Level Of Accuracy Research Articles

Which one is more accurate, BILOG or R program? (a comparison for score test equating)

Evaluation may be carried out using different tests that are not necessarily parallel. Students with lower abilities may get higher scores while those with higher abilities get lower scores. Measurement errors caused by this condition require test equating. Several computer programs, including Bilog and the R program, can be used for test equating. Each program has a different level of accuracy, and the accuracy of the equating results will affect the standard errors of equating. This study aimed to find out the most accurate equating test method and the accuracy of the estimated BILOG and R program. This research used two sets of tests with equivalent group designs. The determination of the most accurate equating method was based on the root mean square deviation (RMSD) value. Equating test packages, A to package B with BILOG program estimation on the mean and Sigma method resulted in RMSD value of 0.320. In the mean and mean method, RMSD value is 0.250. Meanwhile, equating package A to package B using R program on the mean and sigma resulted in RMSD value of 0.300, and the mean and mean method with the RMSD value is 0.272. The mean and mean yield RMSD values smaller than the mean and sigma methods. Therefore, the mean and mean method is more accurate and applicable. Moreover, the estimation results of the BILOG program are more accurate and can be used in test equating.

Comparison of WASPAS and VIKOR methods to determine non-cash food assistance recipients

Non-cash food assistance or bantuan pangan non-tunai (BPNT) is a government program of the Republic of Indonesia by distributes food assistance in non-cash to beneficiary families. The process of distributing BPNT still needs to be done with the data and criteria set, because the existing BPNT distribution is considered not right on target. We need a method that can help provide an objective decision. One method that can be used in making decisions is the weighted aggregated sum product assessment (WASPAS) and Vlsekriterijumsko Koompromisno Rangiranje (VIKOR) methods. The results of the calculations from the two methods will then be chosen which is the best, by conducting sensitivity tests and accuracy tests. This study uses 100 sample data and 16 criteria. The sensitivity test results are 9.780678997% for the WASPAS method and -0.0759182% for the VIKOR method, while the results of the accuracy test show that both methods have the same level of accuracy, which is 80%. Based on the comparison of the sensitivity test and accuracy test of the two methods, the WASPAS method is considered more accurate in determining the recipients of the BPNT program because the WASPAS method has a higher sensitivity test value than the VIKOR method.

An intelligent deep residual learning framework for tomato plant leaf disease classification

Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet(JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as Accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score.

A lightweight YOLOv5 for real-time dangerous weapons detection

Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When it comes to real-world applications, such as weapon detection, these methods are often not suitable for deployment on embedded devices. Because of the huge number of parameters and poor efficiency. The most recent object detection technique, which belongs to the YOLOv5 class, is commonly used for detecting weapons. However, it faces some difficulties such as high computational parameters and an unfavorable detection rate. to solve these shortcomings. an enhanced lightweight Yolov5s approach is suggested. Which consists of a combination of YOLOv5 and GhostNet modules. To evaluate the efficacy of the suggested technique, a set of experiments was performed on the Sohas weapon dataset., which is commonly used as a reference dataset in the field. Compared to the original YOLOv5, the results indicate a slight increase in the proposed model's mean Average Precision (mAP). Furthermore, there has been a reduction of 2.7 in GFLOPs and weights, and the number of model parameters has decreased by 1.42.

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPIcr) equation without race and the estimated glomerular filtration rate (eGFR) equation developed for the Chinese population - displayed suboptimal performance in patients with neurogenic lower urinary tract dysfunction (NLUTD), which limited their clinical application for detecting changes in GFR levels in all cohorts. To develop a neural network model based on multilayer perceptron (MLP) for evaluating GFR in Chinese NLUTD patients, and compare the diagnostic performance with Cr-based multiple linear regression equations for Chinese and the CKD-EPIcr equation without race. Single-center, cross-sectional study of GFR estimation from serum Cr, demographic data, and clinical characteristics in Chinese patients with NLUTD. A total of 204 NLUTD patients, from 27 different geographic regions of China, were selected. A random sample of 141 of these subjects was included in the training sample set, and the remaining 63 patients were included in the testing sample set. The reference GFR (rGFR) was assessed by the technetium-99m-labeled diethylenetriaminepentaacetic acid (99mTc-DTPA) double plasma sample method. A neural network model based on MLP was developed to evaluate GFR in the training sample set, which was then validated in the testing sample set and compared with Cr-based GFR equations. The MLP-based model showed significant performance improvement in evaluating the difference, absolute difference, precision, and accuracy of GFR estimation compared with the Cr-based GFR equations. Additionally, compared with the rGFR, we found that the MLP-based model provided an acceptable level of accuracy (greater than 85%, which was within a 30% deviation from the rGFR). The MLP-based model offered significant advantages in estimating GFR in Chinese NLUTD patients, and its application could be suggested in clinical practice.

