Data Processing Components Research Articles

Stagg:: A data pre-processing R package for climate impacts analysis

The increasing availability of high-resolution climate data has greatly expanded the study of how the climate impacts humans and society. However, the processing of these multi-dimensional datasets poses significant challenges for researchers in this growing field, most of whom are social scientists. This paper introduces stagg, or “space-time aggregator”, a new R package that streamlines three critical components of climate data processing for impacts analysis: nonlinear transformation, spatial and temporal aggregation, and spatial weighting by social or economic variables. The package consolidates the data processing pipeline into a few lines of code, lowering barriers to entry for researchers and facilitating a larger and more diverse research community. The paper provides an overview of stagg's functions, followed by an applied example demonstrating the package's utility in climate impacts research. stagg has the potential to be a valuable tool in generating evidence-based estimates of the likely impacts of future climate change.

Robust seismic data denoising via self-supervised deep learning

Seismic data denoising is a critical component of seismic data processing, yet effectively removing erratic noise, characterized by its non-Gaussian distribution and high amplitude, remains a substantial challenge for conventional methods and deep-learning (DL) algorithms. Supervised learning frameworks typically outperform others, but they require pairs of noisy data sets alongside corresponding clean ground truth, which is impractical for real-world seismic data sets. In contrast, unsupervised learning (UL) methods, which do not rely on ground truth during training, often fall short in performance when compared with their supervised or traditional denoising counterparts. Moreover, current unsupervised DL methods fail to address the specific challenges posed by erratic seismic noise adequately. This paper introduces a novel zero-shot unsupervised DL framework designed specifically to mitigate random and erratic noise, with a particular emphasis on blended noise. Drawing inspiration from Noise2Noise (N2N) and data augmentation principles, we develop a robust self-supervised denoising network called robust Noiser2Noiser. Our approach eliminates the need for paired noisy and clean data sets as required by supervised methods or paired noisy data sets as in N2N. Instead, our framework relies solely on the original noisy seismic data set. Our methodology generates two independent recorrupted data sets from the original noisy data set, using one as the input and the other as the training target. Subsequently, we use a DL-based denoiser, denoising convolutional neural network, for training purposes. To address various types of random and erratic noise, the original noisy data set is recorrupted with the same noise type. Detailed explanations for generating training input and target data for blended data are provided. We apply our network to synthetic and real marine data examples, demonstrating significantly improved noise attenuation performance compared with traditional denoising methods and state-of-the-art UL Codes are available on https://github.com/Ji-seismic/N2N_deblending .

Design and implementation of a scalable correlator based on ROACH2 + GPU cluster for tianlai 96-dual-polarization antenna array

The digital correlator is one of the most crucial data processing components of a radio telescope array. With the scale of radio interferometeric array growing, many efforts have been devoted to developing a cost-effective and scalable correlator in the field of radio astronomy. In this paper, a 192-input digital correlator with six CASPER ROACH2 boards and seven GPU servers has been deployed as the digital signal processing system for Tianlai cylinder pathfinder located in Hongliuxia observatory. The correlator consists of 192 input signals (96 dual-polarization), 125-MHz bandwidth, and full-Stokes output. The correlator inherits the advantages of the CASPER system, for example, low cost, high performance, modular scalability, and a heterogeneous computing architecture. With a rapidly deployable ROACH2 digital sampling system, a commercially expandable 10 Gigabit switching network system, and a flexible upgradable GPU computing system, the correlator forms a low-cost and easily-upgradable system, poised to support scalable large-scale interferometeric array in the future.

