Automata Research Articles

Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan

ABSTRACT The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease’s prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and untrustworthy data sources. Using ensemble learning with optimised k-means helps with these problems; it reduces overfitting and improves reliability by using methods like k-means clustering, frog feap algorithm (FLA), boosting, and bagging to balance datasets and show the complexity of symptoms. In this research, we present a new strategy that makes use of a hybrid optimised K-Means algorithm combined with the FLA, along with thresholding and morphological approaches, and ensemble learning. There are two main stages to our work. In the initial phase, we preprocess low-contrast MRI images and delineate the breast tumour area from the segmented MRI images. Furthermore, we employ Discrete Wavelet Transformation (DWT) to extract the most salient features. In the next step, the retrieved features will be used as input parameters for an ensemble-learned breast tumour classifier. To determine whether a low-contrast breast tumour MRI picture has a benign or malignant tumour after model training, we use a variety of classifiers, such as k-nearest neighbours (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), and artificial neural networks (ANN). Through a voting mechanism that averages the accuracy of all models, our suggested framework achieves a remarkable 98.8% accuracy rate. In addition, proposed model is quite efficient & reliable to detect the kind of breast tumour or cancer with a sensitivity of 98.02% and a specificity of 97.5%.

AI-Based Visual Early Warning System

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model.

From Photography to Radiology: How Physicians Leveraged Early Hospital X-ray Machines to Supplant Photographers.

At the end of the nineteenth century, the advent of x-ray machines fueled American medicine's reliance on technology, transforming hospitals and the medical profession. X-ray manufacturers pursued the nascent hospital market as competition and patent feuds accelerated x-ray machine modifications. Hospitals incorporated clunky new machines and employed x-ray photographers, but as the unruly apparatus stabilized, physicians joining the new specialty of radiology discounted the toils of machine troubleshooting and promoted their medically qualified x-ray interpretations. This article frames early medical radiography in terms of boundary work, highlighting how discourse among physicians, x-ray photographers, and hospital administrators vied to establish a privileged demarcation between radiological science and photographic craft. Ultimately, radiologists supplanted x-ray photographers by leveraging the automation of x-ray machines and capitalizing on the epistemic shift from photographic objectivity to qualified interpretations. By focusing on this overlooked aspect of x-ray incorporation into hospitals, this work provides a unique perspective on how harnessing mechanization and authoritative medical interpretations can shift professional boundaries.

Arduino-Based Automatic Weighing Machine Design

Arduino-Based Automatic Weighing Machine Design

Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search

AbstractSpecification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. However, heavy manual adjustments are time consuming to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre‐defined hyperparameter search space. A total of 16‐year data from Millstone Hill incoherent scatter radar (ISR) are used for the NN models. One single‐layer NN (SLNN) model and one deep NN (DNN) model are both trained with NAS, namely SLNN‐NAS and DNN‐NAS, for Ne prediction and compared with their manually tuned counterparts (SLNN and DNN) based on previous studies. Our results show that SLNN‐NAS and DNN‐NAS outperformed SLNN and DNN, respectively. These NN predictions of Ne daily variation patterns reveal a 27‐day mid‐latitude topside Ne variation, which cannot be reasonably represented by traditional empirical models developed using monthly averages. DNN‐NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction, especially with an improvement in mean absolute error more than 10% compared to the SLNN model. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without the time histories of input parameters.

Analysis of Overall Equipment Effectiveness (OEE) as an Effort to Increase The Productivity Filling Line of Lithos Packaging Lubricant

