High Accuracy Rate Research Articles

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

Text Comment Classification and Sentiment Analysis Model Based on LSTM

This study addresses the challenge of sentiment analysis and text classification in user-generated comments, an essential task for understanding public opinion in the digital age. With the widespread adoption of social media, the sheer volume of text data has increased exponentially, thereby necessitating advanced analytical tools. The study integrates Long Short-Term Memory (LSTM) networks with attention mechanisms to develop a model adept at both classifying and analyzing the sentiment of textual comments. The research methodology commences with data cleaning, tokenization, and feature extraction, subsequently followed by the implementation of a bidirectional LSTM architecture augmented with an attention mechanism. The model underwent rigorous training and testing on a comprehensive dataset, yielding high accuracy rates in sentiment analysis and text classification. The results underscore the model’s proficiency in capturing sentiment nuances and underscore its potential for deployment in e-commerce and content recommendation systems, emphasizing the practical implications of this research for enhancing decision-making processes in businesses and among policymakers.

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

Treatment Outcome of Surgery Followed By Corticosteroid Therapy For Idiopathic Granulomatous Mastitis

Introduction: Idiopathic granulomatous mastitis (IGM) is a rare, benign, and chronic inflammatory breast condition that has been shown to have radiological and clinical similarities to breast cancer. In spite of the fact that the cause of IGM is unknown, various contributing variables have been identified, such as a reaction to chemical substances like OCP, viral disorders, autoimmune diseases, and an immunological response to milk leakage from the breast lobule. Although various methods have been used for IGM treatment (such as surgical excision, steroids, methotrexate, etc), no consensus currently exists regarding the ideal method of treatment. Due to its positive response to steroid treatment, it is hypothesized that IGM is an autoimmune disease and is well responds to corticosteroid therapy. The aim of this study is to observe the treatment outcomes of surgery and corticosteroid therapy in IGM. Materials and Methods: This quasi experimental study was conducted in Department of Surgery, Khulna Medical College Hospital from 8th August 2018 to 8th August 2019. Total 30 patients were enrolled and allocated in two groups a ratio of 1:1. In group-A there were 15 patients, received surgical treatment with corticosteroid therapy and group-B, 15 patients received only surgical treatment. A wide local excision (WLE) was done in surgical procedure to remove area of diseased unhealthy tissue with a margin of normal tissue. Corticosteroid therapy was given orally in the following regimen: 8 weeks of steroid therapy with prednisolone at 0.5mg/kg/day for 4 weeks and then tapered down slowly for 4 weeks. Then subsequent follow up conducted after 2 months and after 4 months of primary treatment to observe the treatment outcomes. Statistical analysis was carried out by using the Statistical Package for Social Sciences version 23.0 for Windows (SPSS Inc., Chicago, Illinois, USA). A descriptive analysis was performed for all data. The mean values were calculated for continuous variables. The quantitative observations were indicated by frequencies and percentages. Unpaired student t-test was used to compare continuous variables. Chi-square test used to compare categorical data. A “p” value of <0.05 was considered as significant. Result was presented by tabulation and graphical presentation in the form of tables, pie chart, graphs, bar diagrams, histogram & charts etc. Result: Maximum numbers of patients (50.0%) were between 31-40 years age group, mean±SD age was 29.7±10.2 years. The left breast was affected in 16(53.3%) patients, right breast in 12(40.0%) patients and bilateral in 2(6.7%) patients. Included 30 patients were allocated in two groups. Among them in group-A there were 15 patients (Surgical treatment with corticosteroid therapy) and group-B, 15 patients (Surgical treatment alone). Wide local excision was performed in all patients. Concomitant corticosteroids therapy was used in selective group (group-A). Study shows that 22(73.3%) of the patients recovered completely (80.0% versus 66.7% in group-A and group-B respectively). There was a statistically significant difference (pd”0.05). Present study revealed that incidence of complications and recurrence rate was 8(26.7%) patients, among them 3(20.0%) were of group-A, while 5(33.3%) patients were group-B. Conclusions: Present study concluded that treatment outcomes of surgery, followed by corticosteroid therapy can be preferred at Idiopathic granulomatous mastitis (IGM) in the means of less early or late postoperative complication, resolution of symptoms with high accuracy rate to relief symptoms. J Dhaka Med Coll. 2023; 32(1) : 71-76

Sex estimation using metrics of the innominate: A test of the DSP2 method.