Edge Impulse Potential to Enhance Object Recognition Through Machine Learning

Machine Learning (ML) is a powerful artificial intelligence branch that can help businesses, whether small or large, in a variety of industries. It is an option for replacing resources with high operating costs. The aim of this study was to use the Edge Impulse platform as an ML tool option. The system applies low-code frameworks which abstracts a series of complex techniques applied in ML, such as data processing and AI components structure. It implies a time reduction during the development period. Using Edge Impulse allows a more user-friendly interface alternative with an easy-to-interpret logic flow. The study focused an application to do an object recognition, aiming the system capacity limit. The autogenerated accuracy value, pointed by the system indicated 97.9 % after the training step and 89 % after retesting the first 20 mices, photographed in different image angles, indicating a possible model overfitting. Even though, the system showed promise in terms of classifying objects. Some adjustments in the image dataset can improve the model capacity of recognition, as the amount of images showed insufficient at the survey's conclusion.

Extending the Digital Twin Ecosystem: A real-time Digital Twin of a LinuxCNC-controlled subtractive manufacturing machine

This research paper describes the design and implementation of a real-time data-driven Digital Twin Ecosystem (DTE) for a LinuxCNC-controlled subtractive manufacturing computer numerical control (CNC) machine. The DTE architecture integrates real-time data acquisition, processing, and synchronization with the physical CNC machine. Our architecture adopts and extends the methodology presented in previous work and utilizes two components, namely the data Acquisition, Processing, and Distribution Component (APDC), and the Virtual Representation Component (VRC), which is developed using the Unity real-time development platform. The DTE accurately models the CNC machine’s behavior under various operating conditions. The DTE is verified and validated through testing and experimentation, particularly focusing on response time, sequencing accuracy, and seamless integration. The DTE offers two modes of operation: the online mode allows for real-time process replication, ideal for process monitoring and remote management, while the offline mode supports asynchronous G-code simulation, enabling what-if analysis and exploration of process variables. In this work, we utilize the offline mode for predictive collision detection. Using the DTE, stakeholders could make informed decisions, optimize machining processes, and ensure cost-effective operations. This research contributes to the advancement of Digital Twin technology, particularly in CNC machining, fostering innovation in the manufacturing sector and promoting the development of practical, efficient, and reliable cyber–physical industrial systems, enhancing operational capabilities and productivity.

Phenomena observed in electron EBRT using a pulse-by-pulse radioluminescence dosimetry system with cloud-based analytics

The pulse-by-pulse characteristics of electron beams from a clinical linear accelerator (linac) at high dose rates are crucial for ensuring precise dose delivery in radiation therapy. In this research, we employ a radioluminescence scintillator made from Ge-doped silica optical fiber to analyse electron beams at standard therapy dose rates 600 cGy/min with energy of 15 MeV. The scintillator is combined with a photomultiplier tube (PMT) and a photon-counting setup to achieve microsecond (μs) gating times, providing high temporal resolution. For sub-microsecond (sub-μs) temporal resolution, we utilize a multi-pixel photon counter and a rapid digital oscilloscope. Our study examines the peak time and the time it takes for pulses to return to baseline, uncovering the effects of afterglow at elevated dose rates and its potential implications for dosimetry precision. Our results indicate that monitor chambers, which have lower temporal resolution than the radioluminescence system outlined in this study, may struggle to accurately track high-rate dose delivery, emphasizing the necessity for advanced monitoring methods in FLASH radiotherapy. The dosimetric data was further analysed for deeper analytics on a cloud-based system. The dosimetric data underwent advanced analysis on a cloud-based platform, employing a prototype analytics software system comprised of Dosi-Client and Dosi-Cloud Backend components for real-time data acquisition and processing. This innovative approach revealed significant insights into radiation dosimetry, including the detection of anomalies through a reconstruction convolutional autoencoder, enhancing the precision and reliability of dosimetric measurements.