This research was conducted at a company engaged in the lubricants business. This study examines the filling station lithos packaging. The lithos packaging filling station consists of 6 filling lines (FL), namely FL-1, FL-2, FL-3, FL-4, FL-5, and FL-6. From each filling, the line will be observed and analyzed in the format of the automation machine program. Based on the observations and analysis, it was found that FL-4 has an availability rate, performance rate, and quality rate below standard, so it is necessary to identify the cause of the low Overall Equipment Effectiveness (OEE) value of FL-4. This study analyzes the factors causing the low Overall Equipment Effectiveness (OEE) score on FL-4 using Six Big Losses and Fishbone diagrams. From the research results, it was found that several factors caused the less productive FL-4, namely breakdown losses with a value of 48%, setup and adjustment losses with a value of 3%, idling and minor stoppages with a value of 7%, reduced speed losses with a value of 41%, and defect losses with a value of 1%. An analysis was carried out using a fishbone diagram to determine the root cause of the high value of breakdown losses, reduced speed losses followed by idle and minor stoppage losses, and setup and adjustment losses. Based on the fishbone diagram, it was found that the factors causing the FL-4's unproductivity in terms of machine factors, method factors, human factors, material factors, and environmental factors. The causes of unproductive FL-4 are damage to the machine, downtime, the operator's less responsiveness to machine problems, operator expertise, decreased work ethic, defective materials, late material stocks, and hot room temperatures. Based on this, the proposed improvements given are to schedule machine maintenance at a specified time (preventive maintenance), not by the factor of damage (breakdown maintenance), make data guide for product change for each machine and attach it to each machine in full so that This downtime can be reduced, placing technical operators who focus on each filling line at least one technical operator, to speed up downtime and not disrupt the production process, add fans to speed up air circulation to reduce heat in the area and change material suppliers to get better quality.

Simulation Test of an Intelligent Vibration System for Concrete under Reinforcing Steel Mesh

Concrete vibration construction sustains high labor intensity, a poor working environment, difficulties in quality control, and other problems. Current research on concrete vibration focuses on monitoring vibration quality, evaluating vibration processes quantitatively, and assessing mechanical vibration of unreinforced mesh concrete (plain concrete). Standardizing concrete vibration under reinforcing steel mesh remains difficult. There is still a lag in the evaluation of the quality of rework and the consumption of human and material resources. To tackle these issues, a vibrating robotic arm system based on automation control technology, machine vision, and kinematic modeling is proposed. Research and simulation tests on intelligent concrete vibration under reinforcing steel mesh aim to enhance construction efficiency and quality. A five-degree-of-freedom robotic arm with a vision module identifies each rebar grid center in the image, extracts the pixel coordinates, and converts them to the mechanical coordinates by the integration of machine vision algorithms. A vibrator point screening algorithm is introduced to determine actual vibrator point locations based on specific insertion spacing, alongside a vibro-module for vertical movement. Real-time assessment of vibration quality is achieved using the YOLOv5 target detection model. Simulation tests confirm the feasibility of automated concrete vibration control under reinforcing steel mesh by a vibrating robot arm system. This research offers a new approach for unmanned vibration technology in concrete under reinforcing steel mesh, supporting future related technological advancements with practical value.

The Physiological Association Between Mean Platelet Volume and Diabetes Mellitus Type 2: Correlation Between HbA1c Score and MPV Index

Objective: To evaluate difference in mean platelet volume between non-diabetics and diabetics individual and to find the relationship between Hba1c level and MPV among type 2 diabetic individuals. Methodology: This was a retrospective study conducted from December 2022 to November 2023 in Muhammad Medical College and Hospital with prior approval from ethical committee via letter No: Physiol dated 10-03-20. By random sampling 370 participants were selected in which 214 were diabetics and 156 were non-diabetics. Their blood pressure, BMI, Hba1c level, fasting and post prandial glucose level was measured. For evaluation of complete blood picture and MPV an automatic (Beckman coulter) machine was used. Statistical analysis was done by Graph Pad Prism 9. Results: it was noted that both male and female show significant association between raised MPV and increased serum glycemic level (P value less than 0.05). In male MPV increased in 21.89% patients (x2 =33.96, df=3 and P value= 0.0001). In female MPV increased in 17.29% patients (Odd ratio3.07 and P value= 0.0006). Conclusion: Mean platelet volume in diabetics was found to be higher as compared to non-diabetic subjects. In diabetic patients with Hba1c less than 6.5% had less raised MPV as compare to diabetics with Hba1c level greater than 6.5%.

Pengaruh Self Service Technology Terhadap Kepuasan Nasabah Pengguna Layanan Digital Bank Pada Bank Syariah Indonesia KCP Palembang Simpang Patal

