Processing Time Research Articles

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

A falls recognition framework based on faster R-CNN and temporal action sequences that can be deployed on home service robots

With the popularity of home service robots (e.g. floor sweepers), robots should be considered to have more features for older adult care. Compared to fixed home monitors with a limited field of view, fall detection with service robots is an ideal solution to keep older adults and disabled people within sight. However, the user’s actions, such as lying on the bed to sleep or slumping on the sofa to rest, cause the traditional fall detection system to generate false alarms, which disrupts the user’s family life. The present work proposed an enhanced faster R-convolutional neural network (CNN) network by incorporating temporal action sequences and fall acceleration computation, demonstrating a reduced misjudgment rate on the service robot platform. Firstly, motion images were captured to obtain the target’s motion area description and action timing at the input stage. Then, the faster R-CNN algorithm was implemented to check the suspected falls further based on the falling acceleration of the detected actions during the training phase. Finally, the proposed temporal action sequences module eliminated the action mistaken for falling. Network training and robotic platform testing demonstrated that the proposed approach distinguished between falls and false alarms, which mitigated the occurrence of false positives. On the service robot platform, experimental results showed that the FAR was 8.19 and processing time was 0.79 s.

Efficient Hand Bone Segmentation for Medical Applications Using Refined DeepLab Model

In the medical field, analyzing various bone structures is crucial due to the rigid nature of bones. X-ray imaging plays an essential role in medical procedures, including bone age evaluation, fracture detection, and implant creation. However, operator involvement can introduce biases and increase processing time. Automating the process could reduce processing time and enhance diagnostic accuracy by minimizing biases and operator involvement. This paper introduces the Refined DeepLab model, a lightweight encoder–decoder-based approach for multiclass segmentation of hand bones. The primary objective is to assist physicians in tasks such as bone age analysis, fracture detection, hand movement analysis, and implant design. The research objectives are organized into three phases, with this work focusing on the first phase of our objectives, which is delineating bones from tissues, studying the bone structure, and multiclass segmentation of hand bones. The model utilizes DenseNet121 as its feature extractor and Sigmoid-weighted Linear Unit (SiLU) as its activation function. Experimental findings demonstrate promising performance in hand bone multiclass segmentation, with a Mean Intersection over Union (mIoU) of 85.02% and a Dice score of 92.2%. The comprehensive analysis of the results confirms that the proposed model excels, particularly in achieving the right balance between computational efficiency and precise segmentation.

Metallographic Etching of Al–Mg–Zn–(Cu) Crossover Alloys

Various alloys demand customized etchants due to their diverse chemical compositions, particularly in the realm of aluminum alloys. Consequently, in this study, a technique is proposed for metallographic visualization of small grain structures within Al–Mg–Zn–(Cu) crossover alloys. In this method, a thermal pretreatment combined with an etching process is relied on. In the study, it is primarily sought to comprehend how grain‐boundary precipitation affects etchability, addressing the complexities of characterizing these alloys. The demonstrated approach facilitates the swift assessment of grain sizes <10 μm using light optical microscopy. Exploring the etchability of Al–Mg–Zn–(Cu) crossover alloys across a standard heat‐treatment pathway identifies the optimal treatment and suitable etchant for grain visualization. Through process refinement, a reduction in processing time is achieved by employing a single‐step preheat treatment lasting 20 min at 180 °C post solution annealing. Transmission electron microscope analysis reveals continuous occupancy of the grain boundary with T‐phase as the key factor influencing the alloy's etchability. Grain size assessment involves line intercept counting and equivalent circle diameter measurement for precise characterization.

Network-based visualisation of frequent sequences.

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.

Mixed-batch scheduling to minimize total tardiness using deep reinforcement learning

This study addresses the issue of scheduling batch machine to minimize total tardiness. Vacuum heat treatment allows multiple jobs to be processed as a batch, as long as they do not exceed the machine's weight and size limits; the weight and size of the jobs both impact the batch processing time. By considering the differences in the release time and due date for each job, a mixed-integer linear programming model is developed and validated using small-scale instances. To tackle large-scale scheduling problems, an intelligent algorithm based on deep reinforcement learning is proposed. Double deep Q-learning networks are designed, and the environmental state feature matrix is extracted as input parameters for the networks. Four job-sorting rules and three scheduling time windows are defined, resulting in twelve action rules within the action space. The most suitable action rule is dynamically selected based on the current batch's environmental status during the scheduling process. Through extensive comparison using large-scale random data, the proposed algorithm demonstrates significantly improved scheduling performance compared to the benchmark algorithms.

Identifying Fraud Financial Reports Based on Signs of Income Management Using Machine Learning Technology: The Case of Listed Companies in Vietnam

This study aims to use a measure of earnings management to predict companies whose financial statements have problems. This is an identification measure other than common measures to predict financial statement fraud such as measuring by the M-Score or the Z-score model that many previous studies have applied. In the income management measure, the author uses a measure of abnormal cash flow and abnormal expense flow to consider whether the corporate financial statements have problems or not. To do this, the author uses data from listed non-financial enterprises in the period from 2018 to 2022, with machine learning and deep learning algorithms, of which we focus on three main algorithms: ANN, SVM and RF. The results show that identifying problematic financial statements based on abnormal cash flows is quite effective with an accuracy of over 78% for the SVM method, while if using the RF method, the accuracy reaches over 82% but it is required to accept an increased processing time.