Integration of Machine Learning and Iot based Multi-Layer Wireless Sensor Networks for Seamless Smart Home Automation

The integration of machine learning algorithms with Internet of Things -based multi-layer wireless sensor networks offers a transformative solution to modern smart home automation and security. This paper seeks to explore both the Sensor Data Processing and Machine Learning Components used. In essence, the machine learning model leverages aggregate sensor data to improve the efficiency and safety of modern living environments. Through attention to detail during the preprocessing and feature engineering of sensor data, the system analyzed learned patterns of household activity, including motion intensity, door/window interactions, and normal occupancy. Subsequently, the features were utilized as input variables in a number of models, including Convolutional Neural Networks , Support Vector Machines, Recurrent Neural Networks, and K-Nearest Neighbors. By simulating both vacation and occupant mode, the models processed the available data using to detect potential threats or other deficiencies in the patterns. Overall the results led by the model suggest that the Convolutional Neural Network model is the best amongst all of the aforementioned algorithms. It demonstrated the best accuracy out of all the other models due to the advanced image processing and facial recognition features, solidified by its 98.56% accuracy rate in recognizing a potential intruder. Other models were also effective or reasonably accurate, with the following results: SVMs – 95.45%; RNNs – 93.4%; and KNNs – 91.23%. The evaluation process also involves the calculation of precision, recall, and F1 score, which also indicates the overall strength of the model. The aggregation of these results implies a considerable potential for internet-of-things systems in combination with machine learning methods to cultivate smarter and safer living environments.

The Copernicus POD Service

The Copernicus Precise Orbit Determination (CPOD) Service is in charge of computing the precise orbits and auxiliary data files of the Copernicus Sentinel-1, −2, −3 and −6 missions, using the GPS&Galileo observations obtained on-board. The orbit products and auxiliary data files are provided to the corresponding Payload Data Ground Segments (PDGS) and Payload Data Acquisition and Processing (PDAP) components and to external users through the Copernicus Data Space Ecosystem (CDSE).The CPOD Service is supported by the CPOD Quality Working Group (QWG), a group of leading experts on GNSS (Global Navigation Satellite System) and POD, which provides independent orbit solutions and techniques to support the quality control (QC) and the validation of the operational products.The CPOD Service was initially developed in 2013, starting its operational phase in April 2014 after the successful launch of Sentinel-1A. The CPOD Service has accumulated almost 10 years of experience, including its initial development, operations, and periodic evolutions, to the point that currently it is a well-recognized POD Service.This paper shows in detail the architecture of the CPOD Service, its performance and its evolution over the years, and its future outlook.

Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review.

The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty. This scoping review followed the PRISMA, PRISMA-ScR guidelines, and fivestage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected. Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review. AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems.

FELIX: First operational experience with the new ATLAS readout system and perspectives for HL-LHC

The Front-End Link eXchange (FELIX) readout system is deployed as the new interface connecting detector front-end electronics with the data acquisition and Timing, Trigger and Control (TTC) systems. FELIX functions as a router between custom serial links from front-end ASICs and FPGAs to data collection and processing components via a commodity switched network. FELIX uses commodity server technology in combination with FPGA-based PCIe I/O cards. FELIX servers run a software routing platform serving data to network clients performing a number of data preparation, monitoring and control functions. This paper covers the design of FELIX as well as the first operational experience gained during the Run 3 start, including the challenges faced commissioning the system for each ATLAS sub-detector. Over the next decade, the ATLAS detector will be required to operate in an increasingly harsh collision environment and challenging data taking conditions. To maintain physics performance, the detector will undergo a series of upgrades. In particular, the readout system capabilities will be improved. The planned evolution of FELIX for High-Luminosity LHC will be described, including architectural changes and status of early integration with detector development projects.

Securing Cloud-Assisted Connected and Autonomous Vehicles: An In-Depth Threat Analysis and Risk Assessment.