Sex estimation is a critical component of the biological profile, and forensic anthropologists may use a variety of sex estimation methods depending upon the degree of completeness and state of preservation of the skeletal remains being analyzed. The innominate is widely accepted to be the most sexually dimorphic skeletal element. The Diagnose Sexuelle Probabiliste (DSP) method, which uses 10 measurements of the innominate, was introduced in 2005 and updated as DSP2 in 2017. While DSP2 has been reported to have high classification accuracy rates in studies of South American and European populations, the method has not been widely tested in US samples, and few US practitioners incorporate this method into their casework. The goal of this study was to test the reliability and accuracy of DSP2 using a large, modern sample from the US (n = 174). Two observers, blinded from demographic information associated with each specimen, collected the DSP2 metrics. Intra- and interobserver error analyses showed acceptable levels of agreement for all measurements, except for IIMT. Classification accuracies exceeded 95%, with minimal sex bias, for both observers and using various measurement combinations; however, an inclusivity sex bias occurred with more males reaching the 0.95 posterior probability threshold required by DSP2 to provide a sex classification estimate. Based on its high accuracy, forensic anthropologists in the US may consider incorporating DSP2 into their casework, although we recommend excluding IIMT and using SPU with caution. Additional methods will continue to be needed when the posterior probability threshold is not reached.

GTasm: a genome assembly method using graph transformers and HiFi reads

MotivationGenome assembly aims to reconstruct the whole chromosome-scale genome sequence. Obtaining accurate and complete chromosome-scale genome sequence serve as an indispensable foundation for downstream genomics analyses. Due to the complex repeat regions contained in genome sequence, the assembly results commonly are fragmented. Long reads with high accuracy rate can greatly enhance the integrity of genome assembly results.ResultsHere we introduce GTasm, an assembly method that uses graph transformer network to find optimal assembly results based on assembly graphs. Based on assembly graph, GTasm first extracts features about vertices and edges. Then, GTasm scores the edges by graph transformer model, and adopt a heuristic algorithm to find optimal paths in the assembly graph, each path corresponding to a contig. The graph transformer model is trained using simulated HiFi reads from CHM13, and GTasm is compared with other assembly methods using real HIFI read set. Through experimental result, GTasm can produce well assembly results, and achieve good performance on NA50 and NGA50 evaluation indicators. Applying deep learning models to genome assembly can improve the continuity and accuracy of assembly results. The code is available from https://github.com/chu-xuezhe/GTasm.

The application and challenges of different face recognition technologies in the three major fields of security, social media, and medical care

Abstract. Face recognition technology is a very important part of modern technology, which can not only be used to ensure security, but also can be used in the field of information organization and content division. However, with the popularization and application of face recognition technology, many problems that need to be solved urgently have emerged: excessive computing resources are consumed in order to pursue high-precision recognition, which brings computing pressure; The basis for improving recall is the need for a lot of power and memory; If security is not guaranteed, it can cause problems such as data breaches. The demand for face recognition technology is different in different use fields, so the purpose of this study is to combine the scene requirements and technical advantages more reasonably. The research results are as follows: the high accuracy and recall rate of 3D convolutional neural networks (3DCNNs) ensure that it can be used safely in high-precision and high-security scenarios. Lightweight Convolutional Neural Network (MobileNetV2) is suitable for resource-constrained environments due to its low memory consumption and low communication cost. Edge computing real-time face recognition (EC-RFERNet) is the most suitable for large-scale popularization and application among the three because of its lowest power consumption and latency. This study deeply explores the advantages and disadvantages of different facial recognition technologies and finds solutions to their shortcomings. According to their unique advantages combined with the requirements of commonly used scenarios, it provides a scientific basis for the deployment of face recognition technology in different fields. However, due to limited information, this paper cannot cover all application scenarios and the latest technologies, so it is hoped that in the future, we can combine the advantages of different technologies to develop more comprehensive face recognition technology and make more reasonable technical planning.

Assessment of teledentistry in improving access to dental care: a systematic review.

Teledentistry has emerged as a promising approach to improving access to dental care, particularly for underserved populations. This systematic review aims to evaluate the effectiveness of teledentistry interventions in enhancing access to dental care. A comprehensive search across multiple databases was conducted to identify studies evaluating teledentistry interventions. Studies were included if they investigated the impact of teledentistry on dental care accessibility, utilization, or patient outcomes. Eligibility criteria were applied systematically to select relevant studies for inclusion. Forty-six studies met the inclusion criteria and were included in the review. The synthesized evidence showed the effectiveness of teledentistry interventions in improving access to oral healthcare services, facilitating timely diagnosis and treatment planning, and enhancing patient outcomes. Despite variations in study designs and methodologies, teledentistry interventions consistently exhibited high diagnostic accuracy and concordance rates compared to traditional face-to-face consultations. The findings highlight teledentistry's transformative potential in modernizing oral healthcare delivery and reducing access disparities.

