User-friendly Graphical Interface Research Articles

The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer

The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model "Modified-DeepSurv" for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model "CPH." The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946-0.974) and 0.836 (95% CI: 0.774-0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908-0.964) and 0.777 (95%CI: 0.633-0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.

VariantSurvival: a tool to identify genotype-treatment response.

Motivation: For a number of neurological diseases, such as Alzheimer's disease, amyotrophic lateral sclerosis, and many others, certain genes are known to be involved in the disease mechanism. A common question is whether a structural variant in any such gene may be related to drug response in clinical trials and how this relationship can contribute to the lifecycle of drug development. Results: To this end, we introduce VariantSurvival, a tool that identifies changes in survival relative to structural variants within target genes. VariantSurvival matches annotated structural variants with genes that are clinically relevant to neurological diseases. A Cox regression model determines the change in survival between the placebo and clinical trial groups with respect to the number of structural variants in the drug target genes. We demonstrate the functionality of our approach with the exemplary case of the SETX gene. VariantSurvival has a user-friendly and lightweight graphical user interface built on the shiny web application package.

An intelligent mushroom strain selection model based on their quality characteristics

The great versatility of mushroom production and the significant nutritional and medicinal properties of the crop make it a highly attractive product that is in continuous expansion around the world. However, its quality can be affected the combination of a large number of evaluable variables that are essential to take into account. Thus, the aim of this work was to build an intelligent model for the prediction of mushroom strains through the development of neural networks (ANNs) that takes into account the control of data processing times, with the use of the minimum possible number of parameters that define their quality control and subsequent selection. In addition, a user-friendly and intuitive graphical interface has been generated that shows the selection of the appropriate mushroom strain and may be useful for potential end-users in this field. For this purpose, 7 mushroom strains (Agaricus bisporus) defined by a total of 27 quality parameters were used (texture, colour, etc.). The results showed that, in the analysis of individual parameter combinations (Rt), the best overall accuracy achieved (OAA) was 52.43%, reaching 81.30% with the combination of four parameters (dry matter (%), crude protein (Nx4. 38. %), Fb and Wt) and 94.32% with 9 parameters (Cap diameter (mm), dry matter (%), crude protein (Nx4.38. %), ΔE, browning index (BI), Fb, Wb = Fb x Db/2, Wr and Rt). The development of this model allows for the identification of some of the most important commercial white hybrid strains of high-yielding mushrooms, while also being a useful tool for the selection of the most important parameters of interest as regards the quality and benefits of this product for the consumer.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Smart inhaler mask for portable medical gas cylinders utilizing IoT technologies

Over the past decade, there has been a significant increase in the development of wearable and smart health devices based on the Internet of Things (IoT), that enable real time remote control and monitoring of health parameters. In this context, the purpose of this article is to present the development and evaluation process of a smart inhaler mask designed for medical gas cylinders, utilizing IoT technologies. Handheld medical gas cylinders, although essential for a specific group of people, often come with a significant cost, and lack features such as dosage measurement and gas storage monitoring. Consequently, a smart inhaler mask was developed to address these limitations by enabling in-situ and real-time measurement of gas dosage, tracking the remaining gas within the cylinder, and assisting users in monitoring the overall usage of the medical gas cylinder. To facilitate the access and interpretation of this information, a newly developed and user-friendly Graphical User Interface (GUI) has been implemented in the form of a mobile application. This study outlines the evaluation process employed to verify the reliability and accuracy of the results obtained from the smart inhaler mask.

Identification and Prediction of Metastatic Tumor in Breast Using Deep Learning Techniques

Abstract: Our work aims to develop a system for breast cancer detection using Image Processing Techniques. It addresses the limitations of mammography, such as painful procedures, by introducing thermal image analysis. For mammogram analysis, morphology techniques are applied to enhance the images before feature extraction. The preprocessed image used to classify mammograms into threecategories: Normal Fatty Breast, Abnormal Fatty Breast, and Glandular Breast. In the case of thermal images, the system analyses the distribution of heat around the breast. Variations in heat levels are used to identify potential cancerous areas. Additionally, the project includes the development of a user-friendly Graphical User Interface (GUI) to enhance accessibility for users. The proposed system provides option on architectures namely CNN, VGG16 and Densenet121 using which the result is obtained.

Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase.

The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. The formation energy is a crucial thermochemical datum, and the accurate calculation of the formation energy of the μ phase contributes to the material design of high-temperature alloys. Traditional first-principles calculations demand significant computational time and resources. In this study, an innovative machine learning (ML)-based approach to accurately predict the formation energy of the μ phase is proposed. This approach involves the utilization of six algorithms and two model evaluation methods to construct the ML models. Leveraging a comprehensive data set containing 1036 binary configurations of the μ phase, the model trained using a 10-fold cross-validation technique, and the multilayer perceptron (MLP) algorithm achieves a mean absolute error (MAE) of 23.906 meV/atom. To validate its generalization performance, the trained model is further validated on 900 ternary configurations, resulting in an MAE of 32.754 meV/atom. Compared with solely using traditional first-principles calculations, our approach significantly reduces the computational time by at least 52%. Moreover, the ML model exhibits exceptional accuracy in predicting the lattice parameters of the μ phase. The MAE values for the a and c parameters are 0.024 and 0.214 Å, respectively, corresponding to low error rates of only 0.479 and 0.578%. Additionally, the ML model was utilized to accurately predict the formation energy of all of the possible ternary configurations. To enhance accessibility to the formation energy data of the μ phase, a user-friendly graphical user interface (GUI) was developed, ensuring convenient usability for researchers and practitioners.

PyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies

The climate change impact and adaptation communities need future scenarios with sufficient high resolution, which are frequently achieved by applying Statistical Downscaling Models (SDMs) over global climate models. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the evaluation and generation of downscaled scenarios following different approaches. In this paper we present a service, ‘pyClim-SDM’, which allows users to generate and evaluate their own downscaled scenarios with a very simple and user-friendly graphical interface. This tool includes a large collection of state-of-the-art methods belonging to different families to downscale daily data of the following surface variables: temperature, precipitation, wind, relative humidity and cloud coverage. Additionally, the software is prepared to be applied over any other user-defined target variable. Thus, multivariable indexes can be tackled as target variables themselves, instead of being calculated from the downscaled primary variables. With this possibility, potential intervariableinconsistencies are avoided. An application example for a Fire Weather Index, dependent on temperature, wind, humidity and precipitation, is shown. The service here presented -mainly based on a new downscaling software and a user-friendly graphical interface- is an essential piece for evaluating and generating high-resolution projection data within the Spanish national climate change adaptation strategy which includes, among other elements, a common database for all sectors, viewer and data distribution portal, etc.

Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions’ subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.

ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis

BackgroundCross-linking mass spectrometry (XL-MS) is a powerful technique for detecting protein–protein interactions (PPIs) and modeling protein structures in a high-throughput manner. In XL-MS experiments, proteins are cross-linked by a chemical reagent (namely cross-linker), fragmented, and then fed into a tandem mass spectrum (MS/MS). Cross-linkers are either cleavable or non-cleavable, and each type requires distinct data analysis tools. However, both types of cross-linkers suffer from imbalanced fragmentation efficiency, resulting in a large number of unidentifiable spectra that hinder the discovery of PPIs and protein conformations. To address this challenge, researchers have sought to improve the sensitivity of XL-MS through invention of novel cross-linking reagents, optimization of sample preparation protocols, and development of data analysis algorithms. One promising approach to developing new data analysis methods is to apply a protein feedback mechanism in the analysis. It has significantly improved the sensitivity of analysis methods in the cleavable cross-linking data. The application of the protein feedback mechanism to the analysis of non-cleavable cross-linking data is expected to have an even greater impact because the majority of XL-MS experiments currently employs non-cleavable cross-linkers.ResultsIn this study, we applied the protein feedback mechanism to the analysis of both non-cleavable and cleavable cross-linking data and observed a substantial improvement in cross-link spectrum matches (CSMs) compared to conventional methods. Furthermore, we developed a new software program, ECL 3.0, that integrates two algorithms and includes a user-friendly graphical interface to facilitate wider applications of this new program.ConclusionsECL 3.0 source code is available at https://github.com/yuweichuan/ECL-PF.git. A quick tutorial is available at https://youtu.be/PpZgbi8V2xI.