As threat vectors and adversarial capabilities evolve, Cloud-Assisted Connected and Autonomous Vehicles (CCAVs) are becoming more vulnerable to cyberattacks. Several established threat analysis and risk assessment (TARA) methodologies are publicly available to address the evolving threat landscape. However, these methodologies inadequately capture the threat data of CCAVs, resulting in poorly defined threat boundaries or the reduced efficacy of the TARA. This is due to multiple factors, including complex hardware-software interactions, rapid technological advancements, outdated security frameworks, heterogeneous standards and protocols, and human errors in CCAV systems. To address these factors, this study begins by systematically evaluating TARA methods and applying the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privileges (STRIDE) threat model and Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) risk assessment to target system architectures. This study identifies vulnerabilities, quantifies risks, and methodically examines defined data processing components. In addition, this study offers an attack tree to delineate attack vectors and provides a novel defense taxonomy against identified risks. This article demonstrates the efficacy of the TARA in systematically capturing compromised security requirements, threats, limits, and associated risks with greater precision. By doing so, we further discuss the challenges in protecting hardware-software assets against multi-staged attacks due to emerging vulnerabilities. As a result, this research informs advanced threat analyses and risk management strategies for enhanced security engineering of cyberphysical CCAV systems.

Patient data management using blockchain technology

<span>The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively.</span>

Fundament for a methodological standard to process hip accelerometer data to a measure of physical activity intensity in middle-aged individuals.

There is a lack of a methodological standard to process accelerometer data to measures of physical activity, which impairs data quality and comparability. This study investigated the effect of different combinations of settings of multiple processing components, on the measure of physical activity and the association with measures of cardiometabolic health in an unselected population of middle-aged individuals. Free-living hip accelerometer data, aerobic fitness, body mass index, HDL:total cholesterol ratio, blood glucose, and systolic blood pressure were achieved from 4391 participants 50-64 years old included in The Swedish CArdioPulmonary bioImage Study (SCAPIS) baseline measurement (cross-sectional). Lab data were also included for calibration of accelerometers to provide comparable measure of physical activity intensity and time spent in different intensity categories, as well as to enhance understanding. The accelerometer data processing components were hardware recalibration, frequency filtering, number of accelerometer axes, epoch length, wear time criterium, time composition (min/24 h vs. % of wear time). Partial least regression and ordinary least regression were used for the association analyses. The setting of frequency filter had the strongest effect on the physical activity intensity measure and time distribution in different intensity categories followed by epoch length and number of accelerometer axes. Wear time criterium and recalibration of accelerometer data were less important. The setting of frequency filter and epoch length also showed consistent important effect on the associations with the different measures of cardiometabolic health, while the effect of recalibration, number of accelerometer axes, wear time criterium and expression of time composition was less consistent and less important. There was a large range in explained variance of the measures of cardiometabolic health depending on the combination of processing settings, for example, 12.1%-20.8% for aerobic fitness and 5.8%-14.0% for body mass index. There was a large variation in the physical activity intensity measure and the association with different measures of cardiometabolic health depending on the combination of settings of accelerometer data processing components. The results provide a fundament for a standard to process hip accelerometer data to assess the physical activity in middle-aged populations.

A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.

Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

Design of sample display system on electronic nose for synthetic flavor classification

This study aimed to design a controlled sample display system on an electronic nose and test its performance for classifying synthetic flavors. There are four primary components to the electronic nose design. They are a controlled sample display system, detector, signal conditioning and preprocessing, and pattern recognition software. The sample display system consists of six vials. The sample room temperature setpoint is set to 40 ℃. The controlled sample display system has one heater and two fans to even the room temperature. The one-time data collection process consists of flushing (120 s), collecting (180 s), and purging (180 s). The samples for the performance test were synthetic flavors with four different aromas; durian, mocca, orange, and strawberry. Data analysis of gas sensor response was done through two stages; pre-treatment data processing and principal component analysis (PCA). The four samples were clearly different from others, according to the PCA results. The scores of the PC-1, PC-2, and PC-3 cumulative variance were 98.28%.