Hybrid Deep Learning Based Model for Removing Grid-Line Artifacts from Radiographical Images

The digital imaging technique known as Computed Radiography (CR) has transformed the medical imaging industry by providing a number of advantages. It eliminates the need for traditional film-based methods, making it more efficient and convenient. A common issue faced with CR images is the presence of grid artifacts and other pattern artifacts, which can have a significant impact on the quality of the images when viewed on a computer screen, especially if a clinic-grade display is not accessible. This paper presents a novel framework for removing grid line artifacts from X-ray images, which is a critical challenge in medical imaging. The framework proposes a hybrid Deep Grid model that combines a Gaussian band-stop filter with ADAM optimization to produce high-quality, grid-line free X-ray images that are suitable for further analysis and diagnosis. Deep learning (DL) models for instance the Convolutional Neural Network (CNN), DenseNet, VGG-Net, and Fast R-CNN were utilized to classify images, and the grid-by-grid removal of grid lines in the image was performed. The proposed framework achieved a high accuracy rate of 98% in eliminating grid line artifacts from X-ray images, demonstrating its possibility for a big improvement the accuracy and reliability of diagnostics for medical based on X-ray images

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors.

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of to , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

Prediction of tennis match results based on logistic regression

More and more people like tennis, and the demand for the accuracy of predicting the results of tennis matches in sports betting is getting higher and higher. In this paper, a model is built to predict the results of tennis matches. In this paper, we first use the random forest model to analyze the factors that will have an impact on the outcome of the game, and the authors have selected out five factors that may have an impact on the outcome, which are: break of serve, Serve, Score, Net points , And they were assigned proportionally and the distribution was Break of serve 34.10%, Serve 24.30%, Score 38.00%, Net points 3.60% and using these proportions the influencing factors were calculated numerically, in this paper, the calculated value is named “momentum”. Then Pearson's correlation coefficient was used to analyze the relationship between momentum and match results, and the analysis found that the correlation between momentum and match results was as high as 0.807. Finally, a logistic regression model was used to predict and test the results of tennis matches. The final test results found that the model established in this paper has a high accuracy rate, and the research results also provide a new idea for tennis betting industry.

Border Gateway Protocol Route Leak Detection Technique Based on Graph Features and Machine Learning

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Protecting Drawings at Production and Regime Facilities Using Neural Network Technology and DLP Systems

The existing methods of object image processing are analyzed. Problems of using the considered methods within the framework of DLP systems are considered. A new object image processing method was presented that allows processing of complex images. A metric of accuracy and completeness of plagiarism image detection was used to evaluate the quality of the developed method. Testing was carried out using image ranking to analyze the ability of the model to search for semantics. Comparative testing was carried out with the method based on raster neural models. The advantages and disadvantages of the developed method were highlighted, as well as options for further developmentAs a result of this work, a method for processing object images in native format has been developed. The main advantage of the developed method is the high rate of accuracy (87 %) and completeness (100 %). This study can be useful for further research in the field of vector image analysis. Also, the developed method can be applied as a tool similarly to raster methods of image processing (image search, classification, search for objects in the image).

An efficient convolution neural network method for copy-move video forgery detection

Unmanned systems play a pivotal role in military surveillance, critical infrastructure protection, law enforcement, search and rescue operations, and border security, showcasing their multifaceted importance across diverse applications. Video fraud detection is integral to multimedia security, where our task involves the precise identification of modified segments within video sequences. Current approaches to video fraud detection often rely on manual feature selection and models tailored to detect specific tampering types, such as copy-move or splicing. The general representation powers of deep learning models and the connection of multiple forensic characteristics are still not fully explored. This research uses a convolutional neural network (CNN) model to identify copy-move video forgeries. Copy-move forgery is a type of video tampering whereby a portion of the video is copied and pasted somewhere different in the same video to cover an essential video characteristic. The method that is being proposed involves dividing the video into individual frames, extracting features from each frame by using a CNN model that has already been trained, and then utilizing these features to train a new CNN model that would classify each frame as either legitimate or fabricated. The proposed method effectively detects copy-move video forgery with an exceptionally high accuracy rate, exceeding current methods of accuracy and computational effort. The proposed method outperformed all other approaches on the SULFA, GRIP, and VTD datasets. The model's accuracy was 85.42 %, 86.16 %, and 81.87 %, respectively, with the shortest times recorded being 9.6 sec, 11.4 sec, and 13.7 sec, respectively. Consequently, specialists can employ the suggested method as a machine-learning instrument for detecting fake videos in real-time.