SI-M/O: Swarm Intelligence-based Modeling and Optimization of complex synthesis reaction processes

Processes with unknown reactions and limited data are commonplace in the chemical industry. However, modeling and optimizing these processes are challenging tasks. In this paper, we propose the Swarm Intelligence-based Modeling and Optimization (SI-M/O) algorithm to address these challenges. The SI-M/O algorithm integrates swarm intelligence with chemical process fundamentals. This fusion empowers SI-M/O to navigate complex chemical landscapes effectively. Swarm intelligence excels at exploring vast solution spaces and adapting dynamically. When combined with first-principle knowledge of chemical reactions and thermodynamics, SI-M/O not only finds optimal solutions but also considers chemical feasibility and physical constraints. To validate its effectiveness, we applied SI-M/O to optimize a production plant, achieving a substantial 5.3 % productivity increase during preliminary testing. We also designed a user-friendly graphical interface for SI-M/O, enhancing accessibility for researchers and practitioners.

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Traditional attendance recording methods can be replaced with artificial intelligence (AI) methods. This paper aims to develop an automated attendance system with a user-friendly graphical interface in Arabic to enhance user interaction. The Python programming language is used to build the system, which employs object detection and feature extraction algorithms. This system processes a live broadcast from an IP camera installed in the classroom. The system employs object detection algorithms to detect the faces in the video. The face locations are then sent to the feature extraction algorithms to extract and compare features with the features stored in the database. Our paper concludes that automating the attendance registration process using facial recognition technology can achieve up to 100% accuracy. Comparing our proposed system with previous studies, we find that it outperforms them in several aspects. Our method handles real-time tests with a larger number of students. It accommodates students positioned at different distances from the camera within the classroom, demonstrating our method's effectiveness in automating the attendance registration process.

A Semi-Autonomous Multi-Vehicle Architecture for Agricultural Applications

The ageing population, climate change, and labour shortages in the agricultural sector are driving the need to reevaluate current farming practices. To address these challenges, the deployment of robot systems can help reduce environmental footprints and increase productivity. However, convincing farmers to adopt new technologies poses difficulties, considering economic viability and ease of use. In this paper, we introduce a management system based on the Robot Operating System (ROS) that integrates heterogeneous vehicles (conventional tractors and mobile robots). The goal of the proposed work is to ease the adoption of mobile robots in an agricultural context by providing to the farmer the initial tools needed to include them alongside the conventional machinery. We provide a comprehensive overview of the system’s architecture, the control laws implemented for fleet navigation within the field, the development of a user-friendly Graphical User Interface, and the charging infrastructure for the deployed vehicles. Additionally, field tests are conducted to demonstrate the effectiveness of the proposed framework.

M6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.

N 6-Methyladenosine (m6A) is one of the most abundant internal chemical modifications on eukaryote mRNA and is involved in numerous essential molecular functions and biological processes. To facilitate the study of this important post-transcriptional modification, we present here m6A-Atlas v2.0, an updated version of m6A-Atlas. It was expanded to include a total of 797 091 reliable m6A sites from 13 high-resolution technologies and two single-cell m6A profiles. Additionally, three methods (exomePeaks2, MACS2 and TRESS) were used to identify >16 million m6A enrichment peaks from 2712 MeRIP-seq experiments covering 651 conditions in 42 species. Quality control results of MeRIP-seq samples were also provided to help users to select reliable peaks. We also estimated the condition-specific quantitative m6A profiles (i.e. differential methylation) under 172 experimental conditions for 19 species. Further, to provide insights into potential functional circuitry, the m6A epitranscriptomics were annotated with various genomic features, interactions with RNA-binding proteins and microRNA, potentially linked splicing events and single nucleotide polymorphisms. The collected m6A sites and their functional annotations can be freely queried and downloaded via a user-friendly graphical interface at: http://rnamd.org/m6a.

A New Reconfigurable Agricultural Vehicle Controlled by a User Graphical Interface: Mechanical and Electronic Aspects

The use of innovative mobile vehicles with increasingly advanced mechatronic aspects in the agricultural sector is becoming, in recent years, a stimulating field of research and comparison. In particular, the problem addressed in the present work refers to improving the locomotion of mobile vehicles on agricultural terrain by reducing the soil damage and improve the overall performance. Agricultural vehicles generally use tracks and wheels for locomotion; the main difference between the two systems is the contact area with the ground and, consequently, the pressure distribution. The present work presents a new reconfigurable agricultural vehicle that can switch from one locomotion system to another, choosing the suitable configuration according to the terrain conditions. All the mechanical and electronic aspects of the prototype developed are analyzed together with an in-depth analysis of the management of the innovative functions through a user-friendly graphical interface able to control the vehicle.

