Reviewerly/exHarmony huggingface dataset:
Blockchain-secure patient Digital Twin in healthcare using smart contracts

Authors:
raw_author_name: Sandro Amofa
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Qi Xia
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Hu Xia
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Isaac Amankona Obiri
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Bonsu Adjei-Arthur
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Jingcong Yang
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Jianbin Gao
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Abstract: Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation

Authors:
raw_author_name: Kartini Rahmat
raw_affiliation_string: Department of Biomedical Imaging, University Malaya Research Imaging Centre, Kuala Lumpur, Malaysia

Abstract: Objective:: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. Methods:: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results. Results:: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p &lt; 0.001) and the BI-RADS category with and without AI (κ =0.74, p &lt; 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively. Conclusion:: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients. Key Messages:: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.
Toward Improving the Generation Quality of Autoregressive Slot VAEs

Authors:
raw_author_name: Patrick Emami
raw_affiliation_string: National Renewable Energy Lab, Golden, CO 80401; University of Florida, Gainesville, FL 32611, U.S.A. Patrick.Emami@nrel.gov
raw_author_name: Pan He
raw_affiliation_string: Auburn University, Auburn, AL 36849, U.S.A. Pan.He@auburn.edu
raw_author_name: Sanjay Ranka
raw_affiliation_string: University of Florida, Gainesville, FL 32611, U.S.A.
raw_author_name: Anand Rangarajan
raw_affiliation_string: University of Florida, Gainesville, FL 32611, U.S.A. anand@cise.ufl.edu

Abstract: Abstract Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (“slots”) from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.
Noise Removal from the Image Using Convolutional Neural Networks-Based Denoising Auto Encoder

Authors:
raw_author_name: Serkan SAVAŞ
raw_affiliation_string: KIRIKKALE ÜNİVERSİTESİ

Abstract: With the exponential growth in the volume of digital images captured daily, there is an escalating demand for elevating image quality to achieve both accuracy and visual appeal. Addressing this need, the development of techniques for reducing image noise while preserving crucial features, such as edges, corners, and sharp structures, has become imperative. This paper delves into the significance of image denoising and introduces a novel approach utilizing a denoising autoencoder based on convolutional neural networks (CNNs). The proposed method adopts a meticulous two-step process to effectively eliminate noise. Initially, input images are segregated into training and testing sets. Subsequently, a denoising autoencoder model is trained using the designated training data. This model is then further refined through training on a CNN, enhancing its noise reduction capabilities. The evaluation of the system's performance is conducted using testing data to gauge its effectiveness. The study employs the MATLAB programming language for implementation and evaluation. Results, measured through RMSE (Root Mean Square Error) and PSNR (Peak Signal-to-Noise Ratio) criteria on two distinct datasets—the Covid19-radiography-database and SIIM-medical-images—reveal that our proposed method outperforms existing approaches significantly. This approach is particularly promising for applications demanding enhanced image quality, such as the resolution enhancement of medical images. The study contributes to the ongoing efforts in noise reduction research, offering a robust solution for improving visual perception in diverse image processing applications.
Science Learning Game (SLG) Based on Augmented Reality Enhances Science Literacy and Critical Thinking Students Skills

Authors:
raw_author_name: Mohamad Fatih
raw_affiliation_string: Universitas Nahdlatul Ulama Blitar
raw_author_name: Cindya Alfi
raw_affiliation_string: Universitas Nahdlatul Ulama Blitar

Abstract: Science literacy is the ability of individuals to understand, evaluate, and apply scientific knowledge in everyday problem-solving, while critical thinking is the ability to analyze, evaluate, and make decisions based on evidence and logic. This research is based on several issues in SDN Sentul2 Kota Blitar in grade 6 post-Covid 19. This research aims to produce a product used as a learning tool in Grade 6, namely Science Learning Game (SLG) Media based on Augmented Reality to Improve Science Literacy and Critical Thinking Skills in Grade 6 Students in Kota Blitar. This research and development (R&amp;D) study uses the ADDIE (analysis, design, development, implementation, and evaluation) model. The instrument used is a questionnaire. The research data analysis results show that the developed media can improve critical thinking skills, as evidenced by comparing the Pre-test results at 72.21% with the Post-test results at 93.57%. This media can also enhance science literacy, as indicated by the increase between the Pre-test results at 74.28% and the Post-test results at 92.14%. The implication is that the science literacy and critical thinking abilities of students in Indonesia still need improvement, which can be achieved by considering the use of instructional media. With positive results in pre-test and post-test scores, this media has the potential to enhance science literacy and critical thinking skills in students.
Hybrid Android Interface Practicum Mode to Develop Students' Science Process Skills

Authors:
raw_author_name: Riandi Riandi
raw_author_name: Rini Solihat

Abstract: Science Process Skills are abilities that students must possess because they are applicable not only to science learning but also to daily life. To be familiar with scientific method activities, students must have direct experience in order to be trained in Science Process Skills. This study aims to enhance students' Science Process Skills through hybrid android interface mode practicum learning. The research data was collected using description questions previously validated by subject matter experts. This study conducted quasi-experiments using the Nonquivalent (pretest-posttest) Control Group Design method. The research participants included control and experimental groups. The research results show an increase in Science Process Skills in the experiment class as evidenced by the achievement of aspects of observation 71.25%, interpretation 78.25%, classification 86.12%, prediction 84.45%, conducting experiments or investigations 83.34%, communicating 63.33%, hypothesizing 79.63%, applying concepts 88.34%, and asking questions 82.43%. It shows that the practicum mode of the hybrid Android interface can develop Science Process Skills
Examining the Developing Influence of Emerging Technologies in the Media Sector of Afghanistan