A New Epileptic Seizure Prediction Framework Based on Electroencephalography Signals

This research seeks to evaluate how effectively seizures can be predicted and managed in epilepsy using a specialized deep learning model based on Long Short-Term Memory (LSTM) neural networks. The model leverages non-invasive scalp electroencephalography (EEG) recordings for predicting seizures. To develop and assess the proposed LSTM neural network model, a comprehensive dataset was gathered. The model emphasizes achieving high sensitivity and reducing false alarms to improve its real-time applicability. The evaluation involved various metrics to measure accuracy, sensitivity, and rates of false positives and false negatives. The effectiveness of the proposed LSTM neural network model was outstanding, with accuracy rates ranging from 99.07% to 99.95%. Notably, the sensitivity score of 1 confirmed precise prediction for all seizure cases. The model demonstrated minimal false positive and false negative rates, highlighting its reliability in predicting seizures. This study emphasizes the promising potential of the proposed LSTM neural network model in providing advanced warning for seizures. The high accuracy and sensitivity rates suggest its usefulness in enabling timely preventive measures for patients, ultimately reducing the occurrence of seizures. This innovative approach holds significance in enhancing the overall management and quality of life for individuals dealing with epilepsy.

Enhancing Speech-to-Text and Translation Capabilities for Developing Arabic Learning Games: Integration of Whisper OpenAI Model and Google API Translate

This study tackles language barriers in computer-mediated communication by developing an application that integrates OpenAI’s Whisper ASR model and Google Translate machine translation to enable real-time, continuous speech transcription and translation and the processing of video and audio files. The application was developed using the Experimental method, incorporating standards for testing and evaluation. The integration expanded language coverage to 133 languages and improved translation accuracy. Efficiency was enhanced through the use of greedy parameters and the Faster Whisper model. Usability evaluations, based on questionnaires, revealed that the application is efficient, effective, and user-friendly, though minor issues in user satisfaction were noted. Overall, the Speech Translate application shows potential in facilitating transcription and translation for video content, especially for language learners and individuals with disabilities. Additionally, this study introduces an Arabic learning game incorporating an Artificial Neural Network using the CNN algorithm. Focusing on the “Speaking” skill, the game applies to voice and image extraction techniques, achieving a high accuracy rate of 95.52%. This game offers an engaging and interactive method for learning Arabic, a language often considered challenging. The incorporation of Artificial Neural Network technology enhances the effectiveness of the learning game, providing users with a unique and innovative language learning experience. By combining voice and image extraction techniques, the game offers a comprehensive approach to enjoyably improving Arabic speaking skills.

Determining domestic violence against women using machine learning methods: The case of Türkiye.

Domestic violence against women is a pervasive issue globally, representing a severe violation of human rights and a significant public health concern. The hidden nature of such violence and its frequent underreporting make it a critical area for research. Recent advancements in artificial intelligence offer new avenues for identifying and predicting instances of domestic violence through machine learning (ML) algorithms. This study aimed to determine the frequency and risk factors of domestic violence against women using ML methods. With a cross-sectional design, this research was conducted with 630 married women between December 2023 and February 2024. Data were obtained using the 'Demographic Information Form' and the 'HITS Domestic Violence Scale'. Data analysis used six ML algorithms (decision tree, random forest, support vector machine [SVM], logistic regression [LR], Naive Bayes and k-nearest neighbour). In our study, the rate of women experiencing violence was determined to be 11%, while the duration of marriage, number of children and level of education were identified as significant risk factors. Threat, insult and injury were common risk factors in all algorithms. SVM and LR algorithms were effective models in predicting violence with a 100% accuracy rate. All ML algorithms' sensitivity ranged from 91.12% to 100%, while specificity ranged from 85% to 100%. The findings of our study demonstrate that ML algorithms have high accuracy rates in determining the frequency and risk factors of domestic violence against women, indicating that they can be used safely.

KAWANKULINER: PERSONALISED FOOD RECOMMENDATION APP USING BMR AND TDEE FOR OPTIMAL DAILY NUTRITION

People often have difficulty in determining the type of food that is suitable for the composition of their body. This is due to the many types of food available, as well as the lack of information about the nutritional content of each type of food. By fulfilling the daily calorie and macro-nutrient needs required by the body, its metabolism can be maintained, preventing various health problems such as malnutrition. Based on the results of the Basic Health Research in 2018 malnutrition is one of the most serious problems in Indonesia. Several studies related to calculating calorie estimates using the BMR approach with the utilization of technology have been conducted.However, these studies were limited to only displaying the amount of daily calorie needs that were not accompanied by what foods were needed to meet these daily needs. The purpose of this research is to build an app to help individuals understand their daily calorie needs based on their Basal Metabolic Rate (BMR) and total daily expenditure energy (TDEE), using smart scale to provide body composition information and spoonacular API to provide food recommendations that match their energy needs and give them recipes on how to make their food meanwhile waterfall model is used as a software development method. The test results show that the app meets the functional and usability requirements well, with a high accuracy rate of 99.86% for calories, protein, fat, and carbohydrates. In terms of user response, 13 respondents had a very positive response, and considered the app useful for making it easier to get information about their calorie and macro-nutrient needs, as well as provide food recommendations based on their daily needs.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.