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.

With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.

CRYSTALpytools: A Python infrastructure for the Crystal code

CRYSTALpytools is an open source Python project available on GitHub that implements a user-friendly interface to the Crystal code for quantum-mechanical condensed matter simulations. CRYSTALpytools provides functionalities to: i) write and read Crystal input and output files for a range of calculations (single-point, electronic structure, geometry optimization, harmonic and quasi-harmonic lattice dynamics, elastic tensor evaluation, topological analysis of the electron density, electron transport, and others); ii) extract relevant information; iii) create workflows; iv) post-process computed quantities, and v) plot results in a variety of styles for rapid and precise visual analysis. Furthermore, CRYSTALpytools allows the user to translate Crystal objects (the central data structure of the project) to and from the Structure and Atoms objects of the pymatgen and ASE libraries, respectively. These tools can be used to create, manipulate and visualise complicated structures and write them efficiently to Crystal input files. Jupyter Notebooks have also been developed for the less Python savvy users to guide them in the use of CRYSTALpytools through a user-friendly graphical interface with predefined workflows to complete different specific tasks. Program summaryProgram Title: CRYSTALpytoolsCPC Library link to program files:https://doi.org/10.17632/p2bp3fsk86.1Developer's repository link:https://github.com/crystal-code-tools/CRYSTALpytoolsLicensing provisions: MITProgramming language: Python and Jupyter NotebookNature of problem: The Crystal code [1,2] is a powerful tool for the calculation of materials properties. It stands out in the condensed matter computational landscape because of the use of local basis sets, heavy parallelisation, efficient implementation of non-local Fock exchange, and extensive use of point and space symmetry. However, it currently lacks an easily programmable interface to access its input/output structure needed to be able to use Crystal calculations within computational materials science workflows. Historically, the use of Crystal in such workflows has been achieved through bespoke scripting implemented in a variety of languages which has hindered code reuse and sharing.Solution method: The CRYSTALpytools project will enable the automation of Crystal calculations in a modular code that can be co-developed by a wide community of users worldwide. It achieves this by transforming standardised input and output files into python objects and providing a suite of functionality to manipulate them. The core implementation is based on a set of data structures denoted the Crystal_objects. CRYSTALpytools, in its current implementation, contains a large variety of functions for input/output manipulation, vibrational and thermodynamic analysis, and visualisation.Additional comments including restrictions and unusual features: The exploitation of point group and space group symmetry is one of the strengths of the Crystal code, therefore, all the geometry modification functions have been developed to optimise the use of symmetry. When a structure is downloaded from a database or is modified by external code, such as pymatgen [3] or ASE [4], a symmetry analysis is performed before transforming the structure to be an optimised Crystal_object. All such objects can be directly transformed to standard structures periodic in 0, 1, 2 or 3 dimensions used by Crystal (ie: .gui or .f34 files).

An open-source behavior controller for associative learning and memory (B-CALM).

Associative learning and memory, i.e., learning and remembering the associations between environmental stimuli, self-generated actions, and outcomes such as rewards or punishments, are critical for the well-being of animals. Hence, the neural mechanisms underlying these processes are extensively studied using behavioral tasks in laboratory animals. Traditionally, these tasks have been controlled using commercial hardware and software, which limits scalability and accessibility due to their cost. More recently, due to the revolution in microcontrollers or microcomputers, several general-purpose and open-source solutions have been advanced for controlling neuroscientific behavioral tasks. While these solutions have great strength due to their flexibility and general-purpose nature, for the same reasons, they suffer from some disadvantages including the need for considerable programming expertise, limited online visualization, or slower than optimal response latencies for any specific task. Here, to mitigate these concerns, we present an open-source behavior controller for associative learning and memory (B-CALM). B-CALM provides an integrated suite that can control a host of associative learning and memory behaviors. As proof of principle for its applicability, we show data from head-fixed mice learning Pavlovian conditioning, operant conditioning, discrimination learning, as well as a timing task and a choice task. These can be run directly from a user-friendly graphical user interface (GUI) written in MATLAB that controls many independently running Arduino Mega microcontrollers in parallel (one per behavior box). In sum, B-CALM will enable researchers to execute a wide variety of associative learning and memory tasks in a scalable, accurate, and user-friendly manner.

