IAES International Journal of Robotics and Automation (IJRA)
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Experimental evaluation of bidirectional encoder representations from transformers models for de-identification of clinical document images
Get PDFMany health institutes maintain patients’ diagnosis and treatment reports as scanned images. For healthcare analytics and research, large volumes of digitally stored patient information have to be accessed, but the privacy requirements of protected health information (PHI) limit the research opportunities. Particularly in this artificial intelligence (AI) era, deep learning models require large datasets for training purposes, which hospitals cannot share unless the PHI fields are de-identified. Manual de-identification is beyond possible, with millions of patient records generated in hospitals every day. Hence, this work aims to automate the de-identification of clinical document images utilizing AI models, particularly pre-trained bidirectional encoder representations from transformers (BERT) models. For the purpose of experimentation, a synthetic dataset of 550 clinical document images was generated, encompassing data obtained from diverse patients across multiple hospitals. This work presents a two-stage transfer learning approach, initially employing Tesseract character recognition (OCR) to convert clinical document images into text. Subsequently, it extracts PHI fields from the text for de-identification. For the purpose of extraction, BERT models were utilized; in this work, we contrasted six pre-trained versions of such models to examine their effectiveness and achieve the F1 score of 92.45%, thus showing better potential for de-identifying PHI data in clinical documents
Evaluating the development and cutting capacity of a one-square computer numeric controlled milling machine
Get PDFTraditional subtractive technology is rapidly losing significance with the advent of digital manufacturing technologies, which offer affordable machining with high accuracy and repeatability. Computer numeric controlled (CNC) machining has been around for a while; however, it has been costly to own one. Since the concept of CNC machining is now broadly understood and open-source software is available for control, designers can make use of available local materials to develop cheaper CNC machines. Hence, this presents the evaluation of the design and development of a one-square-meter CNC milling machine. The control was implemented on Arduino Uno, while open-source Universal G-code Sender (UGS) and G-code reference block library (GRBL) were used for the G-code generation and machine control, respectively. The built CNC was calibrated and tested on wood and plastic materials, and the resulting products were acceptable in accuracy up to ±0.02 mm in the first trial, but attained perfect accuracy by the third trial. Multiple tests repeatedly showed that accuracy was maintained. Since the machine is reconfigurable, future work entails automation and incorporating laser cutting capabilities into the machine
Design of a prototype firefighting robot based on an Arduino microcontroller using machine learning technique
Get PDFThe design and implementation of this paper are mainly based on control of the autonomous firefighting robot. In recent years, robotics has turned out to be an ingredient in which many people have shown their interest. Robotics has gained popularity due to the advancement of many technologies of computing and nanotechnologies. The output of the fire sensor is connected to the Arduino controller that controls the movement of the vehicle and the operation of spraying water. An infrared sensing circuit is designed with the infrared sensors placed in front of the vehicle to avoid collision with the obstacles. A total of two inbuilt reduction geared direct current motors are used in the paper for the robot movement in all the directions forward, backward, right, and left directions. For more practicality, a small water tank with a pumping motor is also arranged over the chassis and the water sprinkler pipe that is firmly fixed over the plate where the sensor is arranged can deliver water with some force. When the sensor detects the fire, the sprinkler is positioned toward fire flames; the pumping motor will be energized automatically to extinguish the fire. The main advantage of the proposed system automatically controls the fire by using advanced control techniques
Optimization model for endurance performance of electric rotorcraft transport drones and its application prospects
Get PDFThe operational parameter configuration and performance optimization of electric rotorcraft transport unmanned aerial vehicles (UAVs) currently lack comprehensive guiding theory, impacting UAV endurance and efficiency, thereby limiting industry growth. This paper analyzes factors affecting UAV endurance and establishes a hover endurance model for electric rotorcraft transport UAVs through theoretical derivation and testing. Based on this model, we introduce the concepts of thrust redundancy coefficient and load cut-off line, proposing an optimal endurance configuration theory. This theory categorizes the parameter configuration range into light load, ideal configuration, load cut-off, and endurance saturation zones. Using current operational parameters, we evaluate and optimize UAV performance. Verification results demonstrate high model accuracy, with error rates ranging from 1.89% to 5.69%. After optimization, the payload capacities of two transport UAVs increased by 6.25%, and their endurance improved by 6.97% and 9.5%, respectively, enhancing overall efficiency. This model provides a solid framework for assessing endurance capabilities and offers targeted optimization suggestions, making it crucial for improving UAV performance
Faraid distribution calculation using AI-based Quranic chatbot
Get PDFFaraid, Islamic inheritance law, refers to that aspect of Shariah law which is not properly understood and has created issues and impediments in the distribution of estates. This paper discusses the development of an AI-based Quranic chatbot to be used by the public to learn the Faraid rules and automate calculations of inheritance distribution. The chatbot has been developed using natural language processing and a rule-based algorithm, which intends to search and get an accurate interpretation from the user queries, retrieve relevant verses of the Quran, and compute the share of inheritance according to the established Islamic jurisprudence. Fuzzy match identifies and corrects variation in queries, enhancing user interaction, ensuring that it appears more intuitive and accessible. The system processes user input regarding heirs of the deceased, estate value, and debts, and applies Faraid rules to generate accurate distribution results that happen to be web-based platforms of this chatbot. It intends to link traditional Islamic knowledge with modern digital solutions, bringing Faraid calculations closer, more comfortable, faster, and transparent. Through rigorous tests and user feedback will prove above, revealing the chatbot’s potential in understanding the application of Islamic inheritance law and promoting digital engagement in all these through Quranic teachings