Determining the possibility of passage through narrow alleys using ultrasonic sensors

The complex road topography of South Korea presents significant challenges to the timely arrival of emergency vehicles. Compounding the issue, obstacles such as legally or illicitly constructed structures, and improperly parked or stationary vehicles, frequently obstruct the path of emergency vehicles. To address these challenges, this study introduces a novel system aimed at enhancing emergency response times. The system employs ultrasonic sensors that can be integrated into personal devices to measure the width of the numerous narrow alleys prevalent in Korea's densely populated regions. Experiments demonstrate that within a 1-meter range in front of a narrow alley with widths varying between 270 cm and 450 cm where vehicle maneuvering is possible, it's possible to accurately gauge the width using two ultrasonic sensors, achieving a precision within a 5 cm margin of error. This level of accuracy enables the practical assessment of whether emergency vehicles can access the area in real-time by identifying the alley's narrowest point. The proposed system is a cost-effective method using easy-to-buy devices for augmenting emergency preparedness and enhancing emergency response times by ensuring that emergency vehicles can navigate through alleys, thereby fostering a safer living environment.

Refining disparity maps using deep learning and edge-aware smoothing filter

Stereo matching algorithm is crucial for applications that rely on three-dimensional (3D) surface reconstruction, producing a disparity map that contains depth information by computing the disparity values between corresponding points from a stereo image pair. In order to yield desirable results, the proposed stereo matching algorithm must possess a high degree of resilience against radiometric variation and edge inconsistencies. In this article convolutional neural network (CNN) is employed in the first stage to generate the raw matching cost, which is subsequently filtered with a bilateral filter (BF) and applied with cross-based cost aggregation (CBCA) during the cost aggregation stage to enhance precision. Winner-take-all (WTA) strategy is implemented to normalise the disparity map values. Finally, the resulting output is subjected to an edge-aware smoothing filter (EASF) to reduce the noise. Due to its resistance to high contrast and brightness, the filter is found to be effective in refining and eliminating noise from the output image. Despite discontinuities like adiron's lost cup handle or artl's shattered rods, this approach, based on experimental research utilizing a Middlebury standard validation benchmark, yields a high level of accuracy, with an average non-occluded error of 6.79%, comparable to other published methods.

Proposed threshold-based and rule-based approaches to detecting duplicates in bibliographic database

Bibliographic databases are used to measure the performance of researchers, universities and research institutions. Thus, high data quality is required and data duplication is avoided. One of the weaknesses of the threshold-based approach in duplication detection is the low accuracy level. Therefore, another approach is required to improve duplication detection. This study proposes a method that combines threshold-based and rule-based approaches to perform duplication detection. These two approaches are implemented in the comparison stage. The cosine similarity function is used to create weight vectors from the features. Then, the comparison operator is used to determine whether the pair of records are grouped as duplication or not. Three research databases: Web of Science (WoS), Scopus, and Google Scholar (GS) on the Science and Technology Index (SINTA) database are investigated. Rule 4 and Rule 5 provide the best performance. For WoS dataset, the accuracy, precision, recall, and F1-measure values were 100.00%. For Scopus dataset, the accuracy and precision values were 100.00%, recall: 98.00%, and the F1-measure value is 98.00%. For GS dataset, the accuracy value was 100.00%, precision: 99.00%, recall: 97.00%, and the F1-measure value is 98.00%. The proposed method is potential tool for accurate detection on duplication records in publication databases.

Assessing the functional impact of protein binding site definition.

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.