Authors:
raw_author_name: Musawer Hakimi
raw_affiliation_string: Assistant Professor, Samangan University, Afghanistan
raw_author_name: Amir Kror Shahidzay
raw_affiliation_string: Associate Professor, Kabul University, Afghanistan

Abstract: The study sets the stage by outlining the growing significance of digital platforms in Afghan media and the transformative potential of emerging technologies in this landscape. The purpose of this research is to comprehensively analyze the current state of affairs, challenges, and opportunities associated with integrating emerging technologies into Afghanistan's media sector. Employing a mixed-methods approach, the study utilizes questionnaires and interviews to gather data from a sample size of 80 participants representing diverse media outlets in Afghanistan. Quantitative analysis involves descriptive statistics and chi-square tests, while qualitative data from interviews are thematically analyzed to provide deeper insights. The results shed light on notable disparities in technology adoption rates, with cloud computing and artificial intelligence emerging as frontrunners. Anticipated integration levels vary across technologies, with cloud computing and AI garnering significant interest. Perceived challenges in integration range from moderately to extremely challenging, while opportunities for media development are viewed as promising. In conclusion, the study underscores the transformative potential of emerging technologies in Afghanistan's media landscape, despite existing challenges. It offers recommendations such as enhancing media literacy and addressing infrastructure gaps to fully harness the benefits of technological advancements.
Sistem Pakar Diagnosa Penyakit Angsa Menggunakan Metode Forward Chaining Berbasis Web

Authors:
raw_author_name: Ade Eviyanti
raw_affiliation_string: Universitas Muhammadiyah Sidoarjo, Sidoarjo
raw_author_name: Hindarto Hindarto
raw_affiliation_string: Universitas Muhammadiyah Sidoarjo, Sidoarjo

Abstract: Geese have many benefits for human needs. Starting from meat, eggs, feathers, fat, and other uniqueness such as guarding other livestock if there are animals or strangers approaching their territory. However, in Indonesia, goose utilization is less desirable and only a few do it. This poses a probӏem for goose breeders due to the absence of knowӏedge about the diseases experienced and the handӏing soӏutions. An expert system is an artificiaӏ inteӏӏigence that supports expert decision-making. The forward chaining method provides conclusions through rules derived from existing facts. This Expert System for Diagnosing Swan Diseases Using the Web-Based Forward Chaining Method is designed to make it easier for farmers or users to consult about the diseases experienced and treatment solutions with 90% diagnostic results. This expert system is made based on a website that can be accessed easily and at any time. Based on black box testing, the results obtained show 100% system functionality, so this expert system application can be used.
Sistem Pendukung Keputusan Penentuan Lokasi Pabrik Baru Menggunakan Metode ROC dan MAUT

Authors:
raw_author_name: Rima Tamara Aldisa
raw_affiliation_string: Universitas Nasional, Jakarta

Abstract: Factories are places where goods and services are mass produced. Factories collect and integrate various resources, such as labor, capital, and machines. Factories have an important role in a country's economy, because they produce goods and services that society needs. Apart from that, factories can also create jobs and improve community welfare. However, factories can also have a negative impact on companies and the environment. Companies that produce goods with certain raw materials must consider the availability of raw materials at the factory location. If raw materials are not available at that location, the company must bring them in from other locations, which can increase production costs. Apart from that, companies must also consider the availability of adequate infrastructure, such as roads, electricity, air and telecommunications. If infrastructure is inadequate, companies have to invest to build it, which can also increase production costs. In this study, the ROC method and the MAUT method were used, the ROC method was used to determine the level of importance of the criteria used in selecting the factory location. The MAUT method is used to rank factory locations. The five criteria used in selecting factory locations are: availability of raw materials, availability of infrastructure, labor costs, transportation costs, and environmental impact. A total of 10 factory locations were selected based on predetermined criteria. The final results show that the best factory location is location E with a final score of 0.682, followed by location H and location F with a final score of 0.619 and 0.453 respectively.
Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Authors:
raw_author_name: Ikbal Danu Setiawan
raw_affiliation_string: Universitas Nasional, Jakarta
raw_author_name: Agung Triayudi
raw_affiliation_string: Universitas Nasional, Jakarta

Abstract: Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.
Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web

Authors:
raw_author_name: Endah Ratna Arumi
raw_affiliation_string: Universitas Muhammadiyah Magelang, Magelang
raw_author_name: Dimas Sasongko
raw_affiliation_string: Universitas Muhammadiyah Magelang, Magelang
raw_author_name: Uky Yudatama
raw_affiliation_string: Universitas Muhammadiyah Magelang, Magelang
raw_author_name: Muhammad Resa Arif Yudianto
raw_affiliation_string: Universitas Muhammadiyah Magelang, Magelang

Abstract: Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.
Penerapan Metode Support Vector Machine Dalam Memprediksi Prediksi Cuaca

Authors:
raw_author_name: Rima Tamara Aldisa
raw_affiliation_string: Universitas Nasional, Jakarta

Abstract: Weather is a natural that has a big impact on humans. Information about weather predictions is really needed by humans, especially in the city of Medan. This information is very useful for knowing weather events around us. Data mining is a process of collecting important information from large data, so that it can help make decisions and make accurate predictions. So researchers are interested in conducting research in predicting the weather in the city of Medan using the Support Vector Machine (SVM) method as a solution for predicting the weather in the city of Medan. The application of data mining using the SVM method helps produce precise accuracy for weather based on predetermined criteria. This method is suitable for weather predictions because it is able to provide clear and accurate assessments with weather predictions of 54.55%.