This research aims to determine the influence of Automatic Teller Machine/ATM, Mobile Banking, Internet Banking and Self Service Technology on customer satisfaction at Bank Syariah Indonesia KCP Palembang Simpang Patal.The reason behind the author's interest in writing this thesis is based on the researcher's sense of suggestion regarding the lack of customer understanding regarding technology, obstacles in using ATMs or Mobile Banking applications and Internet Banking which carry out transactions via cellphones, this is thought to be a lack of potential for improving Self Service Technology services in banks. Indonesian Sharia KCP Palembang Simpang Patal.This type of research is field research obtained directly through distributing questionnaires to BSI KCP Palembang Simpang Patal customers. The technique used in sampling is the Slovin formula. The sample used in this research was 99 customers of Bank Syariah Indonesia KCP Palembang Simpang Patal. The type of data used is primary data. Data analysis techniques in this research consist of validity testing, reliability testing, classical assumption testing, hypothesis testing. Based on the results of the analysis, the Automatic Teller Machine variable has a positive and significant effect on customer satisfaction, Mobile Banking has a positive and significant effect on customer satisfaction, Internet Banking has a positive and significant effect on customer satisfaction, and Self Service Technology simultaneously has an effect on customer satisfaction.

Embedded Communication System for Monitoring and Authenticating Transactions via Proxy on Automatic Teller Machine (ATM)

Embedded Communication System for Monitoring and Authenticating Transactions via Proxy on Automatic Teller Machine (ATM)

Development of novel prototype of orange sorting machine

Orange is one of the most popular fruits in the world. Oranges originate from Southeast Asia, India, and southern China. Nowadays, they are grown in warm regions around the world. Oranges are consumed either fresh or in the form of fruit juice. Depending on the type of orange, when ripe, its surface is orange or green, with a sweet or slightly sour taste. In Vietnam, oranges have significant export value. Therefore, ensuring consistent quality of oranges before export is an important requirement. Currently in Vietnam, this process is done manually or semi-automatically, resulting in low productivity and inadequate output for export purposes. This article introduces a new design of an automatic orange sorting machine. The proposed machine is equipped with functions to classify the size and defects on the orange surface through image processing technique. The experiment results show that the proposed machine works effectively. The size sorting module worked perfectly with absolute accuracy while the image processing module ensures the confidence level of 95%. The proposed machine could replace the manual sorting process with high accuracy.

Atwood machine automation using Arduino and LabVIEW

The Atwood machine is an apparatus used in physics experiments to visualize the concept of Newton’s Second Law of Motion. However, the data collection on the Atwood machine, which is still done manually using a stopwatch, may cause errors and can be time-consuming. To improve the efficiency and accuracy of the experiment, this research developed Atwood machine automation using Arduino Uno microcontroller. The automation enables automatic release of weights and automatic data collection through a graphical user interface developed using LabVIEW. The experiment using the automated machine obtained satisfactory results in verifying the earth’s gravitational acceleration. Therefore, this machine can be used in physics learning, especially in physics experiment. The automation also enables remote utilization through internet connection, thus allowing easier and wider access for users.

The Effect of Automated Teller Machine (ATM) On Customer Satisfaction of Digital Services Users at Afghanistan Selected Commercial Banks (Afghanistan International Bank, Azizi Bank and Islamic Bank of Afghanistan)

This study investigates the impact of Automatic Teller Machines (ATMs) on customer satisfaction among users of digital services at commercial banks in Afghanistan, specifically Afghanistan International Bank, Azizi Bank, and Islamic Bank of Afghanistan. The banking sector in Afghanistan requires further development in expanding ATM infrastructure and enhancing customer satisfaction through improved user experience and support services. Utilizing a quantitative approach with multiple linear regression analysis, the research examines primary data collected through questionnaires as well as secondary data sources. The findings reveal a significant positive relationship between ATM usage and customer satisfaction. To enhance ATM availability and customer satisfaction, commercial banks in Afghanistan should strategically install and maintain ATMs, focusing on key locations in Kabul. Investing in ATM facilities can improve various dimensions of customer satisfaction.

Assessment of haematological parameters in HIV patients attending Kabutare district Hospital

Background: Human Immunodeficiency virus (HIV) infection is characterized by progressive weakening of the immune system attributed to the decrease in the number of circulating CD4+ T-helper cells. This predisposes HIV patients to a variety of opportunistic infections and neoplastic disorders. also lead to defective hematopoiesis and a decrease in cell lineage. Haematological parameters including red blood cells, white blood cells, platelet and hemoglobin are widely used clinical indicators of health and disease. Aim: The aim of this study was to assess haematological parameters in HIV patients attending Kabutare District hospital. Methodology: This study was a cross-sectional study conducted to assess haematological parameters in HIV patients attending Kabutare District hospital. Venous blood sample were collected for complete blood count which was determined using sysmex 500i machine automation analyser. Results: A total of 80 HIV infected patient participated. 7.5% of the study participants were proven to have white blood cells below the normal range as it was below 4.26.25% of patients were found below the normal range of red blood cells. Hemoglobin and hematocrit levels of 35% patients were found to below their normal ranges. 10% of patient were found to have low platelet and 23.5% were found to have low levels of lymphocytes. Association between CD4 counts and hemoglobin levels was significant with p value of 0.000 as it is less than confidence level of 0.05. Conclusion: This study concluded that haematological parameters are affected by HIV infection and there is association between CD4 counts and hemoglobin concentration levels. I would recommend health practioners to also consider complete blood count as prognostic test of HIV as it is clinical health indicator and diseases. Keywords: Haematological parameters, Human Immunodeficiency virus, CD4 counts, acquired immunodeficiency syndrome.

