Extra Data Research Articles

A novel approach for detecting sensor-based semiconductor fault yield classification using convolutional neural networks

<p>In the proposed research, data from the semiconductor industry is considered for analysis. In this research, there is a requirement for significantly more space for storage, processing will take significantly more time, and there will be a significant amount of duplicate data. So, the utilization of dimensionality reduction strategies is required so as to lessen the number of spectral bands while maintaining the maximum amount of relevant information. Our contribution can be broken down into two parts: To begin, we suggest a filter-based technique that we call interband redundancy analysis (IBRA). This method is based on a collinearity analysis that is performed among a band and its neighbors. By performing the given research, redundant bands can be omitted, which in turn significantly brings down the search space. Next, we take the findings of the IBRA and use a wrapper-based technique known as greedy spectral selection (GSS) to choose bands on the basis of the information entropy values of those bands. We are later training a convolutional neural network to evaluate how well the present selection is working. We also propose an optimization algorithm for performance enhancement known as bacterial foraging optimization.</p>

Use of Databases to Manage Sales Results Data at the KDH Sancang Coffee Shop

Technological advances allow entrepreneurs to manage sales data efficiently. Governance management in micro, small and medium enterprises is often an obstacle to the continuity and development of the business. Based on interviews conducted, at KDH Sancang Coffee Shop, data management still uses Microsoft Excel, which sometimes makes it difficult for users to access the desired data. This research recommends using Microsoft Access to create a secure and organized sales database. This solution helps overcome data duplication problems and improve data accessibility and security. Based on the research results, the process of creating a database for sales results includes the creation of item and sales data tables, one to one relationship, Query total sales results, and Form Design for easy data access. This research activity aims to enable micro, small, and medium-sized enterprises to effectively organize their financial records, including providing owners with the necessary tools to manage their business finances more efficiently. The results showed that the use of Microsoft Access increased the efficiency of managing monthly sales data at KDH Sancang Coffee Shop which is expected to facilitate monthly sales evaluation activities at the coffee shop.

Breast Cancer among Cancer Patients Visiting the Department of Internal Medicine of a Tertiary Centre.

Breast cancer is one of the most common cancers worldwide both in terms of incidence and mortality. Its incidence has been on an increasing trend in developing nations including Nepal, however, there is very limited evidence of its demographic profile in our setting. This study aimed to find out the prevalence of breast cancer among cancer patients visiting the Department of Internal Medicine of a tertiary centre. A descriptive cross-sectional study was conducted among cancer patients visiting the Department of Internal Medicine of a tertiary care centre using retrospectively collected data from 1 August 2022 to 30 July 2023 after obtaining ethical approval from the Institutional Review Committee. Histopathologically confirmed cancer patients were included and those with incomplete and duplicated data were excluded. A convenience sampling method was used. The point estimate was calculated at a 95% Confidence Interval. Among 2067 cancer patients, the prevalence of breast cancer was 102 (4.93%) (4.00-5.87, 95% Confidence Interval). The mean age was 50.51±2.08 years. The most commonly affected age group was 40 to 60 years constituting 61 (59.80%) patients. Histologically, invasive breast cancer of no special type was the most common and was found in 91 (89.22%) patients. The prevalence of breast cancer was similar to other studies done in similar settings. breast cancer; cross-sectional study; demography; malignancy; prevalence.

Analysis of environmental health and sustainability of Brazilian municipalities in the Baixo Pardo/Grande River Basin

Population growth, the lack of urban planning and poor sanitation infrastructure have persisted in the management of urban space when the core issue is environmental health and the sustainability of municipalities. This paper aimed to evaluate the environmental health and sustainability of the 12 Brazilian municipalities in the Baixo Pardo/Grande (SP) river basin. The exploratory approach and the case study guided the comparative analysis of sustainability through the Sustainable Cities Development Index (100 indicators) and the Brazilian Environmental Health Index (18 indicators) to obtain an integrated classification of these indices on a municipal scale. The analyses highlighted that 100% of studied municipalities reached medium level (score: 51-75) out for health and sustainability, especially to Altair and Orlândia results Bebedouro, Viradouro and Colina require careful monitoring of the following indicators sanitary sewage and vector control, water and socioeconomic resources, and water and socioeconomic resources, respectively. The basic sanitation, health diseases and infant mortality pillars relating to these indices identified seven similar indicators, that is, they measure the same aspects of the analyzes. Then, they were excluded to avoid duplication of data. The least promising results of Sustainable Development Goals (SDGs) were Gender equality (SDG 5), Industry, innovation and infrastructure (SDG 9), Protect life on Earth (SDG 15) and Partnerships and means of implementation (SDG 17). Finally, the great challenges of the Baixo Pardo Grande River Basin pointed out social, technological and connectivity issues between interinstitutional sectors from a regional perspective.

Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost.

Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation

Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Useful Life (RUL) of Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries. This research utilizes a dataset derived from the Hawaii Natural Energy Institute, encompassing 14 individual batteries subjected to over 1000 cycles under controlled conditions. A multi-step methodology is adopted, starting with data collection and preprocessing, followed by feature selection and outlier elimination. Machine learning models, including XGBoost, BaggingRegressor, LightGBM, CatBoost, and ExtraTreesRegressor, are employed to develop the RUL prediction model. Feature importance analysis aids in identifying critical parameters influencing battery health and lifespan. Statistical evaluations reveal no missing or duplicate data, and outlier removal enhances model accuracy. Notably, XGBoost emerged as the most effective algorithm, providing near-perfect predictions. This research underscores the significance of RUL prediction for enhancing battery lifecycle management, particularly in applications like electric vehicles, ensuring optimal resource utilization, cost efficiency, and environmental sustainability.

Secure Access of Folders and Files after Removal of Duplicacy over the Cloud

Cloud Computing has been the most popular approach of computing due to faster access to folders and files at a low cost. Hence, many organizations are shifting the old long database folders and files over the cloud which may be text, audio, video or in the other formats. Due to large size of the database with multiple storages of folders and files over the cloud, there may be chances of duplicate access of the database folders and files which may cause the loss of time of execution or accessing the database files. In the present work, a technique is developed to remove duplicate files in the form of .txt, .doc, .jpg, .pdf as well as duplicate folders after applying a well-known ElGamal algorithm later on converted as fuzzy ElGamal technique, for faster retrieval of files in a very secure manner. For this purpose, Unified Modelling Language (UML) model is developed which has been implemented through Python programming language. The computed results towards the model’s efficiency have been depicted through tables and graphs, on a large database in the form of folders and files of Indian railway reservation system. The present work is significant for the large organizations and also useful for the users working over the cloud for faster accessing of the folders and files.

SISTEM MANAJEMEN PENGELOLAAN KEUANGAN BERBASIS ANDROID PADA APPEL SPECIALIST REPAIR

Appel Specialist Repair is a business that operates in Smartphone and Computer repair and maintenance services. Currently, the financial management process uses Google services, namely spreadsheets, and recording is still done manually, such as recording financial transactions, checking stock of goods, and preparing financial reports. This manual approach carries the risk of data duplication and accidental data deletion. The research method used in this research is a qualitative method consisting of literature studies related to the thesis title, field studies observations at Appel Specialist Repair, interviews with related parties, analysis of needs related to the system required, design and creation of an Android-based information system, and testing black box financial management application. Based on the research and analysis, UML models for designing and creating an Android-based financial management information system for Apple Specialist Repair in its finance.

A Lightweight Method for Detecting and Correcting Errors in Low-Frequency Measurements for In-Orbit Demonstrators.

In the pursuit of enhancing the technological maturity of innovative magnetic sensing techniques, opportunities presented by in-orbit platforms (IOD/IOV experiments) provide a means to evaluate their in-flight capabilities. The Magnetic Experiments for the Laser Interferometer Space Antenna (MELISA) represent a set of in-flight demonstrators designed to characterize the low-frequency noise performance of a magnetic measurement system within a challenging space environment. In Low Earth Orbit (LEO) satellites, electronic circuits are exposed to high levels of radiation coming from energetic particles trapped by the Earth's magnetic field, solar flares, and galactic cosmic rays. A significant effect is the accidental bit-flipping in memory registers. This work presents an analysis of memory data redundancy resources using auxiliary second flash memory and exposes recovery options to retain critical data utilizing a duplicated data structure. A new and lightweight technique, CCM (Cross-Checking and Mirroring), is proposed to verify the proper performance of these techniques. Four alternative algorithms included in the original version of the MELISA software (Version v0.0) are presented. All the versions have been validated and evaluated according to various merit indicators. The evaluations showed similar performances for the proposed techniques, and they are valid for situations in which the flash memory suffers from more than one bit-flip. The overhead due to the introduction of additional instructions to the main code is negligible, even in the target experiment based on an 8-bit microcontroller.