LoRa-enabled remote-controlled surveillance robot for monitoring and navigation in disaster response missions
Get PDFRescue missions must be conducted within a strict timeframe, and the safety of all rescuers and civilians is prioritized. The proposed system aims to design a remote-operated aerial surveillance robot for disaster-affected areas for search and rescue missions. Real-time video transmission and RS-232 long-range communication enable operators to navigate rough environments and monitor data collected in real-time. This powerful tool ensures the protection of human life while collecting accurate and meaningful data. Cloud storage for data and surveillance strengthens the system, preventing part failure and fostering collaboration among users. This is a significant step towards using Internet of Things systems alongside remote-controlled robots in disaster response. The robot's key contribution to disaster management is identifying the environment, addressing issues of no visibility, complicated terrains, and speed. Its modification and expansion capabilities make it useful in armed surveillance, industrial monitoring, and environmental studies, making it an important innovation for many other fields
Forecasting business exceptions in robotic process automation with machine learning
Get PDFBusiness exceptions interrupt robotic process automation (RPA) workflows and oblige costly human intervention. This paper explores the application of machine learning (ML) time series forecasting techniques to predict business exceptions in RPA. Using RPA robot logs from a financial service company, we employ ARIMA, SARIMAX, and Prophet statistical models, comparing their performance with ML models such as XGBoost and LightGBM. Furthermore, we explore hybrid approaches that combine the strengths of statistical models with ML techniques, specifically integrating Prophet with XGBoost and LightGBM. Our findings reveal that a hybrid LightGBM model substantially outperforms traditional methods, achieving a 40% reduction in the weighted absolute percentage error (WAPE) when compared to the top-performing statistical model. These results suggest the potential of ML forecasting in optimizing RPA operations through the analysis of log-generated data
Analysis and implementation of computation offloading in fog architecture
Get PDFThe fast expansion of connected devices has led to an unparalleled increase in data across sectors like industrial automation, social media, environmental monitoring, and life sciences. The processing of this data presents difficulties owing to its magnitude, temporal urgency, and security stipulations. Computation offloading has arisen as a viable alternative, allowing resource-constrained devices to assign demanding work to more robust platforms, thus improving responsiveness and efficiency. This paper examines decision-making strategies for computing offloading by assessing various algorithms, including a deep neural network with deep reinforcement learning (DNN-DRL), coordinate descent (baseline), AdaBoost, and K-nearest neighbor (KNN). The performance evaluation centers on three primary metrics: system accuracy, training duration, and latency. The computation offloading mitigates these issues by transferring intricate workloads from resource-limited devices to more proficient platforms, thus enhancing efficiency and responsiveness. The evaluation examines accuracy, training duration, and latency as key parameters. The results indicate that KNN attains maximum accuracy and minimal latency, AdaBoost provides a robust balance despite increased training costs, and the baseline underperforms in both efficiency and responsiveness. These findings underscore the trade-offs between computational expense, precision, and real-time application, providing insights for forthcoming IoT and edge-computing systems
Designing high power efficient finite impulse response filters with three-four inexact adder-integrated Booth multiplier
Get PDFFinite impulse response (FIR) filters are widely utilized in several applications in digital signal processing, including data transmission, photography, digital audio, and biomedicine. It is necessary to use high sample rates for FIR filters, while moderate sample rates are needed for low-power circuits. To solve these problems, a Booth multiplier based on three-four inexact adder-based multiplication (TFIE-BM) was proposed. The goal of the proposed TFIE-based FIR Booth multiplier is to lower area usage, latency, and power consumption. The proposed method utilizes the spotted hyena optimizer (SHO) to find the optimal filter coefficient (FC) by minimizing the pass power consumption and Transition bandwidth. Moreover, a high-performance three-four inexact adder (TIFE adder) has been introduced, which uses fewer XOR gates for sum and carry generation, indicating that the logic has been simplified to reduce hardware complexity. By increasing speed and decreasing the FIR filter's critical path delay, a modified Booth multiplier that uses a 5:2 compressor is introduced. The overall delay of the proposed approach is 23.4%, 18.7%, 12.3%, and 5.7% lower than that of the Radix-4 Booth multiplier, CSA Booth multiplier, hybrid multiplier, and traditional Booth multiplier, respectively
IoT-based cricket environment system to maximize egg production and reduce mortality rate
No full textThe deployment of Internet of things (IoT) technologies presents an opportunity to improve efficiency in cricket farming. This study investigates the implementation of an IoT-based system utilizing an ESP32 microcontroller, a suite of environmental sensors, and actuators. The system is supported by a ThingsBoard dashboard for data visualization and a Telegram bot for notifications. The setup was tested on a single cricket cage over a 28-day period and compared against a control group. Each cage contained 20 male and 100 female Cliring crickets. Key parameters analyzed included temperature, humidity, soil moisture, egg yield, food conversion ratio (FCR), and mortality rate. Findings show that the IoT-enabled cage consistently maintained optimal environmental conditions—temperature (20 to 32 °C), humidity (65% to 85%), and soil moisture (60% to 80%)—unlike the control, which experienced greater variability. The IoT cage yielded 87.28 grams of eggs, a 33.33% improvement over the control's 65.46 grams. Additionally, FCR improved from 2.53 to 2.01 grams per egg, and mortality rate dropped from 0.816 to 0.708. These results underscore the effectiveness of IoT systems in enhancing environmental stability, productivity, and survival rates in small- to medium-scale cricket farming operations