Kıyma Kokuşma Analizi İçin Düşük Maliyetli Elektronik Burun Geliştirilmesi

A low-cost, easy-to-use e-nose is developed to detect the spoilage of ground meat. E-nose consists of hardware, software and data processing components. The main elements of hardware component are gas sensors sensitive to hydrogen sulfide (H2S) and ammonia (NH3). Using MIT App Inventor 2 an Android application is developed to run the hardware component, retrieve the data, pre-process and send it to Google Sheets. Classification model is developed, and data management is carried out in Google Colab and Google Script. Logistic regression method is used to develop classification models from the collected signals. The model classified the samples as "spoiled" and "fresh" based on the gas concentrations. The Nessler solution is used to determine the actual spoilage state. Ground beef samples stored in the refrigerator and at room temperature are used to obtain spoiled and fresh samples to develop a logistic regression model. A total of 36 samples are used to develop model. Another set of 24 samples is used to test model and prototype device performance. It is observed that all samples used in the testing phase were classified correctly. The cost of the system has been determined as approximately $100 considering January 2021 exchange rates.

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated.

A comparison of Apache Solr and Elasticsearch technologies in support of large-scale data analysis

In the era of big data, data has never been more important because it contains hidden insights. Additionally, it is necessary and challenging to extract usable information from enormous volumes of data. When attempting to perform data processing and analytics in a variety of domains, developers of data-intensive systems have consequently met several challenges. In addition, full-text search is one of the most significant components of big data processing and analytics for discovering fragments of required data among large volumes of data. Due to the importance of the subject, this article begins with an examination of the characteristics, capabilities, and technical comparisons of full-text search technologies, followed by a systematic comparison of Apache Solr and Elasticsearch in terms of indexing times and queries on three separate datasets. According to our findings, based on default configuration, Apache Solr has better performance when looking at indexing times measured on three machines with different hardware specifications. Likewise, Apache Solr outperforms Elasticsearch in seven out of ten search queries. Regarding our results, on computers with restricted hardware resources, we recommend utilizing Apache Solr instead of Elasticsearch. In addition, this study provides researchers and developers of data-intensive systems with a complete comparison and suggestions for choosing the most effective full-text search engine for their task.

Smart Airtime Vending Machine: A Case of Nyamasheke District, Nyabitekeri Sector

Purpose: The aim of this paper was undertaking modern technology by designing and implementing a smart airtime vending machine known as a self-service airtime vendor machine that will come as an additional method apart from the current airtime selling and buying methods which are mobile money, banking, airtime agent that involves theft of money and airtime loading mistakes and errors. It will help rural citizens to buy airtime using a coin where a customer has to enter the mobile number using a keypad then inset coin in airtime vending machine, and automatically the machine dispenses the airtime equivalent to the amount inserted.
 Methodology: The methodology used consists of an IoT system where the customer will access the vending machine by inserting it into a coin to buy airtime. This research consists of three main parts, the first part is the interconnection of IoT hardware components that build the entire circuit and are linked to the cloud via GPRS/GSM communication technology, this part involves sensing components, data processing components, and actuators components. The second part consists of coding using Arduino IDE that makes IoT system hardware operational and the last part is data processing and analytics using python programming and regression as a machine learning technique. The system monitoring is done through wireless radio, the cloud data storage is secured and can be easily accessible by authorized users via a web interface. The battery is used for powering the system and the solar panel for recharging the battery. All transaction data are recorded and given date returns the day type between working days, weekends, and the session of the day.
 Findings: The results of the research include an IoT system that is developed and implemented to help both airtime agents and customers to sell and buy airtime using coin-based self-service airtime vending machine and the model that analyse machine transaction data using Python programming and regression as a machine learning technique.
 Unique contribution to practice: It improved the airtime vending system's efficiency and sustainable management of airtime agent. It facilitates safe selling and buying of airtime across the country especially in rural areas where getting airtime seemed to be a problem. The Internet is also used for linking cloud platform and application users.it indicates that the vending machine can provide positive impact in society including self-service of airtime to citizens from nearby vendors, the distribution of machines country wide can increase employability.