Lightweight bobbin yarn detection model for auto-coner with yarn bank

The automated replacement of empty tubes in the yarn bank is a critical step in the process of automatic winding machines with yarn banks, as the real-time detection of depleted yarn on spools and accurate positioning of empty tubes directly impact the production efficiency of winding machines. Addressing the shortcomings of traditional methods, such as poor adaptability and low sensitivity in optical and visual tube detection, and aiming to reduce the computational and detection time costs introduced by neural networks, this paper proposes a lightweight yarn spool detection model based on YOLOv8. The model utilizes Darknet-53 as the backbone network, and due to the dense spatial distribution of yarn spool targets, it incorporates large selective kernel units to enhance the recognition and positioning of dense targets. To address the issue of excessive focus on local features by convolutional neural networks, a bi-level routing attention mechanism is introduced to capture long-distance dependencies dynamically. Furthermore, to balance accuracy and detection speed, a FasterNeck is constructed as the neck network, replacing the original convolutional blocks with Ghost convolutions and integrating with FasterNet. This design minimizes the sacrifice of detection accuracy while achieving a significant improvement in inference speed. Lastly, the model employs weighted IoU with a dynamic focusing mechanism as the bounding box loss function. Experimental results on a custom yarn spool dataset demonstrate a notable improvement over the baseline model, with a high-confidence mAP of 94.2% and a compact weight size of only 4.9 MB. The detection speed reaches 223FPS, meeting the requirements for industrial deployment and real-time detection.

Design and Construction of Automatic Facemask and Body Temperature Scanning Entry Barrier Machine

In the context of the global pandemic, public health screening has become paramount as the whole world including developed countries is going through a health crisis. A face mask prevents transpiration and protects against airborne transmitted bacteria or viruses. In the previous scenario of coronavirus, it was critical to eradicate this sickness and preserve our lives. Hence, prevention is better than cure becomes true. Accordingly, many precautionary measures were taken to reduce the spread of the virus. One such method of prevention is wearing a mask and regular monitoring of body temperature. This research dealt with the design and construction of an automatic entry barrier machine that integrates both facemask detection and scanning of individuals exhibiting elevated body temperature functionalities without any human interference. This was accomplished by simulating and synergizing a microprocessor (Raspberry Pi 3 Model B+), a Pi camera, an Infrared Non-contact Temperature sensor, a servo motor, and other components. The barrier arm opens and allows entrance at normal temperature by indicating a green light, displaying ‘allow’ on the LCD, and signaling once or preventing entrance at an elevated unacceptable temperature by indicating a red light, displaying ‘not allowed’ on the LCD, and signaling five (5) times.

Towards Modern Laundry Practice in Urban Socialist Poland

Drawing on ethnographic data and applying practice theory, this article discusses the emergence of modern laundry practice in urban socialist Poland. We follow an “elemental” approach to social practices: practice consists of meanings, materials, and competences. We analyze the transition of laundering from modern meanings (high standards of cleanliness), through modern materials (running water, washing machine) towards modern competences (individual improvisations, finally adjusted to an effortless laundry procedure corresponding to the capabilities of automatic washing machines). We argue that relations between elements of social practice such as tension and adjustment affect the transitions of the social practice. The transitional cleanliness practices involved men, due to the physical strength necessary to balance the tension between high standards of cleanliness and the lack of domestic material infrastructure.