AI-Powered Autonomous Checkout System for Retail Stores

Abstract: Auto Bill offers a smart, automated checkout service for shops, leveraging state-of-the-art machine learning and computer vision technologies to provide an exceptional shopping experience. Designed to minimize interactions for security purposes during the pandemic, the innovation lies in utilizing modern techniques like image processing and weight sensors to create a novel shopping experience. This system aims to surpass traditional weight scales by incorporating advanced image recognition technologies, benefiting customers with a highly user-friendly interface, reduced checkout times, and improved accessibility. With a multifaceted approach involving deep learning neural networks and a web-based interface for transactions and inventory monitoring, Auto Bill envisions a future where retail checkout is taken to new heights. Supermarkets adopting this system can enhance customer service throughits advanced machine learning and camera technology, offering a seamless shopping experience. By integrating easily with existing POS systems, this solution streamlines operations and improves overall efficiency in the retail industry. With continuous testing and adjustments, Auto Bill aims to refine retail checkout to unprecedented levels of accuracy and customer satisfaction.

Computed Tomography- Based Maxillary Sinus Morphometry: A Tool for Gender Differentiation in Libyan Population

Introduction: In forensic contexts, gender identification is a multifaceted process that involves examining various anatomical characteristics to determine an individual's gender. Recent advancements in medical imaging techniques, such as computed tomography (CT) scans have provided researchers and healthcare professionals with more detailed insights into anatomical differences between genders such as the dimensions of paranasal sinuses. Aim: This study aimed to compare the maxillary sinus (MS) dimensions between genders in a Libyan sample using CT scans. Materials and methods: One hundred consecutive Libyan patients, comprising 50 women and 50 men, were included in the study. The Width, height, and length were determined based on CT images. Additionally, the perimeter, area, and volume of the MS were calculated mathematically. The collected data analyzed to compare the MS parameters between genders in both sides. Statistical significance was accepted at p<0.05. Results: Statistically we found that the length, width and length of the MS were significantly different between male & female in both sides, while there was no significant difference in case of sinus area and volume. The accuracy level for correctly classifying gender based on width of right MS was 91.8%, demonstrating strong predictive power of this parameter. The result showed also that 94 % of males and 90% of females were sexed correctly with an overall accuracy of 92%. Conclusion: This study highlights the importance of understanding sinus morphology in forensic anthropology for gender determination.

Various techniques for resolving overlapping ultraviolet spectra of combination pharmaceutical dosage forms containing hydroxychloroquine and paracetamol

Ten novel spectrophotometric approaches were developed for the initial examination of the Hydroxychloroquine and Paracetamol medications. These procedures are straightforward, specific, easy to use, and provide exact and accurate results. The determination was conducted through the utilization of several approaches, including zero order (dual wavelength, zero crossing, advanced absorption subtraction and spectrum subtraction), derivative (first derivative of zero crossing), ratio (ratio difference, ratio derivative) and mathematical (bivariate, simultaneous equation, and Q-absorbance) techniques. After undergoing validation in accordance with ICH criteria, it was established that each of these methods achieved acceptable levels of precision, repeatability, robustness, and accuracy. The advantages and disadvantages of each method are demonstrated, and the proposed and reported methodologies were statistically compared.Graphical

Error analysis of dead reckoning navigation system by considering uncertainties in an underwater vehicle's sensors

Abstract In this paper, a complete introduction to the dead reckoning navigation technique is offered after a discussion of the many forms of navigation, and the benefits and drawbacks associated with each of those types of navigation. After that, the dead reckoning navigation solution is used as an option that is both low-cost and makes use of the sophisticated equations that are used by the system. Moreover, to achieve the highest level of accuracy in navigation, an investigation of navigation errors caused by dead reckoning is calculated. Employing the suggested dead reckoning navigation system, the final position of an underwater vehicle can be established with a high degree of accuracy by using experimental data (from sensors) and the uncertainties that are associated with the system. Finally, to illustrate the correctness of the dead reckoning navigation process, the system error analysis as uncertainty that was carried out using experimental data using the dead reckoning navigation technique is compared with GPS data.

