloys could promote the oxidization of o-phenylenediamine (OPD) to generate a new fluorescence emission peak of 2,3-diaminopenazine (ADP) at 570 nm. Therefore, a turn-on fluorescence method for the detection of Hg2+ was established. As the addition of Cl- can influence the fluorescence of ADP, the fluorescence intensity was constantly quenched to achieve the continuous quantitative detection of Cl-. Therefore, a turn-off fluorescence method for the detection of Cl- was established. This method had good linear ranges for the detection of Hg2+ and Cl- in 8.0-200.0 nM and 5.0-350.0 µM, with a detection limit of 0.8 nM and 27 nM, respectively. Depending on the color change with the detection of Hg2+ and Cl-, a convenient on-site colorimetric method for an analysis of Hg2+ and Cl- was achieved by using digital images combined with smartphones (color recognizers). The digital picture sensor could analyze RGB values in concentrations of Hg2+ or Cl- via a smartphone app. In summary, the proposed Au/Cu/Ti3C2 NSs-based method provided a novel and more comprehensive application for environmental monitoring.

Image Based Search Engine Using Deep Learning

Abstract: Machine the learning's field of image recognition as well as classification is one that is expanding quickly. The business ramifications of object identification, which is a crucial component of picture categorization, are enormous. A procedure to identify and recognize an item or property in a digital video or picture, image recognition is a subset of artificial intelligence. A larger phrase used to describe techniques for obtaining, processing, and analyzing data from the actual environment is computer vision. The highly dimensional data generates judgements that are expressed as numerical or pictorial information. Artificial intelligence (AI) also encompasses event detection, object identification, learning, picture the rebuilding process, and tracking of video in addition to image classification. This project outlines a methodical strategy to organizing using machine learning. digital photos. Convolutional neural network (CNN) and deep neural networks are two classifiers that may be combined to improve classification performance. Over the past several years, Convolutional Neural Networks (CNNs) have emerged as the leading approach for image classification and object recognition tasks. On several of the picture categorization databases, they now outperform humans. Most of these datasets are built around the idea of tangible classes; photographs are categorized according to the kind of item they include. Using Abstract classes, this project will propose a unique picture classifying dataset that should be simple for humans to solve but difficult for CNNs to fully understand. This dataset and potential variants of the dataset are used to assess the classifications performance of common CNN designs. Interesting topics for future study are found

Authorized Automatic Vehicle Allowance System

Abstract: In most organizations, the ongoing vehicle entry registration process for visitors, staff, or students entering the organization includes a security guard checking the driver's identification card or looking for a membership sticker on the vehicle's windscreen, which can easily be lost or stolen. This writing process is tiresome and time-demanding, and it is prone to incorrect notes; additionally, backup and sharing of this vehicle information is difficult due to the data being a physical copy. The goal of the Automatic Authorized Vehicle Allowance System is to create a digital picture processing system with the Raspberry Pi that can be used in an entrance parking system. The Raspberry Pi will identify the car with an ultrasonic sensor and photograph it. The device then processes the picture using digital image processing techniques. The system incorporates various algorithms such as localization, normalization, orientation, segmentation, and optical character recognition (OCR), which examines picture characteristics and forecasts license plate numbers. The obtained number plate information is then cross-checked with records stored in a pre-existing database. An administration is in charge of designating a "whitelist," which is a list of vehicles that can join the suggested system. If the car registration is found in the database, a servo motor circuit connected to a Raspberry Pi will open the gate, and after a monitor delay, the gate will shut. The system is built in such a way that it is simple to apply in a short period of time and is cost-effective.

The Effect of Digital Picture Series Media on Simple Essay Writing Skills in Elementary Schools

This study aims to determine the effect of digital series picture media on simple essay writing skills in the Indonesian language subject for third-grade students at SDN Tawangrejo II Pandaan, classified as low. The research method used in this study is quantitative with a pre-experimental design, specifically the one-group Pre-Test-Post-Test design. The subjects of this study were 32 third-grade students. The sampling technique used in this study was saturated sampling. The data collection technique used in this study was a test to measure third-grade students' simple essay writing skills. The test was conducted with a Pre-Test and a Post-Test. The test consisted of writing a simple essay based on five indicators. The data analysis technique differed significantly between the Pre-Test and Post-Test results. This was indicated by the hypothesis test using the paired sample t-test with a significance value of 0.000, which means the sig value < 0.05. Therefore, the null hypothesis (H0) is rejected, and the alternative hypothesis (Ha) is accepted. The use of digital series picture media significantly affects simple essay writing skills.

Free access to multilingual digital books: a tool to increase book reading?

The objective of the study was to examine how providing access to multilingual digital picture books affected the reading habits and language development of children from bilingual families. The study included 41 children aged 4–5 from two schools whose parents spoke a heritage language distinct from the environmental language (Dutch), and had a low level of education. The children were randomly assigned to two groups - one with access to digital books solely in the environmental language (Dutch) and the other with a choice between the environmental language and their heritage language. A general vocabulary test was administered before and after a six-week intervention period. The findings indicated that access to digital books motivated reading, with roughly one-third of the children reading a substantial number of books during the intervention. The availability of books in the heritage language did not lead to an increase in book reading. Given a choice, only a minority (33%) preferred to read in their heritage language. The conditions exhibited similar growth in vocabulary, but the study uncovered positive associations between the number of books and vocabulary development.

A secure framework for medical image by integrating watermarking and encryption through fuzzy based ROI selection

Security, secrecy, and authenticity problems have arisen as a result of the widespread sharing of medical images in social media. Copyright protection for online photo sharing is becoming a must. In this research, a cutting-edge method for embedding encrypted watermarks into medical images is proposed. The proposed method makes use of fuzzy-based ROI selection and wavelet-transformation to accomplish this. In the first step of the process, a fuzzy search is performed on the original picture to locate relevant places using the center region of interest (RoI) and the radial line along the final intensity. The suggested method takes a digital picture and divides it into 4×4 non-overlapping blocks, with the intent of selecting low information chunks for embedding in order to maximize invisibility. By changing the coefficients, a single watermark bit may be inserted into both the left and right singular SVD matrices. The absence of false positives means the suggested technique can successfully integrate a large amount of data. Watermarks are encrypted using a pseudorandom key before being embedded. Discrete wavelet transform saliency map, block mean method, and cosine functions are used to construct an adaptively-generated pseudo-random key from the cover picture. Images uploaded to social media platforms must have a high degree of invisibility and durability. These watermarking features, however, come with a price. The optimal scaling factor is used to strike a balance between the two in the proposed system. Furthermore, the suggested scheme’s higher performance is confirmed by comparison with the latest state-of-the-art systems.