Influence of Credit Risk Management Practices on Loan Performance: A Case of Selected Microfinance Institutions in Tanzania

This research investigates the influence of credit risk management practices on loan performance of microfinance institutions. The variables of interest in this study are credit appraisal, credit supervision, credit monitoring (as predictor variables) and loan performance (as dependent variable). However, the study includes average credit processing time as a control variable. The study focuses on five microfinance institutions operating in Tanzania. The research utilizes probability and non-probability sampling to select 140 respondents from the study population. The study collected both primary and secondary data. Data analysis was carried out by applying multiple regression analysis following the assumptions of ordinary least squares (OLS). The findings reveal that credit supervision, credit monitoring, and average credit process time are significant in influencing loan performance in microfinance institutions. Contrary, there is no evidence, whatsoever to suggest that credit supervision influences loan performance in microfinance institutions.

Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems.

Adaptive Workload Scheduling Design in Parallel Computational Framework

In the recent years, many researchers have showed a great deal of interest to improve the scheduling of workload in the cloud platforms. On the other hand, to carry out the execution of the scientific workloads in the cloud environment, it consumes much time and is expensive; hence, it is neither time efficient nor cost-efficient. Due to this reason, many research studies have been carried, by which the researchers tend to reduce the processing time and make a cost-efficient method as the users are charged based on the usage. Very few studies have been done to optimize the cost with processing time and energy parameters together in order to meet the Service Level Agreement (SLA) and Quality of Service (QoS) of the workload task. Hence, in this paper, we present an Adaptive Workload Scheduling (AWS) model that ensures the Task Level SLA prerequisites in a heterogeneous distributed-computing environment. This AWS- model approach reduces the amount of energy and time needed to execute a given workloads. Cybershake scientific workload has been utilized for the studying proposed AWS model. When model was compared with the standard workload scheduling approach, our model reduced the consumption of cost, energy and time.

Polar coordinate-based guided wave beamforming imaging using scanning LDV

In this paper, we propose a technique for efficiently detecting damage in plate-like structures using guided waves, polar coordinate-based beamforming imaging, and filtering techniques. The proposed technique uses a laser Doppler vibrometer and a scanning mirror-tilting device for ultrasonic wave imaging, with single-frequency excitation provided by a mounted piezoelectric transducer. Frequency-wavenumber domain filtering and directional filtering are implemented in polar coordinates for efficient detection of damage-reflected waves by removing any incident wave components. The filtered waves are then processed using a delay-and-sum beamforming algorithm within local-area laser scanning through a circular array sensing system. The polar coordinate-based laser scanning system improves the damage detection capability and processing time by removing the need for coordinate transformation in frequency-wavenumber domain filtering and directional filtering. Finally, the proposed technique is validated through experiments on composite and aluminum plates with various types of damage, where it demonstrates improved accuracy and speed when estimating the exact damage location.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.

Electrospinning Nonspinnable Sols to Ceramic Fibers and Springs.

Electrospinning has been applied to produce ceramic fibers using sol gel-based spinning solutions consisting of ceramic precursors, a solvent, and a polymer to control the viscosity of the solution. However, the addition of polymers to the spinning solution makes the process more complex, increases the processing time, and results in porous mechanically weak ceramic fibers. Herein, we develop a coelectrospinning technique, where a nonspinnable sol (<10 mPa s) consisting of only the ceramic precursor(s) and solvent(s) is encapsulated inside a polymeric shell, forming core-shell precursor fibers that are further calcined into ceramic fibers with reduced porosity, decreased surface defects, uniform crystal packing, and controlled diameters. We demonstrate the versatility of this method by applying it to a series of nonspinnable sols and creating high-quality ceramic fibers containing TiO2, ZrO2, SiO2, and Al2O3. The polycrystalline TiO2 fibers possess excellent flexibility and a high Young's modulus reaching 54.3 MPa, solving the extreme brittleness problem of the previously reported TiO2 fibers. The single-component ZrO2 fibers exhibit a Young's modulus and toughness of 130.5 MPa and 11.9 KJ/m3, respectively, significantly superior to the counterparts prepared by conventional sol-gel electrospinning. We also report the creation of ceramic fibers in micro- and nanospring morphologies and examine the formation mechanisms using thermomechanical simulations. The fiber assemblies constructed by the helical fibers exhibit a density-normalized toughness of 3.5-5 times that of the straight fibers due to improved fracture strain. This work expands the selection of the electrospinning solution and enables the development of ceramic fibers with more attractive properties.