The effect of various therapeutic exercises on forward head posture, rounded shoulder, and hyperkyphosis among people with upper crossed syndrome: a systematic review and meta-analysis

ObjectivesThis review study aimed to evaluate the impact of therapeutic exercises on Upper-Crossed Syndrome (UCS). The study utilized a systematic review and meta-analysis approach to investigate the effects of various therapeutic exercises on forward head posture, rounded shoulders, and hyperkyphosis associated with upper crossed syndrome.MethodsThe study identified relevant keywords for each independent and dependent variable and conducted a search in scientific databases, including PubMed, Web of Science, Scopus, and Google Scholar, without any time limitations until 12 August 2023. Overall, 4625 articles were found in the selected databases, which were reduced to 1085 after being entered into the EndNote software and removing duplicate data. The full texts of 30 remaining studies were reviewed; ten articles meeting the criteria were included. Additionally, 12 studies from the Google Scholar database were included, resulting in 22 studies. Using Comprehensive meta-analysis software (CMA ver 3), data heterogeneity was measured with I2 and the Q tests. The Funnel Plot and Egger test methods were utilized to determine the possibility of publication bias. The JBI checklist was used to assess the quality of the studies.ResultsThe results of the meta-analysis showed that therapeutic exercises were effective in improving forward head, rounded shoulders, and thoracic kyphosis angles (CI 95% = -1.85–1.161, P = 0.001, P = 0.001, CI95%=-1.822–1.15, and P = 0.001, CI 95%= -1.83–1.09, respectively).ConclusionBased on the results, it appears that performing therapeutic exercises in the form of strength exercises, stretching, shoulder-based exercises, and incredibly comprehensive exercises that target all muscles may be effective in reducing forward head, rounded shoulders, thoracic kyphosis, and overall UCS.Level of evidence1

Aplikasi Business Intelligence Pada Sistem Penjualan CV. Muda Jeans

This research explains the application of business intelligence in Point of Sales sales applications in CV. Young Jeans. Muda Jeans is a private company that buys, sells, and manufactures women's jeans. The authors used a descriptive method in this study and conducted observations and interviews with store owners and employees. The sales system currently used by CV. Muda Jeans uses a traditional sales system where the entire sales transaction process is carried out directly through store visits and every transaction is recorded in the general ledger, this results in data recording errors, data duplication and data presentation which can cause problems in data utilization. Data utilization is used in decision making in creating and developing new products. This problem can be overcome with Point of Sales applications that apply business intelligence, allowing applications to analyze current and historical data displayed through data visualization. Data visualization in the form of sales graphs per product to make it easier for owners to see, understand and analyze product movements through sales transactions that occur, which products will later be increased production, which products are decided to be reduced or not produced at all.

Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams.

In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of ab initio calculations covering a wide range of compositions and structures. These are essential to building a reliable convex hull diagram. While density functional theory (DFT) provides accurate predictions for many systems, its computational overheads set a throughput limit on the number of hypothetical phases that can be probed. Here, we demonstrate how an ensemble of machine-learning (ML) spectral neighbor-analysis potentials (SNAPs) can be integrated into a workflow for the construction of accurate ternary convex hull diagrams, highlighting regions that are fertile for materials discovery. Our workflow relies on using available binary-alloy data both to train the SNAP models and to create prototypes for ternary phases. From the prototype structures, all unique ternary decorations are created and used to form a pool of candidate compounds. The SNAPs ensemble is then used to prerelax the structures and screen the most favorable prototypes before using DFT to build the final phase diagram. As constructed, the proposed workflow relies on no extra first-principles data to train the ML surrogate model and yields a DFT-level accurate convex hull. We demonstrate its efficacy by investigating the Cu-Ag-Au and Mo-Ta-W ternary systems.