Optimisation of the Transmitter Layout in a VLP System Using an Aperture-Based Receiver

In this paper, we consider a visible light positioning (VLP) system, where an array of photo diodes combined with apertures is used as a directional receiver and a set of inexpensive and energy-efficient light-emitting diodes (LEDs) is used as transmitters. The paper focuses on the optimisation of the layout of the transmitter, i.e., the number and placement of the LEDs, to meet the wanted position estimation accuracy levels. To this end, we evaluate the Cramer–Rao bound (CRB), which is a lower bound on the mean-squared error (MSE) of the position estimate, to analyse the influence of the LEDs’ placement. In contrast to other works, where only the location of the LEDs was considered and/or the optimisation was carried out through simulations, in this work, the optimisation is carried out analytically and considers all the parameters involved in the VLP system as well as the illumination. Based on our results, we formulate simple rules of thumb with which we can determine the spacing between LEDs and the minimum number of LEDs, as well as their position on the ceiling, while also taking into account the requirements for the illumination.

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms—Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)—customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

ResLMFFNet: a real-time semantic segmentation network for precision agriculture

Lightweight multiscale-feature-fusion network (LMFFNet), a proficient real-time CNN architecture, adeptly achieves a balance between inference time and accuracy. Capturing the intricate details of precision agriculture target objects in remote sensing images requires deep SEM-B blocks in the LMFFNet model design. However, employing numerous SEM-B units leads to instability during backward gradient flow. This work proposes the novel residual-LMFFNet (ResLMFFNet) model for ensuring smooth gradient flow within SEM-B blocks. By incorporating residual connections, ResLMFFNet achieves improved accuracy without affecting the inference speed and the number of trainable parameters. The results of the experiments demonstrate that this architecture has achieved superior performance compared to other real-time architectures across diverse precision agriculture applications involving UAV and satellite images. Compared to LMFFNet, the ResLMFFNet architecture enhances the Jaccard Index values by 2.1% for tree detection, 1.4% for crop detection, and 11.2% for wheat-yellow rust detection. Achieving these remarkable accuracy levels involves maintaining almost identical inference time and computational complexity as the LMFFNet model. The source code is available on GitHub: https://github.com/iremulku/Semantic-Segmentation-in-Precision-Agriculture.

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota.

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

The importance of first-principles tools for safety enhancement in the design of passenger ships in the case of flooding events

The design of a passenger ship is a complex process covering multiple aspects of naval architecture and marine engineering to address performance, functionality safety, and cost as primary objectives. Between them, safety is a key element focusing on the people on board. In this sense, ship safety in the case of flooding events needs proper estimation from the first stages of the design process employing an appropriate metric. To this end, safety can be evaluated as a risk by calculating the Potential Loss of Life. Thanks to a multi-level framework developed during project FLARE, it is possible to calculate the risk associated with an accident, increasing the level of reliability as the design process advances. The framework aims at employing first-principles tools from the early stages of the design process, abandoning static calculations and empirical formulae as soon as data is available to set up advanced calculation techniques. Then, the framework adopts rigid-body time-domain calculations for the flooding simulations, advanced evacuation analysis tools, and direct crash simulation to evaluate collision damages. The process allows for testing alternative design solutions for the ship to enhance safety. Investigating risk control options is also possible, considering active or passive systems such as fixed foam installations, deployable barriers, or crashworthiness. Such an approach allows for evaluating safer solutions, respecting other design constraints and cost-related aspects. The present work describes the risk assessment framework for the case of flooding events, together with the different levels of accuracy that can be achieved, showing the improvements that could be reached by employing alternative risk control options.

Unified AI-Based Predictive Models for the Ultimate Capacity of Multi-Planar Gapped KK Steel Pipe Joints

The multi-planar steel pipe joints are widely used in communication towers, industrial structures, and offshore platforms. The current design formulas consider this joint as a uniplanar joint and account for the multi-planar effect using empirical correction factors. Recent studies deal with this multi-planar joint as a 3D joint but considering certain loading conditions. Hence, the aim of this research is to develop more general AI-based predictive models for the ultimate capacity of multi-planar gapped KK steel pipe joints, considering both symmetric and asymmetric loading conditions. Three AI techniques were applied to a database of previously published works. These techniques are “Genetic Programming” (GP), “Artificial Neural Network” (ANN), and “Evolutionary Polynomial Regression” (EPR). The prediction accuracies of the developed AI models were compared against two previously published formulas. The results indicated that the developed AI models are much more accurate than the previously published formulas. Also, the results showed that both the ANN and EPR models have almost the same level of accuracy (about 92%), but the EPR model has the advantage of presenting a closed-form equation that could be implemented either manually or using software. Doi: 10.28991/CEJ-SP2024-010-07 Full Text: PDF