Sistem Otomasi Untuk Menyortir Barang Pada Ruang Produksi Menggunakan Scada dan PLC

The development of the industrial revolution is progressing. Especially on production performance or machine parts. Industrial revolution 4.0 uses the internet and digitalization. So there are lots of new innovations. In a large-scale company, of course there are many tools and machines that need to be regulated whether they are on or not with regular supervision. With regular supervision, it is impossible for human workers to check one by one, thereby wasting time and reducing production levels. Therefore, it is necessary to develop a control system using PLC and HMI (Human Machine Interface) as the brain or control center for the company. In the industrial sector, PLC (Programmable Logic Controller) is an important factor in the operation of automatic machines in factories replacing relay control systems. So that PLC as a control system in the industrial sector can move machines according to needs. One part of the industry is the production room. The production room is the part where there are manufacturing materials or goods up to packaging. This research aims to design and implement a PLC (Programmable Logic Controller) and SCADA by adding sensors and actuators as output. The method used is a rule base system, the system runs according to a predetermined sequence and flow. The results of this research are the PLC, the SHT 20 temperature and humidity sensor can produce temperature and humidity values, the capacitive photosensor can detect items with 35 trials accurately but the distance is limited to only 27cm, the proximity sensor can only detect metal objects by attaching to the sensor and The actuator in the form of a stepper motor can run according to the commands given and is well integrated into the HMI (Human Machine Interface).

Pengungkapan Kinerja Lingkungan Instansi Pemerintah Daerah Melalui Laporan Keberlanjutan Berbasis Global Reporting Initiative

Laundry washing is a crucial process in daily life. Although automatic washing machines have simplified the washing process, utilizing advanced technology like fuzzy logic control can enhance the performance of smart washing machines to achieve optimal washing results. This research aims to implement fuzzy logic control using the Mamdani method on smart washing machines to improve efficiency and washing quality. The research methodology consists of several stages. Firstly, analyzing variables that affect the washing process, such as laundry load, fabric thickness, dirt level, and water temperature. Next, designing a Mamdani fuzzy logic control system by determining membership functions for each variable and creating fuzzy rules to link input and output variables. Lastly, evaluating washing results based on predefined input variables. The research findings demonstrate that implementing Mamdani fuzzy logic control on smart washing machines can significantly enhance washing quality and adaptively determine washing parameters to achieve cleaner washing outcomes. Therefore, integrating Mamdani fuzzy logic control into smart washing machines has the potential to improve overall washing performance. In conclusion, utilizing Mamdani fuzzy logic control on smart washing machines is an effective step towards enhancing efficiency and effectiveness in laundry washing. This research contributes to the development of more adaptive and environmentally-friendly smart washing machine technology and plays a crucial role in advancing fuzzy logic control technology for other smart household applications.

Design and Experiment of Dual-Row Seedling Pick-Up Device for High-Speed Automatic Transplanting Machine

To address the inefficiency and instability of automatic transplanting machines, a dual-row seedling pick-up device and its corresponding control system were developed. Existing seedling end-effectors are primarily mechanically controlled, and the seedling needles can easily cause damage to the interior of the bowl. In order to reduce the damage inflicted by the end-effectors to the bowl, this paper conducted a mechanical analysis of the end-effector. At the same time, a buffer optimization analysis was conducted on the operation of the end-effector, and a flexible pneumatic end-effector for seedling picking was designed. The control system combined the detection of multiple sensors to monitor the process of seedling picking and throwing. By coordinating the lifting cylinder and clamping cylinder, the system effectively reduced seedling pot damage while improving seedling picking efficiency. By setting the operating parameters of the servo motor, the goal of low-speed and high-efficiency seedling picking was achieved. To evaluate the performance of the control system, the linear displacement sensors and acceleration testing systems were used to analyze the performance of the seedling throwing. The results showed that the seedling picking efficiency could reach 180 plants min−1, with no significant difference between the actual measured moving distance and the theoretical setting distance. The positioning error remained stable between 0.5 and 0.9 mm, which met the requirements for seedling picking accuracy. The buffer optimization design reduced the peak acceleration of the end-effector from −22.1 m/s2 to −13.4 m/s2, and the peak value was reduced by 39.4%, which proved the significant effectiveness of the buffer design. A performance test was conducted using 128-hole seed trays and 33-day-old cabbage seedlings for seedling picking and throwing. When the planting frequency reached 90 plants/row·min−1, the average success rate of picking and throwing seedlings was 97.3%. This indicates that the various components of the designed seedling pick-up device work in good coordination during operation, and the control system operates stably. Technical requirements for the automatic mechanical transplanting of tray seedlings were achieved, which can provide reference for research on automatic transplanting machines.