The Diffusion of E-Government Innovation in The Framework of Public Services (A Study on Population Administration Services and Civil Registration in Kediri Regency

Purpose: Public services, a government obligation, have a direct impact on society, particularly in the realm of population services. However, population-related issues persist in Indonesia, such as residents needing more proper identification, duplicate data, and invalid records. Innovation in public services serves as a solution for both central and local governments. The Kediri Regency Government actively implements the concept of governance, adhering to the e-government paradigm in executing population administration services conducted by the Population and Civil Registration Agency of Kediri Regency. Theoretical framework: The Indonesian government has sought various improvements in providing services to address population-related challenges through innovative service models. The recommendation outlined in Minister of Administrative and Bureaucratic Reform Regulation No. 30 of 2014 is a legal foundation for these efforts. Relevant innovations can be employed in the public sector as alternative mechanisms to seek new solutions to longstanding issues (Muluk, 2008;43). Innovation within government agencies is crucial for improving public service delivery by introducing novel approaches and resources. Method: qualitative descriptive-evaluative approach rooted in a meticulous investigation by the author concerning public service innovations based on e-government, encompassing programs, events, activities, processes, and their execution. Result: This research examined the initial conditions of public services related to population. Throughout the process of public services, the government of Kediri Regency has introduced an innovation in the form of the E-Government program within the SAHAJA (Satu Hari Jadi) program for the issuance of ID cards (KTP) and various other population documents. On a practical level, this research can provide benefits by offering suitable methods for implementing innovations in population and civil registration services.

A cross-domain challenge with panoptic segmentation in agriculture

Automation in agriculture is a growing area of research with fundamental societal importance as farmers are expected to produce more and better crop with fewer resources. A key enabling factor is robotic vision techniques allowing us to sense and then interact with the environment. A limiting factor for these robotic vision systems is their cross-domain performance, that is, their ability to operate in a large range of environments. In this paper, we propose the use of auxiliary tasks to enhance cross-domain performance without the need for extra data. We perform experiments using four datasets (two in a glasshouse and two in arable farmland) for four cross-domain evaluations. These experiments demonstrate the effectiveness of our auxiliary tasks to improve network generalisability. In glasshouse experiments, our approach improves the panoptic quality of things from 10.4 to 18.5 and in arable farmland from 16.0 to 27.5; where a score of 100 is the best. To further evaluate the generalisability of our approach, we perform an ablation study using the large Crop and Weed dataset (CAW) where we improve cross-domain performance (panoptic quality of things) from 12.8 to 30.6 for the CAW dataset to our novel WeedAI dataset, and 21.2 to 36.0 from CAW to the other arable farmland dataset. Although our proposed approaches considerably improve cross-domain performance we still do not generally outperform in-domain trained systems. This highlights the potential room for improvement in this area and the importance of cross-domain research for robotic vision systems.

Does integration with national registers improve the data completeness of local COVID-19 contact tracing tools? A register-based study in Norway, May 2020 - September 2021

BackgroundDuring the COVID-19 response in Norway, many municipalities used the Fiks contact tracing tool (FiksCT) to register positive individuals and follow-up contacts. This tool is based on DHIS2, an open source, web-based platform. In this study we examined if data completeness in FiksCT improved after integration with national registers between May 2020 and September 2021.MethodsData from municipalities using FiksCT was extracted from the Norwegian Emergency Preparedness Register for COVID-19 (Beredt C19). We linked FiksCT data to the Norwegian Surveillance System for Communicable Diseases (MSIS), the National Population Register (FREG), and the Norwegian Vaccine Registry (SYSVAK) using unique identification numbers (ID). Completeness for each variable linked with a national register was calculated before and after integration with these registers.ResultsOf the 125 municipalities using FiksCT, 87 (69.6%) agreed to share and upload their data to Beredt C19. Data completeness for positive individuals improved after integration with national registers. After integration with FREG, the proportion of missing values decreased from 12.5 to 1.6% for ID, from 4.5 to 0.9% for sex, and from 1.2 to 0.4% for date of birth. Missing values for vaccine type decreased from 63.0 to 15.2% and 39.3–36.7% for first and second dose, respectively. In addition, direct reporting from FiksCT to MSIS increased the proportion of complete records in MSIS (on the selected variables) from 68.6% before to 77.0% after integration.ConclusionThe completeness of local contact tracing data can be improved by enabling integration with established national registers. In addition, providing the option to submit local data to the national registers could ease workload and reduce the need to collect duplicate data.