An easy-to-use graphical user interface for mass spectrometry imaging analysis

The authors developed a user-friendly graphical interface, MSItoolbox, for beginners without programming knowledge by providing most of the default parameters for guidance. The toolbox can provide 2D and 3D visualization based on mass spectra preprocessing and modeling of MSI data from one or more experiments. The ROI method is applied to significantly compress the original MS data while preserving most of the relevant information of the original data without losing instrument quality accuracy. The toolbox can process the MSI dataset with a large data file size. In the testing example, with appropriate parameters, it was able to compress 244 GB of data files to 14.7 MB in a commonly used laptop without suffering the out-of-memory problem in less than 2.5 h. This toolbox supports importing all pixels or pixels in a specified area in a customized shape. The software provides commonly used functions: single-ion imaging display, MCR-ALS, spatial segmentation, ROI mass spectra average, and image registration. PCA and t-SNE were also provided to help with data interpretation, evaluation of data reliability, and data mining to explore important spatiotemporal relationships in the analyzed samples. Users can import a homemade proteomics mass spectrometry library based on LC‒MS to search for small molecule candidate compounds. Image registration was included to combine the information of stained images with MSI and give the potential to use supervised methods for the analysis of MSI data.

Revolutionizing Small-Scale Retail: Introducing an Intelligent IoT-based Scale for Efficient Fruits and Vegetables Shops

In the bustling streets of Pakistan, small-scale fruits and vegetables shops stand as vital hubs of daily life. These humble establishments are where people flock to satisfy their everyday needs. However, the traditional methods employed by shopkeepers using manual weighing scales have proven to be time-consuming and limit the shopkeepers’ ability to serve multiple customers simultaneously. But in a world rapidly embracing automation and propelled by the wonders of artificial intelligence, a revolution is underway. In this visionary paper, we introduce the concept of an intelligent scale that will revolutionize the retail process. This remarkable scale possesses the power to automate numerous tasks, making the shopping experience seamless and efficient. Imagine a scale that not only determines the weight of the produce but also possesses the ability to detect and identify each fruit and vegetable placed upon it. By harnessing the potential of cutting-edge technology, we fine-tuned pre-trained models, such as YOLOv5n and YOLOv7, on our extensive dataset, consisting of 12 classes and 2622 images. The dataset was collected manually and it closely aligns with real-time scenarios, ensuring that the distribution in our training and validation sets were similar and that it reflected what our models will encounter during testing. As a result, our YOLOv5n and YOLOv7 models have achieved astonishing mean Average Precision (mAP) scores of 0.98 and 0.987, respectively. YOLOv5n demonstrates an impressive processing speed of 20 frames per second (fps) on a CPU, while it reaches an impressive 125 fps on a GPU. Similarly, YOLOv7 achieves a processing speed of 2 fps on a CPU, which escalates to 66.6 fps on a GPU. These extraordinary results testify to the remarkable accuracy and efficacy of our system when subjected to real-world testing scenarios. To ensure accurate weighing, we incorporated a load cell with an hx711 amplifier, providing precise measurements that customers can trust. However, our intelligent scale does not stop there. We understand that determining weight alone is insufficient when it comes to transactions. Hence, a meticulously crafted Python script was developed to map each specific item to its corresponding price based on its weight. With all these incredible features in place, the experience of purchasing from a fruits and vegetables shop is taken to new heights. The intelligent scale is accompanied by a user-friendly graphical user interface (GUI), where customers can conveniently view their order and prices. Once the order is complete, a simple click on the print button generates a neatly printed bill, ensuring a seamless transaction. The implications of this intelligent scale are profound. Shopkeepers can now serve customers faster and more efficiently, effortlessly managing multiple transactions simultaneously. The introduction of automation enhances the overall shopping experience, leaving customers delighted and eager to return. This amalgamation of technology and traditional commerce heralds a new era, where small-scale shops can thrive and adapt to the ever-evolving needs of the modern world.