Electrochemical Oxidation of Ethylene Glycol in Aqueous Media Using Lead Dioxide Titanium Anode

Experiments on the electrochemical oxidation of ethylene glycol (EG) have been carried out in laboratory conditions using lead dioxide titanium anode (LDOTA) in electrochemical cell. It has been found that the chemical oxygen consumption in the solution on LDOTA at a current density of 5.5 A/dm2 and processing time of 120 min is reduced to the values that allow its further bio-purification. It has been concluded that the efficiency of the electrochemical oxidation process, leading to further destruction of the EG molecule, is explained by both direct oxidation, in which the hydroxyl radicals arise that oxidize EG on the anode surface, and indirect oxidation, accompanied by the formation of strong oxidizing agents, for example, peroxodisulfuric acid anions capable of oxidizing the organic matter in the solution volume.

Autoencoder-based Image Denoiser Suitable for Image of Numbers with High Potential for Hardware Implementation

The algorithms for processing images and videos are currently essential for many applications. Many of these applications are specified for processing and analyzing images of numbers, such as smart meter reading, automated document processing, and processing of vehicles and license plate images in traffic monitoring and analysis. Consequently, eliminating noise is frequently used as a pre-processing step to improve subsequent analysis and processing outcomes. In this context, this manuscript proposes using artificial intelligence-based methods to increase the efficiency of the image-denoising process. However, the computational demands of these algorithms necessitate careful consideration of the hardware on which they are implemented. Therefore, this paper proposes using the simple autoencoder approach and evaluates its efficiency compared to the conventional methods. This unsupervised model is trained to identify and remove impulse noise from digital images by replacing some pixels with others from the outer dataset that can clarify the whole image more. The model was trained using handwritten numbers, MNIST, and data set size in the first trial and the FER2013 dataset in the second. The model is superior in the case of the simple dataset. Two versions of autoencoders are considered, the first with three layers and the second with five. The Traditional denoising methods are investigated for comparison purposes. The four conventional filtering procedures, AMF, DBMF, ADBMF, and MDBUTMF, are implemented using the MATLAB simulation environment, and the results are reported and compared with the proposed methodology. The results show that the proposed artificial intelligence-based method significantly outperforms the traditional methods regarding processing efficiency and the resulting image quality. Moreover, the computational intensity for the proposed methodology is chosen as a metric for evaluating the algorithm compliance for the hardware implementation compared to the other Artificial Intelligence (AI)-based denoising algorithms. The suggested technique has minor processing and training time compared to the other AI-based methods with adequate quality in case the images of numbers usually do not contain many details, making it more convenient for hardware implementation.

Blockchain-Based Lean Supply Chain: A Simulation Approach

This paper aims to propose a model based on blockchain and cloud computing technologies to support lean production techniques for industrial supply chain management, evaluating how the combination of these technologies provides improvement in the chain performance. The literature review contextualizes the state of the art in studies related to the application of blockchain technology to lean manufacturing processes, as well as identifies research gaps in this field. Besides, this study models and simulates an architecture based on both the Hyperledger Fabric network and a virtual machine in the cloud environment, assessing its applicability and performance in the industrial supply chain. The analysis addresses lean aspects identified as research gaps, including integration of stakeholders in the collaborative production of customized products, conditions of flexibility and adaptability to demand variations, coordinated relationship of networks involving small and medium-sized enterprises, and behavior of various players acting simultaneously and consensually. The proposed model reveals a decentralized and nonlinear information sharing concept that demonstrates a logic of simultaneous participation of all players involved in the supply chain. The low costs and processing times identified in the simulation indicate the robustness of the system even in transactions involving a large number of companies and a large amount of data. This architecture offers an integration solution between stakeholders to support operational processes, providing insights for researchers and specialists in the adoption of innovative, reliable and inclusive information technologies for companies of different sizes or levels of development, with a focus on lean manufacturing, reduction in resource consumption and cost reduction. The developed model, which combines blockchain and cloud computing technologies applied to stages of lean production systems, presents a differentiated solution on the planning of integrated production systems, indicating the differential in this work.

Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data

Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge. Methods To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works. Use cases We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times. Conclusions The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge.

A Review on Automatic Toll Collection System

One of the most crucial elements for any country's development is logistics, or transportation. Since India is one of the world's largest nations, road transportation plays a vital role in the country's economy. For any government, having fast road transit becomes essential, and toll plazas are essential to this. The most commonly used toll systems in India at the moment are the manual toll collection and FASTag systems. Toll plaza processing times rise as a result of their need for a toll collector or attendant. The Automatic Toll Collection System, or ATCS, is a tool used to automatically collect taxes. Here, radio frequency is used to help in identification. An RFID tag will be attached to a car. This tag is nothing more than an allocated unique identification number. The RTO, or traffic controlling body, will assign this. We shall keep all of his basic data and the amount he has paid in advance for the TOLL collection in line with this number. At the toll collection facility, the reader will be positioned strategically. His prepaid balance will be reduced by the tax amount each time the car passes the toll collection center. This essay discusses several automated toll collection systems that are intended to improve overall transportation efficiency, ease toll payment procedures, and lessen traffic congestion. The suggested method computes toll fees automatically by utilizing cutting-edge technology including mobile communication, GPS, and RFID (Radio Frequency Identification).

Effect of Argon Flow Rate on Power Consumption of a 120‐t Ladle Furnace

Effect of Argon Flow Rate on Power Consumption of a 120‐t Ladle Furnace