Multi-Criteria Decision-Making Framework with Fuzzy Queries for Multimedia Data Fusion

Multimedia Internet of Things (MIoT) is widely explored in many smart applications for connectivity with wireless communication. Such networks are not like ordinary networks because it has to collect a massive amount of data and are further forwarded to processing systems. As MIoT is very limited in terms of resources for healthcare, smart homes, etc., therefore, energy efficiency with reliable data transmission is a significant research challenge. As smart applications rely on bounded constraints, therefore duplicate and unnecessary data transmission should be minimized. In addition, the timely delivery of data in crucial circumstances has a significant impact on any proposed system. Consequently, this paper presents a fuzzy logic-based edge computing framework to provide cooperative decision-making while avoiding inefficient use of the sensing power of smart devices. The proposed framework can be applied to critical applications to improve response time and processing cost. It consists of the following two functional components: Firstly, it provides the automated routing process with a natural language interface at the sink node. Secondly, to ensure reasonable performance, it also transmits semantic data between sensors using fuzzy queries and security. According to the performance evaluation, the proposed framework significantly outperformed related studies in terms of energy consumption, packet overhead, network throughput, and end-to-end delay.

3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms

In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model employs a two-stream 3D convolutional neural network to integrate spatial and temporal streams for capturing video distortion details, with a coding layer configured to remove redundant distortion information. We validated our approach using the LIVE and CSIQ datasets, comparing its performance against the V-BLIINDS and VIDEO schemes across different packet loss rates. Furthermore, we simulated the client-server interaction using a subset of the dataset and assessed the scheme's time efficiency. Our results indicate that the proposed scheme offers a highly efficient solution for video quality assessment.

Perbandingan Kinerja Sistem Operasi Network Attached Storage: Studi Kasus Truenas dan Xigmanas

Network Attached Storage (NAS) is a data storage device that operates through a computer network and provides an efficient and cost-effective storage solution due to its open-source foundation. This research compares the performance of TrueNAS and XigmaNAS as NAS operating systems in terms of data transfer speed and resource utilization over a period of 7 days. The tested parameters include file copy, file classification, file duplicate detection, CPU usage, and memory usage. The research results indicate that the average file transfer speed from server to client in TrueNAS is 7.4 Mbps, while in XigmaNAS it is 7.36 Mbps. The average file classification rate in TrueNAS is 863 files/s, whereas in XigmaNAS it is 813 files/s. The average speed of duplicate file detection in TrueNAS is 1.23 files/s, while in XigmaNAS it is 1.13 files/s. The average CPU usage in TrueNAS is 30.08%, compared to 29.38% in XigmaNAS. The average available memory for TrueNAS is 6.13 GiB, and for XigmaNAS it is 5.41 GiB. Despite the small variations and differences observed in the test results, overall, TrueNAS outperforms XigmaNAS in the analyzed aspects.

Perancangan Sistem Informasi Peminjaman Berkas Rekam Medis di RSUD Al-Ihsan

This study aimed to evaluate current systems and processes as well as design and build a medical record lending information system using Visual Studio 2012 at Al-Ihsan Regional Hospital in Bandung. The research method used is qualitative research with data collection through observation, interviews, internet browsing and information system design within a system development framework built using the waterfall method using the Microsoft Visual Studio 2012 tool. In this study, several problems were found in the medical record borrowing process, among others, inefficiencies in the document borrowing system, the use of using Excel-based manuals, as well as obstacles in obtaining documents. It is suggested that a system designed to record loans be used to speed up the process and manage duplicate data more effectively.